From 9b2c7dae247399f48fa9f8f50057222469d96e15 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Mon, 20 Jan 2025 14:58:51 -0800 Subject: [PATCH 01/57] clean demo folder --- demo/__init__.py | 0 demo/app.py | 479 ------------------------ demo/executor/anthropic_executor.py | 136 ------- demo/gui_agent/anthropic_agent.py | 207 ---------- demo/gui_agent/llm_utils/llm_utils.py | 109 ------ demo/gui_agent/llm_utils/oai.py | 117 ------ demo/gui_agent/llm_utils/qwen.py | 107 ------ demo/gui_agent/llm_utils/run_llm.py | 44 --- demo/loop.py | 236 ------------ demo/omniparser_agent/vlm_agent.py | 417 --------------------- demo/tmp/outputs/screenshot.png | Bin 1436362 -> 0 bytes demo/tools/__init__.py | 16 - demo/tools/base.py | 69 ---- demo/tools/bash.py | 136 ------- demo/tools/collection.py | 34 -- demo/tools/colorful_text.py | 27 -- demo/tools/computer.py | 519 -------------------------- demo/tools/edit.py | 290 -------------- demo/tools/run.py | 42 --- demo/tools/screen_capture.py | 185 --------- 20 files changed, 3170 deletions(-) delete mode 100644 demo/__init__.py delete mode 100644 demo/app.py delete mode 100644 demo/executor/anthropic_executor.py delete mode 100644 demo/gui_agent/anthropic_agent.py delete mode 100644 demo/gui_agent/llm_utils/llm_utils.py delete mode 100644 demo/gui_agent/llm_utils/oai.py delete mode 100644 demo/gui_agent/llm_utils/qwen.py delete mode 100644 demo/gui_agent/llm_utils/run_llm.py delete mode 100644 demo/loop.py delete mode 100644 demo/omniparser_agent/vlm_agent.py delete mode 100644 demo/tmp/outputs/screenshot.png delete mode 100644 demo/tools/__init__.py delete mode 100644 demo/tools/base.py delete mode 100644 demo/tools/bash.py delete mode 100644 demo/tools/collection.py delete mode 100644 demo/tools/colorful_text.py delete mode 100644 demo/tools/computer.py delete mode 100644 demo/tools/edit.py delete mode 100644 demo/tools/run.py delete mode 100644 demo/tools/screen_capture.py diff --git a/demo/__init__.py b/demo/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/demo/app.py b/demo/app.py deleted file mode 100644 index e0394d9..0000000 --- a/demo/app.py +++ /dev/null @@ -1,479 +0,0 @@ -""" -Entrypoint for Gradio, see https://gradio.app/ -""" - -import platform -import asyncio -import base64 -import os -import io -import json -from datetime import datetime -from enum import StrEnum -from functools import partial -from pathlib import Path -from typing import cast, Dict -from PIL import Image - -import gradio as gr -from anthropic import APIResponse -from anthropic.types import TextBlock -from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock -from anthropic.types.tool_use_block import ToolUseBlock - -from screeninfo import get_monitors - -screens = get_monitors() -print(screens) -from loop import ( - PROVIDER_TO_DEFAULT_MODEL_NAME, - APIProvider, - sampling_loop_sync, -) - -from tools import ToolResult -from tools.computer import get_screen_details -SCREEN_NAMES, SELECTED_SCREEN_INDEX = get_screen_details() -# SELECTED_SCREEN_INDEX = None -# SCREEN_NAMES = None - -CONFIG_DIR = Path("~/.anthropic").expanduser() -API_KEY_FILE = CONFIG_DIR / "api_key" - -INTRO_TEXT = ''' -๐Ÿš€๐Ÿค–โœจ It's Play Time! - -Welcome to the OmniParser+X Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. Type a message to play with your beloved assistant. -''' - -class Sender(StrEnum): - USER = "user" - BOT = "assistant" - TOOL = "tool" - - -def setup_state(state): - - if "messages" not in state: - state["messages"] = [] - if "model" not in state: - # state["model"] = "gpt-4o + ShowUI" - state["model"] = "omniparser + gpt-4o" - # _reset_model(state) - if "provider" not in state: - if state["model"] == "qwen2vl + ShowUI": - state["provider"] = "DashScopeAPI" - elif state["model"] == "gpt-4o + ShowUI": - state["provider"] = "openai" - else: - state["provider"] = os.getenv("API_PROVIDER", "anthropic") or "anthropic" - - if "provider_radio" not in state: - state["provider_radio"] = state["provider"] - - if "openai_api_key" not in state: # Fetch API keys from environment variables - state["openai_api_key"] = os.getenv("OPENAI_API_KEY", "") - if "anthropic_api_key" not in state: - state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "") - if "qwen_api_key" not in state: - state["qwen_api_key"] = os.getenv("QWEN_API_KEY", "") - - # Set the initial api_key based on the provider - if "api_key" not in state: - if state["provider"] == "openai": - state["api_key"] = state["openai_api_key"] - elif state["provider"] == "anthropic": - state["api_key"] = state["anthropic_api_key"] - elif state["provider"] == "qwen": - state["api_key"] = state["qwen_api_key"] - else: - state["api_key"] = "" - # print(f"state['api_key']: {state['api_key']}") - if not state["api_key"]: - print("API key not found. Please set it in the environment or paste in textbox.") - - if "selected_screen" not in state: - state['selected_screen'] = SELECTED_SCREEN_INDEX if SCREEN_NAMES else 0 - - if "auth_validated" not in state: - state["auth_validated"] = False - if "responses" not in state: - state["responses"] = {} - if "tools" not in state: - state["tools"] = {} - if "only_n_most_recent_images" not in state: - state["only_n_most_recent_images"] = 10 # 10 - if "custom_system_prompt" not in state: - state["custom_system_prompt"] = load_from_storage("system_prompt") or "" - # remove if want to use default system prompt - device_os_name = "Windows" if platform.system() == "Windows" else "Mac" if platform.system() == "Darwin" else "Linux" - state["custom_system_prompt"] += f"\n\nNOTE: you are operating a {device_os_name} machine" - if "hide_images" not in state: - state["hide_images"] = False - if 'chatbot_messages' not in state: - state['chatbot_messages'] = [] - - - -def _reset_model(state): - state["model"] = PROVIDER_TO_DEFAULT_MODEL_NAME[cast(APIProvider, state["provider"])] - - -async def main(state): - """Render loop for Gradio""" - setup_state(state) - return "Setup completed" - - -def validate_auth(provider: APIProvider, api_key: str | None): - if provider == APIProvider.ANTHROPIC: - if not api_key: - return "Enter your Anthropic API key to continue." - if provider == APIProvider.BEDROCK: - import boto3 - - if not boto3.Session().get_credentials(): - return "You must have AWS credentials set up to use the Bedrock API." - if provider == APIProvider.VERTEX: - import google.auth - from google.auth.exceptions import DefaultCredentialsError - - if not os.environ.get("CLOUD_ML_REGION"): - return "Set the CLOUD_ML_REGION environment variable to use the Vertex API." - try: - google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) - except DefaultCredentialsError: - return "Your google cloud credentials are not set up correctly." - - -def load_from_storage(filename: str) -> str | None: - """Load data from a file in the storage directory.""" - try: - file_path = CONFIG_DIR / filename - if file_path.exists(): - data = file_path.read_text().strip() - if data: - return data - except Exception as e: - print(f"Debug: Error loading {filename}: {e}") - return None - - -def save_to_storage(filename: str, data: str) -> None: - """Save data to a file in the storage directory.""" - try: - CONFIG_DIR.mkdir(parents=True, exist_ok=True) - file_path = CONFIG_DIR / filename - file_path.write_text(data) - # Ensure only user can read/write the file - file_path.chmod(0o600) - except Exception as e: - print(f"Debug: Error saving {filename}: {e}") - - -def _api_response_callback(response: APIResponse[BetaMessage], response_state: dict): - response_id = datetime.now().isoformat() - response_state[response_id] = response - - -def _tool_output_callback(tool_output: ToolResult, tool_id: str, tool_state: dict): - tool_state[tool_id] = tool_output - - -def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="bot"): - - def _render_message(message: str | BetaTextBlock | BetaToolUseBlock | ToolResult, hide_images=False): - - print(f"_render_message: {str(message)[:100]}") - - if isinstance(message, str): - return message - - is_tool_result = not isinstance(message, str) and ( - isinstance(message, ToolResult) - or message.__class__.__name__ == "ToolResult" - or message.__class__.__name__ == "CLIResult" - ) - if not message or ( - is_tool_result - and hide_images - and not hasattr(message, "error") - and not hasattr(message, "output") - ): # return None if hide_images is True - return - # render tool result - if is_tool_result: - message = cast(ToolResult, message) - if message.output: - return message.output - if message.error: - return f"Error: {message.error}" - if message.base64_image and not hide_images: - # somehow can't display via gr.Image - # image_data = base64.b64decode(message.base64_image) - # return gr.Image(value=Image.open(io.BytesIO(image_data))) - return f'' - - elif isinstance(message, BetaTextBlock) or isinstance(message, TextBlock): - return f"Analysis: {message.text}" - elif isinstance(message, BetaToolUseBlock) or isinstance(message, ToolUseBlock): - # return f"Tool Use: {message.name}\nInput: {message.input}" - return f"Next I will perform the following action: {message.input}" - else: - return message - - def _truncate_string(s, max_length=500): - """Truncate long strings for concise printing.""" - if isinstance(s, str) and len(s) > max_length: - return s[:max_length] + "..." - return s - # processing Anthropic messages - message = _render_message(message, hide_images) - - if sender == "bot": - chatbot_state.append((None, message)) - else: - chatbot_state.append((message, None)) - - # Create a concise version of the chatbot state for printing - concise_state = [(_truncate_string(user_msg), _truncate_string(bot_msg)) - for user_msg, bot_msg in chatbot_state] - # print(f"chatbot_output_callback chatbot_state: {concise_state} (truncated)") - -def process_input(user_input, state): - - setup_state(state) - - # Append the user message to state["messages"] - if state["model"] == "gpt-4o + ShowUI" or state["model"] == "qwen2vl + ShowUI": - state["messages"].append( - { - "role": "user", - "content": [TextBlock(type="text", text=user_input)], - } - ) - elif state["model"] == "claude-3-5-sonnet-20241022": - state["messages"].append( - { - "role": Sender.USER, - "content": [TextBlock(type="text", text=user_input)], - } - ) - elif state["model"] == "omniparser + gpt-4o" or state["model"] == "omniparser + phi35v": - state["messages"].append( - { - "role": "user", - "content": [TextBlock(type="text", text=user_input)], - } - ) - - # Append the user's message to chatbot_messages with None for the assistant's reply - state['chatbot_messages'].append((user_input, None)) - yield state['chatbot_messages'] # Yield to update the chatbot UI with the user's message - - # Run sampling_loop_sync with the chatbot_output_callback - for loop_msg in sampling_loop_sync( - system_prompt_suffix=state["custom_system_prompt"], - model=state["model"], - provider=state["provider"], - messages=state["messages"], - output_callback=partial(chatbot_output_callback, chatbot_state=state['chatbot_messages'], hide_images=state["hide_images"]), - tool_output_callback=partial(_tool_output_callback, tool_state=state["tools"]), - api_response_callback=partial(_api_response_callback, response_state=state["responses"]), - api_key=state["api_key"], - only_n_most_recent_images=state["only_n_most_recent_images"], - selected_screen=state['selected_screen'] - ): - if loop_msg is None: - yield state['chatbot_messages'] - print("End of task. Close the loop.") - break - - yield state['chatbot_messages'] # Yield the updated chatbot_messages to update the chatbot UI - - -# with gr.Blocks(theme=gr.themes.Default()) as demo: -with gr.Blocks(theme='YTheme/Minecraft') as demo: - state = gr.State({}) # Use Gradio's state management - - setup_state(state.value) # Initialize the state - - # Retrieve screen details - gr.Markdown("# OmniParser + โœ–๏ธ Demo") - - if not os.getenv("HIDE_WARNING", False): - gr.Markdown(INTRO_TEXT) - - with gr.Accordion("Settings", open=True): - with gr.Row(): - with gr.Column(): - model = gr.Dropdown( - label="Model", - choices=["omniparser + gpt-4o", "omniparser + phi35v", "claude-3-5-sonnet-20241022"], - value="omniparser + gpt-4o", # Set to one of the choices - interactive=True, - ) - with gr.Column(): - provider = gr.Dropdown( - label="API Provider", - choices=[option.value for option in APIProvider], - value="openai", - interactive=False, - ) - with gr.Column(): - api_key = gr.Textbox( - label="API Key", - type="password", - value=state.value.get("api_key", ""), - placeholder="Paste your API key here", - interactive=True, - ) - with gr.Column(): - custom_prompt = gr.Textbox( - label="System Prompt Suffix", - value="", - interactive=True, - ) - with gr.Column(): - screen_options, primary_index = get_screen_details() - SCREEN_NAMES = screen_options - SELECTED_SCREEN_INDEX = primary_index - screen_selector = gr.Dropdown( - label="Select Screen", - choices=screen_options, - value=screen_options[primary_index] if screen_options else None, - interactive=True, - ) - with gr.Column(): - only_n_images = gr.Slider( - label="N most recent screenshots", - minimum=0, - maximum=10, - step=1, - value=2, - interactive=True, - ) - # hide_images = gr.Checkbox(label="Hide screenshots", value=False) - - # Define the merged dictionary with task mappings - # merged_dict = json.load(open("examples/ootb_examples.json", "r")) - merged_dict = {} - - def update_only_n_images(only_n_images_value, state): - state["only_n_most_recent_images"] = only_n_images_value - - # Callback to update the second dropdown based on the first selection - def update_second_menu(selected_category): - return gr.update(choices=list(merged_dict.get(selected_category, {}).keys())) - - # Callback to update the third dropdown based on the second selection - def update_third_menu(selected_category, selected_option): - return gr.update(choices=list(merged_dict.get(selected_category, {}).get(selected_option, {}).keys())) - - # Callback to update the textbox based on the third selection - def update_textbox(selected_category, selected_option, selected_task): - task_data = merged_dict.get(selected_category, {}).get(selected_option, {}).get(selected_task, {}) - prompt = task_data.get("prompt", "") - preview_image = task_data.get("initial_state", "") - task_hint = "Task Hint: " + task_data.get("hint", "") - return prompt, preview_image, task_hint - - # Function to update the global variable when the dropdown changes - def update_selected_screen(selected_screen_name, state): - global SCREEN_NAMES - global SELECTED_SCREEN_INDEX - SELECTED_SCREEN_INDEX = SCREEN_NAMES.index(selected_screen_name) - print(f"Selected screen updated to: {SELECTED_SCREEN_INDEX}") - state['selected_screen'] = SELECTED_SCREEN_INDEX - - def update_model(model_selection, state): - state["model"] = model_selection - print(f"Model updated to: {state['model']}") - - if model_selection == "claude-3-5-sonnet-20241022": - # Provider can be any of the current choices except 'openai' - provider_choices = [option.value for option in APIProvider if option.value != "openai"] - provider_value = "anthropic" # Set default to 'anthropic' - provider_interactive = True - api_key_placeholder = "claude API key" - elif model_selection == "omniparser + gpt-4o" or model_selection == "omniparser + phi35v": - # Provider can be any of the current choices except 'openai' - provider_choices = ["openai"] - provider_value = "openai" - provider_interactive = False - api_key_placeholder = "openai API key" - else: - # Default case - provider_choices = [option.value for option in APIProvider] - provider_value = state.get("provider", provider_choices[0]) - provider_interactive = True - api_key_placeholder = "" - - # Update the provider in state - state["provider"] = provider_value - - # Update api_key in state based on the provider - if provider_value == "openai": - state["api_key"] = state.get("openai_api_key", "") - elif provider_value == "anthropic": - state["api_key"] = state.get("anthropic_api_key", "") - elif provider_value == "qwen": - state["api_key"] = state.get("qwen_api_key", "") - else: - state["api_key"] = "" - - # Use gr.update() instead of gr.Dropdown.update() - provider_update = gr.update( - choices=provider_choices, - value=provider_value, - interactive=provider_interactive - ) - - # Update the API Key textbox - api_key_update = gr.update( - placeholder=api_key_placeholder, - value=state["api_key"] - ) - - return provider_update, api_key_update - - def update_api_key_placeholder(provider_value, model_selection): - if model_selection == "claude-3-5-sonnet-20241022": - if provider_value == "anthropic": - return gr.update(placeholder="anthropic API key") - elif provider_value == "bedrock": - return gr.update(placeholder="bedrock API key") - elif provider_value == "vertex": - return gr.update(placeholder="vertex API key") - else: - return gr.update(placeholder="") - elif model_selection == "gpt-4o + ShowUI": - return gr.update(placeholder="openai API key") - else: - return gr.update(placeholder="") - - def update_system_prompt_suffix(system_prompt_suffix, state): - state["custom_system_prompt"] = system_prompt_suffix - - - api_key.change(fn=lambda key: save_to_storage(API_KEY_FILE, key), inputs=api_key) - - with gr.Row(): - # submit_button = gr.Button("Submit") # Add submit button - with gr.Column(scale=8): - chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Computer Use OOTB...", container=False) - with gr.Column(scale=1, min_width=50): - submit_button = gr.Button(value="Send", variant="primary") - - chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) - - model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key]) - provider.change(fn=update_api_key_placeholder, inputs=[provider, model], outputs=api_key) - screen_selector.change(fn=update_selected_screen, inputs=[screen_selector, state], outputs=None) - only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) - - - # chat_input.submit(process_input, [chat_input, state], chatbot) - submit_button.click(process_input, [chat_input, state], chatbot) - -demo.launch(share=True, server_port=7861, server_name='0.0.0.0') # TODO: allowed_paths diff --git a/demo/executor/anthropic_executor.py b/demo/executor/anthropic_executor.py deleted file mode 100644 index df61810..0000000 --- a/demo/executor/anthropic_executor.py +++ /dev/null @@ -1,136 +0,0 @@ -import asyncio -from typing import Any, Dict, cast -from collections.abc import Callable -from anthropic.types.beta import ( - BetaContentBlock, - BetaContentBlockParam, - BetaImageBlockParam, - BetaMessage, - BetaMessageParam, - BetaTextBlockParam, - BetaToolResultBlockParam, -) -from anthropic.types import TextBlock -from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock -from tools import BashTool, ComputerTool, EditTool, ToolCollection, ToolResult - - -class AnthropicExecutor: - def __init__( - self, - output_callback: Callable[[BetaContentBlockParam], None], - tool_output_callback: Callable[[Any, str], None], - selected_screen: int = 0 - ): - self.tool_collection = ToolCollection( - ComputerTool(selected_screen=selected_screen), - BashTool(), - EditTool(), - ) - self.output_callback = output_callback - self.tool_output_callback = tool_output_callback - - def __call__(self, response: BetaMessage, messages: list[BetaMessageParam]): - new_message = { - "role": "assistant", - "content": cast(list[BetaContentBlockParam], response.content), - } - if new_message not in messages: - messages.append(new_message) - else: - print("new_message already in messages, there are duplicates.") - - tool_result_content: list[BetaToolResultBlockParam] = [] - for content_block in cast(list[BetaContentBlock], response.content): - - self.output_callback(content_block, sender="bot") - # Execute the tool - if content_block.type == "tool_use": - # Run the asynchronous tool execution in a synchronous context - result = asyncio.run(self.tool_collection.run( - name=content_block.name, - tool_input=cast(dict[str, Any], content_block.input), - )) - - self.output_callback(result, sender="bot") - - tool_result_content.append( - _make_api_tool_result(result, content_block.id) - ) - self.tool_output_callback(result, content_block.id) - - # Craft messages based on the content_block - # Note: to display the messages in the gradio, you should organize the messages in the following way (user message, bot message) - - display_messages = _message_display_callback(messages) - # display_messages = [] - - # Send the messages to the gradio - for user_msg, bot_msg in display_messages: - # yield [user_msg, bot_msg], tool_result_content - yield [None, None], tool_result_content - - if not tool_result_content: - return messages - - return tool_result_content - -def _message_display_callback(messages): - display_messages = [] - for msg in messages: - try: - if isinstance(msg["content"][0], TextBlock): - display_messages.append((msg["content"][0].text, None)) # User message - elif isinstance(msg["content"][0], BetaTextBlock): - display_messages.append((None, msg["content"][0].text)) # Bot message - elif isinstance(msg["content"][0], BetaToolUseBlock): - display_messages.append((None, f"Tool Use: {msg['content'][0].name}\nInput: {msg['content'][0].input}")) # Bot message - elif isinstance(msg["content"][0], Dict) and msg["content"][0]["content"][-1]["type"] == "image": - display_messages.append((None, f'')) # Bot message - else: - print(msg["content"][0]) - except Exception as e: - print("error", e) - pass - return display_messages - -def _make_api_tool_result( - result: ToolResult, tool_use_id: str -) -> BetaToolResultBlockParam: - """Convert an agent ToolResult to an API ToolResultBlockParam.""" - tool_result_content: list[BetaTextBlockParam | BetaImageBlockParam] | str = [] - is_error = False - if result.error: - is_error = True - tool_result_content = _maybe_prepend_system_tool_result(result, result.error) - else: - if result.output: - tool_result_content.append( - { - "type": "text", - "text": _maybe_prepend_system_tool_result(result, result.output), - } - ) - if result.base64_image: - tool_result_content.append( - { - "type": "image", - "source": { - "type": "base64", - "media_type": "image/png", - "data": result.base64_image, - }, - } - ) - return { - "type": "tool_result", - "content": tool_result_content, - "tool_use_id": tool_use_id, - "is_error": is_error, - } - - -def _maybe_prepend_system_tool_result(result: ToolResult, result_text: str): - if result.system: - result_text = f"{result.system}\n{result_text}" - return result_text \ No newline at end of file diff --git a/demo/gui_agent/anthropic_agent.py b/demo/gui_agent/anthropic_agent.py deleted file mode 100644 index 09d857e..0000000 --- a/demo/gui_agent/anthropic_agent.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. -""" -import asyncio -import platform -from collections.abc import Callable -from datetime import datetime -from enum import StrEnum -from typing import Any, cast - -from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse -from anthropic.types import ( - ToolResultBlockParam, -) -from anthropic.types.beta import ( - BetaContentBlock, - BetaContentBlockParam, - BetaImageBlockParam, - BetaMessage, - BetaMessageParam, - BetaTextBlockParam, - BetaToolResultBlockParam, -) -from anthropic.types import TextBlock -from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock - -from tools import BashTool, ComputerTool, EditTool, ToolCollection, ToolResult - -from PIL import Image -from io import BytesIO -import gradio as gr -from typing import Dict - - -BETA_FLAG = "computer-use-2024-10-22" - - -class APIProvider(StrEnum): - ANTHROPIC = "anthropic" - BEDROCK = "bedrock" - VERTEX = "vertex" - - -PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { - APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", - APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", - APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", -} - - -# Check OS - -SYSTEM_PROMPT = f""" -* You are utilizing a Windows system with internet access. -* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. - -""" - - -class AnthropicActor: - def __init__( - self, - model: str, - provider: APIProvider, - system_prompt_suffix: str, - api_key: str, - api_response_callback: Callable[[APIResponse[BetaMessage]], None], - max_tokens: int = 4096, - only_n_most_recent_images: int | None = None, - selected_screen: int = 0, - print_usage: bool = True, - ): - self.model = model - self.provider = provider - self.system_prompt_suffix = system_prompt_suffix - self.api_key = api_key - self.api_response_callback = api_response_callback - self.max_tokens = max_tokens - self.only_n_most_recent_images = only_n_most_recent_images - self.selected_screen = selected_screen - - self.tool_collection = ToolCollection( - ComputerTool(selected_screen=selected_screen), - BashTool(), - EditTool(), - ) - - self.system = ( - f"{SYSTEM_PROMPT}{' ' + system_prompt_suffix if system_prompt_suffix else ''}" - ) - - self.total_token_usage = 0 - self.total_cost = 0 - self.print_usage = print_usage - - # Instantiate the appropriate API client based on the provider - if provider == APIProvider.ANTHROPIC: - self.client = Anthropic(api_key=api_key) - elif provider == APIProvider.VERTEX: - self.client = AnthropicVertex() - elif provider == APIProvider.BEDROCK: - self.client = AnthropicBedrock() - - def __call__( - self, - *, - messages: list[BetaMessageParam] - ): - """ - Generate a response given history messages. - """ - if self.only_n_most_recent_images: - _maybe_filter_to_n_most_recent_images(messages, self.only_n_most_recent_images) - - # Call the API synchronously - raw_response = self.client.beta.messages.with_raw_response.create( - max_tokens=self.max_tokens, - messages=messages, - model=self.model, - system=self.system, - tools=self.tool_collection.to_params(), - betas=["computer-use-2024-10-22"], - ) - - self.api_response_callback(cast(APIResponse[BetaMessage], raw_response)) - - response = raw_response.parse() - print(f"AnthropicActor response: {response}") - - self.total_token_usage += response.usage.input_tokens + response.usage.output_tokens - self.total_cost += (response.usage.input_tokens * 3 / 1000000 + response.usage.output_tokens * 15 / 1000000) - - if self.print_usage: - print(f"Claude total token usage so far: {self.total_token_usage}, total cost so far: $USD{self.total_cost}") - - return response - - -def _maybe_filter_to_n_most_recent_images( - messages: list[BetaMessageParam], - images_to_keep: int, - min_removal_threshold: int = 10, -): - """ - With the assumption that images are screenshots that are of diminishing value as - the conversation progresses, remove all but the final `images_to_keep` tool_result - images in place, with a chunk of min_removal_threshold to reduce the amount we - break the implicit prompt cache. - """ - if images_to_keep is None: - return messages - - tool_result_blocks = cast( - list[ToolResultBlockParam], - [ - item - for message in messages - for item in ( - message["content"] if isinstance(message["content"], list) else [] - ) - if isinstance(item, dict) and item.get("type") == "tool_result" - ], - ) - - total_images = sum( - 1 - for tool_result in tool_result_blocks - for content in tool_result.get("content", []) - if isinstance(content, dict) and content.get("type") == "image" - ) - - images_to_remove = total_images - images_to_keep - # for better cache behavior, we want to remove in chunks - images_to_remove -= images_to_remove % min_removal_threshold - - for tool_result in tool_result_blocks: - if isinstance(tool_result.get("content"), list): - new_content = [] - for content in tool_result.get("content", []): - if isinstance(content, dict) and content.get("type") == "image": - if images_to_remove > 0: - images_to_remove -= 1 - continue - new_content.append(content) - tool_result["content"] = new_content - - - -if __name__ == "__main__": - pass - # client = Anthropic(api_key="") - # response = client.beta.messages.with_raw_response.create( - # max_tokens=4096, - # model="claude-3-5-sonnet-20241022", - # system=SYSTEM_PROMPT, - # # tools=ToolCollection( - # # ComputerTool(selected_screen=0), - # # BashTool(), - # # EditTool(), - # # ).to_params(), - # betas=["computer-use-2024-10-22"], - # messages=[ - # {"role": "user", "content": "click on (199, 199)."} - # ], - # ) - - # print(f"AnthropicActor response: {response.parse().usage.input_tokens+response.parse().usage.output_tokens}") \ No newline at end of file diff --git a/demo/gui_agent/llm_utils/llm_utils.py b/demo/gui_agent/llm_utils/llm_utils.py deleted file mode 100644 index fbc4a42..0000000 --- a/demo/gui_agent/llm_utils/llm_utils.py +++ /dev/null @@ -1,109 +0,0 @@ -import os -import re -import ast -import base64 - - -def is_image_path(text): - # Checking if the input text ends with typical image file extensions - image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") - if text.endswith(image_extensions): - return True - else: - return False - - -def encode_image(image_path): - """Encode image file to base64.""" - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode("utf-8") - - -def is_url_or_filepath(input_string): - # Check if input_string is a URL - url_pattern = re.compile( - r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+" - ) - if url_pattern.match(input_string): - return "URL" - - # Check if input_string is a file path - file_path = os.path.abspath(input_string) - if os.path.exists(file_path): - return "File path" - - return "Invalid" - - -def extract_data(input_string, data_type): - # Regular expression to extract content starting from '```python' until the end if there are no closing backticks - pattern = f"```{data_type}" + r"(.*?)(```|$)" - # Extract content - # re.DOTALL allows '.' to match newlines as well - matches = re.findall(pattern, input_string, re.DOTALL) - # Return the first match if exists, trimming whitespace and ignoring potential closing backticks - return matches[0][0].strip() if matches else input_string - - -def parse_input(code): - """Use AST to parse the input string and extract the function name, arguments, and keyword arguments.""" - - def get_target_names(target): - """Recursively get all variable names from the assignment target.""" - if isinstance(target, ast.Name): - return [target.id] - elif isinstance(target, ast.Tuple): - names = [] - for elt in target.elts: - names.extend(get_target_names(elt)) - return names - return [] - - def extract_value(node): - """ๆๅ– AST ่Š‚็‚น็š„ๅฎž้™…ๅ€ผ""" - if isinstance(node, ast.Constant): - return node.value - elif isinstance(node, ast.Name): - # TODO: a better way to handle variables - raise ValueError( - f"Arguments should be a Constant, got a variable {node.id} instead." - ) - # ๆทปๅŠ ๅ…ถไป–้œ€่ฆๅค„็†็š„ AST ่Š‚็‚น็ฑปๅž‹ - return None - - try: - tree = ast.parse(code) - for node in ast.walk(tree): - if isinstance(node, ast.Assign): - targets = [] - for t in node.targets: - targets.extend(get_target_names(t)) - if isinstance(node.value, ast.Call): - func_name = node.value.func.id - args = [ast.dump(arg) for arg in node.value.args] - kwargs = { - kw.arg: extract_value(kw.value) for kw in node.value.keywords - } - print(f"Input: {code.strip()}") - print(f"Output Variables: {targets}") - print(f"Function Name: {func_name}") - print(f"Arguments: {args}") - print(f"Keyword Arguments: {kwargs}") - elif isinstance(node, ast.Expr) and isinstance(node.value, ast.Call): - targets = [] - func_name = extract_value(node.value.func) - args = [extract_value(arg) for arg in node.value.args] - kwargs = {kw.arg: extract_value(kw.value) for kw in node.value.keywords} - - except SyntaxError: - print(f"Input: {code.strip()}") - print("No match found") - - return targets, func_name, args, kwargs - - -if __name__ == "__main__": - import json - s='{"Reasoning": "The Docker icon has been successfully clicked, and the Docker application should now be opening. No further actions are required.", "Next Action": None}' - json_str = json.loads(s) - print(json_str) \ No newline at end of file diff --git a/demo/gui_agent/llm_utils/oai.py b/demo/gui_agent/llm_utils/oai.py deleted file mode 100644 index ad1d54e..0000000 --- a/demo/gui_agent/llm_utils/oai.py +++ /dev/null @@ -1,117 +0,0 @@ - -import os -import logging -import base64 -import requests - -# from computer_use_demo.gui_agent.llm_utils import is_image_path, encode_image - -def is_image_path(text): - # Checking if the input text ends with typical image file extensions - image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") - if text.endswith(image_extensions): - return True - else: - return False - -def encode_image(image_path): - """Encode image file to base64.""" - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode("utf-8") - - - -# from openai import OpenAI -# client = OpenAI( -# api_key=os.environ.get("OPENAI_API_KEY") -# ) - - -def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): - - api_key = api_key or os.environ.get("OPENAI_API_KEY") - if not api_key: - raise ValueError("OPENAI_API_KEY is not set") - - headers = {"Content-Type": "application/json", - "Authorization": f"Bearer {api_key}"} - - final_messages = [{"role": "system", "content": system}] - - # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" - if type(messages) == list: - for item in messages: - contents = [] - if isinstance(item, dict): - for cnt in item["content"]: - if isinstance(cnt, str): - if is_image_path(cnt): - base64_image = encode_image(cnt) - content = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}} - else: - content = {"type": "text", "text": cnt} - else: - # in this case it is a text block from anthropic - content = {"type": "text", "text": str(cnt)} - - contents.append(content) - - message = {"role": 'user', "content": contents} - else: # str - contents.append({"type": "text", "text": item}) - message = {"role": "user", "content": contents} - - final_messages.append(message) - - - elif isinstance(messages, str): - final_messages = [{"role": "user", "content": messages}] - # import pdb; pdb.set_trace() - - print("[oai] sending messages:", {"role": "user", "content": messages}) - - payload = { - "model": llm, - "messages": final_messages, - "max_tokens": max_tokens, - "temperature": temperature, - # "stop": stop, - } - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - - response = requests.post( - "https://api.openai.com/v1/chat/completions", headers=headers, json=payload - ) - - try: - text = response.json()['choices'][0]['message']['content'] - token_usage = int(response.json()['usage']['total_tokens']) - return text, token_usage - - # return error message if the response is not successful - except Exception as e: - print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") - return response.json() - - -if __name__ == "__main__": - - api_key = os.environ.get("OPENAI_API_KEY") - if not api_key: - raise ValueError("OPENAI_API_KEY is not set") - - text, token_usage = run_oai_interleaved( - messages= [{"content": [ - "What is in the screenshot?", - "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"], - "role": "user" - }], - llm="gpt-4o-mini", - system="You are a helpful assistant", - api_key=api_key, - max_tokens=256, - temperature=0) - - print(text, token_usage) - # There is an introduction describing the Calyx... 36986 diff --git a/demo/gui_agent/llm_utils/qwen.py b/demo/gui_agent/llm_utils/qwen.py deleted file mode 100644 index 2d3e6e7..0000000 --- a/demo/gui_agent/llm_utils/qwen.py +++ /dev/null @@ -1,107 +0,0 @@ - -import os -import logging -import base64 -import requests - -import dashscope -# from computer_use_demo.gui_agent.llm_utils import is_image_path, encode_image - -def is_image_path(text): - return False - -def encode_image(image_path): - return "" - - -def run_qwen(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): - - api_key = api_key or os.environ.get("QWEN_API_KEY") - if not api_key: - raise ValueError("QWEN_API_KEY is not set") - - dashscope.api_key = api_key - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - - final_messages = [{"role": "system", "content": [{"text": system}]}] - # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" - if type(messages) == list: - for item in messages: - contents = [] - if isinstance(item, dict): - for cnt in item["content"]: - if isinstance(cnt, str): - if is_image_path(cnt): - # base64_image = encode_image(cnt) - content = [{"image": cnt}] - # content = {"type": "image_url", "image_url": {"url": image_url}} - else: - content = {"text": cnt} - contents.append(content) - - message = {"role": item["role"], "content": contents} - else: # str - contents.append({"text": item}) - message = {"role": "user", "content": contents} - - final_messages.append(message) - - print("[qwen-vl] sending messages:", final_messages) - - response = dashscope.MultiModalConversation.call( - model='qwen-vl-max-0809', - messages=final_messages - ) - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - - try: - text = response.output.choices[0].message.content[0]['text'] - usage = response.usage - - if "total_tokens" not in usage: - token_usage = int(usage["input_tokens"] + usage["output_tokens"]) - else: - token_usage = int(usage["total_tokens"]) - - return text, token_usage - # return response.json()['choices'][0]['message']['content'] - # return error message if the response is not successful - except Exception as e: - print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") - return response.json() - - - -if __name__ == "__main__": - api_key = os.environ.get("QWEN_API_KEY") - if not api_key: - raise ValueError("QWEN_API_KEY is not set") - - dashscope.api_key = api_key - - final_messages = [{"role": "user", - "content": [ - {"text": "What is in the screenshot?"}, - {"image": "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"} - ] - } - ] - response = dashscope.MultiModalConversation.call(model='qwen-vl-max-0809', messages=final_messages) - - print(response) - - text = response.output.choices[0].message.content[0]['text'] - usage = response.usage - - if "total_tokens" not in usage: - if "image_tokens" in usage: - token_usage = usage["input_tokens"] + usage["output_tokens"] + usage["image_tokens"] - else: - token_usage = usage["input_tokens"] + usage["output_tokens"] - else: - token_usage = usage["total_tokens"] - - print(text, token_usage) - # The screenshot is from a video game... 1387 \ No newline at end of file diff --git a/demo/gui_agent/llm_utils/run_llm.py b/demo/gui_agent/llm_utils/run_llm.py deleted file mode 100644 index bc207dd..0000000 --- a/demo/gui_agent/llm_utils/run_llm.py +++ /dev/null @@ -1,44 +0,0 @@ -import base64 -import logging -from .oai import run_oai_interleaved -from .gemini import run_gemini_interleaved - -def run_llm(prompt, llm="gpt-4o-mini", max_tokens=256, temperature=0, stop=None): - log_prompt(prompt) - - # turn string prompt into list - if isinstance(prompt, str): - prompt = [prompt] - elif isinstance(prompt, list): - pass - else: - raise ValueError(f"Invalid prompt type: {type(prompt)}") - - if llm.startswith("gpt"): # gpt series - out = run_oai_interleaved( - prompt, - llm, - max_tokens, - temperature, - stop - ) - elif llm.startswith("gemini"): # gemini series - out = run_gemini_interleaved( - prompt, - llm, - max_tokens, - temperature, - stop - ) - else: - raise ValueError(f"Invalid llm: {llm}") - logging.info( - f"========Output for {llm}=======\n{out}\n============================") - return out - -def log_prompt(prompt): - prompt_display = [prompt] if isinstance(prompt, str) else prompt - prompt_display = "\n\n".join(prompt_display) - logging.info( - f"========Prompt=======\n{prompt_display}\n============================") - \ No newline at end of file diff --git a/demo/loop.py b/demo/loop.py deleted file mode 100644 index 3d506c2..0000000 --- a/demo/loop.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. -""" -import time -import json -import asyncio -import platform -from collections.abc import Callable -from datetime import datetime -from enum import StrEnum -from typing import Any, cast, Dict - -from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse -from anthropic.types import ( - ToolResultBlockParam, - TextBlock, -) -from anthropic.types.beta import ( - BetaContentBlock, - BetaContentBlockParam, - BetaImageBlockParam, - BetaMessage, - BetaMessageParam, - BetaTextBlockParam, - BetaToolResultBlockParam, -) -from tools import BashTool, ComputerTool, EditTool, ToolCollection, ToolResult - -import torch - -from gui_agent.anthropic_agent import AnthropicActor -from executor.anthropic_executor import AnthropicExecutor -from omniparser_agent.vlm_agent import OmniParser, VLMAgent -from tools.colorful_text import colorful_text_showui, colorful_text_vlm -from tools.screen_capture import get_screenshot -from gui_agent.llm_utils.oai import encode_image - - -BETA_FLAG = "computer-use-2024-10-22" - - -class APIProvider(StrEnum): - ANTHROPIC = "anthropic" - BEDROCK = "bedrock" - VERTEX = "vertex" - OPENAI = "openai" - QWEN = "qwen" - - -PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { - APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", - APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", - APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", - # APIProvider.OPENAI: "gpt-4o", - # APIProvider.QWEN: "qwen2vl", -} - - -# This system prompt is optimized for the Docker environment in this repository and -# specific tool combinations enabled. -# We encourage modifying this system prompt to ensure the model has context for the -# environment it is running in, and to provide any additional information that may be -# helpful for the task at hand. -SYSTEM_PROMPT = f""" -* You are utilizing a Windows system with internet access. -* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. - -""" - -import base64 -from PIL import Image -from io import BytesIO - -def sampling_loop_sync( - *, - model: str, - provider: APIProvider | None, - system_prompt_suffix: str, - messages: list[BetaMessageParam], - output_callback: Callable[[BetaContentBlock], None], - tool_output_callback: Callable[[ToolResult, str], None], - api_response_callback: Callable[[APIResponse[BetaMessage]], None], - api_key: str, - only_n_most_recent_images: int | None = 2, - max_tokens: int = 4096, - selected_screen: int = 0 -): - """ - Synchronous agentic sampling loop for the assistant/tool interaction of computer use. - """ - print('in sampling_loop_sync, model:', model) - if model == "claude-3-5-sonnet-20241022": - omniparser = OmniParser(url="http://127.0.0.1:8000/send_text/", - selected_screen=selected_screen,) - - # Register Actor and Executor - actor = AnthropicActor( - model=model, - provider=provider, - system_prompt_suffix=system_prompt_suffix, - api_key=api_key, - api_response_callback=api_response_callback, - max_tokens=max_tokens, - only_n_most_recent_images=only_n_most_recent_images, - selected_screen=selected_screen - ) - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - executor = AnthropicExecutor( - output_callback=output_callback, - tool_output_callback=tool_output_callback, - selected_screen=selected_screen - ) - - elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": - omniparser = OmniParser(url="http://127.0.0.1:8000/send_text/", - selected_screen=selected_screen,) - - actor = VLMAgent( - model=model, - provider=provider, - system_prompt_suffix=system_prompt_suffix, - api_key=api_key, - api_response_callback=api_response_callback, - selected_screen=selected_screen, - output_callback=output_callback, - ) - - executor = AnthropicExecutor( - output_callback=output_callback, - tool_output_callback=tool_output_callback, - selected_screen=selected_screen - ) - - # elif model == "gpt-4o + ShowUI" or model == "qwen2vl + ShowUI": - # planner = VLMPlanner( - # model=model, - # provider=provider, - # system_prompt_suffix=system_prompt_suffix, - # api_key=api_key, - # api_response_callback=api_response_callback, - # selected_screen=selected_screen, - # output_callback=output_callback, - # ) - - # if torch.cuda.is_available(): device = torch.device("cuda") - # elif torch.backends.mps.is_available(): device = torch.device("mps") - # else: device = torch.device("cpu") # support: 'cpu', 'mps', 'cuda' - # print(f"showUI-2B inited on device: {device}.") - - # actor = ShowUIActor( - # model_path="./showui-2b/", - # # Replace with your local path, e.g., "C:\\code\\ShowUI-2B", "/Users/your_username/ShowUI-2B/". - # device=device, - # split='web', # 'web' or 'phone' - # selected_screen=selected_screen, - # output_callback=output_callback, - # ) - - # executor = ShowUIExecutor( - # output_callback=output_callback, - # tool_output_callback=tool_output_callback, - # selected_screen=selected_screen - # ) - - else: - raise ValueError(f"Model {model} not supported") - print(f"Model Inited: {model}, Provider: {provider}") - - tool_result_content = None - - print(f"Start the message loop. User messages: {messages}") - - if model == "claude-3-5-sonnet-20241022": # Anthropic loop - while True: - parsed_screen = omniparser() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} - import pdb; pdb.set_trace() - screen_info_block = TextBlock(text='Below is the structured accessibility information of the current UI screen, which includes text and icons you can operate on, take these information into account when you are making the prediction for the next action. Note you will still need to take screenshot to get the image: \n' + parsed_screen['screen_info'], type='text') - # # messages[-1]['content'].append(screen_info_block) - screen_info_dict = {"role": "user", "content": [screen_info_block]} - messages.append(screen_info_dict) - response = actor(messages=messages) - - for message, tool_result_content in executor(response, messages): - yield message - - if not tool_result_content: - return messages - - messages.append({"content": tool_result_content, "role": "user"}) - - elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": - while True: - parsed_screen = omniparser() - response, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) - - for message, tool_result_content in executor(response, messages): - yield message - - if not tool_result_content: - return messages - - # import pdb; pdb.set_trace() - # messages.append({"role": "user", - # "content": ["History plan:\n" + str(vlm_response_json['Reasoning'])]}) - - # messages.append({"content": tool_result_content, "role": "user"}) - - elif model == "gpt-4o + ShowUI" or model == "qwen2vl + ShowUI": # ShowUI loop - while True: - vlm_response = planner(messages=messages) - - next_action = json.loads(vlm_response).get("Next Action") - yield next_action - - if next_action == None or next_action == "" or next_action == "None": - final_sc, final_sc_path = get_screenshot(selected_screen=selected_screen) - output_callback(f'No more actions from {colorful_text_vlm}. End of task. Final State:\n', - sender="bot") - yield None - - output_callback(f"{colorful_text_vlm} sending action to {colorful_text_showui}:\n{next_action}", sender="bot") - - actor_response = actor(messages=next_action) - yield actor_response - - for message, tool_result_content in executor(actor_response, messages): - time.sleep(1) - yield message - - # since showui executor has no feedback for now, we use "actor_response" to represent its response - # update messages for the next loop - messages.append({"role": "user", - "content": ["History plan:\n" + str(json.loads(vlm_response)) + - "\nHistory actions:\n" + str(actor_response["content"])]}) - print(f"End of loop. Messages: {str(messages)[:100000]}. Total cost: $USD{planner.total_cost:.5f}") \ No newline at end of file diff --git a/demo/omniparser_agent/vlm_agent.py b/demo/omniparser_agent/vlm_agent.py deleted file mode 100644 index 53ba46a..0000000 --- a/demo/omniparser_agent/vlm_agent.py +++ /dev/null @@ -1,417 +0,0 @@ -import json -import asyncio -import platform -from collections.abc import Callable -from datetime import datetime -from enum import StrEnum -from typing import Any, cast, Dict, Callable -import uuid -import requests -from PIL import Image, ImageDraw -import base64 -from io import BytesIO - -from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse -from anthropic.types import TextBlock, ToolResultBlockParam -from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage - -from tools.screen_capture import get_screenshot -from gui_agent.llm_utils.oai import run_oai_interleaved, encode_image -from gui_agent.llm_utils.qwen import run_qwen -from gui_agent.llm_utils.llm_utils import extract_data -from tools.colorful_text import colorful_text_showui, colorful_text_vlm - - -SYSTEM_PROMPT = f""" -* You are utilizing a Windows system with internet access. -* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. - -""" - - -class OmniParser: - def __init__(self, - url: str, - selected_screen: int = 0) -> None: - self.url = url - self.selected_screen = selected_screen - - def __call__(self,): - screenshot, screenshot_path = get_screenshot(selected_screen=self.selected_screen) - screenshot_path = str(screenshot_path) - image_base64 = encode_image(screenshot_path) - - # response = requests.post(self.url, json={"base64_image": image_base64, 'prompt': 'omniparser process'}) - # response_json = response.json() - # example response_json: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "latency": 0.1} - # Debug - response_json = {"som_image_base64": image_base64, "parsed_content_list": ['debug1', 'debug2'], "latency": 0.1} - print('omniparser latency:', response_json['latency']) - response_json = self.reformat_messages(response_json) - return response_json - - def reformat_messages(self, response_json: dict): - parsed_content_list = response_json["parsed_content_list"] - screen_info = "" - # Debug - # for idx, element in enumerate(parsed_content_list): - # element['idx'] = idx - # if element['type'] == 'text': - # # screen_info += f'''

\n''' - # screen_info += f'ID: {idx}, Text: {element["content"]}\n' - # elif element['type'] == 'icon': - # # screen_info += f'''{element['content']} \n''' - # screen_info += f'ID: {idx}, Icon: {element["content"]}\n' - response_json['screen_info'] = screen_info - return response_json - - - -class VLMAgent: - def __init__( - self, - model: str, - provider: str, - system_prompt_suffix: str, - api_key: str, - output_callback: Callable, - api_response_callback: Callable, - max_tokens: int = 4096, - only_n_most_recent_images: int | None = None, - selected_screen: int = 0, - print_usage: bool = True, - ): - if model == "gpt-4o + ShowUI": - self.model = "gpt-4o-2024-11-20" - elif model == "gpt-4o-mini + ShowUI": - self.model = "gpt-4o-mini" # "gpt-4o-mini" - elif model == "qwen2vl + ShowUI": - self.model = "qwen2vl" - elif model == "omniparser + gpt-4o": - self.model = "gpt-4o-2024-11-20" - else: - raise ValueError(f"Model {model} not supported") - - self.provider = provider - self.system_prompt_suffix = system_prompt_suffix - self.api_key = api_key - self.api_response_callback = api_response_callback - self.max_tokens = max_tokens - self.only_n_most_recent_images = only_n_most_recent_images - self.selected_screen = selected_screen - self.output_callback = output_callback - - self.print_usage = print_usage - self.total_token_usage = 0 - self.total_cost = 0 - - self.system = ( - # f"{SYSTEM_PROMPT}{' ' + system_prompt_suffix if system_prompt_suffix else ''}" - f"{system_prompt_suffix}" - ) - - def __call__(self, messages: list, parsed_screen: list[str, list]): - # example parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} - screen_info = parsed_screen["screen_info"] - # drop looping actions msg, byte image etc - planner_messages = messages - # planner_messages = _message_filter_callback(messages) - - print(f"filtered_messages: {planner_messages}\n\n", "full messages:", messages) - # import pdb; pdb.set_trace() - planner_messages = _keep_latest_images(planner_messages) - # if self.only_n_most_recent_images: - # _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) - - system = self._get_system_prompt(screen_info) + self.system_prompt_suffix - - # Take a screenshot - screenshot, screenshot_path = get_screenshot(selected_screen=self.selected_screen) - screen_width, screen_height = screenshot.size - screenshot_path = str(screenshot_path) - image_base64 = encode_image(screenshot_path) - - som_image_data = base64.b64decode(parsed_screen['som_image_base64']) - som_screenshot_path = f"./tmp/outputs/screenshot_som_{uuid.uuid4().hex}.png" - with open(som_screenshot_path, "wb") as f: - f.write(som_image_data) - - self.output_callback(f'Screenshot for {colorful_text_vlm}:\n', - sender="bot") - self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n', sender="bot") - - - if isinstance(planner_messages[-1], dict): - if not isinstance(planner_messages[-1]["content"], list): - planner_messages[-1]["content"] = [planner_messages[-1]["content"]] - planner_messages[-1]["content"].append(screenshot_path) - planner_messages[-1]["content"].append(som_screenshot_path) - - print(f"Sending messages to VLMPlanner : {planner_messages}") - - if "gpt" in self.model: - vlm_response, token_usage = run_oai_interleaved( - messages=planner_messages, - system=system, - llm=self.model, - api_key=self.api_key, - max_tokens=self.max_tokens, - temperature=0, - ) - print(f"oai token usage: {token_usage}") - self.total_token_usage += token_usage - self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ - - elif "qwen" in self.model: - vlm_response, token_usage = run_qwen( - messages=planner_messages, - system=system, - llm=self.model, - api_key=self.api_key, - max_tokens=self.max_tokens, - temperature=0, - ) - print(f"qwen token usage: {token_usage}") - self.total_token_usage += token_usage - self.total_cost += (token_usage * 0.02 / 7.25 / 1000) # 1USD=7.25CNY, https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-plus-api - elif "phi" in self.model: - pass # TODO - else: - raise ValueError(f"Model {self.model} not supported") - - print(f"VLMPlanner response: {vlm_response}") - - if self.print_usage: - print(f"VLMPlanner total token usage so far: {self.total_token_usage}. Total cost so far: $USD{self.total_cost:.5f}") - - vlm_response_json = extract_data(vlm_response, "json") - vlm_response_json = json.loads(vlm_response_json) - - # map "box_id" to "idx" in parsed_screen, and output the xy coordinate of bbox - # TODO add try except for the case when "box_id" is not in the response - # if 'Box ID' in vlm_response_json: - try: - bbox = parsed_screen["parsed_content_list"][int(vlm_response_json["Box ID"])]["bbox"] - vlm_response_json["coordinate"] = [int((bbox[0] + bbox[2]) / 2 * screen_width), int((bbox[1] + bbox[3]) / 2 * screen_height)] - # draw a circle on the screenshot image to indicate the action - self.draw_action(vlm_response_json, image_base64) - except: - print("No Box ID in the response.") - - - # vlm_plan_str = '\n'.join([f'{key}: {value}' for key, value in json.loads(response).items()]) - vlm_plan_str = "" - for key, value in vlm_response_json.items(): - if key == "Reasoning": - vlm_plan_str += f'{value}' - else: - vlm_plan_str += f'\n{key}: {value}' - - # self.output_callback(f"{colorful_text_vlm}:\n{vlm_plan_str}", sender="bot") - - # construct the response so that anthropicExcutor can execute the tool - analysis = BetaTextBlock(text=vlm_plan_str, type='text') - if 'coordinate' in vlm_response_json: - move_cursor_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', - input={'action': 'mouse_move', 'coordinate': vlm_response_json["coordinate"]}, - name='computer', type='tool_use') - response_content = [analysis, move_cursor_block] - else: - response_content = [analysis] - if vlm_response_json["Next Action"] == "type": - click_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': 'left_click'}, name='computer', type='tool_use') - sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', - input={'action': vlm_response_json["Next Action"], 'text': vlm_response_json["value"]}, - name='computer', type='tool_use') - response_content.extend([click_block, sim_content_block]) - elif vlm_response_json["Next Action"] == "None": - print("Task paused/completed.") - else: - sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', - input={'action': vlm_response_json["Next Action"]}, - name='computer', type='tool_use') - response_content.append(sim_content_block) - - response = BetaMessage(id=f'toolu_{uuid.uuid4()}', content=response_content, model='', role='assistant', type='message', stop_reason='tool_use', usage=BetaUsage(input_tokens=0, output_tokens=0)) - return response, vlm_response_json - - - def _api_response_callback(self, response: APIResponse): - self.api_response_callback(response) - - - def reformat_messages(self, messages: list): - pass - - def _get_system_prompt(self, screen_info: str = ""): - datetime_str = datetime.now().strftime("%A, %B %d, %Y") - os_name = platform.system() - return f""" -You are using an {os_name} device. -You are able to use a mouse and keyboard to interact with the computer based on the given task and screenshot. -You can only interact with the desktop GUI (no terminal or application menu access). - -You may be given some history plan and actions, this is the response from the previous loop. -You should carefully consider your plan base on the task, screenshot, and history actions. - -Here is the list of all detected bounding boxes by IDs on the screen and their description:{screen_info} - -Your available "Next Action" only include: -- type: type a string of text. -- left_click: Describe the ui element to be clicked. -- enter: Press an enter key. -- escape: Press an ESCAPE key. -- hover: Describe the ui element to be hovered. -- scroll: Scroll the screen, you must specify up or down. -- press: Describe the ui element to be pressed. - -Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. - -Output format: -```json -{{ - "Reasoning": str, # describe what is in the current screen, taking into account the history, then describe your step-by-step thoughts on how to achieve the task, choose one action from available actions at a time. - "Next Action": "action_type, action description" | "None" # one action at a time, describe it in short and precisely. - 'Box ID': n, - 'value': "xxx" # if the action is type, you should provide the text to type. -}} -``` - -One Example: -```json -{{ - "Reasoning": "The current screen shows google result of amazon, in previous action I have searched amazon on google. Then I need to click on the first search results to go to amazon.com.", - "Next Action": "left_click", - 'Box ID': m, -}} -``` - -Another Example: -```json -{{ - "Reasoning": "The current screen shows the front page of amazon. There is no previous action. Therefore I need to type "Apple watch" in the search bar.", - "Next Action": "type", - 'Box ID': n, - 'value': "Apple watch" -}} -``` - -IMPORTANT NOTES: -1. You should only give a single action at a time. -2. You should give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. -3. Attach the next action prediction in the "Next Action". -4. You should not include other actions, such as keyboard shortcuts. -5. When the task is completed, you should say "Next Action": "None" in the json field. -""" - def draw_action(self, vlm_response_json, image_base64): - # draw a circle using the coordinate in parsed_screen['som_image_base64'] - image_data = base64.b64decode(image_base64) - image = Image.open(BytesIO(image_data)) - - draw = ImageDraw.Draw(image) - x, y = vlm_response_json["coordinate"] - radius = 10 - draw.ellipse((x - radius, y - radius, x + radius, y + radius), outline='red', width=3) - buffered = BytesIO() - image.save('demo.png') - image.save(buffered, format="PNG") - image_with_circle_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") - self.output_callback(f'Action performed on the Screenshot (red circle), for {colorful_text_vlm}:\n', sender="bot") - - -def _keep_latest_images(messages): - for i in range(len(messages)-1): - if isinstance(messages[i]["content"], list): - for cnt in messages[i]["content"]: - if isinstance(cnt, str): - if cnt.endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")): - messages[i]["content"].remove(cnt) - return messages - - -def _maybe_filter_to_n_most_recent_images( - messages: list[BetaMessageParam], - images_to_keep: int, - min_removal_threshold: int = 10, -): - """ - With the assumption that images are screenshots that are of diminishing value as - the conversation progresses, remove all but the final `images_to_keep` tool_result - images in place, with a chunk of min_removal_threshold to reduce the amount we - break the implicit prompt cache. - """ - if images_to_keep is None: - return messages - - tool_result_blocks = cast( - list[ToolResultBlockParam], - [ - item - for message in messages - for item in ( - message["content"] if isinstance(message["content"], list) else [] - ) - if isinstance(item, dict) and item.get("type") == "tool_result" - ], - ) - - total_images = sum( - 1 - for tool_result in tool_result_blocks - for content in tool_result.get("content", []) - if isinstance(content, dict) and content.get("type") == "image" - ) - - images_to_remove = total_images - images_to_keep - # for better cache behavior, we want to remove in chunks - images_to_remove -= images_to_remove % min_removal_threshold - - for tool_result in tool_result_blocks: - if isinstance(tool_result.get("content"), list): - new_content = [] - for content in tool_result.get("content", []): - if isinstance(content, dict) and content.get("type") == "image": - if images_to_remove > 0: - images_to_remove -= 1 - continue - new_content.append(content) - tool_result["content"] = new_content - - -def _message_filter_callback(messages): - filtered_list = [] - try: - for msg in messages: - if msg.get('role') in ['user']: - if not isinstance(msg["content"], list): - msg["content"] = [msg["content"]] - if isinstance(msg["content"][0], TextBlock): - filtered_list.append(str(msg["content"][0].text)) # User message - elif isinstance(msg["content"][0], str): - filtered_list.append(msg["content"][0]) # User message - else: - print("[_message_filter_callback]: drop message", msg) - continue - - # elif msg.get('role') in ['assistant']: - # if isinstance(msg["content"][0], TextBlock): - # msg["content"][0] = str(msg["content"][0].text) - # elif isinstance(msg["content"][0], BetaTextBlock): - # msg["content"][0] = str(msg["content"][0].text) - # elif isinstance(msg["content"][0], BetaToolUseBlock): - # msg["content"][0] = str(msg['content'][0].input) - # elif isinstance(msg["content"][0], Dict) and msg["content"][0]["content"][-1]["type"] == "image": - # msg["content"][0] = f'' - # else: - # print("[_message_filter_callback]: drop message", msg) - # continue - # filtered_list.append(msg["content"][0]) # User message - - else: - print("[_message_filter_callback]: drop message", msg) - continue - - except Exception as e: - print("[_message_filter_callback]: error", e) - - return filtered_list \ No newline at end of file diff --git a/demo/tmp/outputs/screenshot.png b/demo/tmp/outputs/screenshot.png deleted file 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zGzTnG2C#L0d)H<7h8yVM!j!>bZM#p%M%#w>;0ZMgTn#zZRdJnm)Hf%T(cjPSOLqX* N&WOF+>$V-u`46QzFg^eP diff --git a/demo/tools/__init__.py b/demo/tools/__init__.py deleted file mode 100644 index e0bf3e2..0000000 --- a/demo/tools/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -from .base import CLIResult, ToolResult -from .bash import BashTool -from .collection import ToolCollection -from .computer import ComputerTool -from .edit import EditTool -from .screen_capture import get_screenshot - -__ALL__ = [ - BashTool, - CLIResult, - ComputerTool, - EditTool, - ToolCollection, - ToolResult, - get_screenshot, -] diff --git a/demo/tools/base.py b/demo/tools/base.py deleted file mode 100644 index 1acf24a..0000000 --- a/demo/tools/base.py +++ /dev/null @@ -1,69 +0,0 @@ -from abc import ABCMeta, abstractmethod -from dataclasses import dataclass, fields, replace -from typing import Any - -from anthropic.types.beta import BetaToolUnionParam - - -class BaseAnthropicTool(metaclass=ABCMeta): - """Abstract base class for Anthropic-defined tools.""" - - @abstractmethod - def __call__(self, **kwargs) -> Any: - """Executes the tool with the given arguments.""" - ... - - @abstractmethod - def to_params( - self, - ) -> BetaToolUnionParam: - raise NotImplementedError - - -@dataclass(kw_only=True, frozen=True) -class ToolResult: - """Represents the result of a tool execution.""" - - output: str | None = None - error: str | None = None - base64_image: str | None = None - system: str | None = None - - def __bool__(self): - return any(getattr(self, field.name) for field in fields(self)) - - def __add__(self, other: "ToolResult"): - def combine_fields( - field: str | None, other_field: str | None, concatenate: bool = True - ): - if field and other_field: - if concatenate: - return field + other_field - raise ValueError("Cannot combine tool results") - return field or other_field - - return ToolResult( - output=combine_fields(self.output, other.output), - error=combine_fields(self.error, other.error), - base64_image=combine_fields(self.base64_image, other.base64_image, False), - system=combine_fields(self.system, other.system), - ) - - def replace(self, **kwargs): - """Returns a new ToolResult with the given fields replaced.""" - return replace(self, **kwargs) - - -class CLIResult(ToolResult): - """A ToolResult that can be rendered as a CLI output.""" - - -class ToolFailure(ToolResult): - """A ToolResult that represents a failure.""" - - -class ToolError(Exception): - """Raised when a tool encounters an error.""" - - def __init__(self, message): - self.message = message diff --git a/demo/tools/bash.py b/demo/tools/bash.py deleted file mode 100644 index 56eb912..0000000 --- a/demo/tools/bash.py +++ /dev/null @@ -1,136 +0,0 @@ -import asyncio -import os -from typing import ClassVar, Literal - -from anthropic.types.beta import BetaToolBash20241022Param - -from .base import BaseAnthropicTool, CLIResult, ToolError, ToolResult - - -class _BashSession: - """A session of a bash shell.""" - - _started: bool - _process: asyncio.subprocess.Process - - command: str = "/bin/bash" - _output_delay: float = 0.2 # seconds - _timeout: float = 120.0 # seconds - _sentinel: str = "<>" - - def __init__(self): - self._started = False - self._timed_out = False - - async def start(self): - if self._started: - return - - self._process = await asyncio.create_subprocess_shell( - self.command, - shell=False, - stdin=asyncio.subprocess.PIPE, - stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE, - ) - - self._started = True - - def stop(self): - """Terminate the bash shell.""" - if not self._started: - raise ToolError("Session has not started.") - if self._process.returncode is not None: - return - self._process.terminate() - - async def run(self, command: str): - """Execute a command in the bash shell.""" - if not self._started: - raise ToolError("Session has not started.") - if self._process.returncode is not None: - return ToolResult( - system="tool must be restarted", - error=f"bash has exited with returncode {self._process.returncode}", - ) - if self._timed_out: - raise ToolError( - f"timed out: bash has not returned in {self._timeout} seconds and must be restarted", - ) - - # we know these are not None because we created the process with PIPEs - assert self._process.stdin - assert self._process.stdout - assert self._process.stderr - - # send command to the process - self._process.stdin.write( - command.encode() + f"; echo '{self._sentinel}'\n".encode() - ) - await self._process.stdin.drain() - - # read output from the process, until the sentinel is found - output = "" - try: - async with asyncio.timeout(self._timeout): - while True: - await asyncio.sleep(self._output_delay) - data = await self._process.stdout.readline() - if not data: - break - line = data.decode() - output += line - if self._sentinel in line: - output = output.replace(self._sentinel, "") - break - except asyncio.TimeoutError: - self._timed_out = True - raise ToolError( - f"timed out: bash has not returned in {self._timeout} seconds and must be restarted", - ) from None - - error = await self._process.stderr.read() - error = error.decode() - - return CLIResult(output=output.strip(), error=error.strip()) - - -class BashTool(BaseAnthropicTool): - """ - A tool that allows the agent to run bash commands. - The tool parameters are defined by Anthropic and are not editable. - """ - - _session: _BashSession | None - name: ClassVar[Literal["bash"]] = "bash" - api_type: ClassVar[Literal["bash_20241022"]] = "bash_20241022" - - def __init__(self): - self._session = None - super().__init__() - - async def __call__( - self, command: str | None = None, restart: bool = False, **kwargs - ): - if restart: - if self._session: - self._session.stop() - self._session = _BashSession() - await self._session.start() - - return ToolResult(system="tool has been restarted.") - - if self._session is None: - self._session = _BashSession() - await self._session.start() - - if command is not None: - return await self._session.run(command) - - raise ToolError("no command provided.") - - def to_params(self) -> BetaToolBash20241022Param: - return { - "type": self.api_type, - "name": self.name, - } \ No newline at end of file diff --git a/demo/tools/collection.py b/demo/tools/collection.py deleted file mode 100644 index 693a1dd..0000000 --- a/demo/tools/collection.py +++ /dev/null @@ -1,34 +0,0 @@ -"""Collection classes for managing multiple tools.""" - -from typing import Any - -from anthropic.types.beta import BetaToolUnionParam - -from .base import ( - BaseAnthropicTool, - ToolError, - ToolFailure, - ToolResult, -) - - -class ToolCollection: - """A collection of anthropic-defined tools.""" - - def __init__(self, *tools: BaseAnthropicTool): - self.tools = tools - self.tool_map = {tool.to_params()["name"]: tool for tool in tools} - - def to_params( - self, - ) -> list[BetaToolUnionParam]: - return [tool.to_params() for tool in self.tools] - - async def run(self, *, name: str, tool_input: dict[str, Any]) -> ToolResult: - tool = self.tool_map.get(name) - if not tool: - return ToolFailure(error=f"Tool {name} is invalid") - try: - return await tool(**tool_input) - except ToolError as e: - return ToolFailure(error=e.message) diff --git a/demo/tools/colorful_text.py b/demo/tools/colorful_text.py deleted file mode 100644 index 5804187..0000000 --- a/demo/tools/colorful_text.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -Define some colorful stuffs for better visualization in the chat. -""" - -# Define the RGB colors for each letter -colors = { - 'S': 'rgb(106, 158, 210)', - 'h': 'rgb(111, 163, 82)', - 'o': 'rgb(209, 100, 94)', - 'w': 'rgb(238, 171, 106)', - 'U': 'rgb(0, 0, 0)', - 'I': 'rgb(0, 0, 0)', -} - -# Construct the colorful "ShowUI" word -colorful_text_showui = "**"+''.join( - f'{letter}' - for letter in "ShowUI" -)+"**" - - -colorful_text_vlm = "**OmniParser Agent**" - -colorful_text_user = "**User**" - -# print(f"colorful_text_showui: {colorful_text_showui}") -# **ShowUI** \ No newline at end of file diff --git a/demo/tools/computer.py b/demo/tools/computer.py deleted file mode 100644 index e49f932..0000000 --- a/demo/tools/computer.py +++ /dev/null @@ -1,519 +0,0 @@ -import subprocess -import platform -import pyautogui -import asyncio -import base64 -import os -import time -if platform.system() == "Darwin": - import Quartz # uncomment this line if you are on macOS -from enum import StrEnum -from pathlib import Path -from typing import Literal, TypedDict -from uuid import uuid4 -from screeninfo import get_monitors - -from PIL import ImageGrab, Image -from functools import partial - -from anthropic.types.beta import BetaToolComputerUse20241022Param - -from .base import BaseAnthropicTool, ToolError, ToolResult -from .run import run - -OUTPUT_DIR = "./tmp/outputs" - -TYPING_DELAY_MS = 12 -TYPING_GROUP_SIZE = 50 - -Action = Literal[ - "key", - "type", - "mouse_move", - "left_click", - "left_click_drag", - "right_click", - "middle_click", - "double_click", - "screenshot", - "cursor_position", -] - - -class Resolution(TypedDict): - width: int - height: int - - -MAX_SCALING_TARGETS: dict[str, Resolution] = { - "XGA": Resolution(width=1024, height=768), # 4:3 - "WXGA": Resolution(width=1280, height=800), # 16:10 - "FWXGA": Resolution(width=1366, height=768), # ~16:9 -} - - -class ScalingSource(StrEnum): - COMPUTER = "computer" - API = "api" - - -class ComputerToolOptions(TypedDict): - display_height_px: int - display_width_px: int - display_number: int | None - - -def chunks(s: str, chunk_size: int) -> list[str]: - return [s[i : i + chunk_size] for i in range(0, len(s), chunk_size)] - - -def get_screen_details(): - screens = get_monitors() - screen_details = [] - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s.x) - - # Loop through sorted screens and assign positions - primary_index = 0 - for i, screen in enumerate(sorted_screens): - if i == 0: - layout = "Left" - elif i == len(sorted_screens) - 1: - layout = "Right" - else: - layout = "Center" - - if screen.is_primary: - position = "Primary" - primary_index = i - else: - position = "Secondary" - screen_info = f"Screen {i + 1}: {screen.width}x{screen.height}, {layout}, {position}" - screen_details.append(screen_info) - - return screen_details, primary_index - - -class ComputerTool(BaseAnthropicTool): - """ - A tool that allows the agent to interact with the screen, keyboard, and mouse of the current computer. - Adapted for Windows using 'pyautogui'. - """ - - name: Literal["computer"] = "computer" - api_type: Literal["computer_20241022"] = "computer_20241022" - width: int - height: int - display_num: int | None - - _screenshot_delay = 2.0 - _scaling_enabled = True - - @property - def options(self) -> ComputerToolOptions: - width, height = self.scale_coordinates( - ScalingSource.COMPUTER, self.width, self.height - ) - return { - "display_width_px": width, - "display_height_px": height, - "display_number": self.display_num, - } - - def to_params(self) -> BetaToolComputerUse20241022Param: - return {"name": self.name, "type": self.api_type, **self.options} - - def __init__(self, selected_screen: int = 0, is_scaling: bool = False): - super().__init__() - - # Get screen width and height using Windows command - self.display_num = None - self.offset_x = 0 - self.offset_y = 0 - self.selected_screen = selected_screen - self.is_scaling = is_scaling - self.width, self.height = self.get_screen_size() - - # Path to cliclick - self.cliclick = "cliclick" - self.key_conversion = {"Page_Down": "pagedown", - "Page_Up": "pageup", - "Super_L": "win", - "Escape": "esc"} - - system = platform.system() # Detect platform - if system == "Windows": - screens = get_monitors() - sorted_screens = sorted(screens, key=lambda s: s.x) - if self.selected_screen < 0 or self.selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - screen = sorted_screens[self.selected_screen] - bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) - - elif system == "Darwin": # macOS - max_displays = 32 # Maximum number of displays to handle - active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] - screens = [] - for display_id in active_displays: - bounds = Quartz.CGDisplayBounds(display_id) - screens.append({ - 'id': display_id, 'x': int(bounds.origin.x), 'y': int(bounds.origin.y), - 'width': int(bounds.size.width), 'height': int(bounds.size.height), - 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display - }) - sorted_screens = sorted(screens, key=lambda s: s['x']) - if self.selected_screen < 0 or self.selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - screen = sorted_screens[self.selected_screen] - bbox = (screen['x'], screen['y'], screen['x'] + screen['width'], screen['y'] + screen['height']) - else: # Linux or other OS - cmd = "xrandr | grep ' primary' | awk '{print $4}'" - try: - # output = subprocess.check_output(cmd, shell=True).decode() - # resolution = output.strip().split()[0] - # width, height = map(int, resolution.split('x')) - # bbox = (0, 0, width, height) # Assuming single primary screen for simplicity - screen = get_monitors()[0] - bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) - except subprocess.CalledProcessError: - raise RuntimeError("Failed to get screen resolution on Linux.") - - self.offset_x = screen['x'] if system == "Darwin" else screen.x - self.offset_y = screen['y'] if system == "Darwin" else screen.y - self.bbox = bbox - - - async def __call__( - self, - *, - action: Action, - text: str | None = None, - coordinate: tuple[int, int] | None = None, - **kwargs, - ): - print(f"action: {action}, text: {text}, coordinate: {coordinate}, is_scaling: {self.is_scaling}") - if action in ("mouse_move", "left_click_drag"): - if coordinate is None: - raise ToolError(f"coordinate is required for {action}") - if text is not None: - raise ToolError(f"text is not accepted for {action}") - if not isinstance(coordinate, (list, tuple)) or len(coordinate) != 2: - raise ToolError(f"{coordinate} must be a tuple of length 2") - # if not all(isinstance(i, int) and i >= 0 for i in coordinate): - if not all(isinstance(i, int) for i in coordinate): - raise ToolError(f"{coordinate} must be a tuple of non-negative ints") - - if self.is_scaling: - x, y = self.scale_coordinates( - ScalingSource.API, coordinate[0], coordinate[1] - ) - else: - x, y = coordinate - - # print(f"scaled_coordinates: {x}, {y}") - # print(f"offset: {self.offset_x}, {self.offset_y}") - - # x += self.offset_x # TODO - check if this is needed - # y += self.offset_y - - print(f"mouse move to {x}, {y}") - - if action == "mouse_move": - pyautogui.moveTo(x, y) - return ToolResult(output=f"Moved mouse to ({x}, {y})") - elif action == "left_click_drag": - current_x, current_y = pyautogui.position() - pyautogui.dragTo(x, y, duration=0.5) # Adjust duration as needed - return ToolResult(output=f"Dragged mouse from ({current_x}, {current_y}) to ({x}, {y})") - - if action in ("key", "type"): - if text is None: - raise ToolError(f"text is required for {action}") - if coordinate is not None: - raise ToolError(f"coordinate is not accepted for {action}") - if not isinstance(text, str): - raise ToolError(output=f"{text} must be a string") - - if action == "key": - # Handle key combinations - keys = text.split('+') - for key in keys: - key = self.key_conversion.get(key.strip(), key.strip()) - key = key.lower() - pyautogui.keyDown(key) # Press down each key - for key in reversed(keys): - key = self.key_conversion.get(key.strip(), key.strip()) - key = key.lower() - pyautogui.keyUp(key) # Release each key in reverse order - return ToolResult(output=f"Pressed keys: {text}") - - elif action == "type": - pyautogui.typewrite(text, interval=TYPING_DELAY_MS / 1000) # Convert ms to seconds - pyautogui.press('enter') - screenshot_base64 = (await self.screenshot()).base64_image - return ToolResult(output=text, base64_image=screenshot_base64) - - if action in ( - "left_click", - "right_click", - "double_click", - "middle_click", - "screenshot", - "cursor_position", - "left_press", - ): - if text is not None: - raise ToolError(f"text is not accepted for {action}") - if coordinate is not None: - raise ToolError(f"coordinate is not accepted for {action}") - - if action == "screenshot": - return await self.screenshot() - elif action == "cursor_position": - x, y = pyautogui.position() - x, y = self.scale_coordinates(ScalingSource.COMPUTER, x, y) - return ToolResult(output=f"X={x},Y={y}") - else: - if action == "left_click": - pyautogui.click() - elif action == "right_click": - pyautogui.rightClick() - elif action == "middle_click": - pyautogui.middleClick() - elif action == "double_click": - pyautogui.doubleClick() - elif action == "left_press": - pyautogui.mouseDown() - time.sleep(1) - pyautogui.mouseUp() - return ToolResult(output=f"Performed {action}") - - raise ToolError(f"Invalid action: {action}") - - async def screenshot(self): - - import time - time.sleep(1) - - """Take a screenshot of the current screen and return a ToolResult with the base64 encoded image.""" - output_dir = Path(OUTPUT_DIR) - output_dir.mkdir(parents=True, exist_ok=True) - path = output_dir / f"screenshot_{uuid4().hex}.png" - - ImageGrab.grab = partial(ImageGrab.grab, all_screens=True) - - # Detect platform - system = platform.system() - - if system == "Windows": - # Windows: Use screeninfo to get monitor details - screens = get_monitors() - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s.x) - - if self.selected_screen < 0 or self.selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - - screen = sorted_screens[self.selected_screen] - bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) - - elif system == "Darwin": # macOS - # macOS: Use Quartz to get monitor details - max_displays = 32 # Maximum number of displays to handle - active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] - - # Get the display bounds (resolution) for each active display - screens = [] - for display_id in active_displays: - bounds = Quartz.CGDisplayBounds(display_id) - screens.append({ - 'id': display_id, - 'x': int(bounds.origin.x), - 'y': int(bounds.origin.y), - 'width': int(bounds.size.width), - 'height': int(bounds.size.height), - 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display - }) - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s['x']) - - if self.selected_screen < 0 or self.selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - - screen = sorted_screens[self.selected_screen] - bbox = (screen['x'], screen['y'], screen['x'] + screen['width'], screen['y'] + screen['height']) - - else: # Linux or other OS - cmd = "xrandr | grep ' primary' | awk '{print $4}'" - try: - # output = subprocess.check_output(cmd, shell=True).decode() - # resolution = output.strip().split()[0] - # width, height = map(int, resolution.split('x')) - # bbox = (0, 0, width, height) # Assuming single primary screen for simplicity - screen = get_monitors()[0] - bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) - except subprocess.CalledProcessError: - raise RuntimeError("Failed to get screen resolution on Linux.") - - # Take screenshot using the bounding box - screenshot = ImageGrab.grab(bbox=bbox) - - # Set offsets (for potential future use) - self.offset_x = screen['x'] if system == "Darwin" else screen.x - self.offset_y = screen['y'] if system == "Darwin" else screen.y - - print(f"target_dimension {self.target_dimension}") - - if not hasattr(self, 'target_dimension'): - screenshot = self.padding_image(screenshot) - self.target_dimension = MAX_SCALING_TARGETS["WXGA"] - - # Resize if target_dimensions are specified - print(f"offset is {self.offset_x}, {self.offset_y}") - print(f"target_dimension is {self.target_dimension}") - screenshot = screenshot.resize((self.target_dimension["width"], self.target_dimension["height"])) - - # Save the screenshot - screenshot.save(str(path)) - - if path.exists(): - # Return a ToolResult instance instead of a dictionary - return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode()) - - raise ToolError(f"Failed to take screenshot: {path} does not exist.") - - def padding_image(self, screenshot): - """Pad the screenshot to 16:10 aspect ratio, when the aspect ratio is not 16:10.""" - _, height = screenshot.size - new_width = height * 16 // 10 - - padding_image = Image.new("RGB", (new_width, height), (255, 255, 255)) - # padding to top left - padding_image.paste(screenshot, (0, 0)) - return padding_image - - async def shell(self, command: str, take_screenshot=True) -> ToolResult: - """Run a shell command and return the output, error, and optionally a screenshot.""" - _, stdout, stderr = await run(command) - base64_image = None - - if take_screenshot: - # delay to let things settle before taking a screenshot - await asyncio.sleep(self._screenshot_delay) - base64_image = (await self.screenshot()).base64_image - - return ToolResult(output=stdout, error=stderr, base64_image=base64_image) - - def scale_coordinates(self, source: ScalingSource, x: int, y: int): - """Scale coordinates to a target maximum resolution.""" - if not self._scaling_enabled: - return x, y - ratio = self.width / self.height - target_dimension = None - - for target_name, dimension in MAX_SCALING_TARGETS.items(): - # allow some error in the aspect ratio - not ratios are exactly 16:9 - if abs(dimension["width"] / dimension["height"] - ratio) < 0.02: - if dimension["width"] < self.width: - target_dimension = dimension - self.target_dimension = target_dimension - # print(f"target_dimension: {target_dimension}") - break - - if target_dimension is None: - # TODO: currently we force the target to be WXGA (16:10), when it cannot find a match - target_dimension = MAX_SCALING_TARGETS["WXGA"] - self.target_dimension = MAX_SCALING_TARGETS["WXGA"] - - # should be less than 1 - x_scaling_factor = target_dimension["width"] / self.width - y_scaling_factor = target_dimension["height"] / self.height - if source == ScalingSource.API: - if x > self.width or y > self.height: - raise ToolError(f"Coordinates {x}, {y} are out of bounds") - # scale up - return round(x / x_scaling_factor), round(y / y_scaling_factor) - # scale down - return round(x * x_scaling_factor), round(y * y_scaling_factor) - - def get_screen_size(self): - if platform.system() == "Windows": - # Use screeninfo to get primary monitor on Windows - screens = get_monitors() - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s.x) - - if self.selected_screen is None: - primary_monitor = next((m for m in get_monitors() if m.is_primary), None) - return primary_monitor.width, primary_monitor.height - elif self.selected_screen < 0 or self.selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - else: - screen = sorted_screens[self.selected_screen] - return screen.width, screen.height - - elif platform.system() == "Darwin": - # macOS part using Quartz to get screen information - max_displays = 32 # Maximum number of displays to handle - active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] - - # Get the display bounds (resolution) for each active display - screens = [] - for display_id in active_displays: - bounds = Quartz.CGDisplayBounds(display_id) - screens.append({ - 'id': display_id, - 'x': int(bounds.origin.x), - 'y': int(bounds.origin.y), - 'width': int(bounds.size.width), - 'height': int(bounds.size.height), - 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display - }) - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s['x']) - - if self.selected_screen is None: - # Find the primary monitor - primary_monitor = next((screen for screen in screens if screen['is_primary']), None) - if primary_monitor: - return primary_monitor['width'], primary_monitor['height'] - else: - raise RuntimeError("No primary monitor found.") - elif self.selected_screen < 0 or self.selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - else: - # Return the resolution of the selected screen - screen = sorted_screens[self.selected_screen] - return screen['width'], screen['height'] - - else: # Linux or other OS - cmd = "xrandr | grep ' primary' | awk '{print $4}'" - try: - # output = subprocess.check_output(cmd, shell=True).decode() - # resolution = output.strip().split()[0] - # width, height = map(int, resolution.split('x')) - # return width, height - screen = get_monitors()[0] - return screen.width, screen.height - except subprocess.CalledProcessError: - raise RuntimeError("Failed to get screen resolution on Linux.") - - def get_mouse_position(self): - # TODO: enhance this func - from AppKit import NSEvent - from Quartz import CGEventSourceCreate, kCGEventSourceStateCombinedSessionState - - loc = NSEvent.mouseLocation() - # Adjust for different coordinate system - return int(loc.x), int(self.height - loc.y) - - def map_keys(self, text: str): - """Map text to cliclick key codes if necessary.""" - # For simplicity, return text as is - # Implement mapping if special keys are needed - return text \ No newline at end of file diff --git a/demo/tools/edit.py b/demo/tools/edit.py deleted file mode 100644 index fd4e868..0000000 --- a/demo/tools/edit.py +++ /dev/null @@ -1,290 +0,0 @@ -from collections import defaultdict -from pathlib import Path -from typing import Literal, get_args - -from anthropic.types.beta import BetaToolTextEditor20241022Param - -from .base import BaseAnthropicTool, CLIResult, ToolError, ToolResult -from .run import maybe_truncate, run - -Command = Literal[ - "view", - "create", - "str_replace", - "insert", - "undo_edit", -] -SNIPPET_LINES: int = 4 - - -class EditTool(BaseAnthropicTool): - """ - An filesystem editor tool that allows the agent to view, create, and edit files. - The tool parameters are defined by Anthropic and are not editable. - """ - - api_type: Literal["text_editor_20241022"] = "text_editor_20241022" - name: Literal["str_replace_editor"] = "str_replace_editor" - - _file_history: dict[Path, list[str]] - - def __init__(self): - self._file_history = defaultdict(list) - super().__init__() - - def to_params(self) -> BetaToolTextEditor20241022Param: - return { - "name": self.name, - "type": self.api_type, - } - - async def __call__( - self, - *, - command: Command, - path: str, - file_text: str | None = None, - view_range: list[int] | None = None, - old_str: str | None = None, - new_str: str | None = None, - insert_line: int | None = None, - **kwargs, - ): - _path = Path(path) - self.validate_path(command, _path) - if command == "view": - return await self.view(_path, view_range) - elif command == "create": - if not file_text: - raise ToolError("Parameter `file_text` is required for command: create") - self.write_file(_path, file_text) - self._file_history[_path].append(file_text) - return ToolResult(output=f"File created successfully at: {_path}") - elif command == "str_replace": - if not old_str: - raise ToolError( - "Parameter `old_str` is required for command: str_replace" - ) - return self.str_replace(_path, old_str, new_str) - elif command == "insert": - if insert_line is None: - raise ToolError( - "Parameter `insert_line` is required for command: insert" - ) - if not new_str: - raise ToolError("Parameter `new_str` is required for command: insert") - return self.insert(_path, insert_line, new_str) - elif command == "undo_edit": - return self.undo_edit(_path) - raise ToolError( - f'Unrecognized command {command}. The allowed commands for the {self.name} tool are: {", ".join(get_args(Command))}' - ) - - def validate_path(self, command: str, path: Path): - """ - Check that the path/command combination is valid. - """ - # Check if its an absolute path - if not path.is_absolute(): - suggested_path = Path("") / path - raise ToolError( - f"The path {path} is not an absolute path, it should start with `/`. Maybe you meant {suggested_path}?" - ) - # Check if path exists - if not path.exists() and command != "create": - raise ToolError( - f"The path {path} does not exist. Please provide a valid path." - ) - if path.exists() and command == "create": - raise ToolError( - f"File already exists at: {path}. Cannot overwrite files using command `create`." - ) - # Check if the path points to a directory - if path.is_dir(): - if command != "view": - raise ToolError( - f"The path {path} is a directory and only the `view` command can be used on directories" - ) - - async def view(self, path: Path, view_range: list[int] | None = None): - """Implement the view command""" - if path.is_dir(): - if view_range: - raise ToolError( - "The `view_range` parameter is not allowed when `path` points to a directory." - ) - - _, stdout, stderr = await run( - rf"find {path} -maxdepth 2 -not -path '*/\.*'" - ) - if not stderr: - stdout = f"Here's the files and directories up to 2 levels deep in {path}, excluding hidden items:\n{stdout}\n" - return CLIResult(output=stdout, error=stderr) - - file_content = self.read_file(path) - init_line = 1 - if view_range: - if len(view_range) != 2 or not all(isinstance(i, int) for i in view_range): - raise ToolError( - "Invalid `view_range`. It should be a list of two integers." - ) - file_lines = file_content.split("\n") - n_lines_file = len(file_lines) - init_line, final_line = view_range - if init_line < 1 or init_line > n_lines_file: - raise ToolError( - f"Invalid `view_range`: {view_range}. It's first element `{init_line}` should be within the range of lines of the file: {[1, n_lines_file]}" - ) - if final_line > n_lines_file: - raise ToolError( - f"Invalid `view_range`: {view_range}. It's second element `{final_line}` should be smaller than the number of lines in the file: `{n_lines_file}`" - ) - if final_line != -1 and final_line < init_line: - raise ToolError( - f"Invalid `view_range`: {view_range}. It's second element `{final_line}` should be larger or equal than its first `{init_line}`" - ) - - if final_line == -1: - file_content = "\n".join(file_lines[init_line - 1 :]) - else: - file_content = "\n".join(file_lines[init_line - 1 : final_line]) - - return CLIResult( - output=self._make_output(file_content, str(path), init_line=init_line) - ) - - def str_replace(self, path: Path, old_str: str, new_str: str | None): - """Implement the str_replace command, which replaces old_str with new_str in the file content""" - # Read the file content - file_content = self.read_file(path).expandtabs() - old_str = old_str.expandtabs() - new_str = new_str.expandtabs() if new_str is not None else "" - - # Check if old_str is unique in the file - occurrences = file_content.count(old_str) - if occurrences == 0: - raise ToolError( - f"No replacement was performed, old_str `{old_str}` did not appear verbatim in {path}." - ) - elif occurrences > 1: - file_content_lines = file_content.split("\n") - lines = [ - idx + 1 - for idx, line in enumerate(file_content_lines) - if old_str in line - ] - raise ToolError( - f"No replacement was performed. Multiple occurrences of old_str `{old_str}` in lines {lines}. Please ensure it is unique" - ) - - # Replace old_str with new_str - new_file_content = file_content.replace(old_str, new_str) - - # Write the new content to the file - self.write_file(path, new_file_content) - - # Save the content to history - self._file_history[path].append(file_content) - - # Create a snippet of the edited section - replacement_line = file_content.split(old_str)[0].count("\n") - start_line = max(0, replacement_line - SNIPPET_LINES) - end_line = replacement_line + SNIPPET_LINES + new_str.count("\n") - snippet = "\n".join(new_file_content.split("\n")[start_line : end_line + 1]) - - # Prepare the success message - success_msg = f"The file {path} has been edited. " - success_msg += self._make_output( - snippet, f"a snippet of {path}", start_line + 1 - ) - success_msg += "Review the changes and make sure they are as expected. Edit the file again if necessary." - - return CLIResult(output=success_msg) - - def insert(self, path: Path, insert_line: int, new_str: str): - """Implement the insert command, which inserts new_str at the specified line in the file content.""" - file_text = self.read_file(path).expandtabs() - new_str = new_str.expandtabs() - file_text_lines = file_text.split("\n") - n_lines_file = len(file_text_lines) - - if insert_line < 0 or insert_line > n_lines_file: - raise ToolError( - f"Invalid `insert_line` parameter: {insert_line}. It should be within the range of lines of the file: {[0, n_lines_file]}" - ) - - new_str_lines = new_str.split("\n") - new_file_text_lines = ( - file_text_lines[:insert_line] - + new_str_lines - + file_text_lines[insert_line:] - ) - snippet_lines = ( - file_text_lines[max(0, insert_line - SNIPPET_LINES) : insert_line] - + new_str_lines - + file_text_lines[insert_line : insert_line + SNIPPET_LINES] - ) - - new_file_text = "\n".join(new_file_text_lines) - snippet = "\n".join(snippet_lines) - - self.write_file(path, new_file_text) - self._file_history[path].append(file_text) - - success_msg = f"The file {path} has been edited. " - success_msg += self._make_output( - snippet, - "a snippet of the edited file", - max(1, insert_line - SNIPPET_LINES + 1), - ) - success_msg += "Review the changes and make sure they are as expected (correct indentation, no duplicate lines, etc). Edit the file again if necessary." - return CLIResult(output=success_msg) - - def undo_edit(self, path: Path): - """Implement the undo_edit command.""" - if not self._file_history[path]: - raise ToolError(f"No edit history found for {path}.") - - old_text = self._file_history[path].pop() - self.write_file(path, old_text) - - return CLIResult( - output=f"Last edit to {path} undone successfully. {self._make_output(old_text, str(path))}" - ) - - def read_file(self, path: Path): - """Read the content of a file from a given path; raise a ToolError if an error occurs.""" - try: - return path.read_text() - except Exception as e: - raise ToolError(f"Ran into {e} while trying to read {path}") from None - - def write_file(self, path: Path, file: str): - """Write the content of a file to a given path; raise a ToolError if an error occurs.""" - try: - path.write_text(file) - except Exception as e: - raise ToolError(f"Ran into {e} while trying to write to {path}") from None - - def _make_output( - self, - file_content: str, - file_descriptor: str, - init_line: int = 1, - expand_tabs: bool = True, - ): - """Generate output for the CLI based on the content of a file.""" - file_content = maybe_truncate(file_content) - if expand_tabs: - file_content = file_content.expandtabs() - file_content = "\n".join( - [ - f"{i + init_line:6}\t{line}" - for i, line in enumerate(file_content.split("\n")) - ] - ) - return ( - f"Here's the result of running `cat -n` on {file_descriptor}:\n" - + file_content - + "\n" - ) diff --git a/demo/tools/run.py b/demo/tools/run.py deleted file mode 100644 index b9d3481..0000000 --- a/demo/tools/run.py +++ /dev/null @@ -1,42 +0,0 @@ -"""Utility to run shell commands asynchronously with a timeout.""" - -import asyncio - -TRUNCATED_MESSAGE: str = "To save on context only part of this file has been shown to you. You should retry this tool after you have searched inside the file with `grep -n` in order to find the line numbers of what you are looking for." -MAX_RESPONSE_LEN: int = 16000 - - -def maybe_truncate(content: str, truncate_after: int | None = MAX_RESPONSE_LEN): - """Truncate content and append a notice if content exceeds the specified length.""" - return ( - content - if not truncate_after or len(content) <= truncate_after - else content[:truncate_after] + TRUNCATED_MESSAGE - ) - - -async def run( - cmd: str, - timeout: float | None = 120.0, # seconds - truncate_after: int | None = MAX_RESPONSE_LEN, -): - """Run a shell command asynchronously with a timeout.""" - process = await asyncio.create_subprocess_shell( - cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE - ) - - try: - stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=timeout) - return ( - process.returncode or 0, - maybe_truncate(stdout.decode(), truncate_after=truncate_after), - maybe_truncate(stderr.decode(), truncate_after=truncate_after), - ) - except asyncio.TimeoutError as exc: - try: - process.kill() - except ProcessLookupError: - pass - raise TimeoutError( - f"Command '{cmd}' timed out after {timeout} seconds" - ) from exc diff --git a/demo/tools/screen_capture.py b/demo/tools/screen_capture.py deleted file mode 100644 index d425141..0000000 --- a/demo/tools/screen_capture.py +++ /dev/null @@ -1,185 +0,0 @@ -import subprocess -import base64 -from pathlib import Path -from PIL import ImageGrab -from uuid import uuid4 -from screeninfo import get_monitors -import platform -if platform.system() == "Darwin": - import Quartz # uncomment this line if you are on macOS - -from PIL import ImageGrab -from functools import partial -from .base import BaseAnthropicTool, ToolError, ToolResult - - -OUTPUT_DIR = "./tmp/outputs" - -def get_screenshot(selected_screen: int = 0, resize: bool = True, target_width: int = 1920, target_height: int = 1080): - # print(f"get_screenshot selected_screen: {selected_screen}") - - # Get screen width and height using Windows command - display_num = None - offset_x = 0 - offset_y = 0 - selected_screen = selected_screen - width, height = _get_screen_size() - - """Take a screenshot of the current screen and return a ToolResult with the base64 encoded image.""" - output_dir = Path(OUTPUT_DIR) - output_dir.mkdir(parents=True, exist_ok=True) - path = output_dir / f"screenshot_{uuid4().hex}.png" - - ImageGrab.grab = partial(ImageGrab.grab, all_screens=True) - - # Detect platform - system = platform.system() - - if system == "Windows": - # Windows: Use screeninfo to get monitor details - screens = get_monitors() - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s.x) - - if selected_screen < 0 or selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - - screen = sorted_screens[selected_screen] - bbox = (screen.x, screen.y, screen.x + screen.width, screen.y + screen.height) - - elif system == "Darwin": # macOS - # macOS: Use Quartz to get monitor details - max_displays = 32 # Maximum number of displays to handle - active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] - - # Get the display bounds (resolution) for each active display - screens = [] - for display_id in active_displays: - bounds = Quartz.CGDisplayBounds(display_id) - screens.append({ - 'id': display_id, - 'x': int(bounds.origin.x), - 'y': int(bounds.origin.y), - 'width': int(bounds.size.width), - 'height': int(bounds.size.height), - 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display - }) - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s['x']) - # print(f"Darwin sorted_screens: {sorted_screens}") - - if selected_screen < 0 or selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - - screen = sorted_screens[selected_screen] - - bbox = (screen['x'], screen['y'], screen['x'] + screen['width'], screen['y'] + screen['height']) - - else: # Linux or other OS - cmd = "xrandr | grep ' primary' | awk '{print $4}'" - try: - # output = subprocess.check_output(cmd, shell=True).decode() - # resolution = output.strip().split()[0] - # width, height = map(int, resolution.split('x')) - screen = get_monitors()[0] - width, height = screen.width, screen.height - bbox = (0, 0, width, height) # Assuming single primary screen for simplicity - except subprocess.CalledProcessError: - raise RuntimeError("Failed to get screen resolution on Linux.") - - # Take screenshot using the bounding box - screenshot = ImageGrab.grab(bbox=bbox) - import os - if (display_num := os.getenv("DISPLAY_NUM")) is not None: - display_num = int(display_num) - _display_prefix = f"DISPLAY=:{display_num} " - else: - display_num = None - _display_prefix = "" - screenshot_cmd = f"{_display_prefix}scrot -p {path}" - import pdb; pdb.set_trace() - result = subprocess.run(screenshot_cmd, shell=True, capture_output=True) - - # Set offsets (for potential future use) - offset_x = screen['x'] if system == "Darwin" else screen.x - offset_y = screen['y'] if system == "Darwin" else screen.y - - # # Resize if - if resize: - screenshot = screenshot.resize((target_width, target_height)) - - # Save the screenshot - # screenshot.save(str(path)) - - if path.exists(): - # Return a ToolResult instance instead of a dictionary - return screenshot, path - - raise ToolError(f"Failed to take screenshot: {path} does not exist.") - - - - -def _get_screen_size(selected_screen: int = 0): - if platform.system() == "Windows": - # Use screeninfo to get primary monitor on Windows - screens = get_monitors() - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s.x) - if selected_screen is None: - primary_monitor = next((m for m in get_monitors() if m.is_primary), None) - return primary_monitor.width, primary_monitor.height - elif selected_screen < 0 or selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - else: - screen = sorted_screens[selected_screen] - return screen.width, screen.height - elif platform.system() == "Darwin": - # macOS part using Quartz to get screen information - max_displays = 32 # Maximum number of displays to handle - active_displays = Quartz.CGGetActiveDisplayList(max_displays, None, None)[1] - - # Get the display bounds (resolution) for each active display - screens = [] - for display_id in active_displays: - bounds = Quartz.CGDisplayBounds(display_id) - screens.append({ - 'id': display_id, - 'x': int(bounds.origin.x), - 'y': int(bounds.origin.y), - 'width': int(bounds.size.width), - 'height': int(bounds.size.height), - 'is_primary': Quartz.CGDisplayIsMain(display_id) # Check if this is the primary display - }) - - # Sort screens by x position to arrange from left to right - sorted_screens = sorted(screens, key=lambda s: s['x']) - - if selected_screen is None: - # Find the primary monitor - primary_monitor = next((screen for screen in screens if screen['is_primary']), None) - if primary_monitor: - return primary_monitor['width'], primary_monitor['height'] - else: - raise RuntimeError("No primary monitor found.") - elif selected_screen < 0 or selected_screen >= len(screens): - raise IndexError("Invalid screen index.") - else: - # Return the resolution of the selected screen - screen = sorted_screens[selected_screen] - return screen['width'], screen['height'] - - else: # Linux or other OS - cmd = "xrandr | grep ' primary' | awk '{print $4}'" - try: - # output = subprocess.check_output(cmd, shell=True).decode() - # resolution = output.strip().split()[0] - # width, height = map(int, resolution.split('x')) - # return width, height - screen = get_monitors()[0] - return screen.width, screen.height - except subprocess.CalledProcessError: - raise RuntimeError("Failed to get screen resolution on Linux.") From 85f5fc0385fcdcc0a073d3b062be78afcce8bbe7 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Mon, 20 Jan 2025 15:07:05 -0800 Subject: [PATCH 02/57] init gradio demo --- demo/gradio/.gitignore | 1 + demo/gradio/app.py | 364 ++++++++++++++++++ demo/gradio/computer_use_demo/__init__.py | 0 .../gradio/computer_use_demo/colorful_text.py | 17 + .../executor/anthropic_executor.py | 133 +++++++ .../gui_agent/anthropic_agent.py | 203 ++++++++++ .../gui_agent/llm_utils/llm_utils.py | 109 ++++++ .../gui_agent/llm_utils/oai.py | 117 ++++++ .../gui_agent/llm_utils/qwen.py | 107 +++++ .../gui_agent/llm_utils/run_llm.py | 44 +++ demo/gradio/computer_use_demo/loop.py | 176 +++++++++ .../omniparser_agent/vlm_agent.py | 349 +++++++++++++++++ .../computer_use_demo/tools/__init__.py | 12 + demo/gradio/computer_use_demo/tools/base.py | 69 ++++ .../computer_use_demo/tools/collection.py | 34 ++ .../computer_use_demo/tools/computer.py | 319 +++++++++++++++ .../computer_use_demo/tools/screen_capture.py | 29 ++ 17 files changed, 2083 insertions(+) create mode 100644 demo/gradio/.gitignore create mode 100644 demo/gradio/app.py create mode 100644 demo/gradio/computer_use_demo/__init__.py create mode 100644 demo/gradio/computer_use_demo/colorful_text.py create mode 100644 demo/gradio/computer_use_demo/executor/anthropic_executor.py create mode 100644 demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py create mode 100644 demo/gradio/computer_use_demo/gui_agent/llm_utils/llm_utils.py create mode 100644 demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py create mode 100644 demo/gradio/computer_use_demo/gui_agent/llm_utils/qwen.py create mode 100644 demo/gradio/computer_use_demo/gui_agent/llm_utils/run_llm.py create mode 100644 demo/gradio/computer_use_demo/loop.py create mode 100644 demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py create mode 100644 demo/gradio/computer_use_demo/tools/__init__.py create mode 100644 demo/gradio/computer_use_demo/tools/base.py create mode 100644 demo/gradio/computer_use_demo/tools/collection.py create mode 100644 demo/gradio/computer_use_demo/tools/computer.py create mode 100644 demo/gradio/computer_use_demo/tools/screen_capture.py diff --git a/demo/gradio/.gitignore b/demo/gradio/.gitignore new file mode 100644 index 0000000..c036379 --- /dev/null +++ b/demo/gradio/.gitignore @@ -0,0 +1 @@ +tmp/ \ No newline at end of file diff --git a/demo/gradio/app.py b/demo/gradio/app.py new file mode 100644 index 0000000..1dfb9e4 --- /dev/null +++ b/demo/gradio/app.py @@ -0,0 +1,364 @@ +""" +Entrypoint for Gradio, see https://gradio.app/ +""" + +import platform +import asyncio +import base64 +import os +import io +import json +from datetime import datetime +from enum import StrEnum +from functools import partial +from pathlib import Path +from typing import cast, Dict +from PIL import Image + +import gradio as gr +from anthropic import APIResponse +from anthropic.types import TextBlock +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock +from anthropic.types.tool_use_block import ToolUseBlock +from computer_use_demo.loop import ( + PROVIDER_TO_DEFAULT_MODEL_NAME, + APIProvider, + sampling_loop_sync, +) + +from computer_use_demo.tools import ToolResult + +CONFIG_DIR = Path("~/.anthropic").expanduser() +API_KEY_FILE = CONFIG_DIR / "api_key" + +INTRO_TEXT = ''' +๐Ÿš€๐Ÿค–โœจ It's Play Time! + +Welcome to the OmniParser+X Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. Type a message to play with your beloved assistant. +''' + +class Sender(StrEnum): + USER = "user" + BOT = "assistant" + TOOL = "tool" + + +def setup_state(state): + if "messages" not in state: + state["messages"] = [] + if "model" not in state: + state["model"] = "omniparser + gpt-4o" + if "provider" not in state: + state["provider"] = "openai" + if "openai_api_key" not in state: # Fetch API keys from environment variables + state["openai_api_key"] = os.getenv("OPENAI_API_KEY", "") + if "anthropic_api_key" not in state: + state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "") + if "qwen_api_key" not in state: + state["qwen_api_key"] = os.getenv("QWEN_API_KEY", "") + if "api_key" not in state: + state["api_key"] = "" + if "auth_validated" not in state: + state["auth_validated"] = False + if "responses" not in state: + state["responses"] = {} + if "tools" not in state: + state["tools"] = {} + if "only_n_most_recent_images" not in state: + state["only_n_most_recent_images"] = 2 + if "custom_system_prompt" not in state: + state["custom_system_prompt"] = "" + if 'chatbot_messages' not in state: + state['chatbot_messages'] = [] + +async def main(state): + """Render loop for Gradio""" + setup_state(state) + return "Setup completed" + +def validate_auth(provider: APIProvider, api_key: str | None): + if provider == APIProvider.ANTHROPIC: + if not api_key: + return "Enter your Anthropic API key to continue." + if provider == APIProvider.BEDROCK: + import boto3 + + if not boto3.Session().get_credentials(): + return "You must have AWS credentials set up to use the Bedrock API." + if provider == APIProvider.VERTEX: + import google.auth + from google.auth.exceptions import DefaultCredentialsError + + if not os.environ.get("CLOUD_ML_REGION"): + return "Set the CLOUD_ML_REGION environment variable to use the Vertex API." + try: + google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) + except DefaultCredentialsError: + return "Your google cloud credentials are not set up correctly." + +def load_from_storage(filename: str) -> str | None: + """Load data from a file in the storage directory.""" + try: + file_path = CONFIG_DIR / filename + if file_path.exists(): + data = file_path.read_text().strip() + if data: + return data + except Exception as e: + print(f"Debug: Error loading {filename}: {e}") + return None + +def save_to_storage(filename: str, data: str) -> None: + """Save data to a file in the storage directory.""" + try: + CONFIG_DIR.mkdir(parents=True, exist_ok=True) + file_path = CONFIG_DIR / filename + file_path.write_text(data) + # Ensure only user can read/write the file + file_path.chmod(0o600) + except Exception as e: + print(f"Debug: Error saving {filename}: {e}") + +def _api_response_callback(response: APIResponse[BetaMessage], response_state: dict): + response_id = datetime.now().isoformat() + response_state[response_id] = response + +def _tool_output_callback(tool_output: ToolResult, tool_id: str, tool_state: dict): + tool_state[tool_id] = tool_output + +def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="bot"): + def _render_message(message: str | BetaTextBlock | BetaToolUseBlock | ToolResult, hide_images=False): + + print(f"_render_message: {str(message)[:100]}") + + if isinstance(message, str): + return message + + is_tool_result = not isinstance(message, str) and ( + isinstance(message, ToolResult) + or message.__class__.__name__ == "ToolResult" + or message.__class__.__name__ == "CLIResult" + ) + if not message or ( + is_tool_result + and hide_images + and not hasattr(message, "error") + and not hasattr(message, "output") + ): # return None if hide_images is True + return + # render tool result + if is_tool_result: + message = cast(ToolResult, message) + if message.output: + return message.output + if message.error: + return f"Error: {message.error}" + if message.base64_image and not hide_images: + # somehow can't display via gr.Image + # image_data = base64.b64decode(message.base64_image) + # return gr.Image(value=Image.open(io.BytesIO(image_data))) + return f'' + + elif isinstance(message, BetaTextBlock) or isinstance(message, TextBlock): + return f"Analysis: {message.text}" + elif isinstance(message, BetaToolUseBlock) or isinstance(message, ToolUseBlock): + # return f"Tool Use: {message.name}\nInput: {message.input}" + return f"Next I will perform the following action: {message.input}" + else: + return message + + def _truncate_string(s, max_length=500): + """Truncate long strings for concise printing.""" + if isinstance(s, str) and len(s) > max_length: + return s[:max_length] + "..." + return s + # processing Anthropic messages + message = _render_message(message, hide_images) + + if sender == "bot": + chatbot_state.append((None, message)) + else: + chatbot_state.append((message, None)) + + # Create a concise version of the chatbot state for printing + concise_state = [(_truncate_string(user_msg), _truncate_string(bot_msg)) + for user_msg, bot_msg in chatbot_state] + # print(f"chatbot_output_callback chatbot_state: {concise_state} (truncated)") + +def process_input(user_input, state): + # Append the user message to state["messages"] + state["messages"].append( + { + "role": Sender.USER, + "content": [TextBlock(type="text", text=user_input)], + } + ) + + # Append the user's message to chatbot_messages with None for the assistant's reply + state['chatbot_messages'].append((user_input, None)) + yield state['chatbot_messages'] # Yield to update the chatbot UI with the user's message + + print("state") + print(state) + + # Run sampling_loop_sync with the chatbot_output_callback + for loop_msg in sampling_loop_sync( + system_prompt_suffix=state["custom_system_prompt"], + model=state["model"], + provider=state["provider"], + messages=state["messages"], + output_callback=partial(chatbot_output_callback, chatbot_state=state['chatbot_messages'], hide_images=False), + tool_output_callback=partial(_tool_output_callback, tool_state=state["tools"]), + api_response_callback=partial(_api_response_callback, response_state=state["responses"]), + api_key=state["api_key"], + only_n_most_recent_images=state["only_n_most_recent_images"], + selected_screen=0 + ): + if loop_msg is None: + yield state['chatbot_messages'] + print("End of task. Close the loop.") + break + + yield state['chatbot_messages'] # Yield the updated chatbot_messages to update the chatbot UI + +with gr.Blocks(theme=gr.themes.Default()) as demo: + gr.HTML(""" + + """) + state = gr.State({}) # Use Gradio's state management + + setup_state(state.value) # Initialize the state + + # Retrieve screen details + gr.Markdown("# OmniParser + โœ–๏ธ Demo") + + if not os.getenv("HIDE_WARNING", False): + gr.Markdown(INTRO_TEXT) + + with gr.Accordion("Settings", open=True): + with gr.Row(): + with gr.Column(): + model = gr.Dropdown( + label="Model", + choices=["omniparser + gpt-4o", "omniparser + phi35v", "claude-3-5-sonnet-20241022"], + value="omniparser + gpt-4o", # Set to one of the choices + interactive=True, + ) + with gr.Column(): + custom_prompt = gr.Textbox( + label="System Prompt Suffix", + value="", + interactive=True, + ) + with gr.Column(): + only_n_images = gr.Slider( + label="N most recent screenshots", + minimum=0, + maximum=10, + step=1, + value=2, + interactive=True + ) + with gr.Row(): + with gr.Column(1): + provider = gr.Dropdown( + label="API Provider", + choices=[option.value for option in APIProvider], + value="openai", + interactive=False, + ) + with gr.Column(2): + api_key = gr.Textbox( + label="API Key", + type="password", + value=state.value.get("api_key", ""), + placeholder="Paste your API key here", + interactive=True, + ) + # hide_images = gr.Checkbox(label="Hide screenshots", value=False) + + with gr.Row(): + with gr.Column(scale=8): + chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Computer Use OOTB...", container=False) + with gr.Column(scale=1, min_width=50): + submit_button = gr.Button(value="Send", variant="primary") + + with gr.Row(): + with gr.Column(scale=1): + chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) + with gr.Column(scale=3): + iframe = gr.HTML( + '', + container=False, + elem_classes="no-padding" + ) + + def update_model(model_selection, state): + state["model"] = model_selection + print(f"Model updated to: {state['model']}") + + if model_selection == "claude-3-5-sonnet-20241022": + provider_choices = [option.value for option in APIProvider if option.value != "openai"] + elif model_selection == "omniparser + gpt-4o" or model_selection == "omniparser + phi35v": + provider_choices = ["openai"] + else: + provider_choices = [option.value for option in APIProvider] + default_provider_value = provider_choices[0] + provider_interactive = len(provider_choices) > 1 + api_key_placeholder = f"{default_provider_value.title()} API Key" + + # Update state + state["provider"] = default_provider_value + state["api_key"] = state.get(f"{default_provider_value}_api_key", "") + + # Calls to update other components UI + provider_update = gr.update( + choices=provider_choices, + value=default_provider_value, + interactive=provider_interactive + ) + api_key_update = gr.update( + placeholder=api_key_placeholder, + value=state["api_key"] + ) + + return provider_update, api_key_update + + def update_system_prompt_suffix(system_prompt_suffix, state): + state["custom_system_prompt"] = system_prompt_suffix + + def update_only_n_images(only_n_images_value, state): + state["only_n_most_recent_images"] = only_n_images_value + + def update_provider(provider_value, state): + # Update state + state["provider"] = provider_value + state["api_key"] = state.get(f"{provider_value}_api_key", "") + + # Calls to update other components UI + api_key_update = gr.update( + placeholder=f"{provider_value.title()} API Key", + value=state["api_key"] + ) + return api_key_update + + def update_api_key(api_key_value, state): + state["api_key"] = api_key_value + state[f'{state["provider"]}_api_key'] = api_key_value + + model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key]) + custom_prompt.change(fn=update_system_prompt_suffix, inputs=[custom_prompt, state], outputs=None) + only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) + provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key) + api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None) + + submit_button.click(process_input, [chat_input, state], chatbot) + +demo.launch(share=False, allowed_paths=["./"], server_port=7888) diff --git a/demo/gradio/computer_use_demo/__init__.py b/demo/gradio/computer_use_demo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/demo/gradio/computer_use_demo/colorful_text.py b/demo/gradio/computer_use_demo/colorful_text.py new file mode 100644 index 0000000..28d772b --- /dev/null +++ b/demo/gradio/computer_use_demo/colorful_text.py @@ -0,0 +1,17 @@ +""" +Define some colorful stuffs for better visualization in the chat. +""" + +# Define the RGB colors for each letter +colors = { + 'S': 'rgb(106, 158, 210)', + 'h': 'rgb(111, 163, 82)', + 'o': 'rgb(209, 100, 94)', + 'w': 'rgb(238, 171, 106)', + 'U': 'rgb(0, 0, 0)', + 'I': 'rgb(0, 0, 0)', +} + +colorful_text_vlm = "**OmniParser Agent**" + +colorful_text_user = "**User**" \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/executor/anthropic_executor.py b/demo/gradio/computer_use_demo/executor/anthropic_executor.py new file mode 100644 index 0000000..2970be1 --- /dev/null +++ b/demo/gradio/computer_use_demo/executor/anthropic_executor.py @@ -0,0 +1,133 @@ +import asyncio +from typing import Any, Dict, cast +from collections.abc import Callable +from anthropic.types.beta import ( + BetaContentBlock, + BetaContentBlockParam, + BetaImageBlockParam, + BetaMessage, + BetaMessageParam, + BetaTextBlockParam, + BetaToolResultBlockParam, +) +from anthropic.types import TextBlock +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock +from ..tools import ComputerTool, ToolCollection, ToolResult + + +class AnthropicExecutor: + def __init__( + self, + output_callback: Callable[[BetaContentBlockParam], None], + tool_output_callback: Callable[[Any, str], None], + selected_screen: int = 0 + ): + self.tool_collection = ToolCollection( + ComputerTool(selected_screen=selected_screen) + ) + self.output_callback = output_callback + self.tool_output_callback = tool_output_callback + + def __call__(self, response: BetaMessage, messages: list[BetaMessageParam]): + new_message = { + "role": "assistant", + "content": cast(list[BetaContentBlockParam], response.content), + } + if new_message not in messages: + messages.append(new_message) + else: + print("new_message already in messages, there are duplicates.") + + tool_result_content: list[BetaToolResultBlockParam] = [] + for content_block in cast(list[BetaContentBlock], response.content): + self.output_callback(content_block, sender="bot") + # Execute the tool + if content_block.type == "tool_use": + # Run the asynchronous tool execution in a synchronous context + result = asyncio.run(self.tool_collection.run( + name=content_block.name, + tool_input=cast(dict[str, Any], content_block.input), + )) + + self.output_callback(result, sender="bot") + + tool_result_content.append( + _make_api_tool_result(result, content_block.id) + ) + self.tool_output_callback(result, content_block.id) + + # Craft messages based on the content_block + # Note: to display the messages in the gradio, you should organize the messages in the following way (user message, bot message) + + display_messages = _message_display_callback(messages) + # display_messages = [] + + # Send the messages to the gradio + for user_msg, bot_msg in display_messages: + # yield [user_msg, bot_msg], tool_result_content + yield [None, None], tool_result_content + + if not tool_result_content: + return messages + + return tool_result_content + +def _message_display_callback(messages): + display_messages = [] + for msg in messages: + try: + if isinstance(msg["content"][0], TextBlock): + display_messages.append((msg["content"][0].text, None)) # User message + elif isinstance(msg["content"][0], BetaTextBlock): + display_messages.append((None, msg["content"][0].text)) # Bot message + elif isinstance(msg["content"][0], BetaToolUseBlock): + display_messages.append((None, f"Tool Use: {msg['content'][0].name}\nInput: {msg['content'][0].input}")) # Bot message + elif isinstance(msg["content"][0], Dict) and msg["content"][0]["content"][-1]["type"] == "image": + display_messages.append((None, f'')) # Bot message + else: + print(msg["content"][0]) + except Exception as e: + print("error", e) + pass + return display_messages + +def _make_api_tool_result( + result: ToolResult, tool_use_id: str +) -> BetaToolResultBlockParam: + """Convert an agent ToolResult to an API ToolResultBlockParam.""" + tool_result_content: list[BetaTextBlockParam | BetaImageBlockParam] | str = [] + is_error = False + if result.error: + is_error = True + tool_result_content = _maybe_prepend_system_tool_result(result, result.error) + else: + if result.output: + tool_result_content.append( + { + "type": "text", + "text": _maybe_prepend_system_tool_result(result, result.output), + } + ) + if result.base64_image: + tool_result_content.append( + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": result.base64_image, + }, + } + ) + return { + "type": "tool_result", + "content": tool_result_content, + "tool_use_id": tool_use_id, + "is_error": is_error, + } + + +def _maybe_prepend_system_tool_result(result: ToolResult, result_text: str): + if result.system: + result_text = f"{result.system}\n{result_text}" + return result_text \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py b/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py new file mode 100644 index 0000000..fbd66a1 --- /dev/null +++ b/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py @@ -0,0 +1,203 @@ +""" +Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. +""" +import asyncio +import platform +from collections.abc import Callable +from datetime import datetime +from enum import StrEnum +from typing import Any, cast + +from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse +from anthropic.types import ( + ToolResultBlockParam, +) +from anthropic.types.beta import ( + BetaContentBlock, + BetaContentBlockParam, + BetaImageBlockParam, + BetaMessage, + BetaMessageParam, + BetaTextBlockParam, + BetaToolResultBlockParam, +) +from anthropic.types import TextBlock +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock + +from computer_use_demo.tools import ComputerTool, ToolCollection, ToolResult + +from PIL import Image +from io import BytesIO +import gradio as gr +from typing import Dict + + +BETA_FLAG = "computer-use-2024-10-22" + + +class APIProvider(StrEnum): + ANTHROPIC = "anthropic" + BEDROCK = "bedrock" + VERTEX = "vertex" + + +PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { + APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", + APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", + APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", +} + + +# Check OS + +SYSTEM_PROMPT = f""" +* You are utilizing a Windows system with internet access. +* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. + +""" + + +class AnthropicActor: + def __init__( + self, + model: str, + provider: APIProvider, + system_prompt_suffix: str, + api_key: str, + api_response_callback: Callable[[APIResponse[BetaMessage]], None], + max_tokens: int = 4096, + only_n_most_recent_images: int | None = None, + selected_screen: int = 0, + print_usage: bool = True, + ): + self.model = model + self.provider = provider + self.system_prompt_suffix = system_prompt_suffix + self.api_key = api_key + self.api_response_callback = api_response_callback + self.max_tokens = max_tokens + self.only_n_most_recent_images = only_n_most_recent_images + self.selected_screen = selected_screen + + self.tool_collection = ToolCollection( + ComputerTool(selected_screen=selected_screen), + ) + + self.system = ( + f"{SYSTEM_PROMPT}{' ' + system_prompt_suffix if system_prompt_suffix else ''}" + ) + + self.total_token_usage = 0 + self.total_cost = 0 + self.print_usage = print_usage + + # Instantiate the appropriate API client based on the provider + if provider == APIProvider.ANTHROPIC: + self.client = Anthropic(api_key=api_key) + elif provider == APIProvider.VERTEX: + self.client = AnthropicVertex() + elif provider == APIProvider.BEDROCK: + self.client = AnthropicBedrock() + + def __call__( + self, + *, + messages: list[BetaMessageParam] + ): + """ + Generate a response given history messages. + """ + if self.only_n_most_recent_images: + _maybe_filter_to_n_most_recent_images(messages, self.only_n_most_recent_images) + + # Call the API synchronously + raw_response = self.client.beta.messages.with_raw_response.create( + max_tokens=self.max_tokens, + messages=messages, + model=self.model, + system=self.system, + tools=self.tool_collection.to_params(), + betas=["computer-use-2024-10-22"], + ) + + self.api_response_callback(cast(APIResponse[BetaMessage], raw_response)) + + response = raw_response.parse() + print(f"AnthropicActor response: {response}") + + self.total_token_usage += response.usage.input_tokens + response.usage.output_tokens + self.total_cost += (response.usage.input_tokens * 3 / 1000000 + response.usage.output_tokens * 15 / 1000000) + + if self.print_usage: + print(f"Claude total token usage so far: {self.total_token_usage}, total cost so far: $USD{self.total_cost}") + + return response + + +def _maybe_filter_to_n_most_recent_images( + messages: list[BetaMessageParam], + images_to_keep: int, + min_removal_threshold: int = 10, +): + """ + With the assumption that images are screenshots that are of diminishing value as + the conversation progresses, remove all but the final `images_to_keep` tool_result + images in place, with a chunk of min_removal_threshold to reduce the amount we + break the implicit prompt cache. + """ + if images_to_keep is None: + return messages + + tool_result_blocks = cast( + list[ToolResultBlockParam], + [ + item + for message in messages + for item in ( + message["content"] if isinstance(message["content"], list) else [] + ) + if isinstance(item, dict) and item.get("type") == "tool_result" + ], + ) + + total_images = sum( + 1 + for tool_result in tool_result_blocks + for content in tool_result.get("content", []) + if isinstance(content, dict) and content.get("type") == "image" + ) + + images_to_remove = total_images - images_to_keep + # for better cache behavior, we want to remove in chunks + images_to_remove -= images_to_remove % min_removal_threshold + + for tool_result in tool_result_blocks: + if isinstance(tool_result.get("content"), list): + new_content = [] + for content in tool_result.get("content", []): + if isinstance(content, dict) and content.get("type") == "image": + if images_to_remove > 0: + images_to_remove -= 1 + continue + new_content.append(content) + tool_result["content"] = new_content + + + +if __name__ == "__main__": + pass + # client = Anthropic(api_key="") + # response = client.beta.messages.with_raw_response.create( + # max_tokens=4096, + # model="claude-3-5-sonnet-20241022", + # system=SYSTEM_PROMPT, + # # tools=ToolCollection( + # # ComputerTool(selected_screen=0), + # # ).to_params(), + # betas=["computer-use-2024-10-22"], + # messages=[ + # {"role": "user", "content": "click on (199, 199)."} + # ], + # ) + + # print(f"AnthropicActor response: {response.parse().usage.input_tokens+response.parse().usage.output_tokens}") \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/llm_utils.py b/demo/gradio/computer_use_demo/gui_agent/llm_utils/llm_utils.py new file mode 100644 index 0000000..3c01c56 --- /dev/null +++ b/demo/gradio/computer_use_demo/gui_agent/llm_utils/llm_utils.py @@ -0,0 +1,109 @@ +import os +import re +import ast +import base64 + + +def is_image_path(text): + # Checking if the input text ends with typical image file extensions + image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") + if text.endswith(image_extensions): + return True + else: + return False + + +def encode_image(image_path): + """Encode image file to base64.""" + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + + +def is_url_or_filepath(input_string): + # Check if input_string is a URL + url_pattern = re.compile( + r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+" + ) + if url_pattern.match(input_string): + return "URL" + + # Check if input_string is a file path + file_path = os.path.abspath(input_string) + if os.path.exists(file_path): + return "File path" + + return "Invalid" + + +def extract_data(input_string, data_type): + # Regular expression to extract content starting from '```python' until the end if there are no closing backticks + pattern = f"```{data_type}" + r"(.*?)(```|$)" + # Extract content + # re.DOTALL allows '.' to match newlines as well + matches = re.findall(pattern, input_string, re.DOTALL) + # Return the first match if exists, trimming whitespace and ignoring potential closing backticks + return matches[0][0].strip() if matches else input_string + + +def parse_input(code): + """Use AST to parse the input string and extract the function name, arguments, and keyword arguments.""" + + def get_target_names(target): + """Recursively get all variable names from the assignment target.""" + if isinstance(target, ast.Name): + return [target.id] + elif isinstance(target, ast.Tuple): + names = [] + for elt in target.elts: + names.extend(get_target_names(elt)) + return names + return [] + + def extract_value(node): + """ๆๅ– AST ่Š‚็‚น็š„ๅฎž้™…ๅ€ผ""" + if isinstance(node, ast.Constant): + return node.value + elif isinstance(node, ast.Name): + # TODO: a better way to handle variables + raise ValueError( + f"Arguments should be a Constant, got a variable {node.id} instead." + ) + # ๆทปๅŠ ๅ…ถไป–้œ€่ฆๅค„็†็š„ AST ่Š‚็‚น็ฑปๅž‹ + return None + + try: + tree = ast.parse(code) + for node in ast.walk(tree): + if isinstance(node, ast.Assign): + targets = [] + for t in node.targets: + targets.extend(get_target_names(t)) + if isinstance(node.value, ast.Call): + func_name = node.value.func.id + args = [ast.dump(arg) for arg in node.value.args] + kwargs = { + kw.arg: extract_value(kw.value) for kw in node.value.keywords + } + print(f"Input: {code.strip()}") + print(f"Output Variables: {targets}") + print(f"Function Name: {func_name}") + print(f"Arguments: {args}") + print(f"Keyword Arguments: {kwargs}") + elif isinstance(node, ast.Expr) and isinstance(node.value, ast.Call): + targets = [] + func_name = extract_value(node.value.func) + args = [extract_value(arg) for arg in node.value.args] + kwargs = {kw.arg: extract_value(kw.value) for kw in node.value.keywords} + + except SyntaxError: + print(f"Input: {code.strip()}") + print("No match found") + + return targets, func_name, args, kwargs + + +if __name__ == "__main__": + import json + s='{"Reasoning": "The Docker icon has been successfully clicked, and the Docker application should now be opening. No further actions are required.", "Next Action": None}' + json_str = json.loads(s) + print(json_str) \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py b/demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py new file mode 100644 index 0000000..36cf60b --- /dev/null +++ b/demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py @@ -0,0 +1,117 @@ + +import os +import logging +import base64 +import requests + +# from computer_use_demo.gui_agent.llm_utils import is_image_path, encode_image + +def is_image_path(text): + # Checking if the input text ends with typical image file extensions + image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") + if text.endswith(image_extensions): + return True + else: + return False + +def encode_image(image_path): + """Encode image file to base64.""" + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + + + +# from openai import OpenAI +# client = OpenAI( +# api_key=os.environ.get("OPENAI_API_KEY") +# ) + + +def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): + + api_key = api_key or os.environ.get("OPENAI_API_KEY") + if not api_key: + raise ValueError("OPENAI_API_KEY is not set") + + headers = {"Content-Type": "application/json", + "Authorization": f"Bearer {api_key}"} + + final_messages = [{"role": "system", "content": system}] + + # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + if type(messages) == list: + for item in messages: + contents = [] + if isinstance(item, dict): + for cnt in item["content"]: + if isinstance(cnt, str): + if is_image_path(cnt): + base64_image = encode_image(cnt) + content = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}} + else: + content = {"type": "text", "text": cnt} + else: + # in this case it is a text block from anthropic + content = {"type": "text", "text": str(cnt)} + + contents.append(content) + + message = {"role": 'user', "content": contents} + else: # str + contents.append({"type": "text", "text": item}) + message = {"role": "user", "content": contents} + + final_messages.append(message) + + + elif isinstance(messages, str): + final_messages = [{"role": "user", "content": messages}] + # import pdb; pdb.set_trace() + + print("[oai] sending messages:", {"role": "user", "content": messages}) + + payload = { + "model": llm, + "messages": final_messages, + "max_tokens": max_tokens, + "temperature": temperature, + # "stop": stop, + } + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + + response = requests.post( + "https://api.openai.com/v1/chat/completions", headers=headers, json=payload + ) + + try: + text = response.json()['choices'][0]['message']['content'] + token_usage = int(response.json()['usage']['total_tokens']) + return text, token_usage + + # return error message if the response is not successful + except Exception as e: + print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") + return response.json() + + +if __name__ == "__main__": + + api_key = os.environ.get("OPENAI_API_KEY") + if not api_key: + raise ValueError("OPENAI_API_KEY is not set") + + text, token_usage = run_oai_interleaved( + messages= [{"content": [ + "What is in the screenshot?", + "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"], + "role": "user" + }], + llm="gpt-4o-mini", + system="You are a helpful assistant", + api_key=api_key, + max_tokens=256, + temperature=0) + + print(text, token_usage) + # There is an introduction describing the Calyx... 36986 diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/qwen.py b/demo/gradio/computer_use_demo/gui_agent/llm_utils/qwen.py new file mode 100644 index 0000000..0d570cd --- /dev/null +++ b/demo/gradio/computer_use_demo/gui_agent/llm_utils/qwen.py @@ -0,0 +1,107 @@ + +import os +import logging +import base64 +import requests + +import dashscope +# from computer_use_demo.gui_agent.llm_utils import is_image_path, encode_image + +def is_image_path(text): + return False + +def encode_image(image_path): + return "" + + +def run_qwen(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): + + api_key = api_key or os.environ.get("QWEN_API_KEY") + if not api_key: + raise ValueError("QWEN_API_KEY is not set") + + dashscope.api_key = api_key + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + + final_messages = [{"role": "system", "content": [{"text": system}]}] + # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + if type(messages) == list: + for item in messages: + contents = [] + if isinstance(item, dict): + for cnt in item["content"]: + if isinstance(cnt, str): + if is_image_path(cnt): + # base64_image = encode_image(cnt) + content = [{"image": cnt}] + # content = {"type": "image_url", "image_url": {"url": image_url}} + else: + content = {"text": cnt} + contents.append(content) + + message = {"role": item["role"], "content": contents} + else: # str + contents.append({"text": item}) + message = {"role": "user", "content": contents} + + final_messages.append(message) + + print("[qwen-vl] sending messages:", final_messages) + + response = dashscope.MultiModalConversation.call( + model='qwen-vl-max-0809', + messages=final_messages + ) + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + + try: + text = response.output.choices[0].message.content[0]['text'] + usage = response.usage + + if "total_tokens" not in usage: + token_usage = int(usage["input_tokens"] + usage["output_tokens"]) + else: + token_usage = int(usage["total_tokens"]) + + return text, token_usage + # return response.json()['choices'][0]['message']['content'] + # return error message if the response is not successful + except Exception as e: + print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") + return response.json() + + + +if __name__ == "__main__": + api_key = os.environ.get("QWEN_API_KEY") + if not api_key: + raise ValueError("QWEN_API_KEY is not set") + + dashscope.api_key = api_key + + final_messages = [{"role": "user", + "content": [ + {"text": "What is in the screenshot?"}, + {"image": "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"} + ] + } + ] + response = dashscope.MultiModalConversation.call(model='qwen-vl-max-0809', messages=final_messages) + + print(response) + + text = response.output.choices[0].message.content[0]['text'] + usage = response.usage + + if "total_tokens" not in usage: + if "image_tokens" in usage: + token_usage = usage["input_tokens"] + usage["output_tokens"] + usage["image_tokens"] + else: + token_usage = usage["input_tokens"] + usage["output_tokens"] + else: + token_usage = usage["total_tokens"] + + print(text, token_usage) + # The screenshot is from a video game... 1387 \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/run_llm.py b/demo/gradio/computer_use_demo/gui_agent/llm_utils/run_llm.py new file mode 100644 index 0000000..a1de8ba --- /dev/null +++ b/demo/gradio/computer_use_demo/gui_agent/llm_utils/run_llm.py @@ -0,0 +1,44 @@ +import base64 +import logging +from .oai import run_oai_interleaved +from .gemini import run_gemini_interleaved + +def run_llm(prompt, llm="gpt-4o-mini", max_tokens=256, temperature=0, stop=None): + log_prompt(prompt) + + # turn string prompt into list + if isinstance(prompt, str): + prompt = [prompt] + elif isinstance(prompt, list): + pass + else: + raise ValueError(f"Invalid prompt type: {type(prompt)}") + + if llm.startswith("gpt"): # gpt series + out = run_oai_interleaved( + prompt, + llm, + max_tokens, + temperature, + stop + ) + elif llm.startswith("gemini"): # gemini series + out = run_gemini_interleaved( + prompt, + llm, + max_tokens, + temperature, + stop + ) + else: + raise ValueError(f"Invalid llm: {llm}") + logging.info( + f"========Output for {llm}=======\n{out}\n============================") + return out + +def log_prompt(prompt): + prompt_display = [prompt] if isinstance(prompt, str) else prompt + prompt_display = "\n\n".join(prompt_display) + logging.info( + f"========Prompt=======\n{prompt_display}\n============================") + \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py new file mode 100644 index 0000000..a0bc58e --- /dev/null +++ b/demo/gradio/computer_use_demo/loop.py @@ -0,0 +1,176 @@ +""" +Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. +""" +import time +import json +import asyncio +import platform +from collections.abc import Callable +from datetime import datetime +from enum import StrEnum +from typing import Any, cast, Dict + +from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse +from anthropic.types import ( + ToolResultBlockParam, + TextBlock, +) +from anthropic.types.beta import ( + BetaContentBlock, + BetaContentBlockParam, + BetaImageBlockParam, + BetaMessage, + BetaMessageParam, + BetaTextBlockParam, + BetaToolResultBlockParam, +) +from computer_use_demo.tools import ToolResult + +import torch + +from computer_use_demo.gui_agent.anthropic_agent import AnthropicActor +from computer_use_demo.executor.anthropic_executor import AnthropicExecutor +from computer_use_demo.omniparser_agent.vlm_agent import OmniParser, VLMAgent +from computer_use_demo.colorful_text import colorful_text_vlm +from computer_use_demo.tools.screen_capture import get_screenshot +from computer_use_demo.gui_agent.llm_utils.oai import encode_image + + +BETA_FLAG = "computer-use-2024-10-22" + + +class APIProvider(StrEnum): + ANTHROPIC = "anthropic" + BEDROCK = "bedrock" + VERTEX = "vertex" + OPENAI = "openai" + QWEN = "qwen" + + +PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { + APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", + APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", + APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", + # APIProvider.OPENAI: "gpt-4o", + # APIProvider.QWEN: "qwen2vl", +} + + +# This system prompt is optimized for the Docker environment in this repository and +# specific tool combinations enabled. +# We encourage modifying this system prompt to ensure the model has context for the +# environment it is running in, and to provide any additional information that may be +# helpful for the task at hand. +SYSTEM_PROMPT = f""" +* You are utilizing a Windows system with internet access. +* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. + +""" + +import base64 +from PIL import Image +from io import BytesIO + +def sampling_loop_sync( + *, + model: str, + provider: APIProvider | None, + system_prompt_suffix: str, + messages: list[BetaMessageParam], + output_callback: Callable[[BetaContentBlock], None], + tool_output_callback: Callable[[ToolResult, str], None], + api_response_callback: Callable[[APIResponse[BetaMessage]], None], + api_key: str, + only_n_most_recent_images: int | None = 2, + max_tokens: int = 4096, + selected_screen: int = 0 +): + """ + Synchronous agentic sampling loop for the assistant/tool interaction of computer use. + """ + print('in sampling_loop_sync, model:', model) + if model == "claude-3-5-sonnet-20241022": + omniparser = OmniParser(url="http://localhost:8000/send_text/", + selected_screen=selected_screen,) + + # Register Actor and Executor + actor = AnthropicActor( + model=model, + provider=provider, + system_prompt_suffix=system_prompt_suffix, + api_key=api_key, + api_response_callback=api_response_callback, + max_tokens=max_tokens, + only_n_most_recent_images=only_n_most_recent_images, + selected_screen=selected_screen + ) + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + executor = AnthropicExecutor( + output_callback=output_callback, + tool_output_callback=tool_output_callback, + selected_screen=selected_screen + ) + + elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": + omniparser = OmniParser(url="http://localhost:8000/send_text/", + selected_screen=selected_screen,) + + actor = VLMAgent( + model=model, + provider=provider, + system_prompt_suffix=system_prompt_suffix, + api_key=api_key, + api_response_callback=api_response_callback, + selected_screen=selected_screen, + output_callback=output_callback, + ) + + executor = AnthropicExecutor( + output_callback=output_callback, + tool_output_callback=tool_output_callback, + selected_screen=selected_screen + ) + + else: + raise ValueError(f"Model {model} not supported") + print(f"Model Inited: {model}, Provider: {provider}") + + tool_result_content = None + + print(f"Start the message loop. User messages: {messages}") + + if model == "claude-3-5-sonnet-20241022": # Anthropic loop + while True: + parsed_screen = omniparser() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} + import pdb; pdb.set_trace() + screen_info_block = TextBlock(text='Below is the structured accessibility information of the current UI screen, which includes text and icons you can operate on, take these information into account when you are making the prediction for the next action. Note you will still need to take screenshot to get the image: \n' + parsed_screen['screen_info'], type='text') + # # messages[-1]['content'].append(screen_info_block) + screen_info_dict = {"role": "user", "content": [screen_info_block]} + messages.append(screen_info_dict) + tools_use_needed = actor(messages=messages) + + for message, tool_result_content in executor(tools_use_needed, messages): + yield message + + if not tool_result_content: + return messages + + messages.append({"content": tool_result_content, "role": "user"}) + + elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": + while True: + parsed_screen = omniparser() + tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) + + for message, tool_result_content in executor(tools_use_needed, messages): + yield message + + if not tool_result_content: + return messages + + # import pdb; pdb.set_trace() + # messages.append({"role": "user", + # "content": ["History plan:\n" + str(vlm_response_json['Reasoning'])]}) + + # messages.append({"content": tool_result_content, "role": "user"}) \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py new file mode 100644 index 0000000..26542e8 --- /dev/null +++ b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py @@ -0,0 +1,349 @@ +import json +import asyncio +import platform +from collections.abc import Callable +from datetime import datetime +from enum import StrEnum +from typing import Any, cast, Dict, Callable +import uuid +import requests +from PIL import Image, ImageDraw +import base64 +from io import BytesIO +from pathlib import Path + +from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse +from anthropic.types import TextBlock, ToolResultBlockParam +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage + +from computer_use_demo.tools.screen_capture import get_screenshot +from computer_use_demo.gui_agent.llm_utils.oai import run_oai_interleaved, encode_image +from computer_use_demo.gui_agent.llm_utils.qwen import run_qwen +from computer_use_demo.gui_agent.llm_utils.llm_utils import extract_data +from computer_use_demo.colorful_text import colorful_text_vlm + + +SYSTEM_PROMPT = f""" +* You are utilizing a Windows system with internet access. +* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. + +""" +OUTPUT_DIR = "./tmp/outputs" + +class OmniParser: + def __init__(self, + url: str, + selected_screen: int = 0) -> None: + self.url = url + self.selected_screen = selected_screen + + def __call__(self,): + screenshot, screenshot_path = get_screenshot(selected_screen=self.selected_screen) + screenshot_path = str(screenshot_path) + image_base64 = encode_image(screenshot_path) + + response = requests.post(self.url, json={"base64_image": image_base64, 'prompt': 'omniparser process'}) + response_json = response.json() + som_image_data = base64.b64decode(response_json['som_image_base64']) + screenshot_path_uuid = Path(screenshot_path).stem.replace("screenshot_", "") + som_screenshot_path = f"{OUTPUT_DIR}/screenshot_som_{screenshot_path_uuid}.png" + with open(som_screenshot_path, "wb") as f: + f.write(som_image_data) + + response_json['width'] = screenshot.size[0] + response_json['height'] = screenshot.size[1] + response_json['original_screenshot_base64'] = image_base64 + response_json['screenshot_uuid'] = screenshot_path_uuid + # example response_json: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "latency": 0.1} + print('omniparser latency:', response_json['latency']) + response_json = self.reformat_messages(response_json) + return response_json + + def reformat_messages(self, response_json: dict): + screen_info = "" + for idx, element in enumerate(response_json["parsed_content_list"]): + element['idx'] = idx + if element['type'] == 'text': + screen_info += f'''

\n''' + elif element['type'] == 'icon': + screen_info += f'''{element['content']} \n''' + response_json['screen_info'] = screen_info + return response_json + + + +class VLMAgent: + def __init__( + self, + model: str, + provider: str, + system_prompt_suffix: str, + api_key: str, + output_callback: Callable, + api_response_callback: Callable, + max_tokens: int = 4096, + only_n_most_recent_images: int | None = None, + selected_screen: int = 0, + print_usage: bool = True, + ): + if model == "omniparser + gpt-4o": + self.model = "gpt-4o-2024-11-20" + else: + raise ValueError(f"Model {model} not supported") + + self.provider = provider + self.system_prompt_suffix = system_prompt_suffix + self.api_key = api_key + self.api_response_callback = api_response_callback + self.max_tokens = max_tokens + self.only_n_most_recent_images = only_n_most_recent_images + self.selected_screen = selected_screen + self.output_callback = output_callback + + self.print_usage = print_usage + self.total_token_usage = 0 + self.total_cost = 0 + + self.system = system_prompt_suffix + + def __call__(self, messages: list, parsed_screen: list[str, list]): + # Show results of Omniparser + image_base64 = parsed_screen['original_screenshot_base64'] + self.output_callback(f'Screenshot for {colorful_text_vlm}:\n', + sender="bot") + self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n', sender="bot") + screenshot_uuid = parsed_screen['screenshot_uuid'] + screen_width, screen_height = parsed_screen['width'], parsed_screen['height'] + + # example parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} + boxids_and_labels = parsed_screen["screen_info"] + system = self._get_system_prompt(boxids_and_labels) + self.system_prompt_suffix + + # drop looping actions msg, byte image etc + planner_messages = messages + # import pdb; pdb.set_trace() + planner_messages = _keep_latest_images(planner_messages) + # if self.only_n_most_recent_images: + # _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) + print(f"filtered_messages: {planner_messages}\n\n", "full messages:", messages) + + if isinstance(planner_messages[-1], dict): + if not isinstance(planner_messages[-1]["content"], list): + planner_messages[-1]["content"] = [planner_messages[-1]["content"]] + planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_{screenshot_uuid}.png") + planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_som_{screenshot_uuid}.png") + + print(f"Sending messages to VLMPlanner : {planner_messages}") + + if "gpt" in self.model: + vlm_response, token_usage = run_oai_interleaved( + messages=planner_messages, + system=system, + llm=self.model, + api_key=self.api_key, + max_tokens=self.max_tokens, + temperature=0, + ) + print(f"oai token usage: {token_usage}") + self.total_token_usage += token_usage + self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ + + elif "qwen" in self.model: + vlm_response, token_usage = run_qwen( + messages=planner_messages, + system=system, + llm=self.model, + api_key=self.api_key, + max_tokens=self.max_tokens, + temperature=0, + ) + print(f"qwen token usage: {token_usage}") + self.total_token_usage += token_usage + self.total_cost += (token_usage * 0.02 / 7.25 / 1000) # 1USD=7.25CNY, https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-plus-api + elif "phi" in self.model: + pass # TODO + else: + raise ValueError(f"Model {self.model} not supported") + + print(f"VLMPlanner response: {vlm_response}") + + if self.print_usage: + print(f"VLMPlanner total token usage so far: {self.total_token_usage}. Total cost so far: $USD{self.total_cost:.5f}") + + vlm_response_json = extract_data(vlm_response, "json") + vlm_response_json = json.loads(vlm_response_json) + + # map "box_id" to "idx" in parsed_screen, and output the xy coordinate of bbox + try: + bbox = parsed_screen["parsed_content_list"][int(vlm_response_json["Box ID"])]["bbox"] + vlm_response_json["box_centroid_coordinate"] = [int((bbox[0] + bbox[2]) / 2 * screen_width), int((bbox[1] + bbox[3]) / 2 * screen_height)] + # draw a circle on the screenshot image to indicate the action + self.draw_action(vlm_response_json, image_base64) + except: + print("No Box ID in the response.") + + # Convert the VLM output to a string for printing in chat + vlm_plan_str = "" + for key, value in vlm_response_json.items(): + if key == "Reasoning": + vlm_plan_str += f'{value}' + else: + vlm_plan_str += f'\n{key}: {value}' + self.output_callback(f"{colorful_text_vlm}:\n{vlm_plan_str}", sender="bot") + + # construct the response so that anthropicExcutor can execute the tool + response_content = [BetaTextBlock(text=vlm_plan_str, type='text')] + if 'box_centroid_coordinate' in vlm_response_json: + move_cursor_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': 'mouse_move', 'coordinate': vlm_response_json["box_centroid_coordinate"]}, + name='computer', type='tool_use') + response_content.append(move_cursor_block) + if vlm_response_json["Next Action"] == "type": + click_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': 'left_click'}, name='computer', type='tool_use') + sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': vlm_response_json["Next Action"], 'text': vlm_response_json["value"]}, + name='computer', type='tool_use') + response_content.extend([click_block, sim_content_block]) + elif vlm_response_json["Next Action"] == "None": + print("Task paused/completed.") + else: + sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': vlm_response_json["Next Action"]}, + name='computer', type='tool_use') + response_content.append(sim_content_block) + response_message = BetaMessage(id=f'toolu_{uuid.uuid4()}', content=response_content, model='', role='assistant', type='message', stop_reason='tool_use', usage=BetaUsage(input_tokens=0, output_tokens=0)) + return response_message, vlm_response_json + + def _api_response_callback(self, response: APIResponse): + self.api_response_callback(response) + + def _get_system_prompt(self, screen_info: str = ""): + return f""" +You are using a Windows device. +You are able to use a mouse and keyboard to interact with the computer based on the given task and screenshot. +You can only interact with the desktop GUI (no terminal or application menu access). + +You may be given some history plan and actions, this is the response from the previous loop. +You should carefully consider your plan base on the task, screenshot, and history actions. + +Here is the list of all detected bounding boxes by IDs on the screen and their description:{screen_info} + +Your available "Next Action" only include: +- type: type a string of text. +- left_click: Describe the ui element to be clicked. +- enter: Press an enter key. +- escape: Press an ESCAPE key. +- hover: Describe the ui element to be hovered. +- scroll: Scroll the screen, you must specify up or down. +- press: Describe the ui element to be pressed. + +Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. + +Output format: +```json +{{ + "Reasoning": str, # describe what is in the current screen, taking into account the history, then describe your step-by-step thoughts on how to achieve the task, choose one action from available actions at a time. + "Next Action": "action_type, action description" | "None" # one action at a time, describe it in short and precisely. + 'Box ID': n, + 'value': "xxx" # if the action is type, you should provide the text to type. +}} +``` + +One Example: +```json +{{ + "Reasoning": "The current screen shows google result of amazon, in previous action I have searched amazon on google. Then I need to click on the first search results to go to amazon.com.", + "Next Action": "left_click", + 'Box ID': m, +}} +``` + +Another Example: +```json +{{ + "Reasoning": "The current screen shows the front page of amazon. There is no previous action. Therefore I need to type "Apple watch" in the search bar.", + "Next Action": "type", + 'Box ID': n, + 'value': "Apple watch" +}} +``` + +IMPORTANT NOTES: +1. You should only give a single action at a time. +2. You should give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. +3. Attach the next action prediction in the "Next Action". +4. You should not include other actions, such as keyboard shortcuts. +5. When the task is completed, you should say "Next Action": "None" in the json field. +""" + def draw_action(self, vlm_response_json, image_base64): + # draw a circle using the coordinate in parsed_screen['som_image_base64'] + image_data = base64.b64decode(image_base64) + image = Image.open(BytesIO(image_data)) + + draw = ImageDraw.Draw(image) + x, y = vlm_response_json["box_centroid_coordinate"] + radius = 30 + draw.ellipse((x - radius, y - radius, x + radius, y + radius), fill='red') + buffered = BytesIO() + image.save(buffered, format="PNG") + image_with_circle_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") + self.output_callback(f'Action performed on the red circle with centroid ({x}, {y}), for {colorful_text_vlm}:\n', sender="bot") + + +def _keep_latest_images(messages): + for i in range(len(messages)-1): + if isinstance(messages[i]["content"], list): + for cnt in messages[i]["content"]: + if isinstance(cnt, str): + if cnt.endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")): + messages[i]["content"].remove(cnt) + return messages + + +def _maybe_filter_to_n_most_recent_images( + messages: list[BetaMessageParam], + images_to_keep: int, + min_removal_threshold: int = 10, +): + """ + With the assumption that images are screenshots that are of diminishing value as + the conversation progresses, remove all but the final `images_to_keep` tool_result + images in place, with a chunk of min_removal_threshold to reduce the amount we + break the implicit prompt cache. + """ + if images_to_keep is None: + return messages + + tool_result_blocks = cast( + list[ToolResultBlockParam], + [ + item + for message in messages + for item in ( + message["content"] if isinstance(message["content"], list) else [] + ) + if isinstance(item, dict) and item.get("type") == "tool_result" + ], + ) + + total_images = sum( + 1 + for tool_result in tool_result_blocks + for content in tool_result.get("content", []) + if isinstance(content, dict) and content.get("type") == "image" + ) + + images_to_remove = total_images - images_to_keep + # for better cache behavior, we want to remove in chunks + images_to_remove -= images_to_remove % min_removal_threshold + + for tool_result in tool_result_blocks: + if isinstance(tool_result.get("content"), list): + new_content = [] + for content in tool_result.get("content", []): + if isinstance(content, dict) and content.get("type") == "image": + if images_to_remove > 0: + images_to_remove -= 1 + continue + new_content.append(content) + tool_result["content"] = new_content \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/tools/__init__.py b/demo/gradio/computer_use_demo/tools/__init__.py new file mode 100644 index 0000000..bb051ae --- /dev/null +++ b/demo/gradio/computer_use_demo/tools/__init__.py @@ -0,0 +1,12 @@ +from .base import CLIResult, ToolResult +from .collection import ToolCollection +from .computer import ComputerTool +from .screen_capture import get_screenshot + +__ALL__ = [ + CLIResult, + ComputerTool, + ToolCollection, + ToolResult, + get_screenshot, +] diff --git a/demo/gradio/computer_use_demo/tools/base.py b/demo/gradio/computer_use_demo/tools/base.py new file mode 100644 index 0000000..d6f1371 --- /dev/null +++ b/demo/gradio/computer_use_demo/tools/base.py @@ -0,0 +1,69 @@ +from abc import ABCMeta, abstractmethod +from dataclasses import dataclass, fields, replace +from typing import Any + +from anthropic.types.beta import BetaToolUnionParam + + +class BaseAnthropicTool(metaclass=ABCMeta): + """Abstract base class for Anthropic-defined tools.""" + + @abstractmethod + def __call__(self, **kwargs) -> Any: + """Executes the tool with the given arguments.""" + ... + + @abstractmethod + def to_params( + self, + ) -> BetaToolUnionParam: + raise NotImplementedError + + +@dataclass(kw_only=True, frozen=True) +class ToolResult: + """Represents the result of a tool execution.""" + + output: str | None = None + error: str | None = None + base64_image: str | None = None + system: str | None = None + + def __bool__(self): + return any(getattr(self, field.name) for field in fields(self)) + + def __add__(self, other: "ToolResult"): + def combine_fields( + field: str | None, other_field: str | None, concatenate: bool = True + ): + if field and other_field: + if concatenate: + return field + other_field + raise ValueError("Cannot combine tool results") + return field or other_field + + return ToolResult( + output=combine_fields(self.output, other.output), + error=combine_fields(self.error, other.error), + base64_image=combine_fields(self.base64_image, other.base64_image, False), + system=combine_fields(self.system, other.system), + ) + + def replace(self, **kwargs): + """Returns a new ToolResult with the given fields replaced.""" + return replace(self, **kwargs) + + +class CLIResult(ToolResult): + """A ToolResult that can be rendered as a CLI output.""" + + +class ToolFailure(ToolResult): + """A ToolResult that represents a failure.""" + + +class ToolError(Exception): + """Raised when a tool encounters an error.""" + + def __init__(self, message): + self.message = message diff --git a/demo/gradio/computer_use_demo/tools/collection.py b/demo/gradio/computer_use_demo/tools/collection.py new file mode 100644 index 0000000..c4e8c95 --- /dev/null +++ b/demo/gradio/computer_use_demo/tools/collection.py @@ -0,0 +1,34 @@ +"""Collection classes for managing multiple tools.""" + +from typing import Any + +from anthropic.types.beta import BetaToolUnionParam + +from .base import ( + BaseAnthropicTool, + ToolError, + ToolFailure, + ToolResult, +) + + +class ToolCollection: + """A collection of anthropic-defined tools.""" + + def __init__(self, *tools: BaseAnthropicTool): + self.tools = tools + self.tool_map = {tool.to_params()["name"]: tool for tool in tools} + + def to_params( + self, + ) -> list[BetaToolUnionParam]: + return [tool.to_params() for tool in self.tools] + + async def run(self, *, name: str, tool_input: dict[str, Any]) -> ToolResult: + tool = self.tool_map.get(name) + if not tool: + return ToolFailure(error=f"Tool {name} is invalid") + try: + return await tool(**tool_input) + except ToolError as e: + return ToolFailure(error=e.message) diff --git a/demo/gradio/computer_use_demo/tools/computer.py b/demo/gradio/computer_use_demo/tools/computer.py new file mode 100644 index 0000000..67c1ef6 --- /dev/null +++ b/demo/gradio/computer_use_demo/tools/computer.py @@ -0,0 +1,319 @@ +import pyautogui +import base64 +import time +from enum import StrEnum +from typing import Literal, TypedDict + +from PIL import Image + +from anthropic.types.beta import BetaToolComputerUse20241022Param + +from .base import BaseAnthropicTool, ToolError, ToolResult +from .screen_capture import get_screenshot +import requests +import re + +OUTPUT_DIR = "./tmp/outputs" + +TYPING_DELAY_MS = 12 +TYPING_GROUP_SIZE = 50 + +Action = Literal[ + "key", + "type", + "mouse_move", + "left_click", + "left_click_drag", + "right_click", + "middle_click", + "double_click", + "screenshot", + "cursor_position", +] + + +class Resolution(TypedDict): + width: int + height: int + + +MAX_SCALING_TARGETS: dict[str, Resolution] = { + "XGA": Resolution(width=1024, height=768), # 4:3 + "WXGA": Resolution(width=1280, height=800), # 16:10 + "FWXGA": Resolution(width=1366, height=768), # ~16:9 +} + + +class ScalingSource(StrEnum): + COMPUTER = "computer" + API = "api" + + +class ComputerToolOptions(TypedDict): + display_height_px: int + display_width_px: int + display_number: int | None + + +def chunks(s: str, chunk_size: int) -> list[str]: + return [s[i : i + chunk_size] for i in range(0, len(s), chunk_size)] + +class ComputerTool(BaseAnthropicTool): + """ + A tool that allows the agent to interact with the screen, keyboard, and mouse of the current computer. + Adapted for Windows using 'pyautogui'. + """ + + name: Literal["computer"] = "computer" + api_type: Literal["computer_20241022"] = "computer_20241022" + width: int + height: int + display_num: int | None + + _screenshot_delay = 2.0 + _scaling_enabled = True + + @property + def options(self) -> ComputerToolOptions: + width, height = self.scale_coordinates( + ScalingSource.COMPUTER, self.width, self.height + ) + return { + "display_width_px": width, + "display_height_px": height, + "display_number": self.display_num, + } + + def to_params(self) -> BetaToolComputerUse20241022Param: + return {"name": self.name, "type": self.api_type, **self.options} + + def __init__(self, selected_screen: int = 0, is_scaling: bool = False): + super().__init__() + + # Get screen width and height using Windows command + self.display_num = None + self.offset_x = 0 + self.offset_y = 0 + self.selected_screen = selected_screen + self.is_scaling = is_scaling + self.width, self.height = self.get_screen_size() + print(f"screen size: {self.width}, {self.height}") + + self.key_conversion = {"Page_Down": "pagedown", + "Page_Up": "pageup", + "Super_L": "win", + "Escape": "esc"} + + + async def __call__( + self, + *, + action: Action, + text: str | None = None, + coordinate: tuple[int, int] | None = None, + **kwargs, + ): + print(f"action: {action}, text: {text}, coordinate: {coordinate}, is_scaling: {self.is_scaling}") + if action in ("mouse_move", "left_click_drag"): + if coordinate is None: + raise ToolError(f"coordinate is required for {action}") + if text is not None: + raise ToolError(f"text is not accepted for {action}") + if not isinstance(coordinate, (list, tuple)) or len(coordinate) != 2: + raise ToolError(f"{coordinate} must be a tuple of length 2") + # if not all(isinstance(i, int) and i >= 0 for i in coordinate): + if not all(isinstance(i, int) for i in coordinate): + raise ToolError(f"{coordinate} must be a tuple of non-negative ints") + + if self.is_scaling: + x, y = self.scale_coordinates( + ScalingSource.API, coordinate[0], coordinate[1] + ) + else: + x, y = coordinate + + # print(f"scaled_coordinates: {x}, {y}") + # print(f"offset: {self.offset_x}, {self.offset_y}") + + # x += self.offset_x # TODO - check if this is needed + # y += self.offset_y + + print(f"mouse move to {x}, {y}") + + if action == "mouse_move": + self.send_to_vm(f"pyautogui.moveTo({x}, {y})") + return ToolResult(output=f"Moved mouse to ({x}, {y})") + elif action == "left_click_drag": + current_x, current_y = pyautogui.position() + self.send_to_vm(f"pyautogui.dragTo({x}, {y}, duration=0.5)") + return ToolResult(output=f"Dragged mouse from ({current_x}, {current_y}) to ({x}, {y})") + + if action in ("key", "type"): + if text is None: + raise ToolError(f"text is required for {action}") + if coordinate is not None: + raise ToolError(f"coordinate is not accepted for {action}") + if not isinstance(text, str): + raise ToolError(output=f"{text} must be a string") + + if action == "key": + # Handle key combinations + keys = text.split('+') + for key in keys: + key = self.key_conversion.get(key.strip(), key.strip()) + key = key.lower() + self.send_to_vm(f"pyautogui.keyDown('{key}')") # Press down each key + for key in reversed(keys): + key = self.key_conversion.get(key.strip(), key.strip()) + key = key.lower() + self.send_to_vm(f"pyautogui.keyUp('{key}')") # Release each key in reverse order + return ToolResult(output=f"Pressed keys: {text}") + + elif action == "type": + self.send_to_vm(f"pyautogui.typewrite('{text}', interval={TYPING_DELAY_MS / 1000})") # Convert ms to seconds + self.send_to_vm("pyautogui.press('enter')") + screenshot_base64 = (await self.screenshot()).base64_image + return ToolResult(output=text, base64_image=screenshot_base64) + + if action in ( + "left_click", + "right_click", + "double_click", + "middle_click", + "screenshot", + "cursor_position", + "left_press", + ): + if text is not None: + raise ToolError(f"text is not accepted for {action}") + if coordinate is not None: + raise ToolError(f"coordinate is not accepted for {action}") + + if action == "screenshot": + return await self.screenshot() + elif action == "cursor_position": + x, y = self.send_to_vm("pyautogui.position()") + x, y = self.scale_coordinates(ScalingSource.COMPUTER, x, y) + return ToolResult(output=f"X={x},Y={y}") + else: + if action == "left_click": + self.send_to_vm("pyautogui.click()") + elif action == "right_click": + self.send_to_vm("pyautogui.rightClick()") + elif action == "middle_click": + self.send_to_vm("pyautogui.middleClick()") + elif action == "double_click": + self.send_to_vm("pyautogui.doubleClick()") + elif action == "left_press": + self.send_to_vm("pyautogui.mouseDown()") + time.sleep(1) + self.send_to_vm("pyautogui.mouseUp()") + return ToolResult(output=f"Performed {action}") + + raise ToolError(f"Invalid action: {action}") + + def send_to_vm(self, action: str): + """ + Executes a python command on the server. Only return tuple of x,y when action is "pyautogui.position()" + """ + prefix = "import pyautogui; pyautogui.FAILSAFE = False;" + command_list = ["python", "-c", f"{prefix} {action}"] + parse = action == "pyautogui.position()" + if parse: + command_list[-1] = f"{prefix} print({action})" + + try: + print(f"sending to vm: {command_list}") + response = requests.post( + f"http://localhost:5000/execute", + headers={'Content-Type': 'application/json'}, + json={"command": command_list}, + timeout=90 + ) + print(f"action executed") + if response.status_code != 200: + raise ToolError(f"Failed to execute command. Status code: {response.status_code}") + if parse: + output = response.json()['output'].strip() + match = re.search(r'Point\(x=(\d+),\s*y=(\d+)\)', output) + if not match: + raise ToolError(f"Could not parse coordinates from output: {output}") + x, y = map(int, match.groups()) + return x, y + except requests.exceptions.RequestException as e: + raise ToolError(f"An error occurred while trying to execute the command: {str(e)}") + + async def screenshot(self): + if not hasattr(self, 'target_dimension'): + screenshot = self.padding_image(screenshot) + self.target_dimension = MAX_SCALING_TARGETS["WXGA"] + width, height = self.target_dimension["width"], self.target_dimension["height"] + screenshot, path = get_screenshot(selected_screen=0, resize=True, target_width=width, target_height=height) + + # return ToolResult() + return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode()) + + raise ToolError(f"Failed to take screenshot: {path} does not exist.") + + def padding_image(self, screenshot): + """Pad the screenshot to 16:10 aspect ratio, when the aspect ratio is not 16:10.""" + _, height = screenshot.size + new_width = height * 16 // 10 + + padding_image = Image.new("RGB", (new_width, height), (255, 255, 255)) + # padding to top left + padding_image.paste(screenshot, (0, 0)) + return padding_image + + def scale_coordinates(self, source: ScalingSource, x: int, y: int): + """Scale coordinates to a target maximum resolution.""" + if not self._scaling_enabled: + return x, y + ratio = self.width / self.height + target_dimension = None + + for target_name, dimension in MAX_SCALING_TARGETS.items(): + # allow some error in the aspect ratio - not ratios are exactly 16:9 + if abs(dimension["width"] / dimension["height"] - ratio) < 0.02: + if dimension["width"] < self.width: + target_dimension = dimension + self.target_dimension = target_dimension + # print(f"target_dimension: {target_dimension}") + break + + if target_dimension is None: + # TODO: currently we force the target to be WXGA (16:10), when it cannot find a match + target_dimension = MAX_SCALING_TARGETS["WXGA"] + self.target_dimension = MAX_SCALING_TARGETS["WXGA"] + + # should be less than 1 + x_scaling_factor = target_dimension["width"] / self.width + y_scaling_factor = target_dimension["height"] / self.height + if source == ScalingSource.API: + if x > self.width or y > self.height: + raise ToolError(f"Coordinates {x}, {y} are out of bounds") + # scale up + return round(x / x_scaling_factor), round(y / y_scaling_factor) + # scale down + return round(x * x_scaling_factor), round(y * y_scaling_factor) + + def get_screen_size(self): + """Return width and height of the screen""" + try: + response = requests.post( + f"http://localhost:5000/execute", + headers={'Content-Type': 'application/json'}, + json={"command": ["python", "-c", "import pyautogui; print(pyautogui.size())"]}, + timeout=90 + ) + if response.status_code != 200: + raise ToolError(f"Failed to get screen size. Status code: {response.status_code}") + + output = response.json()['output'].strip() + match = re.search(r'Size\(width=(\d+),\s*height=(\d+)\)', output) + if not match: + raise ToolError(f"Could not parse screen size from output: {output}") + width, height = map(int, match.groups()) + return width, height + except requests.exceptions.RequestException as e: + raise ToolError(f"An error occurred while trying to get screen size: {str(e)}") \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/tools/screen_capture.py b/demo/gradio/computer_use_demo/tools/screen_capture.py new file mode 100644 index 0000000..40529d3 --- /dev/null +++ b/demo/gradio/computer_use_demo/tools/screen_capture.py @@ -0,0 +1,29 @@ +from pathlib import Path +from uuid import uuid4 +import requests +from PIL import Image +from .base import BaseAnthropicTool, ToolError +from io import BytesIO + +OUTPUT_DIR = "./tmp/outputs" + +def get_screenshot(selected_screen: int = 0, resize: bool = False, target_width: int = 1920, target_height: int = 1080): + """Capture screenshot by requesting from HTTP endpoint - returns native resolution unless resized""" + output_dir = Path(OUTPUT_DIR) + output_dir.mkdir(parents=True, exist_ok=True) + path = output_dir / f"screenshot_{uuid4().hex}.png" + + try: + response = requests.get('http://localhost:5000/screenshot') + if response.status_code != 200: + raise ToolError(f"Failed to capture screenshot: HTTP {response.status_code}") + + # (1280, 800) + screenshot = Image.open(BytesIO(response.content)) + + if resize and screenshot.size != (target_width, target_height): + screenshot = screenshot.resize((target_width, target_height)) + screenshot.save(path) + return screenshot, path + except Exception as e: + raise ToolError(f"Failed to capture screenshot: {str(e)}") \ No newline at end of file From 6cb310d12485377e860286de6d763910882e1024 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Mon, 20 Jan 2025 18:29:46 -0800 Subject: [PATCH 03/57] remove need to write to disk --- demo/remote_request.py | 19 +++++++------------ utils.py | 30 ++++++++++++++++++++++-------- 2 files changed, 29 insertions(+), 20 deletions(-) diff --git a/demo/remote_request.py b/demo/remote_request.py index d0bc1c3..32c1730 100644 --- a/demo/remote_request.py +++ b/demo/remote_request.py @@ -4,11 +4,12 @@ import sys import os sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model +from utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, get_ocr_bbox import torch from PIL import Image from typing import Dict, Tuple, List import base64 +import io config = { @@ -30,12 +31,9 @@ class Omniparser(object): print('Omniparser initialized!!!') def parse(self, image_base64: str): - image_path = '../imgs/demo_image.jpg' - with open(image_path, "wb") as fh: - fh.write(base64.b64decode(image_base64)) - print('Parsing image:', image_path) - - image = Image.open(image_path) + # Convert base64 to image directly without saving to disk + image_bytes = base64.b64decode(image_base64) + image = Image.open(io.BytesIO(image_bytes)) print('image size:', image.size) box_overlay_ratio = max(image.size) / 3200 @@ -47,11 +45,8 @@ class Omniparser(object): } BOX_TRESHOLD = config['BOX_TRESHOLD'] - ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.8}, use_paddleocr=True) - text, ocr_bbox = ocr_bbox_rslt - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) - with open('../imgs/demo_image_som.jpg', "wb") as fh: - fh.write(base64.b64decode(dino_labled_img)) + text, ocr_bbox = get_ocr_bbox(image) + dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) return dino_labled_img, parsed_content_list diff --git a/utils.py b/utils.py index c4b6f88..6ddf88b 100755 --- a/utils.py +++ b/utils.py @@ -35,7 +35,7 @@ import base64 import os import ast import torch -from typing import Tuple, List +from typing import Tuple, List, Union from torchvision.ops import box_convert import re from torchvision.transforms import ToPILImage @@ -384,20 +384,20 @@ def predict(model, image, caption, box_threshold, text_threshold): return boxes, logits, phrases -def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_threshold=0.7): +def predict_yolo(model, image, box_threshold, imgsz, scale_img, iou_threshold=0.7): """ Use huggingface model to replace the original model """ # model = model['model'] if scale_img: result = model.predict( - source=image_path, + source=image, conf=box_threshold, imgsz=imgsz, iou=iou_threshold, # default 0.7 ) else: result = model.predict( - source=image_path, + source=image, conf=box_threshold, iou=iou_threshold, # default 0.7 ) @@ -408,15 +408,21 @@ def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_thresho return boxes, conf, phrases -def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64): - """ ocr_bbox: list of xyxy format bbox +def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_TRESHOLD=0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64): + """Process either an image path or Image object + + Args: + image_source: Either a file path (str) or PIL Image object + ... """ - image_source = Image.open(img_path).convert("RGB") + if isinstance(image_source, str): + image_source = Image.open(image_source).convert("RGB") + w, h = image_source.size if not imgsz: imgsz = (h, w) # print('image size:', w, h) - xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1) + xyxy, logits, phrases = predict_yolo(model=model, image=image_source, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1) xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device) image_source = np.asarray(image_source) phrases = [str(i) for i in range(len(phrases))] @@ -545,5 +551,13 @@ def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_ # print('bounding box!!!', bb) return (text, bb), goal_filtering +def get_ocr_bbox(image): + text_threshold = 0.8 + result = paddle_ocr.ocr(image, cls=False)[0] + coord = [item[0] for item in result if item[1][1] > text_threshold] + text = [item[1][0] for item in result if item[1][1] > text_threshold] + bb = [get_xyxy(item) for item in coord] + return text, bb + From e0a845d35cb153dac3e7345662d5f6fb28a405f8 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Mon, 20 Jan 2025 19:31:26 -0800 Subject: [PATCH 04/57] remove final use of pyautogui --- demo/gradio/computer_use_demo/tools/computer.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/demo/gradio/computer_use_demo/tools/computer.py b/demo/gradio/computer_use_demo/tools/computer.py index 67c1ef6..912c096 100644 --- a/demo/gradio/computer_use_demo/tools/computer.py +++ b/demo/gradio/computer_use_demo/tools/computer.py @@ -1,4 +1,3 @@ -import pyautogui import base64 import time from enum import StrEnum @@ -144,7 +143,7 @@ class ComputerTool(BaseAnthropicTool): self.send_to_vm(f"pyautogui.moveTo({x}, {y})") return ToolResult(output=f"Moved mouse to ({x}, {y})") elif action == "left_click_drag": - current_x, current_y = pyautogui.position() + current_x, current_y = self.send_to_vm("pyautogui.position()") self.send_to_vm(f"pyautogui.dragTo({x}, {y}, duration=0.5)") return ToolResult(output=f"Dragged mouse from ({current_x}, {current_y}) to ({x}, {y})") From ce199d52ab85f8b838d56da7e7213f86825f837b Mon Sep 17 00:00:00 2001 From: yadonglu Date: Wed, 22 Jan 2025 19:24:34 -0800 Subject: [PATCH 05/57] support remote access using fastapi; add double click/scroll; enable local run omniparser --- demo/gradio/app.py | 47 +- demo/gradio/computer_use_demo/loop.py | 4 +- .../omniparser_agent/omniparser.py | 77 ++ .../omniparser_agent/utils.py | 935 ++++++++++++++++++ .../omniparser_agent/vlm_agent.py | 53 +- .../computer_use_demo/tools/computer.py | 6 + demo/gradio/demo_image.jpg | Bin 0 -> 230959 bytes demo/gradio/demo_image_som.jpg | Bin 0 -> 123201 bytes demo/gradio/fast_api_demo.py | 12 + 9 files changed, 1120 insertions(+), 14 deletions(-) create mode 100644 demo/gradio/computer_use_demo/omniparser_agent/omniparser.py create mode 100755 demo/gradio/computer_use_demo/omniparser_agent/utils.py create mode 100644 demo/gradio/demo_image.jpg create mode 100644 demo/gradio/demo_image_som.jpg create mode 100644 demo/gradio/fast_api_demo.py diff --git a/demo/gradio/app.py b/demo/gradio/app.py index 1dfb9e4..b54a19a 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -14,6 +14,7 @@ from functools import partial from pathlib import Path from typing import cast, Dict from PIL import Image +import socket import gradio as gr from anthropic import APIResponse @@ -294,8 +295,10 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: with gr.Column(scale=1): chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) with gr.Column(scale=3): + # Get the fully qualified domain name of the machine + machine_fqdn = socket.getfqdn() iframe = gr.HTML( - '', + f'', container=False, elem_classes="no-padding" ) @@ -361,4 +364,44 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: submit_button.click(process_input, [chat_input, state], chatbot) -demo.launch(share=False, allowed_paths=["./"], server_port=7888) +from fastapi import FastAPI +import uvicorn +from multiprocessing import Process + +app = FastAPI() + +# Mount the Gradio app under the "/gradio" path +app = gr.mount_gradio_app(app, demo, path="/gradio") + +# Optional: Add a root endpoint that redirects to the Gradio interface +@app.get("/") +async def root(): + return {"message": "Welcome to OmniParser Demo API", + "gradio_interface": "/gradio"} + +# Create a second FastAPI app for VNC +vnc_app = FastAPI() + +@vnc_app.get("/") +async def vnc_root(): + return {"message": "VNC Server"} + +def run_app(app, host, port): + uvicorn.run(app, host=host, port=port) + +# To run this with uvicorn: +if __name__ == "__main__": + # Start the main app on port 7889 + p1 = Process(target=run_app, args=(app, "0.0.0.0", 7889)) + # Start the VNC app on port 8006 + p2 = Process(target=run_app, args=(vnc_app, "0.0.0.0", 8006)) + + p1.start() + p2.start() + + try: + p1.join() + p2.join() + except KeyboardInterrupt: + p1.terminate() + p2.terminate() \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py index a0bc58e..22754f8 100644 --- a/demo/gradio/computer_use_demo/loop.py +++ b/demo/gradio/computer_use_demo/loop.py @@ -113,7 +113,9 @@ def sampling_loop_sync( ) elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": - omniparser = OmniParser(url="http://localhost:8000/send_text/", + # omniparser = OmniParser(url="http://localhost:8000/send_text/", + # selected_screen=selected_screen,) + omniparser = OmniParser(url=None, selected_screen=selected_screen,) actor = VLMAgent( diff --git a/demo/gradio/computer_use_demo/omniparser_agent/omniparser.py b/demo/gradio/computer_use_demo/omniparser_agent/omniparser.py new file mode 100644 index 0000000..81ca124 --- /dev/null +++ b/demo/gradio/computer_use_demo/omniparser_agent/omniparser.py @@ -0,0 +1,77 @@ +# uvicorn remote_request:app --host 0.0.0.0 --port 8000 --reload + +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from computer_use_demo.omniparser_agent.utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box +import torch +from PIL import Image +from typing import Dict, Tuple, List +import base64 +import io + + +# config = { +# 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', +# 'device': 'cpu', +# 'caption_model_name': 'florence2', +# 'caption_model_path': '../weights/icon_caption_florence', +# 'BOX_TRESHOLD': 0.05 +# } + + +class Omniparser(object): + def __init__(self, config: Dict): + self.config = config + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + self.som_model = get_yolo_model(model_path=config['som_model_path']) + self.caption_model_processor = get_caption_model_processor(model_name=config['caption_model_name'], model_name_or_path=config['caption_model_path'], device=device) + print('Omniparser initialized!!!') + + def parse(self, image_base64: str): + image_path = './demo_image.jpg' + with open(image_path, "wb") as fh: + fh.write(base64.b64decode(image_base64)) + print('Parsing image:', image_path) + + image = Image.open(image_path) + print('image size:', image.size) + + box_overlay_ratio = max(image.size) / 3200 + draw_bbox_config = { + 'text_scale': 0.8 * box_overlay_ratio, + 'text_thickness': max(int(2 * box_overlay_ratio), 1), + 'text_padding': max(int(3 * box_overlay_ratio), 1), + 'thickness': max(int(3 * box_overlay_ratio), 1), + } + BOX_TRESHOLD = self.config['BOX_TRESHOLD'] + + ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.8}, use_paddleocr=True) + text, ocr_bbox = ocr_bbox_rslt + dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) + + return dino_labled_img, parsed_content_list + + +# from fastapi import FastAPI +# from pydantic import BaseModel + +# app = FastAPI() + +# class Item(BaseModel): +# base64_image: str +# prompt: str + +# Omniparser = Omniparser(config) + +# @app.post("/send_text/") +# async def send_text(item: Item): +# print('start parsing...') +# import time +# start = time.time() +# dino_labled_img, parsed_content_list = Omniparser.parse(item.base64_image) +# latency = time.time() - start +# print('time:', latency) +# return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency} \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/omniparser_agent/utils.py b/demo/gradio/computer_use_demo/omniparser_agent/utils.py new file mode 100755 index 0000000..87c6f83 --- /dev/null +++ b/demo/gradio/computer_use_demo/omniparser_agent/utils.py @@ -0,0 +1,935 @@ +# from ultralytics import YOLO +import os +import io +import base64 +import time +from PIL import Image, ImageDraw, ImageFont +import json +import requests +# utility function +import os +from openai import AzureOpenAI + +import json +import sys +import os +import cv2 +import numpy as np +# %matplotlib inline +from matplotlib import pyplot as plt +import easyocr +from paddleocr import PaddleOCR +reader = easyocr.Reader(['en', 'ch_sim'], gpu=True) +paddle_ocr = PaddleOCR( + lang='en', # other lang also available + use_angle_cls=False, + use_gpu=False, # using cuda will conflict with pytorch in the same process + show_log=False, + max_batch_size=1024, + use_dilation=True, # improves accuracy + det_db_score_mode='slow', # improves accuracy + rec_batch_num=1024) +import time +import base64 + +import os +import ast +import torch +from typing import Tuple, List +from torchvision.ops import box_convert +import re +from torchvision.transforms import ToPILImage +import supervision as sv +import torchvision.transforms as T + + +def get_caption_model_processor(model_name, model_name_or_path="Salesforce/blip2-opt-2.7b", device=None): + if not device: + device = "cuda" if torch.cuda.is_available() else "cpu" + if model_name == "blip2": + from transformers import Blip2Processor, Blip2ForConditionalGeneration + processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") + if device == 'cpu': + model = Blip2ForConditionalGeneration.from_pretrained( + model_name_or_path, device_map=None, torch_dtype=torch.float32 + ) + else: + model = Blip2ForConditionalGeneration.from_pretrained( + model_name_or_path, device_map=None, torch_dtype=torch.float16 + ).to(device) + elif model_name == "florence2": + from transformers import AutoProcessor, AutoModelForCausalLM + processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True) + if device == 'cpu': + model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float32, trust_remote_code=True) + else: + model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, trust_remote_code=True).to(device) + return {'model': model.to(device), 'processor': processor} + + +def get_yolo_model(model_path): + from ultralytics import YOLO + # Load the model. + model = YOLO(model_path) + return model + + +@torch.inference_mode() +def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=None): + # Number of samples per batch, --> 256 roughly takes 23 GB of GPU memory for florence model + + to_pil = ToPILImage() + if starting_idx: + non_ocr_boxes = filtered_boxes[starting_idx:] + else: + non_ocr_boxes = filtered_boxes + croped_pil_image = [] + t0 = time.time() + for i, coord in enumerate(non_ocr_boxes): + try: + xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) + ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) + cropped_image = image_source[ymin:ymax, xmin:xmax, :] + # resize the image to 224x224 to avoid long overhead in clipimageprocessor # TODO + cropped_image = cv2.resize(cropped_image, (64, 64)) + croped_pil_image.append(to_pil(cropped_image)) + except: + continue + # print('time to prepare bbox:', time.time()-t0) + + model, processor = caption_model_processor['model'], caption_model_processor['processor'] + if not prompt: + if 'florence' in model.config.name_or_path: + prompt = "" + else: + prompt = "The image shows" + + generated_texts = [] + device = model.device + # batch_size = 64 + for i in range(0, len(croped_pil_image), batch_size): + start = time.time() + batch = croped_pil_image[i:i+batch_size] + t1 = time.time() + if model.device.type == 'cuda': + inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt", do_resize=False).to(device=device, dtype=torch.float16) + else: + inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device) + t2 = time.time() + # print('time to process image + tokenize text inputs:', t2-t1) + if 'florence' in model.config.name_or_path: + generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=20,num_beams=1, do_sample=False) + else: + generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True, + t3 = time.time() + # print('time to generate:', t3-t2) + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) + generated_text = [gen.strip() for gen in generated_text] + generated_texts.extend(generated_text) + + return generated_texts + + + +def get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor): + to_pil = ToPILImage() + if ocr_bbox: + non_ocr_boxes = filtered_boxes[len(ocr_bbox):] + else: + non_ocr_boxes = filtered_boxes + croped_pil_image = [] + for i, coord in enumerate(non_ocr_boxes): + xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) + ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) + cropped_image = image_source[ymin:ymax, xmin:xmax, :] + croped_pil_image.append(to_pil(cropped_image)) + + model, processor = caption_model_processor['model'], caption_model_processor['processor'] + device = model.device + messages = [{"role": "user", "content": "<|image_1|>\ndescribe the icon in one sentence"}] + prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + + batch_size = 5 # Number of samples per batch + generated_texts = [] + + for i in range(0, len(croped_pil_image), batch_size): + images = croped_pil_image[i:i+batch_size] + image_inputs = [processor.image_processor(x, return_tensors="pt") for x in images] + inputs ={'input_ids': [], 'attention_mask': [], 'pixel_values': [], 'image_sizes': []} + texts = [prompt] * len(images) + for i, txt in enumerate(texts): + input = processor._convert_images_texts_to_inputs(image_inputs[i], txt, return_tensors="pt") + inputs['input_ids'].append(input['input_ids']) + inputs['attention_mask'].append(input['attention_mask']) + inputs['pixel_values'].append(input['pixel_values']) + inputs['image_sizes'].append(input['image_sizes']) + max_len = max([x.shape[1] for x in inputs['input_ids']]) + for i, v in enumerate(inputs['input_ids']): + inputs['input_ids'][i] = torch.cat([processor.tokenizer.pad_token_id * torch.ones(1, max_len - v.shape[1], dtype=torch.long), v], dim=1) + inputs['attention_mask'][i] = torch.cat([torch.zeros(1, max_len - v.shape[1], dtype=torch.long), inputs['attention_mask'][i]], dim=1) + inputs_cat = {k: torch.concatenate(v).to(device) for k, v in inputs.items()} + + generation_args = { + "max_new_tokens": 25, + "temperature": 0.01, + "do_sample": False, + } + generate_ids = model.generate(**inputs_cat, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) + # # remove input tokens + generate_ids = generate_ids[:, inputs_cat['input_ids'].shape[1]:] + response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) + response = [res.strip('\n').strip() for res in response] + generated_texts.extend(response) + + return generated_texts + +def remove_overlap(boxes, iou_threshold, ocr_bbox=None): + assert ocr_bbox is None or isinstance(ocr_bbox, List) + + def box_area(box): + return (box[2] - box[0]) * (box[3] - box[1]) + + def intersection_area(box1, box2): + x1 = max(box1[0], box2[0]) + y1 = max(box1[1], box2[1]) + x2 = min(box1[2], box2[2]) + y2 = min(box1[3], box2[3]) + return max(0, x2 - x1) * max(0, y2 - y1) + + def IoU(box1, box2): + intersection = intersection_area(box1, box2) + union = box_area(box1) + box_area(box2) - intersection + 1e-6 + if box_area(box1) > 0 and box_area(box2) > 0: + ratio1 = intersection / box_area(box1) + ratio2 = intersection / box_area(box2) + else: + ratio1, ratio2 = 0, 0 + return max(intersection / union, ratio1, ratio2) + + def is_inside(box1, box2): + # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3] + intersection = intersection_area(box1, box2) + ratio1 = intersection / box_area(box1) + return ratio1 > 0.95 + + boxes = boxes.tolist() + filtered_boxes = [] + if ocr_bbox: + filtered_boxes.extend(ocr_bbox) + # print('ocr_bbox!!!', ocr_bbox) + for i, box1 in enumerate(boxes): + # if not any(IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2) for j, box2 in enumerate(boxes) if i != j): + is_valid_box = True + for j, box2 in enumerate(boxes): + # keep the smaller box + if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2): + is_valid_box = False + break + if is_valid_box: + # add the following 2 lines to include ocr bbox + if ocr_bbox: + # only add the box if it does not overlap with any ocr bbox + if not any(IoU(box1, box3) > iou_threshold and not is_inside(box1, box3) for k, box3 in enumerate(ocr_bbox)): + filtered_boxes.append(box1) + else: + filtered_boxes.append(box1) + return torch.tensor(filtered_boxes) + + +def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None): + ''' + ocr_bbox format: [{'type': 'text', 'bbox':[x,y], 'interactivity':False, 'content':str }, ...] + boxes format: [{'type': 'icon', 'bbox':[x,y], 'interactivity':True, 'content':None }, ...] + + ''' + assert ocr_bbox is None or isinstance(ocr_bbox, List) + + def box_area(box): + return (box[2] - box[0]) * (box[3] - box[1]) + + def intersection_area(box1, box2): + x1 = max(box1[0], box2[0]) + y1 = max(box1[1], box2[1]) + x2 = min(box1[2], box2[2]) + y2 = min(box1[3], box2[3]) + return max(0, x2 - x1) * max(0, y2 - y1) + + def IoU(box1, box2): + intersection = intersection_area(box1, box2) + union = box_area(box1) + box_area(box2) - intersection + 1e-6 + if box_area(box1) > 0 and box_area(box2) > 0: + ratio1 = intersection / box_area(box1) + ratio2 = intersection / box_area(box2) + else: + ratio1, ratio2 = 0, 0 + return max(intersection / union, ratio1, ratio2) + + def is_inside(box1, box2): + # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3] + intersection = intersection_area(box1, box2) + ratio1 = intersection / box_area(box1) + return ratio1 > 0.80 + + # boxes = boxes.tolist() + filtered_boxes = [] + if ocr_bbox: + filtered_boxes.extend(ocr_bbox) + # print('ocr_bbox!!!', ocr_bbox) + for i, box1_elem in enumerate(boxes): + box1 = box1_elem['bbox'] + is_valid_box = True + for j, box2_elem in enumerate(boxes): + # keep the smaller box + box2 = box2_elem['bbox'] + if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2): + is_valid_box = False + break + if is_valid_box: + # add the following 2 lines to include ocr bbox + if ocr_bbox: + # keep yolo boxes + prioritize ocr label + box_added = False + ocr_labels = '' + for box3_elem in ocr_bbox: + if not box_added: + box3 = box3_elem['bbox'] + if is_inside(box3, box1): # ocr inside icon + # box_added = True + # delete the box3_elem from ocr_bbox + try: + # filtered_boxes.append({'type': 'text', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': box3_elem['content'], 'source':'box_yolo_content_ocr'}) + # gather all ocr labels + ocr_labels += box3_elem['content'] + ' ' + filtered_boxes.remove(box3_elem) + # print('remove ocr bbox:', box3_elem) + except: + continue + # break + elif is_inside(box1, box3): # icon inside ocr, don't added this icon box, no need to check other ocr bbox bc no overlap between ocr bbox, icon can only be in one ocr box + box_added = True + # try: + # filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None}) + # filtered_boxes.remove(box3_elem) + # except: + # continue + break + else: + continue + if not box_added: + if ocr_labels: + filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': ocr_labels, 'source':'box_yolo_content_ocr'}) + else: + filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None, 'source':'box_yolo_content_yolo'}) + + else: + filtered_boxes.append(box1) + return filtered_boxes # torch.tensor(filtered_boxes) + + +def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]: + transform = T.Compose( + [ + T.RandomResize([800], max_size=1333), + T.ToTensor(), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ] + ) + image_source = Image.open(image_path).convert("RGB") + image = np.asarray(image_source) + image_transformed, _ = transform(image_source, None) + return image, image_transformed + + +def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str], text_scale: float, + text_padding=5, text_thickness=2, thickness=3) -> np.ndarray: + """ + This function annotates an image with bounding boxes and labels. + + Parameters: + image_source (np.ndarray): The source image to be annotated. + boxes (torch.Tensor): A tensor containing bounding box coordinates. in cxcywh format, pixel scale + logits (torch.Tensor): A tensor containing confidence scores for each bounding box. + phrases (List[str]): A list of labels for each bounding box. + text_scale (float): The scale of the text to be displayed. 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web + + Returns: + np.ndarray: The annotated image. + """ + h, w, _ = image_source.shape + boxes = boxes * torch.Tensor([w, h, w, h]) + xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() + xywh = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xywh").numpy() + detections = sv.Detections(xyxy=xyxy) + + labels = [f"{phrase}" for phrase in range(boxes.shape[0])] + + # from util.box_annotator import BoxAnnotator + box_annotator = BoxAnnotator(text_scale=text_scale, text_padding=text_padding,text_thickness=text_thickness,thickness=thickness) # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web + annotated_frame = image_source.copy() + annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels, image_size=(w,h)) + + label_coordinates = {f"{phrase}": v for phrase, v in zip(phrases, xywh)} + return annotated_frame, label_coordinates + + +def predict(model, image, caption, box_threshold, text_threshold): + """ Use huggingface model to replace the original model + """ + model, processor = model['model'], model['processor'] + device = model.device + + inputs = processor(images=image, text=caption, return_tensors="pt").to(device) + with torch.no_grad(): + outputs = model(**inputs) + + results = processor.post_process_grounded_object_detection( + outputs, + inputs.input_ids, + box_threshold=box_threshold, # 0.4, + text_threshold=text_threshold, # 0.3, + target_sizes=[image.size[::-1]] + )[0] + boxes, logits, phrases = results["boxes"], results["scores"], results["labels"] + return boxes, logits, phrases + + +def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_threshold=0.7): + """ Use huggingface model to replace the original model + """ + # model = model['model'] + if scale_img: + result = model.predict( + source=image_path, + conf=box_threshold, + imgsz=imgsz, + iou=iou_threshold, # default 0.7 + ) + else: + result = model.predict( + source=image_path, + conf=box_threshold, + iou=iou_threshold, # default 0.7 + ) + boxes = result[0].boxes.xyxy#.tolist() # in pixel space + conf = result[0].boxes.conf + phrases = [str(i) for i in range(len(boxes))] + + return boxes, conf, phrases + + +def int_box_area(box, w, h): + x1, y1, x2, y2 = box + int_box = [int(x1*w), int(y1*h), int(x2*w), int(y2*h)] + area = (int_box[2] - int_box[0]) * (int_box[3] - int_box[1]) + return area + + +def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64): + """ ocr_bbox: list of xyxy format bbox + """ + image_source = Image.open(img_path).convert("RGB") + w, h = image_source.size + if not imgsz: + imgsz = (h, w) + # print('image size:', w, h) + xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1) + xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device) + image_source = np.asarray(image_source) + phrases = [str(i) for i in range(len(phrases))] + + + # annotate the image with labels + if ocr_bbox: + ocr_bbox = torch.tensor(ocr_bbox) / torch.Tensor([w, h, w, h]) + ocr_bbox=ocr_bbox.tolist() + else: + print('no ocr bbox!!!') + ocr_bbox = None + + ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt, 'source': 'box_ocr_content_ocr'} for box, txt in zip(ocr_bbox, ocr_text) if int_box_area(box, w, h) > 0] + xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist() if int_box_area(box, w, h) > 0] + filtered_boxes = remove_overlap_new(boxes=xyxy_elem, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox_elem) + + + # sort the filtered_boxes so that the one with 'content': None is at the end, and get the index of the first 'content': None + filtered_boxes_elem = sorted(filtered_boxes, key=lambda x: x['content'] is None) + # get the index of the first 'content': None + starting_idx = next((i for i, box in enumerate(filtered_boxes_elem) if box['content'] is None), -1) + filtered_boxes = torch.tensor([box['bbox'] for box in filtered_boxes_elem]) + print('len(filtered_boxes):', len(filtered_boxes), starting_idx) + + # get parsed icon local semantics + time1 = time.time() + if use_local_semantics: + caption_model = caption_model_processor['model'] + if 'phi3_v' in caption_model.config.model_type: + parsed_content_icon = get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor) + else: + parsed_content_icon = get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=prompt,batch_size=batch_size) + ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] + icon_start = len(ocr_text) + parsed_content_icon_ls = [] + # fill the filtered_boxes_elem None content with parsed_content_icon in order + for i, box in enumerate(filtered_boxes_elem): + if box['content'] is None: + box['content'] = parsed_content_icon.pop(0) + for i, txt in enumerate(parsed_content_icon): + parsed_content_icon_ls.append(f"Icon Box ID {str(i+icon_start)}: {txt}") + parsed_content_merged = ocr_text + parsed_content_icon_ls + else: + ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] + parsed_content_merged = ocr_text + print('time to get parsed content:', time.time()-time1) + + filtered_boxes = box_convert(boxes=filtered_boxes, in_fmt="xyxy", out_fmt="cxcywh") + + phrases = [i for i in range(len(filtered_boxes))] + + # draw boxes + if draw_bbox_config: + annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, **draw_bbox_config) + else: + annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, text_scale=text_scale, text_padding=text_padding) + + pil_img = Image.fromarray(annotated_frame) + buffered = io.BytesIO() + pil_img.save(buffered, format="PNG") + encoded_image = base64.b64encode(buffered.getvalue()).decode('ascii') + if output_coord_in_ratio: + # h, w, _ = image_source.shape + label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()} + assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0] + + return encoded_image, label_coordinates, filtered_boxes_elem + + +def get_xywh(input): + x, y, w, h = input[0][0], input[0][1], input[2][0] - input[0][0], input[2][1] - input[0][1] + x, y, w, h = int(x), int(y), int(w), int(h) + return x, y, w, h + +def get_xyxy(input): + x, y, xp, yp = input[0][0], input[0][1], input[2][0], input[2][1] + x, y, xp, yp = int(x), int(y), int(xp), int(yp) + return x, y, xp, yp + +def get_xywh_yolo(input): + x, y, w, h = input[0], input[1], input[2] - input[0], input[3] - input[1] + x, y, w, h = int(x), int(y), int(w), int(h) + return x, y, w, h + + + +def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False): + if use_paddleocr: + if easyocr_args is None: + text_threshold = 0.5 + else: + text_threshold = easyocr_args['text_threshold'] + result = paddle_ocr.ocr(image_path, cls=False)[0] + # conf = [item[1] for item in result] + coord = [item[0] for item in result if item[1][1] > text_threshold] + text = [item[1][0] for item in result if item[1][1] > text_threshold] + else: # EasyOCR + if easyocr_args is None: + easyocr_args = {} + result = reader.readtext(image_path, **easyocr_args) + # print('goal filtering pred:', result[-5:]) + coord = [item[0] for item in result] + text = [item[1] for item in result] + # read the image using cv2 + if display_img: + opencv_img = cv2.imread(image_path) + opencv_img = cv2.cvtColor(opencv_img, cv2.COLOR_RGB2BGR) + bb = [] + for item in coord: + x, y, a, b = get_xywh(item) + # print(x, y, a, b) + bb.append((x, y, a, b)) + cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2) + + # Display the image + plt.imshow(opencv_img) + else: + if output_bb_format == 'xywh': + bb = [get_xywh(item) for item in coord] + elif output_bb_format == 'xyxy': + bb = [get_xyxy(item) for item in coord] + # print('bounding box!!!', bb) + return (text, bb), goal_filtering + + + +from typing import List, Optional, Union, Tuple + +import cv2 +import numpy as np + +from supervision.detection.core import Detections +from supervision.draw.color import Color, ColorPalette + + +class BoxAnnotator: + """ + A class for drawing bounding boxes on an image using detections provided. + + Attributes: + color (Union[Color, ColorPalette]): The color to draw the bounding box, + can be a single color or a color palette + thickness (int): The thickness of the bounding box lines, default is 2 + text_color (Color): The color of the text on the bounding box, default is white + text_scale (float): The scale of the text on the bounding box, default is 0.5 + text_thickness (int): The thickness of the text on the bounding box, + default is 1 + text_padding (int): The padding around the text on the bounding box, + default is 5 + + """ + + def __init__( + self, + color: Union[Color, ColorPalette] = ColorPalette.DEFAULT, + thickness: int = 3, # 1 for seeclick 2 for mind2web and 3 for demo + text_color: Color = Color.BLACK, + text_scale: float = 0.5, # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web + text_thickness: int = 2, #1, # 2 for demo + text_padding: int = 10, + avoid_overlap: bool = True, + ): + self.color: Union[Color, ColorPalette] = color + self.thickness: int = thickness + self.text_color: Color = text_color + self.text_scale: float = text_scale + self.text_thickness: int = text_thickness + self.text_padding: int = text_padding + self.avoid_overlap: bool = avoid_overlap + + def annotate( + self, + scene: np.ndarray, + detections: Detections, + labels: Optional[List[str]] = None, + skip_label: bool = False, + image_size: Optional[Tuple[int, int]] = None, + ) -> np.ndarray: + """ + Draws bounding boxes on the frame using the detections provided. + + Args: + scene (np.ndarray): The image on which the bounding boxes will be drawn + detections (Detections): The detections for which the + bounding boxes will be drawn + labels (Optional[List[str]]): An optional list of labels + corresponding to each detection. If `labels` are not provided, + corresponding `class_id` will be used as label. + skip_label (bool): Is set to `True`, skips bounding box label annotation. + Returns: + np.ndarray: The image with the bounding boxes drawn on it + + Example: + ```python + import supervision as sv + + classes = ['person', ...] + image = ... + detections = sv.Detections(...) + + box_annotator = sv.BoxAnnotator() + labels = [ + f"{classes[class_id]} {confidence:0.2f}" + for _, _, confidence, class_id, _ in detections + ] + annotated_frame = box_annotator.annotate( + scene=image.copy(), + detections=detections, + labels=labels + ) + ``` + """ + font = cv2.FONT_HERSHEY_SIMPLEX + for i in range(len(detections)): + x1, y1, x2, y2 = detections.xyxy[i].astype(int) + class_id = ( + detections.class_id[i] if detections.class_id is not None else None + ) + idx = class_id if class_id is not None else i + color = ( + self.color.by_idx(idx) + if isinstance(self.color, ColorPalette) + else self.color + ) + cv2.rectangle( + img=scene, + pt1=(x1, y1), + pt2=(x2, y2), + color=color.as_bgr(), + thickness=self.thickness, + ) + if skip_label: + continue + + text = ( + f"{class_id}" + if (labels is None or len(detections) != len(labels)) + else labels[i] + ) + + text_width, text_height = cv2.getTextSize( + text=text, + fontFace=font, + fontScale=self.text_scale, + thickness=self.text_thickness, + )[0] + + if not self.avoid_overlap: + text_x = x1 + self.text_padding + text_y = y1 - self.text_padding + + text_background_x1 = x1 + text_background_y1 = y1 - 2 * self.text_padding - text_height + + text_background_x2 = x1 + 2 * self.text_padding + text_width + text_background_y2 = y1 + # text_x = x1 - self.text_padding - text_width + # text_y = y1 + self.text_padding + text_height + # text_background_x1 = x1 - 2 * self.text_padding - text_width + # text_background_y1 = y1 + # text_background_x2 = x1 + # text_background_y2 = y1 + 2 * self.text_padding + text_height + else: + text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 = get_optimal_label_pos(self.text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size) + + cv2.rectangle( + img=scene, + pt1=(text_background_x1, text_background_y1), + pt2=(text_background_x2, text_background_y2), + color=color.as_bgr(), + thickness=cv2.FILLED, + ) + box_color = color.as_rgb() + luminance = 0.299 * box_color[0] + 0.587 * box_color[1] + 0.114 * box_color[2] + text_color = (0,0,0) if luminance > 160 else (255,255,255) + cv2.putText( + img=scene, + text=text, + org=(text_x, text_y), + fontFace=font, + fontScale=self.text_scale, + # color=self.text_color.as_rgb(), + color=text_color, + thickness=self.text_thickness, + lineType=cv2.LINE_AA, + ) + return scene + + +def box_area(box): + return (box[2] - box[0]) * (box[3] - box[1]) + +def intersection_area(box1, box2): + x1 = max(box1[0], box2[0]) + y1 = max(box1[1], box2[1]) + x2 = min(box1[2], box2[2]) + y2 = min(box1[3], box2[3]) + return max(0, x2 - x1) * max(0, y2 - y1) + +def IoU(box1, box2, return_max=True): + intersection = intersection_area(box1, box2) + union = box_area(box1) + box_area(box2) - intersection + if box_area(box1) > 0 and box_area(box2) > 0: + ratio1 = intersection / box_area(box1) + ratio2 = intersection / box_area(box2) + else: + ratio1, ratio2 = 0, 0 + if return_max: + return max(intersection / union, ratio1, ratio2) + else: + return intersection / union + + +def get_optimal_label_pos(text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size): + """ check overlap of text and background detection box, and get_optimal_label_pos, + pos: str, position of the text, must be one of 'top left', 'top right', 'outer left', 'outer right' TODO: if all are overlapping, return the last one, i.e. outer right + Threshold: default to 0.3 + """ + + def get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size): + is_overlap = False + for i in range(len(detections)): + detection = detections.xyxy[i].astype(int) + if IoU([text_background_x1, text_background_y1, text_background_x2, text_background_y2], detection) > 0.3: + is_overlap = True + break + # check if the text is out of the image + if text_background_x1 < 0 or text_background_x2 > image_size[0] or text_background_y1 < 0 or text_background_y2 > image_size[1]: + is_overlap = True + return is_overlap + + # if pos == 'top left': + text_x = x1 + text_padding + text_y = y1 - text_padding + + text_background_x1 = x1 + text_background_y1 = y1 - 2 * text_padding - text_height + + text_background_x2 = x1 + 2 * text_padding + text_width + text_background_y2 = y1 + is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) + if not is_overlap: + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + # elif pos == 'outer left': + text_x = x1 - text_padding - text_width + text_y = y1 + text_padding + text_height + + text_background_x1 = x1 - 2 * text_padding - text_width + text_background_y1 = y1 + + text_background_x2 = x1 + text_background_y2 = y1 + 2 * text_padding + text_height + is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) + if not is_overlap: + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + + # elif pos == 'outer right': + text_x = x2 + text_padding + text_y = y1 + text_padding + text_height + + text_background_x1 = x2 + text_background_y1 = y1 + + text_background_x2 = x2 + 2 * text_padding + text_width + text_background_y2 = y1 + 2 * text_padding + text_height + + is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) + if not is_overlap: + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + # elif pos == 'top right': + text_x = x2 - text_padding - text_width + text_y = y1 - text_padding + + text_background_x1 = x2 - 2 * text_padding - text_width + text_background_y1 = y1 - 2 * text_padding - text_height + + text_background_x2 = x2 + text_background_y2 = y1 + + is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) + if not is_overlap: + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 + + + +import re +def extract_dict_from_text(text): + # Define the regex pattern for a dictionary-like structure + pattern = r"\{\s*'(?P.*?)':\s*'(?P.*?)',\s*'(?P.*?)':\s*'(?P.*?)'\s*\}" + + # Search for the dictionary in the text + match = re.search(pattern, text) + + if match: + # Extract matched groups into a dictionary + return { + match.group('key1'): match.group('value1'), + match.group('key2'): match.group('value2'), + } + else: + raise ValueError("No valid dictionary structure found in the text.") + + +def get_phi3v_model_dict(): + from PIL import Image + import requests + from transformers import AutoModelForCausalLM + from transformers import AutoProcessor + + model_id = "microsoft/Phi-3.5-vision-instruct" + model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') + processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) + print('phi3v model loaded!!!') + return {'model': model, 'processor': processor} + + +def call_phi3v(messages, image_base64, model_dict): + model, processor = model_dict['model'], model_dict['processor'] + device = model.device + prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + if isinstance(image_base64, tuple): + image_base64, dino_labled_img = image_base64 + image = Image.open(io.BytesIO(base64.b64decode(image_base64))) + dino_labled_img = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) + inputs = processor(prompt, [image, dino_labled_img], return_tensors="pt").to(device) + else: + image = Image.open(io.BytesIO(base64.b64decode(image_base64))) + inputs = processor(prompt, [image], return_tensors="pt").to(device) + + generation_args = { + "max_new_tokens": 512, + "temperature": 0.01, + "do_sample": False, + } + + generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) + # remove input tokens + generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] + ans = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + return ans + + +def get_pred_phi3v(message_text, image_base64, label_coordinates, id_key='Click ID', model_dict=None): + # messages = [ + # {"role": "system", "content": '''You are an expert at completing instructions on GUI screens. + # You will be presented with two images. The first is the original screenshot. The second is the same screenshot with some numeric tags. You will also be provided with some descriptions of the bbox, and your task is to choose the numeric bbox idx you want to click in order to complete the user instruction.'''}, + # ] + messages = [ + {"role": "system", "content": '''You are an expert at completing instructions on GUI screens. You will also be provided with some descriptions of the bbox, and your task is to choose the numeric bbox idx you want to click in order to complete the user instruction.'''}, + ] + messages = [] + if isinstance(image_base64, tuple): + messages.append({"role": "user", "content": '<|image_1|>\n' + '<|image_2|>\n' + message_text}) + else: + messages.append({"role": "user", "content": '<|image_1|>\n' + message_text}) + + response_text = call_phi3v(messages, image_base64, model_dict) + print(response_text) + + try: + response_text = ast.literal_eval(response_text) + + icon_id = response_text['Click BBox ID'] + bbox = label_coordinates[str(icon_id)] + click_point = [bbox[0] + bbox[2]/2, bbox[1] + bbox[3]/2] + except: + print('error parsing, use regex to parse!!!') + import pdb; pdb.set_trace() + response_text = extract_dict_from_text(response_text) + icon_id = response_text['Click BBox ID'] + bbox = label_coordinates[str(icon_id)] + click_point = [bbox[0] + bbox[2]/2, bbox[1] + bbox[3]/2] + return icon_id, bbox, click_point, response_text + + # try: + # match = re.search(r"```(.*?)```", ans, re.DOTALL) + # if match: + # result = match.group(1).strip() + # pred = result.split('In summary, the next action I will perform is:')[-1].strip().replace('\\', '') + # pred = ast.literal_eval(pred) + # else: + # pred = ans.split('In summary, the next action I will perform is:')[-1].strip().replace('\\', '') + # pred = ast.literal_eval(pred) + + # if pred[id_key]: + # icon_id = pred[id_key] + # bbox = label_coordinates[str(icon_id)] + # pred['click_point'] = [bbox[0] + bbox[2]/2, bbox[1] + bbox[3]/2] + # except: + # print('phi3v action regex extract fail!!!') + # pred = {'action_type': 'CLICK', 'click_point': [0, 0], 'value': 'None', 'is_completed': False} + + # step_pred_summary = None + # return pred, [True, ans, None, step_pred_summary] \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py index 26542e8..66c740a 100644 --- a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py +++ b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py @@ -21,7 +21,8 @@ from computer_use_demo.gui_agent.llm_utils.oai import run_oai_interleaved, encod from computer_use_demo.gui_agent.llm_utils.qwen import run_qwen from computer_use_demo.gui_agent.llm_utils.llm_utils import extract_data from computer_use_demo.colorful_text import colorful_text_vlm - +import time +# start = time.time() SYSTEM_PROMPT = f""" * You are utilizing a Windows system with internet access. @@ -36,14 +37,34 @@ class OmniParser: selected_screen: int = 0) -> None: self.url = url self.selected_screen = selected_screen + if not self.url: + config = { + 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', + 'device': 'cpu', + 'caption_model_name': 'florence2', + 'caption_model_path': '../weights/icon_caption_florence', + 'BOX_TRESHOLD': 0.05 + } + from computer_use_demo.omniparser_agent.omniparser import Omniparser as Omniparser_class + self.omniparser = Omniparser_class(config=config) def __call__(self,): screenshot, screenshot_path = get_screenshot(selected_screen=self.selected_screen) screenshot_path = str(screenshot_path) image_base64 = encode_image(screenshot_path) + if self.url: + response = requests.post(self.url, json={"base64_image": image_base64, 'prompt': 'omniparser process'}) + response_json = response.json() + else: + start = time.time() + dino_labled_img, parsed_content_list = self.omniparser.parse(image_base64) + latency = time.time() - start + response_json = { + 'som_image_base64': dino_labled_img, + 'parsed_content_list': parsed_content_list, + 'latency': latency + } - response = requests.post(self.url, json={"base64_image": image_base64, 'prompt': 'omniparser process'}) - response_json = response.json() som_image_data = base64.b64decode(response_json['som_image_base64']) screenshot_path_uuid = Path(screenshot_path).stem.replace("screenshot_", "") som_screenshot_path = f"{OUTPUT_DIR}/screenshot_som_{screenshot_path_uuid}.png" @@ -64,9 +85,11 @@ class OmniParser: for idx, element in enumerate(response_json["parsed_content_list"]): element['idx'] = idx if element['type'] == 'text': - screen_info += f'''

\n''' + # screen_info += f'''

\n''' + screen_info += f'ID: {idx}, Text: {element["content"]}\n' elif element['type'] == 'icon': - screen_info += f'''{element['content']} \n''' + # screen_info += f'''{element['content']} \n''' + screen_info += f'ID: {idx}, Icon: {element["content"]}\n' response_json['screen_info'] = screen_info return response_json @@ -106,12 +129,16 @@ class VLMAgent: self.system = system_prompt_suffix - def __call__(self, messages: list, parsed_screen: list[str, list]): + def __call__(self, messages: list, parsed_screen: list[str, list, dict]): # Show results of Omniparser image_base64 = parsed_screen['original_screenshot_base64'] + latency_omniparser = parsed_screen['latency'] self.output_callback(f'Screenshot for {colorful_text_vlm}:\n', sender="bot") self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n', sender="bot") + screen_info = str(parsed_screen['screen_info']) + self.output_callback(f'Screen Info for {colorful_text_vlm}:\n{screen_info}', sender="bot") + screenshot_uuid = parsed_screen['screenshot_uuid'] screen_width, screen_height = parsed_screen['width'], parsed_screen['height'] @@ -125,7 +152,7 @@ class VLMAgent: planner_messages = _keep_latest_images(planner_messages) # if self.only_n_most_recent_images: # _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) - print(f"filtered_messages: {planner_messages}\n\n", "full messages:", messages) + # print(f"filtered_messages: {planner_messages}\n\n", "full messages:", messages) if isinstance(planner_messages[-1], dict): if not isinstance(planner_messages[-1]["content"], list): @@ -134,7 +161,7 @@ class VLMAgent: planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_som_{screenshot_uuid}.png") print(f"Sending messages to VLMPlanner : {planner_messages}") - + start = time.time() if "gpt" in self.model: vlm_response, token_usage = run_oai_interleaved( messages=planner_messages, @@ -164,6 +191,8 @@ class VLMAgent: pass # TODO else: raise ValueError(f"Model {self.model} not supported") + latency_vlm = time.time() - start + self.output_callback(f"VLMPlanner latency: {latency_vlm}, Omniparser latency: {latency_omniparser}", sender="bot") print(f"VLMPlanner response: {vlm_response}") @@ -189,7 +218,7 @@ class VLMAgent: vlm_plan_str += f'{value}' else: vlm_plan_str += f'\n{key}: {value}' - self.output_callback(f"{colorful_text_vlm}:\n{vlm_plan_str}", sender="bot") + # self.output_callback(f"{colorful_text_vlm}:\n{vlm_plan_str}", sender="bot") # construct the response so that anthropicExcutor can execute the tool response_content = [BetaTextBlock(text=vlm_plan_str, type='text')] @@ -231,10 +260,12 @@ Here is the list of all detected bounding boxes by IDs on the screen and their d Your available "Next Action" only include: - type: type a string of text. - left_click: Describe the ui element to be clicked. -- enter: Press an enter key. +- double_click: Describe the ui element to be double clicked. +- right_click: Describe the ui element to be right clicked. - escape: Press an ESCAPE key. - hover: Describe the ui element to be hovered. -- scroll: Scroll the screen, you must specify up or down. +- scroll_up: Scroll the screen up. +- scroll_down: Scroll the screen down. - press: Describe the ui element to be pressed. Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. diff --git a/demo/gradio/computer_use_demo/tools/computer.py b/demo/gradio/computer_use_demo/tools/computer.py index 912c096..7086a62 100644 --- a/demo/gradio/computer_use_demo/tools/computer.py +++ b/demo/gradio/computer_use_demo/tools/computer.py @@ -208,6 +208,12 @@ class ComputerTool(BaseAnthropicTool): time.sleep(1) self.send_to_vm("pyautogui.mouseUp()") return ToolResult(output=f"Performed {action}") + if action in ("scroll_up", "scroll_down"): + if action == "scroll_up": + self.send_to_vm("pyautogui.scroll(100)") + elif action == "scroll_down": + self.send_to_vm("pyautogui.scroll(-100)") + return ToolResult(output=f"Performed {action}") raise ToolError(f"Invalid action: {action}") diff --git a/demo/gradio/demo_image.jpg b/demo/gradio/demo_image.jpg new 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zRl}HqAlPg+x7%$r8ns$&oz`>ui(72I_;=%{Z|wpY3>6yWzKb35?$w7-NsegE4kE9Co{%Qfe}p8aJ-9*K5O>e*@$un12}z#@)%V%lWr@ z33cPvE_nX@`8TcCodOfU3H@QE%!%7ED?BBm%QJw%GisHe82XDUkydBoSU@+=Lqhl;He4Swen89E$7!1a5 z3YW_zNm5u?Sao^nOj`7G4^to(;3?frH8c=K(cy5&vRt#?4utOAyZc&SyLRmwl-~^ucnBhx31u#lt4;vT^27|$1+>V$`CYQ@qR8-`(XI5rfJDApv2dK$Y8UO(7 z7cbhYJEu~qv|4TO>K6dIb?erxTeslHxzpFcU@#aBO??C~t8L3*Fc=I5<95biFc=Jm zTfGVfgTY|jb_AQVfx%!f7z_r3!C){Lj5`7r1I%DB7z_r3@tft_?`lcn#b7WPcNI(k xGZ+j8gTY`l2)A5(j(Vu{)-Hg-U@&es{vWpBf72L{_22*i002ovPDHLkV1n~_RfPZm literal 0 HcmV?d00001 diff --git a/demo/gradio/fast_api_demo.py b/demo/gradio/fast_api_demo.py new file mode 100644 index 0000000..d50bbd4 --- /dev/null +++ b/demo/gradio/fast_api_demo.py @@ -0,0 +1,12 @@ +from fastapi import FastAPI +import gradio as gr + +app = FastAPI() + +def greet(name): + return f"Hello, {name}!" + +gradio_interface = gr.Interface(fn=greet, inputs="text", outputs="text") +gradio_app = gr.routes.App.create_app(gradio_interface) + +app.mount("/gradio", gradio_app) From 6e389fe851519c2dc69ecb0f1c3cdc0466f02f2b Mon Sep 17 00:00:00 2001 From: yadonglu Date: Wed, 22 Jan 2025 19:25:01 -0800 Subject: [PATCH 06/57] remove debug file --- demo/gradio/fast_api_demo.py | 12 ------------ 1 file changed, 12 deletions(-) delete mode 100644 demo/gradio/fast_api_demo.py diff --git a/demo/gradio/fast_api_demo.py b/demo/gradio/fast_api_demo.py deleted file mode 100644 index d50bbd4..0000000 --- a/demo/gradio/fast_api_demo.py +++ /dev/null @@ -1,12 +0,0 @@ -from fastapi import FastAPI -import gradio as gr - -app = FastAPI() - -def greet(name): - return f"Hello, {name}!" - -gradio_interface = gr.Interface(fn=greet, inputs="text", outputs="text") -gradio_app = gr.routes.App.create_app(gradio_interface) - -app.mount("/gradio", gradio_app) From c29ac5064a41a315c255dc105dedb3e3ca39cf74 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 22 Jan 2025 20:59:44 -0800 Subject: [PATCH 07/57] remove custom prompt functionality --- demo/gradio/app.py | 13 ------------- .../computer_use_demo/gui_agent/anthropic_agent.py | 8 ++------ demo/gradio/computer_use_demo/loop.py | 5 +---- .../computer_use_demo/omniparser_agent/vlm_agent.py | 13 ++----------- 4 files changed, 5 insertions(+), 34 deletions(-) diff --git a/demo/gradio/app.py b/demo/gradio/app.py index b54a19a..72d6d66 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -67,8 +67,6 @@ def setup_state(state): state["tools"] = {} if "only_n_most_recent_images" not in state: state["only_n_most_recent_images"] = 2 - if "custom_system_prompt" not in state: - state["custom_system_prompt"] = "" if 'chatbot_messages' not in state: state['chatbot_messages'] = [] @@ -204,7 +202,6 @@ def process_input(user_input, state): # Run sampling_loop_sync with the chatbot_output_callback for loop_msg in sampling_loop_sync( - system_prompt_suffix=state["custom_system_prompt"], model=state["model"], provider=state["provider"], messages=state["messages"], @@ -252,12 +249,6 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: value="omniparser + gpt-4o", # Set to one of the choices interactive=True, ) - with gr.Column(): - custom_prompt = gr.Textbox( - label="System Prompt Suffix", - value="", - interactive=True, - ) with gr.Column(): only_n_images = gr.Slider( label="N most recent screenshots", @@ -333,9 +324,6 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: ) return provider_update, api_key_update - - def update_system_prompt_suffix(system_prompt_suffix, state): - state["custom_system_prompt"] = system_prompt_suffix def update_only_n_images(only_n_images_value, state): state["only_n_most_recent_images"] = only_n_images_value @@ -357,7 +345,6 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: state[f'{state["provider"]}_api_key'] = api_key_value model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key]) - custom_prompt.change(fn=update_system_prompt_suffix, inputs=[custom_prompt, state], outputs=None) only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key) api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None) diff --git a/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py b/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py index fbd66a1..e7b2071 100644 --- a/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py +++ b/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py @@ -61,8 +61,7 @@ class AnthropicActor: def __init__( self, model: str, - provider: APIProvider, - system_prompt_suffix: str, + provider: APIProvider, api_key: str, api_response_callback: Callable[[APIResponse[BetaMessage]], None], max_tokens: int = 4096, @@ -72,7 +71,6 @@ class AnthropicActor: ): self.model = model self.provider = provider - self.system_prompt_suffix = system_prompt_suffix self.api_key = api_key self.api_response_callback = api_response_callback self.max_tokens = max_tokens @@ -83,9 +81,7 @@ class AnthropicActor: ComputerTool(selected_screen=selected_screen), ) - self.system = ( - f"{SYSTEM_PROMPT}{' ' + system_prompt_suffix if system_prompt_suffix else ''}" - ) + self.system = SYSTEM_PROMPT self.total_token_usage = 0 self.total_cost = 0 diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py index 22754f8..2a7c076 100644 --- a/demo/gradio/computer_use_demo/loop.py +++ b/demo/gradio/computer_use_demo/loop.py @@ -75,7 +75,6 @@ def sampling_loop_sync( *, model: str, provider: APIProvider | None, - system_prompt_suffix: str, messages: list[BetaMessageParam], output_callback: Callable[[BetaContentBlock], None], tool_output_callback: Callable[[ToolResult, str], None], @@ -96,8 +95,7 @@ def sampling_loop_sync( # Register Actor and Executor actor = AnthropicActor( model=model, - provider=provider, - system_prompt_suffix=system_prompt_suffix, + provider=provider, api_key=api_key, api_response_callback=api_response_callback, max_tokens=max_tokens, @@ -121,7 +119,6 @@ def sampling_loop_sync( actor = VLMAgent( model=model, provider=provider, - system_prompt_suffix=system_prompt_suffix, api_key=api_key, api_response_callback=api_response_callback, selected_screen=selected_screen, diff --git a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py index 66c740a..26d5190 100644 --- a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py +++ b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py @@ -22,13 +22,7 @@ from computer_use_demo.gui_agent.llm_utils.qwen import run_qwen from computer_use_demo.gui_agent.llm_utils.llm_utils import extract_data from computer_use_demo.colorful_text import colorful_text_vlm import time -# start = time.time() -SYSTEM_PROMPT = f""" -* You are utilizing a Windows system with internet access. -* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. - -""" OUTPUT_DIR = "./tmp/outputs" class OmniParser: @@ -94,13 +88,11 @@ class OmniParser: return response_json - class VLMAgent: def __init__( self, model: str, provider: str, - system_prompt_suffix: str, api_key: str, output_callback: Callable, api_response_callback: Callable, @@ -115,7 +107,6 @@ class VLMAgent: raise ValueError(f"Model {model} not supported") self.provider = provider - self.system_prompt_suffix = system_prompt_suffix self.api_key = api_key self.api_response_callback = api_response_callback self.max_tokens = max_tokens @@ -127,7 +118,7 @@ class VLMAgent: self.total_token_usage = 0 self.total_cost = 0 - self.system = system_prompt_suffix + self.system = '' def __call__(self, messages: list, parsed_screen: list[str, list, dict]): # Show results of Omniparser @@ -144,7 +135,7 @@ class VLMAgent: # example parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} boxids_and_labels = parsed_screen["screen_info"] - system = self._get_system_prompt(boxids_and_labels) + self.system_prompt_suffix + system = self._get_system_prompt(boxids_and_labels) # drop looping actions msg, byte image etc planner_messages = messages From 9db016b52f49314d3eec1c4c7ee67464fd6ab754 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 22 Jan 2025 21:14:21 -0800 Subject: [PATCH 08/57] unused file cleanup --- .../gui_agent/anthropic_agent.py | 13 --- .../gui_agent/llm_utils/llm_utils.py | 109 ------------------ .../gui_agent/llm_utils/qwen.py | 107 ----------------- .../gui_agent/llm_utils/run_llm.py | 44 ------- demo/gradio/computer_use_demo/loop.py | 42 +------ .../omniparser_agent/vlm_agent.py | 25 ++-- 6 files changed, 13 insertions(+), 327 deletions(-) delete mode 100644 demo/gradio/computer_use_demo/gui_agent/llm_utils/llm_utils.py delete mode 100644 demo/gradio/computer_use_demo/gui_agent/llm_utils/qwen.py delete mode 100644 demo/gradio/computer_use_demo/gui_agent/llm_utils/run_llm.py diff --git a/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py b/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py index e7b2071..0e49850 100644 --- a/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py +++ b/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py @@ -31,32 +31,19 @@ from io import BytesIO import gradio as gr from typing import Dict - BETA_FLAG = "computer-use-2024-10-22" - class APIProvider(StrEnum): ANTHROPIC = "anthropic" BEDROCK = "bedrock" VERTEX = "vertex" - -PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { - APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", - APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", - APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", -} - - -# Check OS - SYSTEM_PROMPT = f""" * You are utilizing a Windows system with internet access. * The current date is {datetime.today().strftime('%A, %B %d, %Y')}. """ - class AnthropicActor: def __init__( self, diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/llm_utils.py b/demo/gradio/computer_use_demo/gui_agent/llm_utils/llm_utils.py deleted file mode 100644 index 3c01c56..0000000 --- a/demo/gradio/computer_use_demo/gui_agent/llm_utils/llm_utils.py +++ /dev/null @@ -1,109 +0,0 @@ -import os -import re -import ast -import base64 - - -def is_image_path(text): - # Checking if the input text ends with typical image file extensions - image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") - if text.endswith(image_extensions): - return True - else: - return False - - -def encode_image(image_path): - """Encode image file to base64.""" - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode("utf-8") - - -def is_url_or_filepath(input_string): - # Check if input_string is a URL - url_pattern = re.compile( - r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+" - ) - if url_pattern.match(input_string): - return "URL" - - # Check if input_string is a file path - file_path = os.path.abspath(input_string) - if os.path.exists(file_path): - return "File path" - - return "Invalid" - - -def extract_data(input_string, data_type): - # Regular expression to extract content starting from '```python' until the end if there are no closing backticks - pattern = f"```{data_type}" + r"(.*?)(```|$)" - # Extract content - # re.DOTALL allows '.' to match newlines as well - matches = re.findall(pattern, input_string, re.DOTALL) - # Return the first match if exists, trimming whitespace and ignoring potential closing backticks - return matches[0][0].strip() if matches else input_string - - -def parse_input(code): - """Use AST to parse the input string and extract the function name, arguments, and keyword arguments.""" - - def get_target_names(target): - """Recursively get all variable names from the assignment target.""" - if isinstance(target, ast.Name): - return [target.id] - elif isinstance(target, ast.Tuple): - names = [] - for elt in target.elts: - names.extend(get_target_names(elt)) - return names - return [] - - def extract_value(node): - """ๆๅ– AST ่Š‚็‚น็š„ๅฎž้™…ๅ€ผ""" - if isinstance(node, ast.Constant): - return node.value - elif isinstance(node, ast.Name): - # TODO: a better way to handle variables - raise ValueError( - f"Arguments should be a Constant, got a variable {node.id} instead." - ) - # ๆทปๅŠ ๅ…ถไป–้œ€่ฆๅค„็†็š„ AST ่Š‚็‚น็ฑปๅž‹ - return None - - try: - tree = ast.parse(code) - for node in ast.walk(tree): - if isinstance(node, ast.Assign): - targets = [] - for t in node.targets: - targets.extend(get_target_names(t)) - if isinstance(node.value, ast.Call): - func_name = node.value.func.id - args = [ast.dump(arg) for arg in node.value.args] - kwargs = { - kw.arg: extract_value(kw.value) for kw in node.value.keywords - } - print(f"Input: {code.strip()}") - print(f"Output Variables: {targets}") - print(f"Function Name: {func_name}") - print(f"Arguments: {args}") - print(f"Keyword Arguments: {kwargs}") - elif isinstance(node, ast.Expr) and isinstance(node.value, ast.Call): - targets = [] - func_name = extract_value(node.value.func) - args = [extract_value(arg) for arg in node.value.args] - kwargs = {kw.arg: extract_value(kw.value) for kw in node.value.keywords} - - except SyntaxError: - print(f"Input: {code.strip()}") - print("No match found") - - return targets, func_name, args, kwargs - - -if __name__ == "__main__": - import json - s='{"Reasoning": "The Docker icon has been successfully clicked, and the Docker application should now be opening. No further actions are required.", "Next Action": None}' - json_str = json.loads(s) - print(json_str) \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/qwen.py b/demo/gradio/computer_use_demo/gui_agent/llm_utils/qwen.py deleted file mode 100644 index 0d570cd..0000000 --- a/demo/gradio/computer_use_demo/gui_agent/llm_utils/qwen.py +++ /dev/null @@ -1,107 +0,0 @@ - -import os -import logging -import base64 -import requests - -import dashscope -# from computer_use_demo.gui_agent.llm_utils import is_image_path, encode_image - -def is_image_path(text): - return False - -def encode_image(image_path): - return "" - - -def run_qwen(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): - - api_key = api_key or os.environ.get("QWEN_API_KEY") - if not api_key: - raise ValueError("QWEN_API_KEY is not set") - - dashscope.api_key = api_key - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - - final_messages = [{"role": "system", "content": [{"text": system}]}] - # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" - if type(messages) == list: - for item in messages: - contents = [] - if isinstance(item, dict): - for cnt in item["content"]: - if isinstance(cnt, str): - if is_image_path(cnt): - # base64_image = encode_image(cnt) - content = [{"image": cnt}] - # content = {"type": "image_url", "image_url": {"url": image_url}} - else: - content = {"text": cnt} - contents.append(content) - - message = {"role": item["role"], "content": contents} - else: # str - contents.append({"text": item}) - message = {"role": "user", "content": contents} - - final_messages.append(message) - - print("[qwen-vl] sending messages:", final_messages) - - response = dashscope.MultiModalConversation.call( - model='qwen-vl-max-0809', - messages=final_messages - ) - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - - try: - text = response.output.choices[0].message.content[0]['text'] - usage = response.usage - - if "total_tokens" not in usage: - token_usage = int(usage["input_tokens"] + usage["output_tokens"]) - else: - token_usage = int(usage["total_tokens"]) - - return text, token_usage - # return response.json()['choices'][0]['message']['content'] - # return error message if the response is not successful - except Exception as e: - print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") - return response.json() - - - -if __name__ == "__main__": - api_key = os.environ.get("QWEN_API_KEY") - if not api_key: - raise ValueError("QWEN_API_KEY is not set") - - dashscope.api_key = api_key - - final_messages = [{"role": "user", - "content": [ - {"text": "What is in the screenshot?"}, - {"image": "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"} - ] - } - ] - response = dashscope.MultiModalConversation.call(model='qwen-vl-max-0809', messages=final_messages) - - print(response) - - text = response.output.choices[0].message.content[0]['text'] - usage = response.usage - - if "total_tokens" not in usage: - if "image_tokens" in usage: - token_usage = usage["input_tokens"] + usage["output_tokens"] + usage["image_tokens"] - else: - token_usage = usage["input_tokens"] + usage["output_tokens"] - else: - token_usage = usage["total_tokens"] - - print(text, token_usage) - # The screenshot is from a video game... 1387 \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/run_llm.py b/demo/gradio/computer_use_demo/gui_agent/llm_utils/run_llm.py deleted file mode 100644 index a1de8ba..0000000 --- a/demo/gradio/computer_use_demo/gui_agent/llm_utils/run_llm.py +++ /dev/null @@ -1,44 +0,0 @@ -import base64 -import logging -from .oai import run_oai_interleaved -from .gemini import run_gemini_interleaved - -def run_llm(prompt, llm="gpt-4o-mini", max_tokens=256, temperature=0, stop=None): - log_prompt(prompt) - - # turn string prompt into list - if isinstance(prompt, str): - prompt = [prompt] - elif isinstance(prompt, list): - pass - else: - raise ValueError(f"Invalid prompt type: {type(prompt)}") - - if llm.startswith("gpt"): # gpt series - out = run_oai_interleaved( - prompt, - llm, - max_tokens, - temperature, - stop - ) - elif llm.startswith("gemini"): # gemini series - out = run_gemini_interleaved( - prompt, - llm, - max_tokens, - temperature, - stop - ) - else: - raise ValueError(f"Invalid llm: {llm}") - logging.info( - f"========Output for {llm}=======\n{out}\n============================") - return out - -def log_prompt(prompt): - prompt_display = [prompt] if isinstance(prompt, str) else prompt - prompt_display = "\n\n".join(prompt_display) - logging.info( - f"========Prompt=======\n{prompt_display}\n============================") - \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py index 2a7c076..449e37e 100644 --- a/demo/gradio/computer_use_demo/loop.py +++ b/demo/gradio/computer_use_demo/loop.py @@ -1,76 +1,40 @@ """ Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. """ -import time -import json -import asyncio -import platform from collections.abc import Callable -from datetime import datetime from enum import StrEnum -from typing import Any, cast, Dict -from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse +from anthropic import APIResponse from anthropic.types import ( - ToolResultBlockParam, TextBlock, ) from anthropic.types.beta import ( BetaContentBlock, - BetaContentBlockParam, - BetaImageBlockParam, BetaMessage, - BetaMessageParam, - BetaTextBlockParam, - BetaToolResultBlockParam, + BetaMessageParam ) from computer_use_demo.tools import ToolResult -import torch - from computer_use_demo.gui_agent.anthropic_agent import AnthropicActor from computer_use_demo.executor.anthropic_executor import AnthropicExecutor from computer_use_demo.omniparser_agent.vlm_agent import OmniParser, VLMAgent -from computer_use_demo.colorful_text import colorful_text_vlm -from computer_use_demo.tools.screen_capture import get_screenshot -from computer_use_demo.gui_agent.llm_utils.oai import encode_image - BETA_FLAG = "computer-use-2024-10-22" - class APIProvider(StrEnum): ANTHROPIC = "anthropic" BEDROCK = "bedrock" VERTEX = "vertex" OPENAI = "openai" - QWEN = "qwen" PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", - # APIProvider.OPENAI: "gpt-4o", - # APIProvider.QWEN: "qwen2vl", + APIProvider.OPENAI: "gpt-4o", } - -# This system prompt is optimized for the Docker environment in this repository and -# specific tool combinations enabled. -# We encourage modifying this system prompt to ensure the model has context for the -# environment it is running in, and to provide any additional information that may be -# helpful for the task at hand. -SYSTEM_PROMPT = f""" -* You are utilizing a Windows system with internet access. -* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. - -""" - -import base64 -from PIL import Image -from io import BytesIO - def sampling_loop_sync( *, model: str, diff --git a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py index 26d5190..421e196 100644 --- a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py +++ b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py @@ -18,13 +18,21 @@ from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, B from computer_use_demo.tools.screen_capture import get_screenshot from computer_use_demo.gui_agent.llm_utils.oai import run_oai_interleaved, encode_image -from computer_use_demo.gui_agent.llm_utils.qwen import run_qwen -from computer_use_demo.gui_agent.llm_utils.llm_utils import extract_data from computer_use_demo.colorful_text import colorful_text_vlm import time +import re OUTPUT_DIR = "./tmp/outputs" +def extract_data(input_string, data_type): + # Regular expression to extract content starting from '```python' until the end if there are no closing backticks + pattern = f"```{data_type}" + r"(.*?)(```|$)" + # Extract content + # re.DOTALL allows '.' to match newlines as well + matches = re.findall(pattern, input_string, re.DOTALL) + # Return the first match if exists, trimming whitespace and ignoring potential closing backticks + return matches[0][0].strip() if matches else input_string + class OmniParser: def __init__(self, url: str, @@ -165,19 +173,6 @@ class VLMAgent: print(f"oai token usage: {token_usage}") self.total_token_usage += token_usage self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ - - elif "qwen" in self.model: - vlm_response, token_usage = run_qwen( - messages=planner_messages, - system=system, - llm=self.model, - api_key=self.api_key, - max_tokens=self.max_tokens, - temperature=0, - ) - print(f"qwen token usage: {token_usage}") - self.total_token_usage += token_usage - self.total_cost += (token_usage * 0.02 / 7.25 / 1000) # 1USD=7.25CNY, https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-plus-api elif "phi" in self.model: pass # TODO else: From 93d9e2e12fe82889e522824a020bb6318d6565e8 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 22 Jan 2025 21:24:09 -0800 Subject: [PATCH 09/57] cleanup gradio app.py --- demo/gradio/app.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/demo/gradio/app.py b/demo/gradio/app.py index 72d6d66..bc55797 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -22,7 +22,6 @@ from anthropic.types import TextBlock from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock from anthropic.types.tool_use_block import ToolUseBlock from computer_use_demo.loop import ( - PROVIDER_TO_DEFAULT_MODEL_NAME, APIProvider, sampling_loop_sync, ) @@ -54,9 +53,7 @@ def setup_state(state): if "openai_api_key" not in state: # Fetch API keys from environment variables state["openai_api_key"] = os.getenv("OPENAI_API_KEY", "") if "anthropic_api_key" not in state: - state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "") - if "qwen_api_key" not in state: - state["qwen_api_key"] = os.getenv("QWEN_API_KEY", "") + state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "") if "api_key" not in state: state["api_key"] = "" if "auth_validated" not in state: From bb018460d7a3de640d76f94236e4bebb8b02c4be Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 22 Jan 2025 21:25:40 -0800 Subject: [PATCH 10/57] clean oai --- .../computer_use_demo/gui_agent/llm_utils/oai.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py b/demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py index 36cf60b..0ab3760 100644 --- a/demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py +++ b/demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py @@ -4,8 +4,6 @@ import logging import base64 import requests -# from computer_use_demo.gui_agent.llm_utils import is_image_path, encode_image - def is_image_path(text): # Checking if the input text ends with typical image file extensions image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") @@ -18,14 +16,6 @@ def encode_image(image_path): """Encode image file to base64.""" with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode("utf-8") - - - -# from openai import OpenAI -# client = OpenAI( -# api_key=os.environ.get("OPENAI_API_KEY") -# ) - def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): From b1cd705f1b92c49e8e9bdd8886edbdd6d060a8fb Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 22 Jan 2025 21:38:59 -0800 Subject: [PATCH 11/57] add in omniparser_url box --- demo/gradio/app.py | 16 +++++++++++++++- demo/gradio/computer_use_demo/loop.py | 13 ++++--------- 2 files changed, 19 insertions(+), 10 deletions(-) diff --git a/demo/gradio/app.py b/demo/gradio/app.py index bc55797..94b478f 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -66,6 +66,8 @@ def setup_state(state): state["only_n_most_recent_images"] = 2 if 'chatbot_messages' not in state: state['chatbot_messages'] = [] + if "omniparser_url" not in state: + state["omniparser_url"] = "localhost:8000" async def main(state): """Render loop for Gradio""" @@ -207,7 +209,8 @@ def process_input(user_input, state): api_response_callback=partial(_api_response_callback, response_state=state["responses"]), api_key=state["api_key"], only_n_most_recent_images=state["only_n_most_recent_images"], - selected_screen=0 + selected_screen=0, + omniparser_url=state["omniparser_url"] ): if loop_msg is None: yield state['chatbot_messages'] @@ -271,6 +274,13 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: placeholder="Paste your API key here", interactive=True, ) + with gr.Row(): + omniparser_url = gr.Textbox( + label="OmniParser Base URL", + value="localhost:8000", + placeholder="Enter OmniParser base URL (e.g. localhost:8000)", + interactive=True + ) # hide_images = gr.Checkbox(label="Hide screenshots", value=False) with gr.Row(): @@ -341,10 +351,14 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: state["api_key"] = api_key_value state[f'{state["provider"]}_api_key'] = api_key_value + def update_omniparser_url(url_value, state): + state["omniparser_url"] = url_value + model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key]) only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key) api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None) + omniparser_url.change(fn=update_omniparser_url, inputs=[omniparser_url, state], outputs=None) submit_button.click(process_input, [chat_input, state], chatbot) diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py index 449e37e..3e4e3bf 100644 --- a/demo/gradio/computer_use_demo/loop.py +++ b/demo/gradio/computer_use_demo/loop.py @@ -46,16 +46,16 @@ def sampling_loop_sync( api_key: str, only_n_most_recent_images: int | None = 2, max_tokens: int = 4096, - selected_screen: int = 0 + selected_screen: int = 0, + omniparser_url: str ): """ Synchronous agentic sampling loop for the assistant/tool interaction of computer use. """ print('in sampling_loop_sync, model:', model) + omniparser = OmniParser(url=f"http://{omniparser_url}/send_text/" if omniparser_url else None, + selected_screen=selected_screen,) if model == "claude-3-5-sonnet-20241022": - omniparser = OmniParser(url="http://localhost:8000/send_text/", - selected_screen=selected_screen,) - # Register Actor and Executor actor = AnthropicActor( model=model, @@ -75,11 +75,6 @@ def sampling_loop_sync( ) elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": - # omniparser = OmniParser(url="http://localhost:8000/send_text/", - # selected_screen=selected_screen,) - omniparser = OmniParser(url=None, - selected_screen=selected_screen,) - actor = VLMAgent( model=model, provider=provider, From 8778970aff7466ec06c6d51b3a356a625ec78f4b Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 22 Jan 2025 21:45:32 -0800 Subject: [PATCH 12/57] remove selected screen as only 1 screen --- demo/gradio/app.py | 1 - .../computer_use_demo/executor/anthropic_executor.py | 3 +-- .../computer_use_demo/gui_agent/anthropic_agent.py | 8 ++------ demo/gradio/computer_use_demo/loop.py | 12 +++--------- .../computer_use_demo/omniparser_agent/vlm_agent.py | 8 ++------ demo/gradio/computer_use_demo/tools/computer.py | 5 ++--- .../gradio/computer_use_demo/tools/screen_capture.py | 2 +- 7 files changed, 11 insertions(+), 28 deletions(-) diff --git a/demo/gradio/app.py b/demo/gradio/app.py index 94b478f..0004e3c 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -209,7 +209,6 @@ def process_input(user_input, state): api_response_callback=partial(_api_response_callback, response_state=state["responses"]), api_key=state["api_key"], only_n_most_recent_images=state["only_n_most_recent_images"], - selected_screen=0, omniparser_url=state["omniparser_url"] ): if loop_msg is None: diff --git a/demo/gradio/computer_use_demo/executor/anthropic_executor.py b/demo/gradio/computer_use_demo/executor/anthropic_executor.py index 2970be1..a5d5837 100644 --- a/demo/gradio/computer_use_demo/executor/anthropic_executor.py +++ b/demo/gradio/computer_use_demo/executor/anthropic_executor.py @@ -20,10 +20,9 @@ class AnthropicExecutor: self, output_callback: Callable[[BetaContentBlockParam], None], tool_output_callback: Callable[[Any, str], None], - selected_screen: int = 0 ): self.tool_collection = ToolCollection( - ComputerTool(selected_screen=selected_screen) + ComputerTool() ) self.output_callback = output_callback self.tool_output_callback = tool_output_callback diff --git a/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py b/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py index 0e49850..b1a40c0 100644 --- a/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py +++ b/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py @@ -53,7 +53,6 @@ class AnthropicActor: api_response_callback: Callable[[APIResponse[BetaMessage]], None], max_tokens: int = 4096, only_n_most_recent_images: int | None = None, - selected_screen: int = 0, print_usage: bool = True, ): self.model = model @@ -62,11 +61,8 @@ class AnthropicActor: self.api_response_callback = api_response_callback self.max_tokens = max_tokens self.only_n_most_recent_images = only_n_most_recent_images - self.selected_screen = selected_screen - self.tool_collection = ToolCollection( - ComputerTool(selected_screen=selected_screen), - ) + self.tool_collection = ToolCollection(ComputerTool()) self.system = SYSTEM_PROMPT @@ -175,7 +171,7 @@ if __name__ == "__main__": # model="claude-3-5-sonnet-20241022", # system=SYSTEM_PROMPT, # # tools=ToolCollection( - # # ComputerTool(selected_screen=0), + # # ComputerTool(), # # ).to_params(), # betas=["computer-use-2024-10-22"], # messages=[ diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py index 3e4e3bf..c400ec4 100644 --- a/demo/gradio/computer_use_demo/loop.py +++ b/demo/gradio/computer_use_demo/loop.py @@ -46,15 +46,13 @@ def sampling_loop_sync( api_key: str, only_n_most_recent_images: int | None = 2, max_tokens: int = 4096, - selected_screen: int = 0, omniparser_url: str ): """ Synchronous agentic sampling loop for the assistant/tool interaction of computer use. """ print('in sampling_loop_sync, model:', model) - omniparser = OmniParser(url=f"http://{omniparser_url}/send_text/" if omniparser_url else None, - selected_screen=selected_screen,) + omniparser = OmniParser(url=f"http://{omniparser_url}/send_text/" if omniparser_url else None) if model == "claude-3-5-sonnet-20241022": # Register Actor and Executor actor = AnthropicActor( @@ -63,15 +61,13 @@ def sampling_loop_sync( api_key=api_key, api_response_callback=api_response_callback, max_tokens=max_tokens, - only_n_most_recent_images=only_n_most_recent_images, - selected_screen=selected_screen + only_n_most_recent_images=only_n_most_recent_images ) # from IPython.core.debugger import Pdb; Pdb().set_trace() executor = AnthropicExecutor( output_callback=output_callback, - tool_output_callback=tool_output_callback, - selected_screen=selected_screen + tool_output_callback=tool_output_callback ) elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": @@ -80,14 +76,12 @@ def sampling_loop_sync( provider=provider, api_key=api_key, api_response_callback=api_response_callback, - selected_screen=selected_screen, output_callback=output_callback, ) executor = AnthropicExecutor( output_callback=output_callback, tool_output_callback=tool_output_callback, - selected_screen=selected_screen ) else: diff --git a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py index 421e196..92a7901 100644 --- a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py +++ b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py @@ -35,10 +35,8 @@ def extract_data(input_string, data_type): class OmniParser: def __init__(self, - url: str, - selected_screen: int = 0) -> None: + url: str) -> None: self.url = url - self.selected_screen = selected_screen if not self.url: config = { 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', @@ -51,7 +49,7 @@ class OmniParser: self.omniparser = Omniparser_class(config=config) def __call__(self,): - screenshot, screenshot_path = get_screenshot(selected_screen=self.selected_screen) + screenshot, screenshot_path = get_screenshot() screenshot_path = str(screenshot_path) image_base64 = encode_image(screenshot_path) if self.url: @@ -106,7 +104,6 @@ class VLMAgent: api_response_callback: Callable, max_tokens: int = 4096, only_n_most_recent_images: int | None = None, - selected_screen: int = 0, print_usage: bool = True, ): if model == "omniparser + gpt-4o": @@ -119,7 +116,6 @@ class VLMAgent: self.api_response_callback = api_response_callback self.max_tokens = max_tokens self.only_n_most_recent_images = only_n_most_recent_images - self.selected_screen = selected_screen self.output_callback = output_callback self.print_usage = print_usage diff --git a/demo/gradio/computer_use_demo/tools/computer.py b/demo/gradio/computer_use_demo/tools/computer.py index 7086a62..6f9fbe6 100644 --- a/demo/gradio/computer_use_demo/tools/computer.py +++ b/demo/gradio/computer_use_demo/tools/computer.py @@ -86,14 +86,13 @@ class ComputerTool(BaseAnthropicTool): def to_params(self) -> BetaToolComputerUse20241022Param: return {"name": self.name, "type": self.api_type, **self.options} - def __init__(self, selected_screen: int = 0, is_scaling: bool = False): + def __init__(self, is_scaling: bool = False): super().__init__() # Get screen width and height using Windows command self.display_num = None self.offset_x = 0 self.offset_y = 0 - self.selected_screen = selected_screen self.is_scaling = is_scaling self.width, self.height = self.get_screen_size() print(f"screen size: {self.width}, {self.height}") @@ -253,7 +252,7 @@ class ComputerTool(BaseAnthropicTool): screenshot = self.padding_image(screenshot) self.target_dimension = MAX_SCALING_TARGETS["WXGA"] width, height = self.target_dimension["width"], self.target_dimension["height"] - screenshot, path = get_screenshot(selected_screen=0, resize=True, target_width=width, target_height=height) + screenshot, path = get_screenshot(resize=True, target_width=width, target_height=height) # return ToolResult() return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode()) diff --git a/demo/gradio/computer_use_demo/tools/screen_capture.py b/demo/gradio/computer_use_demo/tools/screen_capture.py index 40529d3..1c1ad04 100644 --- a/demo/gradio/computer_use_demo/tools/screen_capture.py +++ b/demo/gradio/computer_use_demo/tools/screen_capture.py @@ -7,7 +7,7 @@ from io import BytesIO OUTPUT_DIR = "./tmp/outputs" -def get_screenshot(selected_screen: int = 0, resize: bool = False, target_width: int = 1920, target_height: int = 1080): +def get_screenshot(resize: bool = False, target_width: int = 1920, target_height: int = 1080): """Capture screenshot by requesting from HTTP endpoint - returns native resolution unless resized""" output_dir = Path(OUTPUT_DIR) output_dir.mkdir(parents=True, exist_ok=True) From 04000ee008d30a115e65efb6a23b2acee2d8b9e8 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 22 Jan 2025 22:01:47 -0800 Subject: [PATCH 13/57] trash clears history --- demo/gradio/app.py | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/demo/gradio/app.py b/demo/gradio/app.py index 0004e3c..5928007 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -34,7 +34,9 @@ API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' ๐Ÿš€๐Ÿค–โœจ It's Play Time! -Welcome to the OmniParser+X Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. Type a message to play with your beloved assistant. +Welcome to the OmniParser+X Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. + +Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history. ''' class Sender(StrEnum): @@ -284,7 +286,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: with gr.Row(): with gr.Column(scale=8): - chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Computer Use OOTB...", container=False) + chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Omniparser + X ...", container=False) with gr.Column(scale=1, min_width=50): submit_button = gr.Button(value="Send", variant="primary") @@ -353,11 +355,20 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: def update_omniparser_url(url_value, state): state["omniparser_url"] = url_value + def clear_chat(state): + # Reset message-related state + state["messages"] = [] + state["responses"] = {} + state["tools"] = {} + state['chatbot_messages'] = [] + return state['chatbot_messages'] + model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key]) only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key) api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None) omniparser_url.change(fn=update_omniparser_url, inputs=[omniparser_url, state], outputs=None) + chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot]) submit_button.click(process_input, [chat_input, state], chatbot) From 651bbe29241007ad9d5deca78ccb325220372ee3 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 22 Jan 2025 23:14:12 -0800 Subject: [PATCH 14/57] if run on localhost want it to auto start local server --- demo/gradio/computer_use_demo/loop.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py index c400ec4..c4129e5 100644 --- a/demo/gradio/computer_use_demo/loop.py +++ b/demo/gradio/computer_use_demo/loop.py @@ -52,7 +52,7 @@ def sampling_loop_sync( Synchronous agentic sampling loop for the assistant/tool interaction of computer use. """ print('in sampling_loop_sync, model:', model) - omniparser = OmniParser(url=f"http://{omniparser_url}/send_text/" if omniparser_url else None) + omniparser = OmniParser(url=f"http://{omniparser_url}/send_text/" if omniparser_url and omniparser_url != "localhost:8000" else None) if model == "claude-3-5-sonnet-20241022": # Register Actor and Executor actor = AnthropicActor( From 869323bfd95c9be99138e132f25ecd97425e4e63 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Mon, 27 Jan 2025 18:25:19 +0000 Subject: [PATCH 15/57] Clean create gradio --- demo/gradio/app.py | 47 +++------------------------------------------- 1 file changed, 3 insertions(+), 44 deletions(-) diff --git a/demo/gradio/app.py b/demo/gradio/app.py index 5928007..842738f 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -294,10 +294,8 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: with gr.Column(scale=1): chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) with gr.Column(scale=3): - # Get the fully qualified domain name of the machine - machine_fqdn = socket.getfqdn() iframe = gr.HTML( - f'', + f'', container=False, elem_classes="no-padding" ) @@ -371,45 +369,6 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot]) submit_button.click(process_input, [chat_input, state], chatbot) - -from fastapi import FastAPI -import uvicorn -from multiprocessing import Process - -app = FastAPI() - -# Mount the Gradio app under the "/gradio" path -app = gr.mount_gradio_app(app, demo, path="/gradio") - -# Optional: Add a root endpoint that redirects to the Gradio interface -@app.get("/") -async def root(): - return {"message": "Welcome to OmniParser Demo API", - "gradio_interface": "/gradio"} - -# Create a second FastAPI app for VNC -vnc_app = FastAPI() - -@vnc_app.get("/") -async def vnc_root(): - return {"message": "VNC Server"} - -def run_app(app, host, port): - uvicorn.run(app, host=host, port=port) - -# To run this with uvicorn: + if __name__ == "__main__": - # Start the main app on port 7889 - p1 = Process(target=run_app, args=(app, "0.0.0.0", 7889)) - # Start the VNC app on port 8006 - p2 = Process(target=run_app, args=(vnc_app, "0.0.0.0", 8006)) - - p1.start() - p2.start() - - try: - p1.join() - p2.join() - except KeyboardInterrupt: - p1.terminate() - p2.terminate() \ No newline at end of file + demo.launch(server_name="0.0.0.0", server_port=7888) \ No newline at end of file From 5cf55e116f98e9f8d0180d4e6e11a3c51e00c157 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Mon, 27 Jan 2025 13:56:53 -0800 Subject: [PATCH 16/57] time delay for vm to process action --- demo/gradio/computer_use_demo/tools/computer.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/demo/gradio/computer_use_demo/tools/computer.py b/demo/gradio/computer_use_demo/tools/computer.py index 6f9fbe6..adad9a2 100644 --- a/demo/gradio/computer_use_demo/tools/computer.py +++ b/demo/gradio/computer_use_demo/tools/computer.py @@ -234,6 +234,7 @@ class ComputerTool(BaseAnthropicTool): json={"command": command_list}, timeout=90 ) + time.sleep(0.7) # avoid async error as actions take time to complete print(f"action executed") if response.status_code != 200: raise ToolError(f"Failed to execute command. Status code: {response.status_code}") @@ -253,7 +254,7 @@ class ComputerTool(BaseAnthropicTool): self.target_dimension = MAX_SCALING_TARGETS["WXGA"] width, height = self.target_dimension["width"], self.target_dimension["height"] screenshot, path = get_screenshot(resize=True, target_width=width, target_height=height) - + time.sleep(0.7) # avoid async error as actions take time to complete # return ToolResult() return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode()) From 9cb2263545e5c174ded5a3caeaa3c95481ba1419 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Mon, 27 Jan 2025 22:08:54 -0800 Subject: [PATCH 17/57] align main utils with demo utils --- utils.py | 59 ++++++++++++++++++++++++-------------------------------- 1 file changed, 25 insertions(+), 34 deletions(-) diff --git a/utils.py b/utils.py index 6ddf88b..a4e3f00 100755 --- a/utils.py +++ b/utils.py @@ -41,6 +41,7 @@ import re from torchvision.transforms import ToPILImage import supervision as sv import torchvision.transforms as T +from util.box_annotator import BoxAnnotator def get_caption_model_processor(model_name, model_name_or_path="Salesforce/blip2-opt-2.7b", device=None): @@ -77,22 +78,21 @@ def get_yolo_model(model_path): @torch.inference_mode() def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=None): # Number of samples per batch, --> 256 roughly takes 23 GB of GPU memory for florence model - to_pil = ToPILImage() if starting_idx: non_ocr_boxes = filtered_boxes[starting_idx:] else: non_ocr_boxes = filtered_boxes croped_pil_image = [] - t0 = time.time() for i, coord in enumerate(non_ocr_boxes): - xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) - ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) - cropped_image = image_source[ymin:ymax, xmin:xmax, :] - # resize the image to 224x224 to avoid long overhead in clipimageprocessor # TODO - cropped_image = cv2.resize(cropped_image, (224, 224)) - croped_pil_image.append(to_pil(cropped_image)) - print('time to prepare bbox:', time.time()-t0) + try: + xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) + ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) + cropped_image = image_source[ymin:ymax, xmin:xmax, :] + cropped_image = cv2.resize(cropped_image, (64, 64)) + croped_pil_image.append(to_pil(cropped_image)) + except: + continue model, processor = caption_model_processor['model'], caption_model_processor['processor'] if not prompt: @@ -112,14 +112,10 @@ def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_ inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt", do_resize=False).to(device=device, dtype=torch.float16) else: inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device) - t2 = time.time() - print('time to process image + tokenize text inputs:', t2-t1) if 'florence' in model.config.name_or_path: generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=20,num_beams=1, do_sample=False) else: generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True, - t3 = time.time() - print('time to generate:', t3-t2) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) generated_text = [gen.strip() for gen in generated_text] generated_texts.extend(generated_text) @@ -282,10 +278,10 @@ def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None): is_valid_box = False break if is_valid_box: - # add the following 2 lines to include ocr bbox if ocr_bbox: # keep yolo boxes + prioritize ocr label box_added = False + ocr_labels = '' for box3_elem in ocr_bbox: if not box_added: box3 = box3_elem['bbox'] @@ -293,25 +289,22 @@ def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None): # box_added = True # delete the box3_elem from ocr_bbox try: - filtered_boxes.append({'type': 'text', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': box3_elem['content']}) + # gather all ocr labels + ocr_labels += box3_elem['content'] + ' ' filtered_boxes.remove(box3_elem) - # print('remove ocr bbox:', box3_elem) except: continue # break - elif is_inside(box1, box3): # icon inside ocr + elif is_inside(box1, box3): # icon inside ocr, don't added this icon box, no need to check other ocr bbox bc no overlap between ocr bbox, icon can only be in one ocr box box_added = True - # try: - # filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None}) - # filtered_boxes.remove(box3_elem) - # except: - # continue break else: continue if not box_added: - filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None}) - + if ocr_labels: + filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': ocr_labels, 'source':'box_yolo_content_ocr'}) + else: + filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None, 'source':'box_yolo_content_yolo'}) else: filtered_boxes.append(box1) return filtered_boxes # torch.tensor(filtered_boxes) @@ -354,7 +347,6 @@ def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor labels = [f"{phrase}" for phrase in range(boxes.shape[0])] - from util.box_annotator import BoxAnnotator box_annotator = BoxAnnotator(text_scale=text_scale, text_padding=text_padding,text_thickness=text_thickness,thickness=thickness) # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web annotated_frame = image_source.copy() annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels, image_size=(w,h)) @@ -407,7 +399,12 @@ def predict_yolo(model, image, box_threshold, imgsz, scale_img, iou_threshold=0. return boxes, conf, phrases - +def int_box_area(box, w, h): + x1, y1, x2, y2 = box + int_box = [int(x1*w), int(y1*h), int(x2*w), int(y2*h)] + area = (int_box[2] - int_box[0]) * (int_box[3] - int_box[1]) + return area + def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_TRESHOLD=0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64): """Process either an image path or Image object @@ -428,19 +425,15 @@ def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_T phrases = [str(i) for i in range(len(phrases))] # annotate the image with labels - h, w, _ = image_source.shape if ocr_bbox: ocr_bbox = torch.tensor(ocr_bbox) / torch.Tensor([w, h, w, h]) ocr_bbox=ocr_bbox.tolist() else: print('no ocr bbox!!!') ocr_bbox = None - # filtered_boxes = remove_overlap(boxes=xyxy, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox) - # starting_idx = len(ocr_bbox) - # print('len(filtered_boxes):', len(filtered_boxes), starting_idx) - ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt} for box, txt in zip(ocr_bbox, ocr_text)] - xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist()] + ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt, 'source': 'box_ocr_content_ocr'} for box, txt in zip(ocr_bbox, ocr_text) if int_box_area(box, w, h) > 0] + xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist() if int_box_area(box, w, h) > 0] filtered_boxes = remove_overlap_new(boxes=xyxy_elem, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox_elem) # sort the filtered_boxes so that the one with 'content': None is at the end, and get the index of the first 'content': None @@ -450,7 +443,6 @@ def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_T filtered_boxes = torch.tensor([box['bbox'] for box in filtered_boxes_elem]) print('len(filtered_boxes):', len(filtered_boxes), starting_idx) - # get parsed icon local semantics time1 = time.time() if use_local_semantics: @@ -489,7 +481,6 @@ def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_T pil_img.save(buffered, format="PNG") encoded_image = base64.b64encode(buffered.getvalue()).decode('ascii') if output_coord_in_ratio: - # h, w, _ = image_source.shape label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()} assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0] From 72040b9ded62f34212e567e08ed072f00a6167cc Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Mon, 27 Jan 2025 22:38:05 -0800 Subject: [PATCH 18/57] move back to check_ocr_box --- demo/remote_request.py | 4 ++-- utils.py | 36 +++++++++++++++++++----------------- 2 files changed, 21 insertions(+), 19 deletions(-) diff --git a/demo/remote_request.py b/demo/remote_request.py index 32c1730..95f3595 100644 --- a/demo/remote_request.py +++ b/demo/remote_request.py @@ -4,7 +4,7 @@ import sys import os sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, get_ocr_bbox +from utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box import torch from PIL import Image from typing import Dict, Tuple, List @@ -45,7 +45,7 @@ class Omniparser(object): } BOX_TRESHOLD = config['BOX_TRESHOLD'] - text, ocr_bbox = get_ocr_bbox(image) + (text, ocr_bbox), _ = check_ocr_box(image, display_img=False, output_bb_format='xyxy', easyocr_args={'text_threshold': 0.8}, use_paddleocr=False) dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) return dino_labled_img, parsed_content_list diff --git a/utils.py b/utils.py index a4e3f00..1da7e68 100755 --- a/utils.py +++ b/utils.py @@ -501,50 +501,52 @@ def get_xywh_yolo(input): x, y, w, h = input[0], input[1], input[2] - input[0], input[3] - input[1] x, y, w, h = int(x), int(y), int(w), int(h) return x, y, w, h - - -def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False): +def check_ocr_box(image_source: Union[str, Image.Image], display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False): + if isinstance(image_source, str): + image_source = Image.open(image_source) + if image_source.mode == 'RGBA': + # Convert RGBA to RGB to avoid alpha channel issues + image_source = image_source.convert('RGB') + image_np = np.array(image_source) + w, h = image_source.size if use_paddleocr: if easyocr_args is None: text_threshold = 0.5 else: text_threshold = easyocr_args['text_threshold'] - result = paddle_ocr.ocr(image_path, cls=False)[0] - # conf = [item[1] for item in result] + result = paddle_ocr.ocr(image_np, cls=False)[0] coord = [item[0] for item in result if item[1][1] > text_threshold] text = [item[1][0] for item in result if item[1][1] > text_threshold] else: # EasyOCR if easyocr_args is None: easyocr_args = {} - result = reader.readtext(image_path, **easyocr_args) - # print('goal filtering pred:', result[-5:]) + result = reader.readtext(image_np, **easyocr_args) coord = [item[0] for item in result] text = [item[1] for item in result] - # read the image using cv2 if display_img: - opencv_img = cv2.imread(image_path) - opencv_img = cv2.cvtColor(opencv_img, cv2.COLOR_RGB2BGR) + opencv_img = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR) bb = [] for item in coord: x, y, a, b = get_xywh(item) - # print(x, y, a, b) bb.append((x, y, a, b)) cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2) - - # Display the image - plt.imshow(opencv_img) + # matplotlib expects RGB + plt.imshow(cv2.cvtColor(opencv_img, cv2.COLOR_BGR2RGB)) else: if output_bb_format == 'xywh': bb = [get_xywh(item) for item in coord] elif output_bb_format == 'xyxy': bb = [get_xyxy(item) for item in coord] - # print('bounding box!!!', bb) return (text, bb), goal_filtering -def get_ocr_bbox(image): +def get_ocr_bbox(image: Image.Image): + if image.mode == 'RGBA': + # Convert RGBA to RGB to avoid alpha channel issues + image = image.convert('RGB') + image_np = np.array(image) + result = paddle_ocr.ocr(image_np, cls=False)[0] text_threshold = 0.8 - result = paddle_ocr.ocr(image, cls=False)[0] coord = [item[0] for item in result if item[1][1] > text_threshold] text = [item[1][0] for item in result if item[1][1] > text_threshold] bb = [get_xyxy(item) for item in coord] From cda479527015217e813252f532c3bd81f6c5d5b4 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Mon, 27 Jan 2025 22:40:37 -0800 Subject: [PATCH 19/57] add omniparse is ready endpoint --- demo/remote_request.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/demo/remote_request.py b/demo/remote_request.py index 95f3595..84dd3a9 100644 --- a/demo/remote_request.py +++ b/demo/remote_request.py @@ -3,14 +3,15 @@ import sys import os sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - +import time from utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box import torch from PIL import Image from typing import Dict, Tuple, List import base64 import io - +from fastapi import FastAPI +from pydantic import BaseModel config = { 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', @@ -51,8 +52,7 @@ class Omniparser(object): return dino_labled_img, parsed_content_list -from fastapi import FastAPI -from pydantic import BaseModel + app = FastAPI() @@ -65,9 +65,13 @@ Omniparser = Omniparser(config) @app.post("/send_text/") async def send_text(item: Item): print('start parsing...') - import time + start = time.time() dino_labled_img, parsed_content_list = Omniparser.parse(item.base64_image) latency = time.time() - start print('time:', latency) - return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency} \ No newline at end of file + return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency} + +@app.get("/") +async def root(): + return {"message": "Omniparser API ready"} \ No newline at end of file From 16570a9bf390022e78f0d0c04ccf71d50ed892d4 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Mon, 27 Jan 2025 22:52:55 -0800 Subject: [PATCH 20/57] remove unused function --- utils.py | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/utils.py b/utils.py index 1da7e68..62278cb 100755 --- a/utils.py +++ b/utils.py @@ -404,7 +404,7 @@ def int_box_area(box, w, h): int_box = [int(x1*w), int(y1*h), int(x2*w), int(y2*h)] area = (int_box[2] - int_box[0]) * (int_box[3] - int_box[1]) return area - + def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_TRESHOLD=0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64): """Process either an image path or Image object @@ -540,17 +540,4 @@ def check_ocr_box(image_source: Union[str, Image.Image], display_img = True, out bb = [get_xyxy(item) for item in coord] return (text, bb), goal_filtering -def get_ocr_bbox(image: Image.Image): - if image.mode == 'RGBA': - # Convert RGBA to RGB to avoid alpha channel issues - image = image.convert('RGB') - image_np = np.array(image) - result = paddle_ocr.ocr(image_np, cls=False)[0] - text_threshold = 0.8 - coord = [item[0] for item in result if item[1][1] > text_threshold] - text = [item[1][0] for item in result if item[1][1] > text_threshold] - bb = [get_xyxy(item) for item in coord] - return text, bb - - From 7ea2239e1038b4a94b16492b9a3b9a592ac8bd16 Mon Sep 17 00:00:00 2001 From: yadonglu Date: Tue, 28 Jan 2025 21:33:58 -0800 Subject: [PATCH 21/57] add drop down for omni output in the chat; add args for app.py; --- .gitignore | 1 + demo/gradio/app.py | 59 ++++++++++++------ demo/gradio/computer_use_demo/loop.py | 2 +- .../omniparser_agent/vlm_agent.py | 12 +++- demo/remote_request.py | 41 +++++++++--- util/__pycache__/__init__.cpython-312.pyc | Bin 139 -> 0 bytes util/__pycache__/__init__.cpython-39.pyc | Bin 141 -> 0 bytes .../action_matching.cpython-39.pyc | Bin 8488 -> 0 bytes .../__pycache__/box_annotator.cpython-312.pyc | Bin 9804 -> 0 bytes util/__pycache__/box_annotator.cpython-39.pyc | Bin 6574 -> 0 bytes 10 files changed, 86 insertions(+), 29 deletions(-) delete mode 100644 util/__pycache__/__init__.cpython-312.pyc delete mode 100644 util/__pycache__/__init__.cpython-39.pyc delete mode 100644 util/__pycache__/action_matching.cpython-39.pyc delete mode 100644 util/__pycache__/box_annotator.cpython-312.pyc delete mode 100644 util/__pycache__/box_annotator.cpython-39.pyc diff --git a/.gitignore b/.gitignore index 2ad14a7..40c4d8c 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ weights/icon_detect_v1_5_2/ .gradio __pycache__/ debug.ipynb +util/__pycache__/ diff --git a/demo/gradio/app.py b/demo/gradio/app.py index 842738f..d005c49 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -1,5 +1,6 @@ """ Entrypoint for Gradio, see https://gradio.app/ +python app.py --windows_host_url xxxx:8006/ --omniparser_host_url localhost:8000 """ import platform @@ -15,6 +16,7 @@ from pathlib import Path from typing import cast, Dict from PIL import Image import socket +import argparse import gradio as gr from anthropic import APIResponse @@ -39,6 +41,18 @@ Welcome to the OmniParser+X Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history. ''' +def parse_arguments(): + parser = argparse.ArgumentParser(description="Gradio App") + parser.add_argument("--windows_host_url", type=str, default='GCRSANDBOX336.redmond.corp.microsoft.com:8006/') # http://gcrsandbox336.redmond.corp.microsoft.com/ + parser.add_argument("--omniparser_host_url", type=str, default="localhost:8000") + return parser.parse_args() +args = parse_arguments() +windows_host_url = args.windows_host_url +omniparser_host_url = args.omniparser_host_url +print(f"Windows host URL: {windows_host_url}") +print(f"OmniParser host URL: {omniparser_host_url}") + + class Sender(StrEnum): USER = "user" BOT = "assistant" @@ -68,8 +82,8 @@ def setup_state(state): state["only_n_most_recent_images"] = 2 if 'chatbot_messages' not in state: state['chatbot_messages'] = [] - if "omniparser_url" not in state: - state["omniparser_url"] = "localhost:8000" + # if "omniparser_url" not in state: + # state["omniparser_url"] = "localhost:8000" async def main(state): """Render loop for Gradio""" @@ -211,7 +225,7 @@ def process_input(user_input, state): api_response_callback=partial(_api_response_callback, response_state=state["responses"]), api_key=state["api_key"], only_n_most_recent_images=state["only_n_most_recent_images"], - omniparser_url=state["omniparser_url"] + omniparser_url=omniparser_host_url #state["omniparser_url"] ): if loop_msg is None: yield state['chatbot_messages'] @@ -275,13 +289,13 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: placeholder="Paste your API key here", interactive=True, ) - with gr.Row(): - omniparser_url = gr.Textbox( - label="OmniParser Base URL", - value="localhost:8000", - placeholder="Enter OmniParser base URL (e.g. localhost:8000)", - interactive=True - ) + # with gr.Row(): + # omniparser_url = gr.Textbox( + # label="OmniParser Base URL", + # value="localhost:8000", + # placeholder="Enter OmniParser base URL (e.g. localhost:8000)", + # interactive=True + # ) # hide_images = gr.Checkbox(label="Hide screenshots", value=False) with gr.Row(): @@ -294,11 +308,20 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: with gr.Column(scale=1): chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) with gr.Column(scale=3): - iframe = gr.HTML( - f'', - container=False, - elem_classes="no-padding" - ) + if not windows_host_url: + iframe = gr.HTML( + f'', + container=False, + elem_classes="no-padding" + ) + else: + # machine_fqdn = socket.getfqdn() + # print('machine_fqdn:', machine_fqdn) + iframe = gr.HTML( + f'', + container=False, + elem_classes="no-padding" + ) def update_model(model_selection, state): state["model"] = model_selection @@ -350,8 +373,8 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: state["api_key"] = api_key_value state[f'{state["provider"]}_api_key'] = api_key_value - def update_omniparser_url(url_value, state): - state["omniparser_url"] = url_value + # def update_omniparser_url(url_value, state): + # state["omniparser_url"] = url_value def clear_chat(state): # Reset message-related state @@ -365,7 +388,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key) api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None) - omniparser_url.change(fn=update_omniparser_url, inputs=[omniparser_url, state], outputs=None) + # omniparser_url.change(fn=update_omniparser_url, inputs=[omniparser_url, state], outputs=None) chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot]) submit_button.click(process_input, [chat_input, state], chatbot) diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py index c4129e5..c400ec4 100644 --- a/demo/gradio/computer_use_demo/loop.py +++ b/demo/gradio/computer_use_demo/loop.py @@ -52,7 +52,7 @@ def sampling_loop_sync( Synchronous agentic sampling loop for the assistant/tool interaction of computer use. """ print('in sampling_loop_sync, model:', model) - omniparser = OmniParser(url=f"http://{omniparser_url}/send_text/" if omniparser_url and omniparser_url != "localhost:8000" else None) + omniparser = OmniParser(url=f"http://{omniparser_url}/send_text/" if omniparser_url else None) if model == "claude-3-5-sonnet-20241022": # Register Actor and Executor actor = AnthropicActor( diff --git a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py index 92a7901..35f57c9 100644 --- a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py +++ b/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py @@ -132,7 +132,15 @@ class VLMAgent: sender="bot") self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n', sender="bot") screen_info = str(parsed_screen['screen_info']) - self.output_callback(f'Screen Info for {colorful_text_vlm}:\n{screen_info}', sender="bot") + # self.output_callback(f'Screen Info for {colorful_text_vlm}:\n{screen_info}', sender="bot") + self.output_callback( + f'

' + f' Screen Info for {colorful_text_vlm}' + f' 
{screen_info}
' + f'
', + sender="bot" + ) + screenshot_uuid = parsed_screen['screenshot_uuid'] screen_width, screen_height = parsed_screen['width'], parsed_screen['height'] @@ -155,7 +163,7 @@ class VLMAgent: planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_{screenshot_uuid}.png") planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_som_{screenshot_uuid}.png") - print(f"Sending messages to VLMPlanner : {planner_messages}") + # print(f"Sending messages to VLMPlanner : {planner_messages}") start = time.time() if "gpt" in self.model: vlm_response, token_usage = run_oai_interleaved( diff --git a/demo/remote_request.py b/demo/remote_request.py index 84dd3a9..7efafc3 100644 --- a/demo/remote_request.py +++ b/demo/remote_request.py @@ -1,4 +1,7 @@ # uvicorn remote_request:app --host 0.0.0.0 --port 8000 --reload +''' +python -m remote_request --som_model_path ../weights/icon_detect_v1_5/model_v1_5.pt --caption_model_name florence2 --caption_model_path ../weights/icon_detect_v1_5/model_v1_5.pt --device cuda --BOX_TRESHOLD 0.05 +''' import sys import os @@ -12,14 +15,31 @@ import base64 import io from fastapi import FastAPI from pydantic import BaseModel +import argparse -config = { - 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', - 'device': 'cpu', - 'caption_model_name': 'florence2', - 'caption_model_path': '../weights/icon_caption_florence', - 'BOX_TRESHOLD': 0.05 -} +def parse_arguments(): + parser = argparse.ArgumentParser(description='Omniparser API') + parser.add_argument('--som_model_path', type=str, default='../weights/icon_detect_v1_5/model_v1_5.pt', help='Path to the som model') + parser.add_argument('--caption_model_name', type=str, default='florence2', help='Name of the caption model') + parser.add_argument('--caption_model_path', type=str, default='../weights/icon_caption_florence', help='Path to the caption model') + parser.add_argument('--device', type=str, default='cpu', help='Device to run the model') + parser.add_argument('--BOX_TRESHOLD', type=float, default=0.05, help='Threshold for box detection') + parser.add_argument('--host', type=str, default='0.0.0.0', help='Host for the API') + parser.add_argument('--port', type=int, default=8000, help='Port for the API') + args = parser.parse_args() + return args + +args = parse_arguments() +config = vars(args) + + +# config = { +# 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', +# 'device': 'cpu', +# 'caption_model_name': 'florence2', +# 'caption_model_path': '../weights/icon_caption_florence', +# 'BOX_TRESHOLD': 0.05 +# } class Omniparser(object): @@ -74,4 +94,9 @@ async def send_text(item: Item): @app.get("/") async def root(): - return {"message": "Omniparser API ready"} \ No newline at end of file + return {"message": "Omniparser API ready"} + + +if __name__ == "__main__": + import uvicorn + uvicorn.run("remote_request:app", host=args.host, port=args.port, reload=True) \ No newline at end of file diff --git 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22/57] correct launch command --- demo/remote_request.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/demo/remote_request.py b/demo/remote_request.py index 7efafc3..414be81 100644 --- a/demo/remote_request.py +++ b/demo/remote_request.py @@ -1,6 +1,5 @@ -# uvicorn remote_request:app --host 0.0.0.0 --port 8000 --reload ''' -python -m remote_request --som_model_path ../weights/icon_detect_v1_5/model_v1_5.pt --caption_model_name florence2 --caption_model_path ../weights/icon_detect_v1_5/model_v1_5.pt --device cuda --BOX_TRESHOLD 0.05 +python -m remote_request --som_model_path ../weights/icon_detect_v1_5/model_v1_5.pt --caption_model_name florence2 --caption_model_path ../weights/icon_caption_florence --device cuda --BOX_TRESHOLD 0.05 ''' import sys From 124d9f6fb6a75398e19e58ce3710838cdf51e173 Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" Date: Wed, 29 Jan 2025 21:54:35 -0800 Subject: [PATCH 23/57] Clean up folder structure --- Dockerfile | 201 ---- demo/gradio/app.py | 29 +- .../{gui_agent => agent}/anthropic_agent.py | 0 .../{gui_agent => agent}/llm_utils/oai.py | 0 .../agent/llm_utils/omniparserclient.py | 44 + .../{omniparser_agent => agent}/vlm_agent.py | 76 +- demo/gradio/computer_use_demo/loop.py | 11 +- .../omniparser_agent/omniparser.py | 77 -- .../omniparser_agent/utils.py | 935 ------------------ demo/gradio/demo_image.jpg | Bin 230959 -> 0 bytes demo/gradio/demo_image_som.jpg | Bin 123201 -> 0 bytes demo/omniparserserver/omniparser.py | 33 + demo/omniparserserver/remote_request.py | 50 + demo/remote_request.py | 101 -- gradio_demo.py | 6 +- omniparser.py | 60 -- util/action_matching.py | 425 -------- util/action_type.py | 45 - utils.py => util/utils.py | 0 19 files changed, 141 insertions(+), 1952 deletions(-) delete mode 100644 Dockerfile rename demo/gradio/computer_use_demo/{gui_agent => agent}/anthropic_agent.py (100%) rename demo/gradio/computer_use_demo/{gui_agent => agent}/llm_utils/oai.py (100%) create mode 100644 demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py rename demo/gradio/computer_use_demo/{omniparser_agent => agent}/vlm_agent.py (80%) delete mode 100644 demo/gradio/computer_use_demo/omniparser_agent/omniparser.py delete mode 100755 demo/gradio/computer_use_demo/omniparser_agent/utils.py delete mode 100644 demo/gradio/demo_image.jpg delete mode 100644 demo/gradio/demo_image_som.jpg create mode 100644 demo/omniparserserver/omniparser.py create mode 100644 demo/omniparserserver/remote_request.py delete mode 100644 demo/remote_request.py delete mode 100644 omniparser.py delete mode 100644 util/action_matching.py delete mode 100644 util/action_type.py rename utils.py => util/utils.py (100%) mode change 100755 => 100644 diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 5737680..0000000 --- a/Dockerfile +++ /dev/null @@ -1,201 +0,0 @@ -# Dockerfile for OmniParser with GPU support and OpenGL libraries -# -# This Dockerfile is intended to create an environment with NVIDIA CUDA -# support and the necessary dependencies to run the OmniParser project. -# The configuration is designed to support applications that rely on -# Python 3.12, OpenCV, Hugging Face transformers, and Gradio. Additionally, -# it includes steps to pull large files from Git LFS and a script to -# convert model weights from .safetensor to .pt format. The container -# runs a Gradio server by default, exposed on port 7861. -# -# Base image: nvidia/cuda:12.3.1-devel-ubuntu22.04 -# -# Key features: -# - System dependencies for OpenGL to support graphical libraries. -# - Miniconda for Python 3.12, allowing for environment management. -# - Git Large File Storage (LFS) setup for handling large model files. -# - Requirement file installation, including specific versions of -# OpenCV and Hugging Face Hub. -# - Entrypoint script execution with Gradio server configuration for -# external access. - -FROM nvidia/cuda:12.3.1-devel-ubuntu22.04 - -# Install system dependencies with explicit OpenGL libraries -RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y \ - git \ - git-lfs \ - wget \ - libgl1 \ - libglib2.0-0 \ - libsm6 \ - libxext6 \ - libxrender1 \ - libglu1-mesa \ - libglib2.0-0 \ - libsm6 \ - libxrender1 \ - libxext6 \ - python3-opencv \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* \ - && git lfs install - -# Install Miniconda for Python 3.12 -RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh && \ - bash miniconda.sh -b -p /opt/conda && \ - rm miniconda.sh -ENV PATH="/opt/conda/bin:$PATH" - -# Create and activate Conda environment with Python 3.12, and set it as the default -RUN conda create -n omni python=3.12 && \ - echo "source activate omni" > ~/.bashrc -ENV CONDA_DEFAULT_ENV=omni -ENV PATH="/opt/conda/envs/omni/bin:$PATH" - -# Set the working directory in the container -WORKDIR /usr/src/app - -# Copy project files and requirements -COPY . . -COPY requirements.txt /usr/src/app/requirements.txt - -# Initialize Git LFS and pull LFS files -RUN git lfs install && \ - git lfs pull - -# Install dependencies from requirements.txt with specific opencv-python-headless version -RUN . /opt/conda/etc/profile.d/conda.sh && conda activate omni && \ - pip uninstall -y opencv-python opencv-python-headless && \ - pip install --no-cache-dir opencv-python-headless==4.8.1.78 && \ - pip install -r requirements.txt && \ - pip install huggingface_hub - -# Run download.py to fetch model weights and convert safetensors to .pt format -# RUN . /opt/conda/etc/profile.d/conda.sh && conda activate omni && \ -# python download.py && \ -# echo "Contents of weights directory:" && \ -# ls -lR weights && \ -# python weights/convert_safetensor_to_pt.py - -# Expose the default Gradio port -EXPOSE 7861 - -# Configure Gradio to be accessible externally -ENV GRADIO_SERVER_NAME="0.0.0.0" - -# Copy and set permissions for entrypoint script -# COPY entrypoint.sh /usr/src/app/entrypoint.sh -# RUN chmod +x /usr/src/app/entrypoint.sh - -# To debug, keep the container running -# CMD ["tail", "-f", "/dev/null"] - -################################################################################################ -# virtual display related setup --> from anthropic-quickstarts/computer-use-demo/Dockerfile - -ENV DEBIAN_FRONTEND=noninteractive -ENV DEBIAN_PRIORITY=high - -RUN apt-get update && \ - apt-get -y upgrade && \ - apt-get -y install \ - # UI Requirements - xvfb \ - xterm \ - xdotool \ - scrot \ - imagemagick \ - sudo \ - mutter \ - x11vnc \ - # Python/pyenv reqs - build-essential \ - libssl-dev \ - zlib1g-dev \ - libbz2-dev \ - libreadline-dev \ - libsqlite3-dev \ - curl \ - git \ - libncursesw5-dev \ - xz-utils \ - tk-dev \ - libxml2-dev \ - libxmlsec1-dev \ - libffi-dev \ - liblzma-dev \ - # Network tools - net-tools \ - netcat \ - # PPA req - software-properties-common && \ - # Userland apps - sudo add-apt-repository ppa:mozillateam/ppa && \ - sudo apt-get install -y --no-install-recommends \ - libreoffice \ - firefox-esr \ - x11-apps \ - xpdf \ - gedit \ - xpaint \ - tint2 \ - galculator \ - pcmanfm \ - unzip && \ - apt-get clean - -# Install noVNC -RUN git clone --branch v1.5.0 https://github.com/novnc/noVNC.git /opt/noVNC && \ - git clone --branch v0.12.0 https://github.com/novnc/websockify /opt/noVNC/utils/websockify && \ - ln -s /opt/noVNC/vnc.html /opt/noVNC/index.html - -# setup user -ENV USERNAME=computeruse -ENV HOME=/home/$USERNAME -RUN useradd -m -s /bin/bash -d $HOME $USERNAME -RUN echo "${USERNAME} ALL=(ALL) NOPASSWD: ALL" >> /etc/sudoers -USER computeruse -WORKDIR $HOME - -# setup python -RUN git clone https://github.com/pyenv/pyenv.git ~/.pyenv && \ - cd ~/.pyenv && src/configure && make -C src && cd .. && \ - echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc && \ - echo 'command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc && \ - echo 'eval "$(pyenv init -)"' >> ~/.bashrc -ENV PYENV_ROOT="$HOME/.pyenv" -ENV PATH="$PYENV_ROOT/bin:$PATH" -ENV PYENV_VERSION_MAJOR=3 -ENV PYENV_VERSION_MINOR=11 -ENV PYENV_VERSION_PATCH=6 -ENV PYENV_VERSION=$PYENV_VERSION_MAJOR.$PYENV_VERSION_MINOR.$PYENV_VERSION_PATCH -RUN eval "$(pyenv init -)" && \ - pyenv install $PYENV_VERSION && \ - pyenv global $PYENV_VERSION && \ - pyenv rehash - -ENV PATH="$HOME/.pyenv/shims:$HOME/.pyenv/bin:$PATH" - -RUN python -m pip install --upgrade pip==23.1.2 setuptools==58.0.4 wheel==0.40.0 && \ - python -m pip config set global.disable-pip-version-check true - -# only reinstall if requirements.txt changes -# COPY --chown=$USERNAME:$USERNAME computer_use_demo/requirements.txt $HOME/computer_use_demo/requirements.txt -# RUN python -m pip install -r $HOME/computer_use_demo/requirements.txt - -# setup desktop env & app -# COPY --chown=$USERNAME:$USERNAME image/ $HOME -# COPY --chown=$USERNAME:$USERNAME computer_use_demo/ $HOME/computer_use_demo/ - -ARG DISPLAY_NUM=1 -ARG HEIGHT=768 -ARG WIDTH=1024 -ENV DISPLAY_NUM=$DISPLAY_NUM -ENV HEIGHT=$HEIGHT -ENV WIDTH=$WIDTH - -# Set the entrypoint -# ENTRYPOINT ["/usr/src/app/entrypoint.sh"] - -# docker build . -t omniparser-x-demo:local # manually build the docker image (optional) diff --git a/demo/gradio/app.py b/demo/gradio/app.py index d005c49..068d17c 100644 --- a/demo/gradio/app.py +++ b/demo/gradio/app.py @@ -3,21 +3,13 @@ Entrypoint for Gradio, see https://gradio.app/ python app.py --windows_host_url xxxx:8006/ --omniparser_host_url localhost:8000 """ -import platform -import asyncio -import base64 import os -import io -import json from datetime import datetime from enum import StrEnum from functools import partial from pathlib import Path -from typing import cast, Dict -from PIL import Image -import socket +from typing import cast import argparse - import gradio as gr from anthropic import APIResponse from anthropic.types import TextBlock @@ -27,7 +19,6 @@ from computer_use_demo.loop import ( APIProvider, sampling_loop_sync, ) - from computer_use_demo.tools import ToolResult CONFIG_DIR = Path("~/.anthropic").expanduser() @@ -43,7 +34,7 @@ Type a message and press submit to start OmniParser+X. Press the trash icon in t def parse_arguments(): parser = argparse.ArgumentParser(description="Gradio App") - parser.add_argument("--windows_host_url", type=str, default='GCRSANDBOX336.redmond.corp.microsoft.com:8006/') # http://gcrsandbox336.redmond.corp.microsoft.com/ + parser.add_argument("--windows_host_url", type=str, default='localhost:8006') parser.add_argument("--omniparser_host_url", type=str, default="localhost:8000") return parser.parse_args() args = parse_arguments() @@ -82,8 +73,6 @@ def setup_state(state): state["only_n_most_recent_images"] = 2 if 'chatbot_messages' not in state: state['chatbot_messages'] = [] - # if "omniparser_url" not in state: - # state["omniparser_url"] = "localhost:8000" async def main(state): """Render loop for Gradio""" @@ -225,7 +214,7 @@ def process_input(user_input, state): api_response_callback=partial(_api_response_callback, response_state=state["responses"]), api_key=state["api_key"], only_n_most_recent_images=state["only_n_most_recent_images"], - omniparser_url=omniparser_host_url #state["omniparser_url"] + omniparser_url=omniparser_host_url ): if loop_msg is None: yield state['chatbot_messages'] @@ -289,14 +278,6 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: placeholder="Paste your API key here", interactive=True, ) - # with gr.Row(): - # omniparser_url = gr.Textbox( - # label="OmniParser Base URL", - # value="localhost:8000", - # placeholder="Enter OmniParser base URL (e.g. localhost:8000)", - # interactive=True - # ) - # hide_images = gr.Checkbox(label="Hide screenshots", value=False) with gr.Row(): with gr.Column(scale=8): @@ -373,9 +354,6 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: state["api_key"] = api_key_value state[f'{state["provider"]}_api_key'] = api_key_value - # def update_omniparser_url(url_value, state): - # state["omniparser_url"] = url_value - def clear_chat(state): # Reset message-related state state["messages"] = [] @@ -388,7 +366,6 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key) api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None) - # omniparser_url.change(fn=update_omniparser_url, inputs=[omniparser_url, state], outputs=None) chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot]) submit_button.click(process_input, [chat_input, state], chatbot) diff --git a/demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py b/demo/gradio/computer_use_demo/agent/anthropic_agent.py similarity index 100% rename from demo/gradio/computer_use_demo/gui_agent/anthropic_agent.py rename to demo/gradio/computer_use_demo/agent/anthropic_agent.py diff --git a/demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py b/demo/gradio/computer_use_demo/agent/llm_utils/oai.py similarity index 100% rename from demo/gradio/computer_use_demo/gui_agent/llm_utils/oai.py rename to demo/gradio/computer_use_demo/agent/llm_utils/oai.py diff --git a/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py b/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py new file mode 100644 index 0000000..7b44069 --- /dev/null +++ b/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py @@ -0,0 +1,44 @@ +import requests +import base64 +from pathlib import Path +from computer_use_demo.tools.screen_capture import get_screenshot +from computer_use_demo.gui_agent.llm_utils.oai import encode_image + +OUTPUT_DIR = "./tmp/outputs" + +class OmniParserClient: + def __init__(self, + url: str) -> None: + self.url = url + + def __call__(self,): + screenshot, screenshot_path = get_screenshot() + screenshot_path = str(screenshot_path) + image_base64 = encode_image(screenshot_path) + response = requests.post(self.url, json={"base64_image": image_base64}) + response_json = response.json() + print('omniparser latency:', response_json['latency']) + + som_image_data = base64.b64decode(response_json['som_image_base64']) + screenshot_path_uuid = Path(screenshot_path).stem.replace("screenshot_", "") + som_screenshot_path = f"{OUTPUT_DIR}/screenshot_som_{screenshot_path_uuid}.png" + with open(som_screenshot_path, "wb") as f: + f.write(som_image_data) + + response_json['width'] = screenshot.size[0] + response_json['height'] = screenshot.size[1] + response_json['original_screenshot_base64'] = image_base64 + response_json['screenshot_uuid'] = screenshot_path_uuid + response_json = self.reformat_messages(response_json) + return response_json + + def reformat_messages(self, response_json: dict): + screen_info = "" + for idx, element in enumerate(response_json["parsed_content_list"]): + element['idx'] = idx + if element['type'] == 'text': + screen_info += f'ID: {idx}, Text: {element["content"]}\n' + elif element['type'] == 'icon': + screen_info += f'ID: {idx}, Icon: {element["content"]}\n' + response_json['screen_info'] = screen_info + return response_json \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py b/demo/gradio/computer_use_demo/agent/vlm_agent.py similarity index 80% rename from demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py rename to demo/gradio/computer_use_demo/agent/vlm_agent.py index 35f57c9..0ce905a 100644 --- a/demo/gradio/computer_use_demo/omniparser_agent/vlm_agent.py +++ b/demo/gradio/computer_use_demo/agent/vlm_agent.py @@ -1,23 +1,16 @@ import json -import asyncio -import platform from collections.abc import Callable -from datetime import datetime -from enum import StrEnum -from typing import Any, cast, Dict, Callable +from typing import cast, Callable import uuid -import requests from PIL import Image, ImageDraw import base64 from io import BytesIO -from pathlib import Path -from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse -from anthropic.types import TextBlock, ToolResultBlockParam +from anthropic import APIResponse +from anthropic.types import ToolResultBlockParam from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage -from computer_use_demo.tools.screen_capture import get_screenshot -from computer_use_demo.gui_agent.llm_utils.oai import run_oai_interleaved, encode_image +from computer_use_demo.gui_agent.llm_utils.oai import run_oai_interleaved from computer_use_demo.colorful_text import colorful_text_vlm import time import re @@ -33,67 +26,6 @@ def extract_data(input_string, data_type): # Return the first match if exists, trimming whitespace and ignoring potential closing backticks return matches[0][0].strip() if matches else input_string -class OmniParser: - def __init__(self, - url: str) -> None: - self.url = url - if not self.url: - config = { - 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', - 'device': 'cpu', - 'caption_model_name': 'florence2', - 'caption_model_path': '../weights/icon_caption_florence', - 'BOX_TRESHOLD': 0.05 - } - from computer_use_demo.omniparser_agent.omniparser import Omniparser as Omniparser_class - self.omniparser = Omniparser_class(config=config) - - def __call__(self,): - screenshot, screenshot_path = get_screenshot() - screenshot_path = str(screenshot_path) - image_base64 = encode_image(screenshot_path) - if self.url: - response = requests.post(self.url, json={"base64_image": image_base64, 'prompt': 'omniparser process'}) - response_json = response.json() - else: - start = time.time() - dino_labled_img, parsed_content_list = self.omniparser.parse(image_base64) - latency = time.time() - start - response_json = { - 'som_image_base64': dino_labled_img, - 'parsed_content_list': parsed_content_list, - 'latency': latency - } - - som_image_data = base64.b64decode(response_json['som_image_base64']) - screenshot_path_uuid = Path(screenshot_path).stem.replace("screenshot_", "") - som_screenshot_path = f"{OUTPUT_DIR}/screenshot_som_{screenshot_path_uuid}.png" - with open(som_screenshot_path, "wb") as f: - f.write(som_image_data) - - response_json['width'] = screenshot.size[0] - response_json['height'] = screenshot.size[1] - response_json['original_screenshot_base64'] = image_base64 - response_json['screenshot_uuid'] = screenshot_path_uuid - # example response_json: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "latency": 0.1} - print('omniparser latency:', response_json['latency']) - response_json = self.reformat_messages(response_json) - return response_json - - def reformat_messages(self, response_json: dict): - screen_info = "" - for idx, element in enumerate(response_json["parsed_content_list"]): - element['idx'] = idx - if element['type'] == 'text': - # screen_info += f'''

\n''' - screen_info += f'ID: {idx}, Text: {element["content"]}\n' - elif element['type'] == 'icon': - # screen_info += f'''{element['content']} \n''' - screen_info += f'ID: {idx}, Icon: {element["content"]}\n' - response_json['screen_info'] = screen_info - return response_json - - class VLMAgent: def __init__( self, diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py index c400ec4..e702b10 100644 --- a/demo/gradio/computer_use_demo/loop.py +++ b/demo/gradio/computer_use_demo/loop.py @@ -15,9 +15,10 @@ from anthropic.types.beta import ( ) from computer_use_demo.tools import ToolResult -from computer_use_demo.gui_agent.anthropic_agent import AnthropicActor +from computer_use_demo.agent.llm_utils.omniparserclient import OmniParserClient +from computer_use_demo.agent.anthropic_agent import AnthropicActor +from computer_use_demo.agent.vlm_agent import VLMAgent from computer_use_demo.executor.anthropic_executor import AnthropicExecutor -from computer_use_demo.omniparser_agent.vlm_agent import OmniParser, VLMAgent BETA_FLAG = "computer-use-2024-10-22" @@ -52,7 +53,7 @@ def sampling_loop_sync( Synchronous agentic sampling loop for the assistant/tool interaction of computer use. """ print('in sampling_loop_sync, model:', model) - omniparser = OmniParser(url=f"http://{omniparser_url}/send_text/" if omniparser_url else None) + omniparser_client = OmniParserClient(url=f"http://{omniparser_url}/parse/") if model == "claude-3-5-sonnet-20241022": # Register Actor and Executor actor = AnthropicActor( @@ -94,7 +95,7 @@ def sampling_loop_sync( if model == "claude-3-5-sonnet-20241022": # Anthropic loop while True: - parsed_screen = omniparser() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} + parsed_screen = omniparser_client() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} import pdb; pdb.set_trace() screen_info_block = TextBlock(text='Below is the structured accessibility information of the current UI screen, which includes text and icons you can operate on, take these information into account when you are making the prediction for the next action. Note you will still need to take screenshot to get the image: \n' + parsed_screen['screen_info'], type='text') # # messages[-1]['content'].append(screen_info_block) @@ -112,7 +113,7 @@ def sampling_loop_sync( elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": while True: - parsed_screen = omniparser() + parsed_screen = omniparser_client() tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) for message, tool_result_content in executor(tools_use_needed, messages): diff --git a/demo/gradio/computer_use_demo/omniparser_agent/omniparser.py b/demo/gradio/computer_use_demo/omniparser_agent/omniparser.py deleted file mode 100644 index 81ca124..0000000 --- a/demo/gradio/computer_use_demo/omniparser_agent/omniparser.py +++ /dev/null @@ -1,77 +0,0 @@ -# uvicorn remote_request:app --host 0.0.0.0 --port 8000 --reload - -import sys -import os -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from computer_use_demo.omniparser_agent.utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box -import torch -from PIL import Image -from typing import Dict, Tuple, List -import base64 -import io - - -# config = { -# 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', -# 'device': 'cpu', -# 'caption_model_name': 'florence2', -# 'caption_model_path': '../weights/icon_caption_florence', -# 'BOX_TRESHOLD': 0.05 -# } - - -class Omniparser(object): - def __init__(self, config: Dict): - self.config = config - device = 'cuda' if torch.cuda.is_available() else 'cpu' - - self.som_model = get_yolo_model(model_path=config['som_model_path']) - self.caption_model_processor = get_caption_model_processor(model_name=config['caption_model_name'], model_name_or_path=config['caption_model_path'], device=device) - print('Omniparser initialized!!!') - - def parse(self, image_base64: str): - image_path = './demo_image.jpg' - with open(image_path, "wb") as fh: - fh.write(base64.b64decode(image_base64)) - print('Parsing image:', image_path) - - image = Image.open(image_path) - print('image size:', image.size) - - box_overlay_ratio = max(image.size) / 3200 - draw_bbox_config = { - 'text_scale': 0.8 * box_overlay_ratio, - 'text_thickness': max(int(2 * box_overlay_ratio), 1), - 'text_padding': max(int(3 * box_overlay_ratio), 1), - 'thickness': max(int(3 * box_overlay_ratio), 1), - } - BOX_TRESHOLD = self.config['BOX_TRESHOLD'] - - ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.8}, use_paddleocr=True) - text, ocr_bbox = ocr_bbox_rslt - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) - - return dino_labled_img, parsed_content_list - - -# from fastapi import FastAPI -# from pydantic import BaseModel - -# app = FastAPI() - -# class Item(BaseModel): -# base64_image: str -# prompt: str - -# Omniparser = Omniparser(config) - -# @app.post("/send_text/") -# async def send_text(item: Item): -# print('start parsing...') -# import time -# start = time.time() -# dino_labled_img, parsed_content_list = Omniparser.parse(item.base64_image) -# latency = time.time() - start -# print('time:', latency) -# return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency} \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/omniparser_agent/utils.py b/demo/gradio/computer_use_demo/omniparser_agent/utils.py deleted file mode 100755 index 87c6f83..0000000 --- a/demo/gradio/computer_use_demo/omniparser_agent/utils.py +++ /dev/null @@ -1,935 +0,0 @@ -# from ultralytics import YOLO -import os -import io -import base64 -import time -from PIL import Image, ImageDraw, ImageFont -import json -import requests -# utility function -import os -from openai import AzureOpenAI - -import json -import sys -import os -import cv2 -import numpy as np -# %matplotlib inline -from matplotlib import pyplot as plt -import easyocr -from paddleocr import PaddleOCR -reader = easyocr.Reader(['en', 'ch_sim'], gpu=True) -paddle_ocr = PaddleOCR( - lang='en', # other lang also available - use_angle_cls=False, - use_gpu=False, # using cuda will conflict with pytorch in the same process - show_log=False, - max_batch_size=1024, - use_dilation=True, # improves accuracy - det_db_score_mode='slow', # improves accuracy - rec_batch_num=1024) -import time -import base64 - -import os -import ast -import torch -from typing import Tuple, List -from torchvision.ops import box_convert -import re -from torchvision.transforms import ToPILImage -import supervision as sv -import torchvision.transforms as T - - -def get_caption_model_processor(model_name, model_name_or_path="Salesforce/blip2-opt-2.7b", device=None): - if not device: - device = "cuda" if torch.cuda.is_available() else "cpu" - if model_name == "blip2": - from transformers import Blip2Processor, Blip2ForConditionalGeneration - processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") - if device == 'cpu': - model = Blip2ForConditionalGeneration.from_pretrained( - model_name_or_path, device_map=None, torch_dtype=torch.float32 - ) - else: - model = Blip2ForConditionalGeneration.from_pretrained( - model_name_or_path, device_map=None, torch_dtype=torch.float16 - ).to(device) - elif model_name == "florence2": - from transformers import AutoProcessor, AutoModelForCausalLM - processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True) - if device == 'cpu': - model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float32, trust_remote_code=True) - else: - model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, trust_remote_code=True).to(device) - return {'model': model.to(device), 'processor': processor} - - -def get_yolo_model(model_path): - from ultralytics import YOLO - # Load the model. - model = YOLO(model_path) - return model - - -@torch.inference_mode() -def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=None): - # Number of samples per batch, --> 256 roughly takes 23 GB of GPU memory for florence model - - to_pil = ToPILImage() - if starting_idx: - non_ocr_boxes = filtered_boxes[starting_idx:] - else: - non_ocr_boxes = filtered_boxes - croped_pil_image = [] - t0 = time.time() - for i, coord in enumerate(non_ocr_boxes): - try: - xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) - ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) - cropped_image = image_source[ymin:ymax, xmin:xmax, :] - # resize the image to 224x224 to avoid long overhead in clipimageprocessor # TODO - cropped_image = cv2.resize(cropped_image, (64, 64)) - croped_pil_image.append(to_pil(cropped_image)) - except: - continue - # print('time to prepare bbox:', time.time()-t0) - - model, processor = caption_model_processor['model'], caption_model_processor['processor'] - if not prompt: - if 'florence' in model.config.name_or_path: - prompt = "" - else: - prompt = "The image shows" - - generated_texts = [] - device = model.device - # batch_size = 64 - for i in range(0, len(croped_pil_image), batch_size): - start = time.time() - batch = croped_pil_image[i:i+batch_size] - t1 = time.time() - if model.device.type == 'cuda': - inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt", do_resize=False).to(device=device, dtype=torch.float16) - else: - inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device) - t2 = time.time() - # print('time to process image + tokenize text inputs:', t2-t1) - if 'florence' in model.config.name_or_path: - generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=20,num_beams=1, do_sample=False) - else: - generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True, - t3 = time.time() - # print('time to generate:', t3-t2) - generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) - generated_text = [gen.strip() for gen in generated_text] - generated_texts.extend(generated_text) - - return generated_texts - - - -def get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor): - to_pil = ToPILImage() - if ocr_bbox: - non_ocr_boxes = filtered_boxes[len(ocr_bbox):] - else: - non_ocr_boxes = filtered_boxes - croped_pil_image = [] - for i, coord in enumerate(non_ocr_boxes): - xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1]) - ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0]) - cropped_image = image_source[ymin:ymax, xmin:xmax, :] - croped_pil_image.append(to_pil(cropped_image)) - - model, processor = caption_model_processor['model'], caption_model_processor['processor'] - device = model.device - messages = [{"role": "user", "content": "<|image_1|>\ndescribe the icon in one sentence"}] - prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) - - batch_size = 5 # Number of samples per batch - generated_texts = [] - - for i in range(0, len(croped_pil_image), batch_size): - images = croped_pil_image[i:i+batch_size] - image_inputs = [processor.image_processor(x, return_tensors="pt") for x in images] - inputs ={'input_ids': [], 'attention_mask': [], 'pixel_values': [], 'image_sizes': []} - texts = [prompt] * len(images) - for i, txt in enumerate(texts): - input = processor._convert_images_texts_to_inputs(image_inputs[i], txt, return_tensors="pt") - inputs['input_ids'].append(input['input_ids']) - inputs['attention_mask'].append(input['attention_mask']) - inputs['pixel_values'].append(input['pixel_values']) - inputs['image_sizes'].append(input['image_sizes']) - max_len = max([x.shape[1] for x in inputs['input_ids']]) - for i, v in enumerate(inputs['input_ids']): - inputs['input_ids'][i] = torch.cat([processor.tokenizer.pad_token_id * torch.ones(1, max_len - v.shape[1], dtype=torch.long), v], dim=1) - inputs['attention_mask'][i] = torch.cat([torch.zeros(1, max_len - v.shape[1], dtype=torch.long), inputs['attention_mask'][i]], dim=1) - inputs_cat = {k: torch.concatenate(v).to(device) for k, v in inputs.items()} - - generation_args = { - "max_new_tokens": 25, - "temperature": 0.01, - "do_sample": False, - } - generate_ids = model.generate(**inputs_cat, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) - # # remove input tokens - generate_ids = generate_ids[:, inputs_cat['input_ids'].shape[1]:] - response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) - response = [res.strip('\n').strip() for res in response] - generated_texts.extend(response) - - return generated_texts - -def remove_overlap(boxes, iou_threshold, ocr_bbox=None): - assert ocr_bbox is None or isinstance(ocr_bbox, List) - - def box_area(box): - return (box[2] - box[0]) * (box[3] - box[1]) - - def intersection_area(box1, box2): - x1 = max(box1[0], box2[0]) - y1 = max(box1[1], box2[1]) - x2 = min(box1[2], box2[2]) - y2 = min(box1[3], box2[3]) - return max(0, x2 - x1) * max(0, y2 - y1) - - def IoU(box1, box2): - intersection = intersection_area(box1, box2) - union = box_area(box1) + box_area(box2) - intersection + 1e-6 - if box_area(box1) > 0 and box_area(box2) > 0: - ratio1 = intersection / box_area(box1) - ratio2 = intersection / box_area(box2) - else: - ratio1, ratio2 = 0, 0 - return max(intersection / union, ratio1, ratio2) - - def is_inside(box1, box2): - # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3] - intersection = intersection_area(box1, box2) - ratio1 = intersection / box_area(box1) - return ratio1 > 0.95 - - boxes = boxes.tolist() - filtered_boxes = [] - if ocr_bbox: - filtered_boxes.extend(ocr_bbox) - # print('ocr_bbox!!!', ocr_bbox) - for i, box1 in enumerate(boxes): - # if not any(IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2) for j, box2 in enumerate(boxes) if i != j): - is_valid_box = True - for j, box2 in enumerate(boxes): - # keep the smaller box - if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2): - is_valid_box = False - break - if is_valid_box: - # add the following 2 lines to include ocr bbox - if ocr_bbox: - # only add the box if it does not overlap with any ocr bbox - if not any(IoU(box1, box3) > iou_threshold and not is_inside(box1, box3) for k, box3 in enumerate(ocr_bbox)): - filtered_boxes.append(box1) - else: - filtered_boxes.append(box1) - return torch.tensor(filtered_boxes) - - -def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None): - ''' - ocr_bbox format: [{'type': 'text', 'bbox':[x,y], 'interactivity':False, 'content':str }, ...] - boxes format: [{'type': 'icon', 'bbox':[x,y], 'interactivity':True, 'content':None }, ...] - - ''' - assert ocr_bbox is None or isinstance(ocr_bbox, List) - - def box_area(box): - return (box[2] - box[0]) * (box[3] - box[1]) - - def intersection_area(box1, box2): - x1 = max(box1[0], box2[0]) - y1 = max(box1[1], box2[1]) - x2 = min(box1[2], box2[2]) - y2 = min(box1[3], box2[3]) - return max(0, x2 - x1) * max(0, y2 - y1) - - def IoU(box1, box2): - intersection = intersection_area(box1, box2) - union = box_area(box1) + box_area(box2) - intersection + 1e-6 - if box_area(box1) > 0 and box_area(box2) > 0: - ratio1 = intersection / box_area(box1) - ratio2 = intersection / box_area(box2) - else: - ratio1, ratio2 = 0, 0 - return max(intersection / union, ratio1, ratio2) - - def is_inside(box1, box2): - # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3] - intersection = intersection_area(box1, box2) - ratio1 = intersection / box_area(box1) - return ratio1 > 0.80 - - # boxes = boxes.tolist() - filtered_boxes = [] - if ocr_bbox: - filtered_boxes.extend(ocr_bbox) - # print('ocr_bbox!!!', ocr_bbox) - for i, box1_elem in enumerate(boxes): - box1 = box1_elem['bbox'] - is_valid_box = True - for j, box2_elem in enumerate(boxes): - # keep the smaller box - box2 = box2_elem['bbox'] - if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2): - is_valid_box = False - break - if is_valid_box: - # add the following 2 lines to include ocr bbox - if ocr_bbox: - # keep yolo boxes + prioritize ocr label - box_added = False - ocr_labels = '' - for box3_elem in ocr_bbox: - if not box_added: - box3 = box3_elem['bbox'] - if is_inside(box3, box1): # ocr inside icon - # box_added = True - # delete the box3_elem from ocr_bbox - try: - # filtered_boxes.append({'type': 'text', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': box3_elem['content'], 'source':'box_yolo_content_ocr'}) - # gather all ocr labels - ocr_labels += box3_elem['content'] + ' ' - filtered_boxes.remove(box3_elem) - # print('remove ocr bbox:', box3_elem) - except: - continue - # break - elif is_inside(box1, box3): # icon inside ocr, don't added this icon box, no need to check other ocr bbox bc no overlap between ocr bbox, icon can only be in one ocr box - box_added = True - # try: - # filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None}) - # filtered_boxes.remove(box3_elem) - # except: - # continue - break - else: - continue - if not box_added: - if ocr_labels: - filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': ocr_labels, 'source':'box_yolo_content_ocr'}) - else: - filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None, 'source':'box_yolo_content_yolo'}) - - else: - filtered_boxes.append(box1) - return filtered_boxes # torch.tensor(filtered_boxes) - - -def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]: - transform = T.Compose( - [ - T.RandomResize([800], max_size=1333), - T.ToTensor(), - T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ] - ) - image_source = Image.open(image_path).convert("RGB") - image = np.asarray(image_source) - image_transformed, _ = transform(image_source, None) - return image, image_transformed - - -def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str], text_scale: float, - text_padding=5, text_thickness=2, thickness=3) -> np.ndarray: - """ - This function annotates an image with bounding boxes and labels. - - Parameters: - image_source (np.ndarray): The source image to be annotated. - boxes (torch.Tensor): A tensor containing bounding box coordinates. in cxcywh format, pixel scale - logits (torch.Tensor): A tensor containing confidence scores for each bounding box. - phrases (List[str]): A list of labels for each bounding box. - text_scale (float): The scale of the text to be displayed. 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web - - Returns: - np.ndarray: The annotated image. - """ - h, w, _ = image_source.shape - boxes = boxes * torch.Tensor([w, h, w, h]) - xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() - xywh = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xywh").numpy() - detections = sv.Detections(xyxy=xyxy) - - labels = [f"{phrase}" for phrase in range(boxes.shape[0])] - - # from util.box_annotator import BoxAnnotator - box_annotator = BoxAnnotator(text_scale=text_scale, text_padding=text_padding,text_thickness=text_thickness,thickness=thickness) # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web - annotated_frame = image_source.copy() - annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels, image_size=(w,h)) - - label_coordinates = {f"{phrase}": v for phrase, v in zip(phrases, xywh)} - return annotated_frame, label_coordinates - - -def predict(model, image, caption, box_threshold, text_threshold): - """ Use huggingface model to replace the original model - """ - model, processor = model['model'], model['processor'] - device = model.device - - inputs = processor(images=image, text=caption, return_tensors="pt").to(device) - with torch.no_grad(): - outputs = model(**inputs) - - results = processor.post_process_grounded_object_detection( - outputs, - inputs.input_ids, - box_threshold=box_threshold, # 0.4, - text_threshold=text_threshold, # 0.3, - target_sizes=[image.size[::-1]] - )[0] - boxes, logits, phrases = results["boxes"], results["scores"], results["labels"] - return boxes, logits, phrases - - -def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_threshold=0.7): - """ Use huggingface model to replace the original model - """ - # model = model['model'] - if scale_img: - result = model.predict( - source=image_path, - conf=box_threshold, - imgsz=imgsz, - iou=iou_threshold, # default 0.7 - ) - else: - result = model.predict( - source=image_path, - conf=box_threshold, - iou=iou_threshold, # default 0.7 - ) - boxes = result[0].boxes.xyxy#.tolist() # in pixel space - conf = result[0].boxes.conf - phrases = [str(i) for i in range(len(boxes))] - - return boxes, conf, phrases - - -def int_box_area(box, w, h): - x1, y1, x2, y2 = box - int_box = [int(x1*w), int(y1*h), int(x2*w), int(y2*h)] - area = (int_box[2] - int_box[0]) * (int_box[3] - int_box[1]) - return area - - -def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64): - """ ocr_bbox: list of xyxy format bbox - """ - image_source = Image.open(img_path).convert("RGB") - w, h = image_source.size - if not imgsz: - imgsz = (h, w) - # print('image size:', w, h) - xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1) - xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device) - image_source = np.asarray(image_source) - phrases = [str(i) for i in range(len(phrases))] - - - # annotate the image with labels - if ocr_bbox: - ocr_bbox = torch.tensor(ocr_bbox) / torch.Tensor([w, h, w, h]) - ocr_bbox=ocr_bbox.tolist() - else: - print('no ocr bbox!!!') - ocr_bbox = None - - ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt, 'source': 'box_ocr_content_ocr'} for box, txt in zip(ocr_bbox, ocr_text) if int_box_area(box, w, h) > 0] - xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist() if int_box_area(box, w, h) > 0] - filtered_boxes = remove_overlap_new(boxes=xyxy_elem, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox_elem) - - - # sort the filtered_boxes so that the one with 'content': None is at the end, and get the index of the first 'content': None - filtered_boxes_elem = sorted(filtered_boxes, key=lambda x: x['content'] is None) - # get the index of the first 'content': None - starting_idx = next((i for i, box in enumerate(filtered_boxes_elem) if box['content'] is None), -1) - filtered_boxes = torch.tensor([box['bbox'] for box in filtered_boxes_elem]) - print('len(filtered_boxes):', len(filtered_boxes), starting_idx) - - # get parsed icon local semantics - time1 = time.time() - if use_local_semantics: - caption_model = caption_model_processor['model'] - if 'phi3_v' in caption_model.config.model_type: - parsed_content_icon = get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor) - else: - parsed_content_icon = get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=prompt,batch_size=batch_size) - ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] - icon_start = len(ocr_text) - parsed_content_icon_ls = [] - # fill the filtered_boxes_elem None content with parsed_content_icon in order - for i, box in enumerate(filtered_boxes_elem): - if box['content'] is None: - box['content'] = parsed_content_icon.pop(0) - for i, txt in enumerate(parsed_content_icon): - parsed_content_icon_ls.append(f"Icon Box ID {str(i+icon_start)}: {txt}") - parsed_content_merged = ocr_text + parsed_content_icon_ls - else: - ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)] - parsed_content_merged = ocr_text - print('time to get parsed content:', time.time()-time1) - - filtered_boxes = box_convert(boxes=filtered_boxes, in_fmt="xyxy", out_fmt="cxcywh") - - phrases = [i for i in range(len(filtered_boxes))] - - # draw boxes - if draw_bbox_config: - annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, **draw_bbox_config) - else: - annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, text_scale=text_scale, text_padding=text_padding) - - pil_img = Image.fromarray(annotated_frame) - buffered = io.BytesIO() - pil_img.save(buffered, format="PNG") - encoded_image = base64.b64encode(buffered.getvalue()).decode('ascii') - if output_coord_in_ratio: - # h, w, _ = image_source.shape - label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()} - assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0] - - return encoded_image, label_coordinates, filtered_boxes_elem - - -def get_xywh(input): - x, y, w, h = input[0][0], input[0][1], input[2][0] - input[0][0], input[2][1] - input[0][1] - x, y, w, h = int(x), int(y), int(w), int(h) - return x, y, w, h - -def get_xyxy(input): - x, y, xp, yp = input[0][0], input[0][1], input[2][0], input[2][1] - x, y, xp, yp = int(x), int(y), int(xp), int(yp) - return x, y, xp, yp - -def get_xywh_yolo(input): - x, y, w, h = input[0], input[1], input[2] - input[0], input[3] - input[1] - x, y, w, h = int(x), int(y), int(w), int(h) - return x, y, w, h - - - -def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False): - if use_paddleocr: - if easyocr_args is None: - text_threshold = 0.5 - else: - text_threshold = easyocr_args['text_threshold'] - result = paddle_ocr.ocr(image_path, cls=False)[0] - # conf = [item[1] for item in result] - coord = [item[0] for item in result if item[1][1] > text_threshold] - text = [item[1][0] for item in result if item[1][1] > text_threshold] - else: # EasyOCR - if easyocr_args is None: - easyocr_args = {} - result = reader.readtext(image_path, **easyocr_args) - # print('goal filtering pred:', result[-5:]) - coord = [item[0] for item in result] - text = [item[1] for item in result] - # read the image using cv2 - if display_img: - opencv_img = cv2.imread(image_path) - opencv_img = cv2.cvtColor(opencv_img, cv2.COLOR_RGB2BGR) - bb = [] - for item in coord: - x, y, a, b = get_xywh(item) - # print(x, y, a, b) - bb.append((x, y, a, b)) - cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2) - - # Display the image - plt.imshow(opencv_img) - else: - if output_bb_format == 'xywh': - bb = [get_xywh(item) for item in coord] - elif output_bb_format == 'xyxy': - bb = [get_xyxy(item) for item in coord] - # print('bounding box!!!', bb) - return (text, bb), goal_filtering - - - -from typing import List, Optional, Union, Tuple - -import cv2 -import numpy as np - -from supervision.detection.core import Detections -from supervision.draw.color import Color, ColorPalette - - -class BoxAnnotator: - """ - A class for drawing bounding boxes on an image using detections provided. - - Attributes: - color (Union[Color, ColorPalette]): The color to draw the bounding box, - can be a single color or a color palette - thickness (int): The thickness of the bounding box lines, default is 2 - text_color (Color): The color of the text on the bounding box, default is white - text_scale (float): The scale of the text on the bounding box, default is 0.5 - text_thickness (int): The thickness of the text on the bounding box, - default is 1 - text_padding (int): The padding around the text on the bounding box, - default is 5 - - """ - - def __init__( - self, - color: Union[Color, ColorPalette] = ColorPalette.DEFAULT, - thickness: int = 3, # 1 for seeclick 2 for mind2web and 3 for demo - text_color: Color = Color.BLACK, - text_scale: float = 0.5, # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web - text_thickness: int = 2, #1, # 2 for demo - text_padding: int = 10, - avoid_overlap: bool = True, - ): - self.color: Union[Color, ColorPalette] = color - self.thickness: int = thickness - self.text_color: Color = text_color - self.text_scale: float = text_scale - self.text_thickness: int = text_thickness - self.text_padding: int = text_padding - self.avoid_overlap: bool = avoid_overlap - - def annotate( - self, - scene: np.ndarray, - detections: Detections, - labels: Optional[List[str]] = None, - skip_label: bool = False, - image_size: Optional[Tuple[int, int]] = None, - ) -> np.ndarray: - """ - Draws bounding boxes on the frame using the detections provided. - - Args: - scene (np.ndarray): The image on which the bounding boxes will be drawn - detections (Detections): The detections for which the - bounding boxes will be drawn - labels (Optional[List[str]]): An optional list of labels - corresponding to each detection. If `labels` are not provided, - corresponding `class_id` will be used as label. - skip_label (bool): Is set to `True`, skips bounding box label annotation. - Returns: - np.ndarray: The image with the bounding boxes drawn on it - - Example: - ```python - import supervision as sv - - classes = ['person', ...] - image = ... - detections = sv.Detections(...) - - box_annotator = sv.BoxAnnotator() - labels = [ - f"{classes[class_id]} {confidence:0.2f}" - for _, _, confidence, class_id, _ in detections - ] - annotated_frame = box_annotator.annotate( - scene=image.copy(), - detections=detections, - labels=labels - ) - ``` - """ - font = cv2.FONT_HERSHEY_SIMPLEX - for i in range(len(detections)): - x1, y1, x2, y2 = detections.xyxy[i].astype(int) - class_id = ( - detections.class_id[i] if detections.class_id is not None else None - ) - idx = class_id if class_id is not None else i - color = ( - self.color.by_idx(idx) - if isinstance(self.color, ColorPalette) - else self.color - ) - cv2.rectangle( - img=scene, - pt1=(x1, y1), - pt2=(x2, y2), - color=color.as_bgr(), - thickness=self.thickness, - ) - if skip_label: - continue - - text = ( - f"{class_id}" - if (labels is None or len(detections) != len(labels)) - else labels[i] - ) - - text_width, text_height = cv2.getTextSize( - text=text, - fontFace=font, - fontScale=self.text_scale, - thickness=self.text_thickness, - )[0] - - if not self.avoid_overlap: - text_x = x1 + self.text_padding - text_y = y1 - self.text_padding - - text_background_x1 = x1 - text_background_y1 = y1 - 2 * self.text_padding - text_height - - text_background_x2 = x1 + 2 * self.text_padding + text_width - text_background_y2 = y1 - # text_x = x1 - self.text_padding - text_width - # text_y = y1 + self.text_padding + text_height - # text_background_x1 = x1 - 2 * self.text_padding - text_width - # text_background_y1 = y1 - # text_background_x2 = x1 - # text_background_y2 = y1 + 2 * self.text_padding + text_height - else: - text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 = get_optimal_label_pos(self.text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size) - - cv2.rectangle( - img=scene, - pt1=(text_background_x1, text_background_y1), - pt2=(text_background_x2, text_background_y2), - color=color.as_bgr(), - thickness=cv2.FILLED, - ) - box_color = color.as_rgb() - luminance = 0.299 * box_color[0] + 0.587 * box_color[1] + 0.114 * box_color[2] - text_color = (0,0,0) if luminance > 160 else (255,255,255) - cv2.putText( - img=scene, - text=text, - org=(text_x, text_y), - fontFace=font, - fontScale=self.text_scale, - # color=self.text_color.as_rgb(), - color=text_color, - thickness=self.text_thickness, - lineType=cv2.LINE_AA, - ) - return scene - - -def box_area(box): - return (box[2] - box[0]) * (box[3] - box[1]) - -def intersection_area(box1, box2): - x1 = max(box1[0], box2[0]) - y1 = max(box1[1], box2[1]) - x2 = min(box1[2], box2[2]) - y2 = min(box1[3], box2[3]) - return max(0, x2 - x1) * max(0, y2 - y1) - -def IoU(box1, box2, return_max=True): - intersection = intersection_area(box1, box2) - union = box_area(box1) + box_area(box2) - intersection - if box_area(box1) > 0 and box_area(box2) > 0: - ratio1 = intersection / box_area(box1) - ratio2 = intersection / box_area(box2) - else: - ratio1, ratio2 = 0, 0 - if return_max: - return max(intersection / union, ratio1, ratio2) - else: - return intersection / union - - -def get_optimal_label_pos(text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size): - """ check overlap of text and background detection box, and get_optimal_label_pos, - pos: str, position of the text, must be one of 'top left', 'top right', 'outer left', 'outer right' TODO: if all are overlapping, return the last one, i.e. outer right - Threshold: default to 0.3 - """ - - def get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size): - is_overlap = False - for i in range(len(detections)): - detection = detections.xyxy[i].astype(int) - if IoU([text_background_x1, text_background_y1, text_background_x2, text_background_y2], detection) > 0.3: - is_overlap = True - break - # check if the text is out of the image - if text_background_x1 < 0 or text_background_x2 > image_size[0] or text_background_y1 < 0 or text_background_y2 > image_size[1]: - is_overlap = True - return is_overlap - - # if pos == 'top left': - text_x = x1 + text_padding - text_y = y1 - text_padding - - text_background_x1 = x1 - text_background_y1 = y1 - 2 * text_padding - text_height - - text_background_x2 = x1 + 2 * text_padding + text_width - text_background_y2 = y1 - is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) - if not is_overlap: - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - # elif pos == 'outer left': - text_x = x1 - text_padding - text_width - text_y = y1 + text_padding + text_height - - text_background_x1 = x1 - 2 * text_padding - text_width - text_background_y1 = y1 - - text_background_x2 = x1 - text_background_y2 = y1 + 2 * text_padding + text_height - is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) - if not is_overlap: - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - - # elif pos == 'outer right': - text_x = x2 + text_padding - text_y = y1 + text_padding + text_height - - text_background_x1 = x2 - text_background_y1 = y1 - - text_background_x2 = x2 + 2 * text_padding + text_width - text_background_y2 = y1 + 2 * text_padding + text_height - - is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) - if not is_overlap: - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - # elif pos == 'top right': - text_x = x2 - text_padding - text_width - text_y = y1 - text_padding - - text_background_x1 = x2 - 2 * text_padding - text_width - text_background_y1 = y1 - 2 * text_padding - text_height - - text_background_x2 = x2 - text_background_y2 = y1 - - is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size) - if not is_overlap: - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 - - - -import re -def extract_dict_from_text(text): - # Define the regex pattern for a dictionary-like structure - pattern = r"\{\s*'(?P.*?)':\s*'(?P.*?)',\s*'(?P.*?)':\s*'(?P.*?)'\s*\}" - - # Search for the dictionary in the text - match = re.search(pattern, text) - - if match: - # Extract matched groups into a dictionary - return { - match.group('key1'): match.group('value1'), - match.group('key2'): match.group('value2'), - } - else: - raise ValueError("No valid dictionary structure found in the text.") - - -def get_phi3v_model_dict(): - from PIL import Image - import requests - from transformers import AutoModelForCausalLM - from transformers import AutoProcessor - - model_id = "microsoft/Phi-3.5-vision-instruct" - model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') - processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) - print('phi3v model loaded!!!') - return {'model': model, 'processor': processor} - - -def call_phi3v(messages, image_base64, model_dict): - model, processor = model_dict['model'], model_dict['processor'] - device = model.device - prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) - if isinstance(image_base64, tuple): - image_base64, dino_labled_img = image_base64 - image = Image.open(io.BytesIO(base64.b64decode(image_base64))) - dino_labled_img = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) - inputs = processor(prompt, [image, dino_labled_img], return_tensors="pt").to(device) - else: - image = Image.open(io.BytesIO(base64.b64decode(image_base64))) - inputs = processor(prompt, [image], return_tensors="pt").to(device) - - generation_args = { - "max_new_tokens": 512, - "temperature": 0.01, - "do_sample": False, - } - - generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) - # remove input tokens - generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] - ans = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] - return ans - - -def get_pred_phi3v(message_text, image_base64, label_coordinates, id_key='Click ID', model_dict=None): - # messages = [ - # {"role": "system", "content": '''You are an expert at completing instructions on GUI screens. - # You will be presented with two images. The first is the original screenshot. The second is the same screenshot with some numeric tags. You will also be provided with some descriptions of the bbox, and your task is to choose the numeric bbox idx you want to click in order to complete the user instruction.'''}, - # ] - messages = [ - {"role": "system", "content": '''You are an expert at completing instructions on GUI screens. You will also be provided with some descriptions of the bbox, and your task is to choose the numeric bbox idx you want to click in order to complete the user instruction.'''}, - ] - messages = [] - if isinstance(image_base64, tuple): - messages.append({"role": "user", "content": '<|image_1|>\n' + '<|image_2|>\n' + message_text}) - else: - messages.append({"role": "user", "content": '<|image_1|>\n' + message_text}) - - response_text = call_phi3v(messages, image_base64, model_dict) - print(response_text) - - try: - response_text = ast.literal_eval(response_text) - - icon_id = response_text['Click BBox ID'] - bbox = label_coordinates[str(icon_id)] - click_point = [bbox[0] + bbox[2]/2, bbox[1] + bbox[3]/2] - except: - print('error parsing, use regex to parse!!!') - import pdb; pdb.set_trace() - response_text = extract_dict_from_text(response_text) - icon_id = response_text['Click BBox ID'] - bbox = label_coordinates[str(icon_id)] - click_point = [bbox[0] + bbox[2]/2, bbox[1] + bbox[3]/2] - return icon_id, bbox, click_point, response_text - - # try: - # match = re.search(r"```(.*?)```", ans, re.DOTALL) - # if match: - # result = match.group(1).strip() - # pred = result.split('In summary, the next action I will perform is:')[-1].strip().replace('\\', '') - # pred = ast.literal_eval(pred) - # else: - # pred = ans.split('In summary, the next action I will perform is:')[-1].strip().replace('\\', '') - # pred = ast.literal_eval(pred) - - # if pred[id_key]: - # icon_id = pred[id_key] - # bbox = label_coordinates[str(icon_id)] - # pred['click_point'] = [bbox[0] + bbox[2]/2, bbox[1] + bbox[3]/2] - # except: - # print('phi3v action regex extract fail!!!') - # pred = {'action_type': 'CLICK', 'click_point': [0, 0], 'value': 'None', 'is_completed': False} - - # step_pred_summary = None - # return pred, [True, ans, None, step_pred_summary] \ No newline at end of file diff --git a/demo/gradio/demo_image.jpg b/demo/gradio/demo_image.jpg deleted file mode 100644 index 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zRl}HqAlPg+x7%$r8ns$&oz`>ui(72I_;=%{Z|wpY3>6yWzKb35?$w7-NsegE4kE9Co{%Qfe}p8aJ-9*K5O>e*@$un12}z#@)%V%lWr@ z33cPvE_nX@`8TcCodOfU3H@QE%!%7ED?BBm%QJw%GisHe82XDUkydBoSU@+=Lqhl;He4Swen89E$7!1a5 z3YW_zNm5u?Sao^nOj`7G4^to(;3?frH8c=K(cy5&vRt#?4utOAyZc&SyLRmwl-~^ucnBhx31u#lt4;vT^27|$1+>V$`CYQ@qR8-`(XI5rfJDApv2dK$Y8UO(7 z7cbhYJEu~qv|4TO>K6dIb?erxTeslHxzpFcU@#aBO??C~t8L3*Fc=I5<95biFc=Jm zTfGVfgTY|jb_AQVfx%!f7z_r3!C){Lj5`7r1I%DB7z_r3@tft_?`lcn#b7WPcNI(k xGZ+j8gTY`l2)A5(j(Vu{)-Hg-U@&es{vWpBf72L{_22*i002ovPDHLkV1n~_RfPZm diff --git a/demo/omniparserserver/omniparser.py b/demo/omniparserserver/omniparser.py new file mode 100644 index 0000000..424430f --- /dev/null +++ b/demo/omniparserserver/omniparser.py @@ -0,0 +1,33 @@ +from ...util.utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box +import torch +from PIL import Image +import io +import base64 + +class Omniparser(object): + def __init__(self, config: Dict): + self.config = config + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + self.som_model = get_yolo_model(model_path=config['som_model_path']) + self.caption_model_processor = get_caption_model_processor(model_name=config['caption_model_name'], model_name_or_path=config['caption_model_path'], device=device) + print('Omniparser initialized!!!') + + def parse(self, image_base64: str): + image_bytes = base64.b64decode(image_base64) + image = Image.open(io.BytesIO(image_bytes)) + print('image size:', image.size) + + box_overlay_ratio = max(image.size) / 3200 + draw_bbox_config = { + 'text_scale': 0.8 * box_overlay_ratio, + 'text_thickness': max(int(2 * box_overlay_ratio), 1), + 'text_padding': max(int(3 * box_overlay_ratio), 1), + 'thickness': max(int(3 * box_overlay_ratio), 1), + } + BOX_TRESHOLD = self.config['BOX_TRESHOLD'] + + (text, ocr_bbox), _ = check_ocr_box(image, display_img=False, output_bb_format='xyxy', easyocr_args={'text_threshold': 0.8}, use_paddleocr=False) + dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) + + return dino_labled_img, parsed_content_list \ No newline at end of file diff --git a/demo/omniparserserver/remote_request.py b/demo/omniparserserver/remote_request.py new file mode 100644 index 0000000..816940a --- /dev/null +++ b/demo/omniparserserver/remote_request.py @@ -0,0 +1,50 @@ +''' +python -m remote_request --som_model_path ../weights/icon_detect_v1_5/model_v1_5.pt --caption_model_name florence2 --caption_model_path ../weights/icon_caption_florence --device cuda --BOX_TRESHOLD 0.05 +''' + +import sys +import os +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +import time +from fastapi import FastAPI +from pydantic import BaseModel +import argparse +import uvicorn +from omniparser import Omniparser + +def parse_arguments(): + parser = argparse.ArgumentParser(description='Omniparser API') + parser.add_argument('--som_model_path', type=str, default='../weights/icon_detect_v1_5/model_v1_5.pt', help='Path to the som model') + parser.add_argument('--caption_model_name', type=str, default='florence2', help='Name of the caption model') + parser.add_argument('--caption_model_path', type=str, default='../weights/icon_caption_florence', help='Path to the caption model') + parser.add_argument('--device', type=str, default='cpu', help='Device to run the model') + parser.add_argument('--BOX_TRESHOLD', type=float, default=0.05, help='Threshold for box detection') + parser.add_argument('--host', type=str, default='0.0.0.0', help='Host for the API') + parser.add_argument('--port', type=int, default=8000, help='Port for the API') + args = parser.parse_args() + return args + +args = parse_arguments() +config = vars(args) + +app = FastAPI() +omniparser = Omniparser(config) + +class ParseRequest(BaseModel): + base64_image: str + +@app.post("/parse/") +async def parse(parse_request: ParseRequest): + print('start parsing...') + start = time.time() + dino_labled_img, parsed_content_list = omniparser.parse(parse_request.base64_image) + latency = time.time() - start + print('time:', latency) + return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency} + +@app.get("/probe/") +async def root(): + return {"message": "Omniparser API ready"} + +if __name__ == "__main__": + uvicorn.run("remote_request:app", host=args.host, port=args.port, reload=True) \ No newline at end of file diff --git a/demo/remote_request.py b/demo/remote_request.py deleted file mode 100644 index 414be81..0000000 --- a/demo/remote_request.py +++ /dev/null @@ -1,101 +0,0 @@ -''' -python -m remote_request --som_model_path ../weights/icon_detect_v1_5/model_v1_5.pt --caption_model_name florence2 --caption_model_path ../weights/icon_caption_florence --device cuda --BOX_TRESHOLD 0.05 -''' - -import sys -import os -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -import time -from utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box -import torch -from PIL import Image -from typing import Dict, Tuple, List -import base64 -import io -from fastapi import FastAPI -from pydantic import BaseModel -import argparse - -def parse_arguments(): - parser = argparse.ArgumentParser(description='Omniparser API') - parser.add_argument('--som_model_path', type=str, default='../weights/icon_detect_v1_5/model_v1_5.pt', help='Path to the som model') - parser.add_argument('--caption_model_name', type=str, default='florence2', help='Name of the caption model') - parser.add_argument('--caption_model_path', type=str, default='../weights/icon_caption_florence', help='Path to the caption model') - parser.add_argument('--device', type=str, default='cpu', help='Device to run the model') - parser.add_argument('--BOX_TRESHOLD', type=float, default=0.05, help='Threshold for box detection') - parser.add_argument('--host', type=str, default='0.0.0.0', help='Host for the API') - parser.add_argument('--port', type=int, default=8000, help='Port for the API') - args = parser.parse_args() - return args - -args = parse_arguments() -config = vars(args) - - -# config = { -# 'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt', -# 'device': 'cpu', -# 'caption_model_name': 'florence2', -# 'caption_model_path': '../weights/icon_caption_florence', -# 'BOX_TRESHOLD': 0.05 -# } - - -class Omniparser(object): - def __init__(self, config: Dict): - self.config = config - device = 'cuda' if torch.cuda.is_available() else 'cpu' - - self.som_model = get_yolo_model(model_path=config['som_model_path']) - self.caption_model_processor = get_caption_model_processor(model_name=config['caption_model_name'], model_name_or_path=config['caption_model_path'], device=device) - print('Omniparser initialized!!!') - - def parse(self, image_base64: str): - # Convert base64 to image directly without saving to disk - image_bytes = base64.b64decode(image_base64) - image = Image.open(io.BytesIO(image_bytes)) - print('image size:', image.size) - - box_overlay_ratio = max(image.size) / 3200 - draw_bbox_config = { - 'text_scale': 0.8 * box_overlay_ratio, - 'text_thickness': max(int(2 * box_overlay_ratio), 1), - 'text_padding': max(int(3 * box_overlay_ratio), 1), - 'thickness': max(int(3 * box_overlay_ratio), 1), - } - BOX_TRESHOLD = config['BOX_TRESHOLD'] - - (text, ocr_bbox), _ = check_ocr_box(image, display_img=False, output_bb_format='xyxy', easyocr_args={'text_threshold': 0.8}, use_paddleocr=False) - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) - - return dino_labled_img, parsed_content_list - - - - -app = FastAPI() - -class Item(BaseModel): - base64_image: str - prompt: str - -Omniparser = Omniparser(config) - -@app.post("/send_text/") -async def send_text(item: Item): - print('start parsing...') - - start = time.time() - dino_labled_img, parsed_content_list = Omniparser.parse(item.base64_image) - latency = time.time() - start - print('time:', latency) - return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency} - -@app.get("/") -async def root(): - return {"message": "Omniparser API ready"} - - -if __name__ == "__main__": - import uvicorn - uvicorn.run("remote_request:app", host=args.host, port=args.port, reload=True) \ No newline at end of file diff --git a/gradio_demo.py b/gradio_demo.py index b09168b..15d46a7 100644 --- a/gradio_demo.py +++ b/gradio_demo.py @@ -8,7 +8,7 @@ import io import base64, os -from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img +from util.utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img import torch from PIL import Image @@ -17,8 +17,6 @@ yolo_model = get_yolo_model(model_path='weights/icon_detect_v1_5/best.pt') caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence") # caption_model_processor = get_caption_model_processor(model_name="blip2", model_name_or_path="weights/icon_caption_blip2") - - MARKDOWN = """ # OmniParser for Pure Vision Based General GUI Agent ๐Ÿ”ฅ

@@ -65,8 +63,6 @@ def process( # parsed_content_list = str(parsed_content_list) return image, str(parsed_content_list) - - with gr.Blocks() as demo: gr.Markdown(MARKDOWN) with gr.Row(): diff --git a/omniparser.py b/omniparser.py deleted file mode 100644 index 634ae9f..0000000 --- a/omniparser.py +++ /dev/null @@ -1,60 +0,0 @@ -from utils import get_som_labeled_img, check_ocr_box, get_yolo_model -import torch -from ultralytics import YOLO -from PIL import Image -from typing import Dict, Tuple, List -import io -import base64 - - -config = { - 'som_model_path': 'finetuned_icon_detect.pt', - 'device': 'cpu', - 'caption_model_path': 'Salesforce/blip2-opt-2.7b', - 'draw_bbox_config': { - 'text_scale': 0.8, - 'text_thickness': 2, - 'text_padding': 3, - 'thickness': 3, - }, - 'BOX_TRESHOLD': 0.05 -} - - -class Omniparser(object): - def __init__(self, config: Dict): - self.config = config - - self.som_model = get_yolo_model(model_path=config['som_model_path']) - # self.caption_model_processor = get_caption_model_processor(config['caption_model_path'], device=cofig['device']) - # self.caption_model_processor['model'].to(torch.float32) - - def parse(self, image_path: str): - print('Parsing image:', image_path) - ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}) - text, ocr_bbox = ocr_bbox_rslt - - draw_bbox_config = self.config['draw_bbox_config'] - BOX_TRESHOLD = self.config['BOX_TRESHOLD'] - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=None, ocr_text=text,use_local_semantics=False) - - image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) - # formating output - return_list = [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]}, - 'text': parsed_content_list[i].split(': ')[1], 'type':'text'} for i, (k, coord) in enumerate(label_coordinates.items()) if i < len(parsed_content_list)] - return_list.extend( - [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]}, - 'text': 'None', 'type':'icon'} for i, (k, coord) in enumerate(label_coordinates.items()) if i >= len(parsed_content_list)] - ) - - return [image, return_list] - -parser = Omniparser(config) -image_path = 'examples/pc_1.png' - -# time the parser -import time -s = time.time() -image, parsed_content_list = parser.parse(image_path) -device = config['device'] -print(f'Time taken for Omniparser on {device}:', time.time() - s) diff --git a/util/action_matching.py b/util/action_matching.py deleted file mode 100644 index 51c5359..0000000 --- a/util/action_matching.py +++ /dev/null @@ -1,425 +0,0 @@ -''' -Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild -''' - -import jax -import jax.numpy as jnp -import numpy as np - -# import action_type as action_type_lib -import enum - -class ActionType(enum.IntEnum): - # Placeholders for unused enum values - UNUSED_0 = 0 - UNUSED_1 = 1 - UNUSED_2 = 2 - UNUSED_8 = 8 - UNUSED_9 = 9 - - ########### Agent actions ########### - - # A type action that sends text to the emulator. Note that this simply sends - # text and does not perform any clicks for element focus or enter presses for - # submitting text. - TYPE = 3 - - # The dual point action used to represent all gestures. - DUAL_POINT = 4 - - # These actions differentiate pressing the home and back button from touches. - # They represent explicit presses of back and home performed using ADB. - PRESS_BACK = 5 - PRESS_HOME = 6 - - # An action representing that ADB command for hitting enter was performed. - PRESS_ENTER = 7 - - ########### Episode status actions ########### - - # An action used to indicate the desired task has been completed and resets - # the environment. This action should also be used in the case that the task - # has already been completed and there is nothing to do. - # e.g. The task is to turn on the Wi-Fi when it is already on - STATUS_TASK_COMPLETE = 10 - - # An action used to indicate that desired task is impossible to complete and - # resets the environment. This can be a result of many different things - # including UI changes, Android version differences, etc. - STATUS_TASK_IMPOSSIBLE = 11 - - -_TAP_DISTANCE_THRESHOLD = 0.14 # Fraction of the screen -ANNOTATION_WIDTH_AUGMENT_FRACTION = 1.4 -ANNOTATION_HEIGHT_AUGMENT_FRACTION = 1.4 - -# Interval determining if an action is a tap or a swipe. -_SWIPE_DISTANCE_THRESHOLD = 0.04 - - -def _yx_in_bounding_boxes( - yx, bounding_boxes -): - """Check if the (y,x) point is contained in each bounding box. - - Args: - yx: The (y, x) coordinate in pixels of the point. - bounding_boxes: A 2D int array of shape (num_bboxes, 4), where each row - represents a bounding box: (y_top_left, x_top_left, box_height, - box_width). Note: containment is inclusive of the bounding box edges. - - Returns: - is_inside: A 1D bool array where each element specifies if the point is - contained within the respective box. - """ - y, x = yx - - # `bounding_boxes` has shape (n_elements, 4); we extract each array along the - # last axis into shape (n_elements, 1), then squeeze unneeded dimension. - top, left, height, width = [ - jnp.squeeze(v, axis=-1) for v in jnp.split(bounding_boxes, 4, axis=-1) - ] - - # The y-axis is inverted for AndroidEnv, so bottom = top + height. - bottom, right = top + height, left + width - - return jnp.logical_and(y >= top, y <= bottom) & jnp.logical_and( - x >= left, x <= right) - - -def _resize_annotation_bounding_boxes( - annotation_positions, annotation_width_augment_fraction, - annotation_height_augment_fraction): - """Resize the bounding boxes by the given fractions. - - Args: - annotation_positions: Array of shape (N, 4), where each row represents the - (y, x, height, width) of the bounding boxes. - annotation_width_augment_fraction: The fraction to augment the box widths, - E.g., 1.4 == 240% total increase. - annotation_height_augment_fraction: Same as described for width, but for box - height. - - Returns: - Resized bounding box. - - """ - height_change = ( - annotation_height_augment_fraction * annotation_positions[:, 2]) - width_change = ( - annotation_width_augment_fraction * annotation_positions[:, 3]) - - # Limit bounding box positions to the screen. - resized_annotations = jnp.stack([ - jnp.maximum(0, annotation_positions[:, 0] - (height_change / 2)), - jnp.maximum(0, annotation_positions[:, 1] - (width_change / 2)), - jnp.minimum(1, annotation_positions[:, 2] + height_change), - jnp.minimum(1, annotation_positions[:, 3] + width_change), - ], - axis=1) - return resized_annotations - - -def is_tap_action(normalized_start_yx, - normalized_end_yx): - distance = jnp.linalg.norm( - jnp.array(normalized_start_yx) - jnp.array(normalized_end_yx)) - return distance <= _SWIPE_DISTANCE_THRESHOLD - - -def _is_non_dual_point_action(action_type): - return jnp.not_equal(action_type, ActionType.DUAL_POINT) - - -def _check_tap_actions_match( - tap_1_yx, - tap_2_yx, - annotation_positions, - matching_tap_distance_threshold_screen_percentage, - annotation_width_augment_fraction, - annotation_height_augment_fraction, -): - """Determines if two tap actions are the same.""" - resized_annotation_positions = _resize_annotation_bounding_boxes( - annotation_positions, - annotation_width_augment_fraction, - annotation_height_augment_fraction, - ) - - # Check if the ground truth tap action falls in an annotation's bounding box. - tap1_in_box = _yx_in_bounding_boxes(tap_1_yx, resized_annotation_positions) - tap2_in_box = _yx_in_bounding_boxes(tap_2_yx, resized_annotation_positions) - both_in_box = jnp.max(tap1_in_box & tap2_in_box) - - # If the ground-truth tap action falls outside any of the annotation - # bounding boxes or one of the actions is inside a bounding box and the other - # is outside bounding box or vice versa, compare the points using Euclidean - # distance. - within_threshold = ( - jnp.linalg.norm(jnp.array(tap_1_yx) - jnp.array(tap_2_yx)) - <= matching_tap_distance_threshold_screen_percentage - ) - return jnp.logical_or(both_in_box, within_threshold) - - -def _check_drag_actions_match( - drag_1_touch_yx, - drag_1_lift_yx, - drag_2_touch_yx, - drag_2_lift_yx, -): - """Determines if two drag actions are the same.""" - # Store drag deltas (the change in the y and x coordinates from touch to - # lift), magnitudes, and the index of the main axis, which is the axis with - # the greatest change in coordinate value (e.g. a drag starting at (0, 0) and - # ending at (0.3, 0.5) has a main axis index of 1). - drag_1_deltas = drag_1_lift_yx - drag_1_touch_yx - drag_1_magnitudes = jnp.abs(drag_1_deltas) - drag_1_main_axis = np.argmax(drag_1_magnitudes) - drag_2_deltas = drag_2_lift_yx - drag_2_touch_yx - drag_2_magnitudes = jnp.abs(drag_2_deltas) - drag_2_main_axis = np.argmax(drag_2_magnitudes) - - return jnp.equal(drag_1_main_axis, drag_2_main_axis) - - -def check_actions_match( - action_1_touch_yx, - action_1_lift_yx, - action_1_action_type, - action_2_touch_yx, - action_2_lift_yx, - action_2_action_type, - annotation_positions, - tap_distance_threshold = _TAP_DISTANCE_THRESHOLD, - annotation_width_augment_fraction = ANNOTATION_WIDTH_AUGMENT_FRACTION, - annotation_height_augment_fraction = ANNOTATION_HEIGHT_AUGMENT_FRACTION, -): - """Determines if two actions are considered to be the same. - - Two actions being "the same" is defined here as two actions that would result - in a similar screen state. - - Args: - action_1_touch_yx: The (y, x) coordinates of the first action's touch. - action_1_lift_yx: The (y, x) coordinates of the first action's lift. - action_1_action_type: The action type of the first action. - action_2_touch_yx: The (y, x) coordinates of the second action's touch. - action_2_lift_yx: The (y, x) coordinates of the second action's lift. - action_2_action_type: The action type of the second action. - annotation_positions: The positions of the UI annotations for the screen. It - is A 2D int array of shape (num_bboxes, 4), where each row represents a - bounding box: (y_top_left, x_top_left, box_height, box_width). Note that - containment is inclusive of the bounding box edges. - tap_distance_threshold: The threshold that determines if two taps result in - a matching screen state if they don't fall the same bounding boxes. - annotation_width_augment_fraction: The fraction to increase the width of the - bounding box by. - annotation_height_augment_fraction: The fraction to increase the height of - of the bounding box by. - - Returns: - A boolean representing whether the two given actions are the same or not. - """ - action_1_touch_yx = jnp.asarray(action_1_touch_yx) - action_1_lift_yx = jnp.asarray(action_1_lift_yx) - action_2_touch_yx = jnp.asarray(action_2_touch_yx) - action_2_lift_yx = jnp.asarray(action_2_lift_yx) - - # Checks if at least one of the actions is global (i.e. not DUAL_POINT), - # because if that is the case, only the actions' types need to be compared. - has_non_dual_point_action = jnp.logical_or( - _is_non_dual_point_action(action_1_action_type), - _is_non_dual_point_action(action_2_action_type), - ) - #print("non dual point: "+str(has_non_dual_point_action)) - - different_dual_point_types = jnp.logical_xor( - is_tap_action(action_1_touch_yx, action_1_lift_yx), - is_tap_action(action_2_touch_yx, action_2_lift_yx), - ) - #print("different dual type: "+str(different_dual_point_types)) - - is_tap = jnp.logical_and( - is_tap_action(action_1_touch_yx, action_1_lift_yx), - is_tap_action(action_2_touch_yx, action_2_lift_yx), - ) - #print("is tap: "+str(is_tap)) - - taps_match = _check_tap_actions_match( - action_1_touch_yx, - action_2_touch_yx, - annotation_positions, - tap_distance_threshold, - annotation_width_augment_fraction, - annotation_height_augment_fraction, - ) - #print("tap match: "+str(taps_match)) - - taps_match = jnp.logical_and(is_tap, taps_match) - #print("tap match: "+str(taps_match)) - - drags_match = _check_drag_actions_match( - action_1_touch_yx, action_1_lift_yx, action_2_touch_yx, action_2_lift_yx - ) - drags_match = jnp.where(is_tap, False, drags_match) - #print("drag match: "+str(drags_match)) - - return jnp.where( - has_non_dual_point_action, - jnp.equal(action_1_action_type, action_2_action_type), - jnp.where( - different_dual_point_types, - False, - jnp.logical_or(taps_match, drags_match), - ), - ) - - -def action_2_format(step_data): - # ๆŠŠtestๆ•ฐๆฎ้›†ไธญ็š„ๅŠจไฝœๆ ผๅผ่ฝฌๆขไธบ่ฎก็ฎ—matching score็š„ๆ ผๅผ - action_type = step_data["action_type_id"] - - if action_type == 4: - if step_data["action_type_text"] == 'click': # ็‚นๅ‡ป - touch_point = step_data["touch"] - lift_point = step_data["lift"] - else: # ไธŠไธ‹ๅทฆๅณๆป‘ๅŠจ - if step_data["action_type_text"] == 'scroll down': - touch_point = [0.5, 0.8] - lift_point = [0.5, 0.2] - elif step_data["action_type_text"] == 'scroll up': - touch_point = [0.5, 0.2] - lift_point = [0.5, 0.8] - elif step_data["action_type_text"] == 'scroll left': - touch_point = [0.2, 0.5] - lift_point = [0.8, 0.5] - elif step_data["action_type_text"] == 'scroll right': - touch_point = [0.8, 0.5] - lift_point = [0.2, 0.5] - else: - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - - if action_type == 3: - typed_text = step_data["type_text"] - else: - typed_text = "" - - action = {"action_type": action_type, "touch_point": touch_point, "lift_point": lift_point, - "typed_text": typed_text} - - action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] - action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] - action["typed_text"] = action["typed_text"].lower() - - return action - - -def pred_2_format(step_data): - # ๆŠŠๆจกๅž‹่พ“ๅ‡บ็š„ๅ†…ๅฎน่ฝฌๆขไธบ่ฎก็ฎ—action_matching็š„ๆ ผๅผ - action_type = step_data["action_type"] - - if action_type == 4: # ็‚นๅ‡ป - action_type_new = 4 - touch_point = step_data["click_point"] - lift_point = step_data["click_point"] - typed_text = "" - elif action_type == 0: - action_type_new = 4 - touch_point = [0.5, 0.8] - lift_point = [0.5, 0.2] - typed_text = "" - elif action_type == 1: - action_type_new = 4 - touch_point = [0.5, 0.2] - lift_point = [0.5, 0.8] - typed_text = "" - elif action_type == 8: - action_type_new = 4 - touch_point = [0.2, 0.5] - lift_point = [0.8, 0.5] - typed_text = "" - elif action_type == 9: - action_type_new = 4 - touch_point = [0.8, 0.5] - lift_point = [0.2, 0.5] - typed_text = "" - else: - action_type_new = action_type - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = "" - if action_type_new == 3: - typed_text = step_data["typed_text"] - - action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point, - "typed_text": typed_text} - - action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] - action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] - action["typed_text"] = action["typed_text"].lower() - - return action - - -def pred_2_format_simplified(step_data): - # ๆŠŠๆจกๅž‹่พ“ๅ‡บ็š„ๅ†…ๅฎน่ฝฌๆขไธบ่ฎก็ฎ—action_matching็š„ๆ ผๅผ - action_type = step_data["action_type"] - - if action_type == 'click' : # ็‚นๅ‡ป - action_type_new = 4 - touch_point = step_data["click_point"] - lift_point = step_data["click_point"] - typed_text = "" - elif action_type == 'scroll' and step_data["direction"] == 'down': - action_type_new = 4 - touch_point = [0.5, 0.8] - lift_point = [0.5, 0.2] - typed_text = "" - elif action_type == 'scroll' and step_data["direction"] == 'up': - action_type_new = 4 - touch_point = [0.5, 0.2] - lift_point = [0.5, 0.8] - typed_text = "" - elif action_type == 'scroll' and step_data["direction"] == 'left': - action_type_new = 4 - touch_point = [0.2, 0.5] - lift_point = [0.8, 0.5] - typed_text = "" - elif action_type == 'scroll' and step_data["direction"] == 'right': - action_type_new = 4 - touch_point = [0.8, 0.5] - lift_point = [0.2, 0.5] - typed_text = "" - elif action_type == 'type': - action_type_new = 3 - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = step_data["text"] - elif action_type == 'navigate_back': - action_type_new = 5 - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = "" - elif action_type == 'navigate_home': - action_type_new = 6 - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = "" - else: - action_type_new = action_type - touch_point = [-1.0, -1.0] - lift_point = [-1.0, -1.0] - typed_text = "" - # if action_type_new == 'type': - # typed_text = step_data["text"] - - action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point, - "typed_text": typed_text} - - action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]] - action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]] - action["typed_text"] = action["typed_text"].lower() - - return action \ No newline at end of file diff --git a/util/action_type.py b/util/action_type.py deleted file mode 100644 index 2e2b06d..0000000 --- a/util/action_type.py +++ /dev/null @@ -1,45 +0,0 @@ -''' -Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild -''' - -import enum - -class ActionType(enum.IntEnum): - - # Placeholders for unused enum values - UNUSED_0 = 0 - UNUSED_1 = 1 - UNUSED_2 = 2 - UNUSED_8 = 8 - UNUSED_9 = 9 - - ########### Agent actions ########### - - # A type action that sends text to the emulator. Note that this simply sends - # text and does not perform any clicks for element focus or enter presses for - # submitting text. - TYPE = 3 - - # The dual point action used to represent all gestures. - DUAL_POINT = 4 - - # These actions differentiate pressing the home and back button from touches. - # They represent explicit presses of back and home performed using ADB. - PRESS_BACK = 5 - PRESS_HOME = 6 - - # An action representing that ADB command for hitting enter was performed. - PRESS_ENTER = 7 - - ########### Episode status actions ########### - - # An action used to indicate the desired task has been completed and resets - # the environment. This action should also be used in the case that the task - # has already been completed and there is nothing to do. - # e.g. The task is to turn on the Wi-Fi when it is already on - STATUS_TASK_COMPLETE = 10 - - # An action used to indicate that desired task is impossible to complete and - # resets the environment. This can be a result of many different things - # including UI changes, Android version differences, etc. - STATUS_TASK_IMPOSSIBLE = 11 \ No newline at end of file diff --git a/utils.py b/util/utils.py old mode 100755 new mode 100644 similarity index 100% rename from utils.py rename to util/utils.py From 41464ccf1c1bc0c8682a6f67df7767e8b2f3e69c Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Thu, 30 Jan 2025 06:18:24 +0000 Subject: [PATCH 24/57] Fix import path issues --- demo.ipynb | 27 +++++++------------ ...{remote_request.py => omniparserserver.py} | 11 ++++---- {demo/omniparserserver => util}/omniparser.py | 7 +++-- 3 files changed, 19 insertions(+), 26 deletions(-) rename demo/omniparserserver/{remote_request.py => omniparserserver.py} (75%) rename {demo/omniparserserver => util}/omniparser.py (74%) diff --git a/demo.ipynb b/demo.ipynb index 7489fbe..4be80a2 100644 --- a/demo.ipynb +++ b/demo.ipynb @@ -14,7 +14,7 @@ } ], "source": [ - "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", + "from util.utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", "import torch\n", "from ultralytics import YOLO\n", "from PIL import Image\n", @@ -48,9 +48,9 @@ "source": [ "# two choices for caption model: fine-tuned blip2 or florence2\n", "import importlib\n", - "import utils\n", - "importlib.reload(utils)\n", - "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", + "import util.utils\n", + "importlib.reload(util.utils)\n", + "from util.utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", "# caption_model_processor = get_caption_model_processor(model_name=\"blip2\", model_name_or_path=\"weights/icon_caption_blip2\", device=device)\n", "caption_model_processor = get_caption_model_processor(model_name=\"florence2\", model_name_or_path=\"weights/icon_caption_florence\", device=device)\n", "\n" @@ -102,9 +102,9 @@ "source": [ "# reload utils\n", "import importlib\n", - "import utils\n", - "importlib.reload(utils)\n", - "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", + "import util.utils\n", + "importlib.reload(util.utils)\n", + "from util.utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", "\n", "image_path = 'imgs/google_page.png'\n", "image_path = 'imgs/windows_home.png'\n", @@ -167,9 +167,9 @@ "# run on cpu!!!\n", "# reload utils\n", "import importlib\n", - "import utils\n", - "importlib.reload(utils)\n", - "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", + "import util.utils\n", + "importlib.reload(util.utils)\n", + "from util.utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n", "\n", "image_path = 'imgs/google_page.png'\n", "image_path = 'imgs/windows_home.png'\n", @@ -447,13 +447,6 @@ "source": [ "parsed_content_list[-1]" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/demo/omniparserserver/remote_request.py b/demo/omniparserserver/omniparserserver.py similarity index 75% rename from demo/omniparserserver/remote_request.py rename to demo/omniparserserver/omniparserserver.py index 816940a..4cc7030 100644 --- a/demo/omniparserserver/remote_request.py +++ b/demo/omniparserserver/omniparserserver.py @@ -1,22 +1,23 @@ ''' -python -m remote_request --som_model_path ../weights/icon_detect_v1_5/model_v1_5.pt --caption_model_name florence2 --caption_model_path ../weights/icon_caption_florence --device cuda --BOX_TRESHOLD 0.05 +python -m omniparserserver --som_model_path ../../weights/icon_detect_v1_5/model_v1_5.pt --caption_model_name florence2 --caption_model_path ../../weights/icon_caption_florence --device cuda --BOX_TRESHOLD 0.05 ''' import sys import os -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import time from fastapi import FastAPI from pydantic import BaseModel import argparse import uvicorn -from omniparser import Omniparser +root_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(root_dir) +from util.omniparser import Omniparser def parse_arguments(): parser = argparse.ArgumentParser(description='Omniparser API') - parser.add_argument('--som_model_path', type=str, default='../weights/icon_detect_v1_5/model_v1_5.pt', help='Path to the som model') + parser.add_argument('--som_model_path', type=str, default='../../weights/icon_detect_v1_5/model_v1_5.pt', help='Path to the som model') parser.add_argument('--caption_model_name', type=str, default='florence2', help='Name of the caption model') - parser.add_argument('--caption_model_path', type=str, default='../weights/icon_caption_florence', help='Path to the caption model') + parser.add_argument('--caption_model_path', type=str, default='../../weights/icon_caption_florence', help='Path to the caption model') parser.add_argument('--device', type=str, default='cpu', help='Device to run the model') parser.add_argument('--BOX_TRESHOLD', type=float, default=0.05, help='Threshold for box detection') parser.add_argument('--host', type=str, default='0.0.0.0', help='Host for the API') diff --git a/demo/omniparserserver/omniparser.py b/util/omniparser.py similarity index 74% rename from demo/omniparserserver/omniparser.py rename to util/omniparser.py index 424430f..536385e 100644 --- a/demo/omniparserserver/omniparser.py +++ b/util/omniparser.py @@ -1,9 +1,9 @@ -from ...util.utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box +from util.utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box import torch from PIL import Image import io import base64 - +from typing import Dict class Omniparser(object): def __init__(self, config: Dict): self.config = config @@ -25,9 +25,8 @@ class Omniparser(object): 'text_padding': max(int(3 * box_overlay_ratio), 1), 'thickness': max(int(3 * box_overlay_ratio), 1), } - BOX_TRESHOLD = self.config['BOX_TRESHOLD'] (text, ocr_bbox), _ = check_ocr_box(image, display_img=False, output_bb_format='xyxy', easyocr_args={'text_threshold': 0.8}, use_paddleocr=False) - dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) + dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = self.config['BOX_TRESHOLD'], output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) return dino_labled_img, parsed_content_list \ No newline at end of file From 17d02bc8c0ef41d991583ab79227ee963982dad5 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Wed, 29 Jan 2025 22:23:35 -0800 Subject: [PATCH 25/57] Fix gradio imports --- .../computer_use_demo/agent/llm_utils/omniparserclient.py | 2 +- demo/gradio/computer_use_demo/agent/vlm_agent.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py b/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py index 7b44069..010e82a 100644 --- a/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py +++ b/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py @@ -2,7 +2,7 @@ import requests import base64 from pathlib import Path from computer_use_demo.tools.screen_capture import get_screenshot -from computer_use_demo.gui_agent.llm_utils.oai import encode_image +from computer_use_demo.agent.llm_utils.oai import encode_image OUTPUT_DIR = "./tmp/outputs" diff --git a/demo/gradio/computer_use_demo/agent/vlm_agent.py b/demo/gradio/computer_use_demo/agent/vlm_agent.py index 0ce905a..58e243e 100644 --- a/demo/gradio/computer_use_demo/agent/vlm_agent.py +++ b/demo/gradio/computer_use_demo/agent/vlm_agent.py @@ -10,7 +10,7 @@ from anthropic import APIResponse from anthropic.types import ToolResultBlockParam from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage -from computer_use_demo.gui_agent.llm_utils.oai import run_oai_interleaved +from computer_use_demo.agent.llm_utils.oai import run_oai_interleaved from computer_use_demo.colorful_text import colorful_text_vlm import time import re From 746507b9d9832337ee4fd506a6017e1cfbc4d6de Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Wed, 29 Jan 2025 22:39:25 -0800 Subject: [PATCH 26/57] Rename folder names --- computer_use_demo/gradio/__init__.py | 0 .../gradio/agent/anthropic_agent.py | 182 +++++++++ .../gradio/agent/llm_utils/oai.py | 107 +++++ .../agent/llm_utils/omniparserclient.py | 44 +++ computer_use_demo/gradio/agent/vlm_agent.py | 302 ++++++++++++++ computer_use_demo/gradio/app.py | 374 ++++++++++++++++++ computer_use_demo/gradio/colorful_text.py | 17 + .../gradio/executor/anthropic_executor.py | 132 +++++++ computer_use_demo/gradio/loop.py | 129 ++++++ computer_use_demo/gradio/tools/__init__.py | 12 + computer_use_demo/gradio/tools/base.py | 69 ++++ computer_use_demo/gradio/tools/collection.py | 34 ++ computer_use_demo/gradio/tools/computer.py | 324 +++++++++++++++ .../gradio/tools/screen_capture.py | 29 ++ 14 files changed, 1755 insertions(+) create mode 100644 computer_use_demo/gradio/__init__.py create mode 100644 computer_use_demo/gradio/agent/anthropic_agent.py create mode 100644 computer_use_demo/gradio/agent/llm_utils/oai.py create mode 100644 computer_use_demo/gradio/agent/llm_utils/omniparserclient.py create mode 100644 computer_use_demo/gradio/agent/vlm_agent.py create mode 100644 computer_use_demo/gradio/app.py create mode 100644 computer_use_demo/gradio/colorful_text.py create mode 100644 computer_use_demo/gradio/executor/anthropic_executor.py create mode 100644 computer_use_demo/gradio/loop.py create mode 100644 computer_use_demo/gradio/tools/__init__.py create mode 100644 computer_use_demo/gradio/tools/base.py create mode 100644 computer_use_demo/gradio/tools/collection.py create mode 100644 computer_use_demo/gradio/tools/computer.py create mode 100644 computer_use_demo/gradio/tools/screen_capture.py diff --git a/computer_use_demo/gradio/__init__.py b/computer_use_demo/gradio/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/computer_use_demo/gradio/agent/anthropic_agent.py b/computer_use_demo/gradio/agent/anthropic_agent.py new file mode 100644 index 0000000..d891f1f --- /dev/null +++ b/computer_use_demo/gradio/agent/anthropic_agent.py @@ -0,0 +1,182 @@ +""" +Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. +""" +import asyncio +import platform +from collections.abc import Callable +from datetime import datetime +from enum import StrEnum +from typing import Any, cast + +from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse +from anthropic.types import ( + ToolResultBlockParam, +) +from anthropic.types.beta import ( + BetaContentBlock, + BetaContentBlockParam, + BetaImageBlockParam, + BetaMessage, + BetaMessageParam, + BetaTextBlockParam, + BetaToolResultBlockParam, +) +from anthropic.types import TextBlock +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock + +from tools import ComputerTool, ToolCollection, ToolResult + +from PIL import Image +from io import BytesIO +import gradio as gr +from typing import Dict + +BETA_FLAG = "computer-use-2024-10-22" + +class APIProvider(StrEnum): + ANTHROPIC = "anthropic" + BEDROCK = "bedrock" + VERTEX = "vertex" + +SYSTEM_PROMPT = f""" +* You are utilizing a Windows system with internet access. +* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. + +""" + +class AnthropicActor: + def __init__( + self, + model: str, + provider: APIProvider, + api_key: str, + api_response_callback: Callable[[APIResponse[BetaMessage]], None], + max_tokens: int = 4096, + only_n_most_recent_images: int | None = None, + print_usage: bool = True, + ): + self.model = model + self.provider = provider + self.api_key = api_key + self.api_response_callback = api_response_callback + self.max_tokens = max_tokens + self.only_n_most_recent_images = only_n_most_recent_images + + self.tool_collection = ToolCollection(ComputerTool()) + + self.system = SYSTEM_PROMPT + + self.total_token_usage = 0 + self.total_cost = 0 + self.print_usage = print_usage + + # Instantiate the appropriate API client based on the provider + if provider == APIProvider.ANTHROPIC: + self.client = Anthropic(api_key=api_key) + elif provider == APIProvider.VERTEX: + self.client = AnthropicVertex() + elif provider == APIProvider.BEDROCK: + self.client = AnthropicBedrock() + + def __call__( + self, + *, + messages: list[BetaMessageParam] + ): + """ + Generate a response given history messages. + """ + if self.only_n_most_recent_images: + _maybe_filter_to_n_most_recent_images(messages, self.only_n_most_recent_images) + + # Call the API synchronously + raw_response = self.client.beta.messages.with_raw_response.create( + max_tokens=self.max_tokens, + messages=messages, + model=self.model, + system=self.system, + tools=self.tool_collection.to_params(), + betas=["computer-use-2024-10-22"], + ) + + self.api_response_callback(cast(APIResponse[BetaMessage], raw_response)) + + response = raw_response.parse() + print(f"AnthropicActor response: {response}") + + self.total_token_usage += response.usage.input_tokens + response.usage.output_tokens + self.total_cost += (response.usage.input_tokens * 3 / 1000000 + response.usage.output_tokens * 15 / 1000000) + + if self.print_usage: + print(f"Claude total token usage so far: {self.total_token_usage}, total cost so far: $USD{self.total_cost}") + + return response + + +def _maybe_filter_to_n_most_recent_images( + messages: list[BetaMessageParam], + images_to_keep: int, + min_removal_threshold: int = 10, +): + """ + With the assumption that images are screenshots that are of diminishing value as + the conversation progresses, remove all but the final `images_to_keep` tool_result + images in place, with a chunk of min_removal_threshold to reduce the amount we + break the implicit prompt cache. + """ + if images_to_keep is None: + return messages + + tool_result_blocks = cast( + list[ToolResultBlockParam], + [ + item + for message in messages + for item in ( + message["content"] if isinstance(message["content"], list) else [] + ) + if isinstance(item, dict) and item.get("type") == "tool_result" + ], + ) + + total_images = sum( + 1 + for tool_result in tool_result_blocks + for content in tool_result.get("content", []) + if isinstance(content, dict) and content.get("type") == "image" + ) + + images_to_remove = total_images - images_to_keep + # for better cache behavior, we want to remove in chunks + images_to_remove -= images_to_remove % min_removal_threshold + + for tool_result in tool_result_blocks: + if isinstance(tool_result.get("content"), list): + new_content = [] + for content in tool_result.get("content", []): + if isinstance(content, dict) and content.get("type") == "image": + if images_to_remove > 0: + images_to_remove -= 1 + continue + new_content.append(content) + tool_result["content"] = new_content + + + +if __name__ == "__main__": + pass + # client = Anthropic(api_key="") + # response = client.beta.messages.with_raw_response.create( + # max_tokens=4096, + # model="claude-3-5-sonnet-20241022", + # system=SYSTEM_PROMPT, + # # tools=ToolCollection( + # # ComputerTool(), + # # ).to_params(), + # betas=["computer-use-2024-10-22"], + # messages=[ + # {"role": "user", "content": "click on (199, 199)."} + # ], + # ) + + # print(f"AnthropicActor response: {response.parse().usage.input_tokens+response.parse().usage.output_tokens}") \ No newline at end of file diff --git a/computer_use_demo/gradio/agent/llm_utils/oai.py b/computer_use_demo/gradio/agent/llm_utils/oai.py new file mode 100644 index 0000000..0ab3760 --- /dev/null +++ b/computer_use_demo/gradio/agent/llm_utils/oai.py @@ -0,0 +1,107 @@ + +import os +import logging +import base64 +import requests + +def is_image_path(text): + # Checking if the input text ends with typical image file extensions + image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") + if text.endswith(image_extensions): + return True + else: + return False + +def encode_image(image_path): + """Encode image file to base64.""" + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + +def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): + + api_key = api_key or os.environ.get("OPENAI_API_KEY") + if not api_key: + raise ValueError("OPENAI_API_KEY is not set") + + headers = {"Content-Type": "application/json", + "Authorization": f"Bearer {api_key}"} + + final_messages = [{"role": "system", "content": system}] + + # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + if type(messages) == list: + for item in messages: + contents = [] + if isinstance(item, dict): + for cnt in item["content"]: + if isinstance(cnt, str): + if is_image_path(cnt): + base64_image = encode_image(cnt) + content = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}} + else: + content = {"type": "text", "text": cnt} + else: + # in this case it is a text block from anthropic + content = {"type": "text", "text": str(cnt)} + + contents.append(content) + + message = {"role": 'user', "content": contents} + else: # str + contents.append({"type": "text", "text": item}) + message = {"role": "user", "content": contents} + + final_messages.append(message) + + + elif isinstance(messages, str): + final_messages = [{"role": "user", "content": messages}] + # import pdb; pdb.set_trace() + + print("[oai] sending messages:", {"role": "user", "content": messages}) + + payload = { + "model": llm, + "messages": final_messages, + "max_tokens": max_tokens, + "temperature": temperature, + # "stop": stop, + } + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + + response = requests.post( + "https://api.openai.com/v1/chat/completions", headers=headers, json=payload + ) + + try: + text = response.json()['choices'][0]['message']['content'] + token_usage = int(response.json()['usage']['total_tokens']) + return text, token_usage + + # return error message if the response is not successful + except Exception as e: + print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") + return response.json() + + +if __name__ == "__main__": + + api_key = os.environ.get("OPENAI_API_KEY") + if not api_key: + raise ValueError("OPENAI_API_KEY is not set") + + text, token_usage = run_oai_interleaved( + messages= [{"content": [ + "What is in the screenshot?", + "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"], + "role": "user" + }], + llm="gpt-4o-mini", + system="You are a helpful assistant", + api_key=api_key, + max_tokens=256, + temperature=0) + + print(text, token_usage) + # There is an introduction describing the Calyx... 36986 diff --git a/computer_use_demo/gradio/agent/llm_utils/omniparserclient.py b/computer_use_demo/gradio/agent/llm_utils/omniparserclient.py new file mode 100644 index 0000000..c29eb58 --- /dev/null +++ b/computer_use_demo/gradio/agent/llm_utils/omniparserclient.py @@ -0,0 +1,44 @@ +import requests +import base64 +from pathlib import Path +from tools.screen_capture import get_screenshot +from agent.llm_utils.oai import encode_image + +OUTPUT_DIR = "./tmp/outputs" + +class OmniParserClient: + def __init__(self, + url: str) -> None: + self.url = url + + def __call__(self,): + screenshot, screenshot_path = get_screenshot() + screenshot_path = str(screenshot_path) + image_base64 = encode_image(screenshot_path) + response = requests.post(self.url, json={"base64_image": image_base64}) + response_json = response.json() + print('omniparser latency:', response_json['latency']) + + som_image_data = base64.b64decode(response_json['som_image_base64']) + screenshot_path_uuid = Path(screenshot_path).stem.replace("screenshot_", "") + som_screenshot_path = f"{OUTPUT_DIR}/screenshot_som_{screenshot_path_uuid}.png" + with open(som_screenshot_path, "wb") as f: + f.write(som_image_data) + + response_json['width'] = screenshot.size[0] + response_json['height'] = screenshot.size[1] + response_json['original_screenshot_base64'] = image_base64 + response_json['screenshot_uuid'] = screenshot_path_uuid + response_json = self.reformat_messages(response_json) + return response_json + + def reformat_messages(self, response_json: dict): + screen_info = "" + for idx, element in enumerate(response_json["parsed_content_list"]): + element['idx'] = idx + if element['type'] == 'text': + screen_info += f'ID: {idx}, Text: {element["content"]}\n' + elif element['type'] == 'icon': + screen_info += f'ID: {idx}, Icon: {element["content"]}\n' + response_json['screen_info'] = screen_info + return response_json \ No newline at end of file diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py new file mode 100644 index 0000000..6703ee7 --- /dev/null +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -0,0 +1,302 @@ +import json +from collections.abc import Callable +from typing import cast, Callable +import uuid +from PIL import Image, ImageDraw +import base64 +from io import BytesIO + +from anthropic import APIResponse +from anthropic.types import ToolResultBlockParam +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage + +from agent.llm_utils.oai import run_oai_interleaved +from colorful_text import colorful_text_vlm +import time +import re + +OUTPUT_DIR = "./tmp/outputs" + +def extract_data(input_string, data_type): + # Regular expression to extract content starting from '```python' until the end if there are no closing backticks + pattern = f"```{data_type}" + r"(.*?)(```|$)" + # Extract content + # re.DOTALL allows '.' to match newlines as well + matches = re.findall(pattern, input_string, re.DOTALL) + # Return the first match if exists, trimming whitespace and ignoring potential closing backticks + return matches[0][0].strip() if matches else input_string + +class VLMAgent: + def __init__( + self, + model: str, + provider: str, + api_key: str, + output_callback: Callable, + api_response_callback: Callable, + max_tokens: int = 4096, + only_n_most_recent_images: int | None = None, + print_usage: bool = True, + ): + if model == "omniparser + gpt-4o": + self.model = "gpt-4o-2024-11-20" + else: + raise ValueError(f"Model {model} not supported") + + self.provider = provider + self.api_key = api_key + self.api_response_callback = api_response_callback + self.max_tokens = max_tokens + self.only_n_most_recent_images = only_n_most_recent_images + self.output_callback = output_callback + + self.print_usage = print_usage + self.total_token_usage = 0 + self.total_cost = 0 + + self.system = '' + + def __call__(self, messages: list, parsed_screen: list[str, list, dict]): + # Show results of Omniparser + image_base64 = parsed_screen['original_screenshot_base64'] + latency_omniparser = parsed_screen['latency'] + self.output_callback(f'Screenshot for {colorful_text_vlm}:\n', + sender="bot") + self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n', sender="bot") + screen_info = str(parsed_screen['screen_info']) + # self.output_callback(f'Screen Info for {colorful_text_vlm}:\n{screen_info}', sender="bot") + self.output_callback( + f'
' + f' Screen Info for {colorful_text_vlm}' + f' 
{screen_info}
' + f'
', + sender="bot" + ) + + + screenshot_uuid = parsed_screen['screenshot_uuid'] + screen_width, screen_height = parsed_screen['width'], parsed_screen['height'] + + # example parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} + boxids_and_labels = parsed_screen["screen_info"] + system = self._get_system_prompt(boxids_and_labels) + + # drop looping actions msg, byte image etc + planner_messages = messages + # import pdb; pdb.set_trace() + planner_messages = _keep_latest_images(planner_messages) + # if self.only_n_most_recent_images: + # _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) + # print(f"filtered_messages: {planner_messages}\n\n", "full messages:", messages) + + if isinstance(planner_messages[-1], dict): + if not isinstance(planner_messages[-1]["content"], list): + planner_messages[-1]["content"] = [planner_messages[-1]["content"]] + planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_{screenshot_uuid}.png") + planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_som_{screenshot_uuid}.png") + + # print(f"Sending messages to VLMPlanner : {planner_messages}") + start = time.time() + if "gpt" in self.model: + vlm_response, token_usage = run_oai_interleaved( + messages=planner_messages, + system=system, + llm=self.model, + api_key=self.api_key, + max_tokens=self.max_tokens, + temperature=0, + ) + print(f"oai token usage: {token_usage}") + self.total_token_usage += token_usage + self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ + elif "phi" in self.model: + pass # TODO + else: + raise ValueError(f"Model {self.model} not supported") + latency_vlm = time.time() - start + self.output_callback(f"VLMPlanner latency: {latency_vlm}, Omniparser latency: {latency_omniparser}", sender="bot") + + print(f"VLMPlanner response: {vlm_response}") + + if self.print_usage: + print(f"VLMPlanner total token usage so far: {self.total_token_usage}. Total cost so far: $USD{self.total_cost:.5f}") + + vlm_response_json = extract_data(vlm_response, "json") + vlm_response_json = json.loads(vlm_response_json) + + # map "box_id" to "idx" in parsed_screen, and output the xy coordinate of bbox + try: + bbox = parsed_screen["parsed_content_list"][int(vlm_response_json["Box ID"])]["bbox"] + vlm_response_json["box_centroid_coordinate"] = [int((bbox[0] + bbox[2]) / 2 * screen_width), int((bbox[1] + bbox[3]) / 2 * screen_height)] + # draw a circle on the screenshot image to indicate the action + self.draw_action(vlm_response_json, image_base64) + except: + print("No Box ID in the response.") + + # Convert the VLM output to a string for printing in chat + vlm_plan_str = "" + for key, value in vlm_response_json.items(): + if key == "Reasoning": + vlm_plan_str += f'{value}' + else: + vlm_plan_str += f'\n{key}: {value}' + # self.output_callback(f"{colorful_text_vlm}:\n{vlm_plan_str}", sender="bot") + + # construct the response so that anthropicExcutor can execute the tool + response_content = [BetaTextBlock(text=vlm_plan_str, type='text')] + if 'box_centroid_coordinate' in vlm_response_json: + move_cursor_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': 'mouse_move', 'coordinate': vlm_response_json["box_centroid_coordinate"]}, + name='computer', type='tool_use') + response_content.append(move_cursor_block) + if vlm_response_json["Next Action"] == "type": + click_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': 'left_click'}, name='computer', type='tool_use') + sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': vlm_response_json["Next Action"], 'text': vlm_response_json["value"]}, + name='computer', type='tool_use') + response_content.extend([click_block, sim_content_block]) + elif vlm_response_json["Next Action"] == "None": + print("Task paused/completed.") + else: + sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', + input={'action': vlm_response_json["Next Action"]}, + name='computer', type='tool_use') + response_content.append(sim_content_block) + response_message = BetaMessage(id=f'toolu_{uuid.uuid4()}', content=response_content, model='', role='assistant', type='message', stop_reason='tool_use', usage=BetaUsage(input_tokens=0, output_tokens=0)) + return response_message, vlm_response_json + + def _api_response_callback(self, response: APIResponse): + self.api_response_callback(response) + + def _get_system_prompt(self, screen_info: str = ""): + return f""" +You are using a Windows device. +You are able to use a mouse and keyboard to interact with the computer based on the given task and screenshot. +You can only interact with the desktop GUI (no terminal or application menu access). + +You may be given some history plan and actions, this is the response from the previous loop. +You should carefully consider your plan base on the task, screenshot, and history actions. + +Here is the list of all detected bounding boxes by IDs on the screen and their description:{screen_info} + +Your available "Next Action" only include: +- type: type a string of text. +- left_click: Describe the ui element to be clicked. +- double_click: Describe the ui element to be double clicked. +- right_click: Describe the ui element to be right clicked. +- escape: Press an ESCAPE key. +- hover: Describe the ui element to be hovered. +- scroll_up: Scroll the screen up. +- scroll_down: Scroll the screen down. +- press: Describe the ui element to be pressed. + +Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. + +Output format: +```json +{{ + "Reasoning": str, # describe what is in the current screen, taking into account the history, then describe your step-by-step thoughts on how to achieve the task, choose one action from available actions at a time. + "Next Action": "action_type, action description" | "None" # one action at a time, describe it in short and precisely. + 'Box ID': n, + 'value': "xxx" # if the action is type, you should provide the text to type. +}} +``` + +One Example: +```json +{{ + "Reasoning": "The current screen shows google result of amazon, in previous action I have searched amazon on google. Then I need to click on the first search results to go to amazon.com.", + "Next Action": "left_click", + 'Box ID': m, +}} +``` + +Another Example: +```json +{{ + "Reasoning": "The current screen shows the front page of amazon. There is no previous action. Therefore I need to type "Apple watch" in the search bar.", + "Next Action": "type", + 'Box ID': n, + 'value': "Apple watch" +}} +``` + +IMPORTANT NOTES: +1. You should only give a single action at a time. +2. You should give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. +3. Attach the next action prediction in the "Next Action". +4. You should not include other actions, such as keyboard shortcuts. +5. When the task is completed, you should say "Next Action": "None" in the json field. +""" + def draw_action(self, vlm_response_json, image_base64): + # draw a circle using the coordinate in parsed_screen['som_image_base64'] + image_data = base64.b64decode(image_base64) + image = Image.open(BytesIO(image_data)) + + draw = ImageDraw.Draw(image) + x, y = vlm_response_json["box_centroid_coordinate"] + radius = 30 + draw.ellipse((x - radius, y - radius, x + radius, y + radius), fill='red') + buffered = BytesIO() + image.save(buffered, format="PNG") + image_with_circle_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") + self.output_callback(f'Action performed on the red circle with centroid ({x}, {y}), for {colorful_text_vlm}:\n', sender="bot") + + +def _keep_latest_images(messages): + for i in range(len(messages)-1): + if isinstance(messages[i]["content"], list): + for cnt in messages[i]["content"]: + if isinstance(cnt, str): + if cnt.endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")): + messages[i]["content"].remove(cnt) + return messages + + +def _maybe_filter_to_n_most_recent_images( + messages: list[BetaMessageParam], + images_to_keep: int, + min_removal_threshold: int = 10, +): + """ + With the assumption that images are screenshots that are of diminishing value as + the conversation progresses, remove all but the final `images_to_keep` tool_result + images in place, with a chunk of min_removal_threshold to reduce the amount we + break the implicit prompt cache. + """ + if images_to_keep is None: + return messages + + tool_result_blocks = cast( + list[ToolResultBlockParam], + [ + item + for message in messages + for item in ( + message["content"] if isinstance(message["content"], list) else [] + ) + if isinstance(item, dict) and item.get("type") == "tool_result" + ], + ) + + total_images = sum( + 1 + for tool_result in tool_result_blocks + for content in tool_result.get("content", []) + if isinstance(content, dict) and content.get("type") == "image" + ) + + images_to_remove = total_images - images_to_keep + # for better cache behavior, we want to remove in chunks + images_to_remove -= images_to_remove % min_removal_threshold + + for tool_result in tool_result_blocks: + if isinstance(tool_result.get("content"), list): + new_content = [] + for content in tool_result.get("content", []): + if isinstance(content, dict) and content.get("type") == "image": + if images_to_remove > 0: + images_to_remove -= 1 + continue + new_content.append(content) + tool_result["content"] = new_content \ No newline at end of file diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py new file mode 100644 index 0000000..5e216c6 --- /dev/null +++ b/computer_use_demo/gradio/app.py @@ -0,0 +1,374 @@ +""" +Entrypoint for Gradio, see https://gradio.app/ +python app.py --windows_host_url xxxx:8006/ --omniparser_host_url localhost:8000 +""" + +import os +from datetime import datetime +from enum import StrEnum +from functools import partial +from pathlib import Path +from typing import cast +import argparse +import gradio as gr +from anthropic import APIResponse +from anthropic.types import TextBlock +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock +from anthropic.types.tool_use_block import ToolUseBlock +from loop import ( + APIProvider, + sampling_loop_sync, +) +from tools import ToolResult + +CONFIG_DIR = Path("~/.anthropic").expanduser() +API_KEY_FILE = CONFIG_DIR / "api_key" + +INTRO_TEXT = ''' +๐Ÿš€๐Ÿค–โœจ It's Play Time! + +Welcome to the OmniParser+X Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. + +Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history. +''' + +def parse_arguments(): + parser = argparse.ArgumentParser(description="Gradio App") + parser.add_argument("--windows_host_url", type=str, default='localhost:8006') + parser.add_argument("--omniparser_host_url", type=str, default="localhost:8000") + return parser.parse_args() +args = parse_arguments() +windows_host_url = args.windows_host_url +omniparser_host_url = args.omniparser_host_url +print(f"Windows host URL: {windows_host_url}") +print(f"OmniParser host URL: {omniparser_host_url}") + + +class Sender(StrEnum): + USER = "user" + BOT = "assistant" + TOOL = "tool" + + +def setup_state(state): + if "messages" not in state: + state["messages"] = [] + if "model" not in state: + state["model"] = "omniparser + gpt-4o" + if "provider" not in state: + state["provider"] = "openai" + if "openai_api_key" not in state: # Fetch API keys from environment variables + state["openai_api_key"] = os.getenv("OPENAI_API_KEY", "") + if "anthropic_api_key" not in state: + state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "") + if "api_key" not in state: + state["api_key"] = "" + if "auth_validated" not in state: + state["auth_validated"] = False + if "responses" not in state: + state["responses"] = {} + if "tools" not in state: + state["tools"] = {} + if "only_n_most_recent_images" not in state: + state["only_n_most_recent_images"] = 2 + if 'chatbot_messages' not in state: + state['chatbot_messages'] = [] + +async def main(state): + """Render loop for Gradio""" + setup_state(state) + return "Setup completed" + +def validate_auth(provider: APIProvider, api_key: str | None): + if provider == APIProvider.ANTHROPIC: + if not api_key: + return "Enter your Anthropic API key to continue." + if provider == APIProvider.BEDROCK: + import boto3 + + if not boto3.Session().get_credentials(): + return "You must have AWS credentials set up to use the Bedrock API." + if provider == APIProvider.VERTEX: + import google.auth + from google.auth.exceptions import DefaultCredentialsError + + if not os.environ.get("CLOUD_ML_REGION"): + return "Set the CLOUD_ML_REGION environment variable to use the Vertex API." + try: + google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) + except DefaultCredentialsError: + return "Your google cloud credentials are not set up correctly." + +def load_from_storage(filename: str) -> str | None: + """Load data from a file in the storage directory.""" + try: + file_path = CONFIG_DIR / filename + if file_path.exists(): + data = file_path.read_text().strip() + if data: + return data + except Exception as e: + print(f"Debug: Error loading {filename}: {e}") + return None + +def save_to_storage(filename: str, data: str) -> None: + """Save data to a file in the storage directory.""" + try: + CONFIG_DIR.mkdir(parents=True, exist_ok=True) + file_path = CONFIG_DIR / filename + file_path.write_text(data) + # Ensure only user can read/write the file + file_path.chmod(0o600) + except Exception as e: + print(f"Debug: Error saving {filename}: {e}") + +def _api_response_callback(response: APIResponse[BetaMessage], response_state: dict): + response_id = datetime.now().isoformat() + response_state[response_id] = response + +def _tool_output_callback(tool_output: ToolResult, tool_id: str, tool_state: dict): + tool_state[tool_id] = tool_output + +def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="bot"): + def _render_message(message: str | BetaTextBlock | BetaToolUseBlock | ToolResult, hide_images=False): + + print(f"_render_message: {str(message)[:100]}") + + if isinstance(message, str): + return message + + is_tool_result = not isinstance(message, str) and ( + isinstance(message, ToolResult) + or message.__class__.__name__ == "ToolResult" + or message.__class__.__name__ == "CLIResult" + ) + if not message or ( + is_tool_result + and hide_images + and not hasattr(message, "error") + and not hasattr(message, "output") + ): # return None if hide_images is True + return + # render tool result + if is_tool_result: + message = cast(ToolResult, message) + if message.output: + return message.output + if message.error: + return f"Error: {message.error}" + if message.base64_image and not hide_images: + # somehow can't display via gr.Image + # image_data = base64.b64decode(message.base64_image) + # return gr.Image(value=Image.open(io.BytesIO(image_data))) + return f'' + + elif isinstance(message, BetaTextBlock) or isinstance(message, TextBlock): + return f"Analysis: {message.text}" + elif isinstance(message, BetaToolUseBlock) or isinstance(message, ToolUseBlock): + # return f"Tool Use: {message.name}\nInput: {message.input}" + return f"Next I will perform the following action: {message.input}" + else: + return message + + def _truncate_string(s, max_length=500): + """Truncate long strings for concise printing.""" + if isinstance(s, str) and len(s) > max_length: + return s[:max_length] + "..." + return s + # processing Anthropic messages + message = _render_message(message, hide_images) + + if sender == "bot": + chatbot_state.append((None, message)) + else: + chatbot_state.append((message, None)) + + # Create a concise version of the chatbot state for printing + concise_state = [(_truncate_string(user_msg), _truncate_string(bot_msg)) + for user_msg, bot_msg in chatbot_state] + # print(f"chatbot_output_callback chatbot_state: {concise_state} (truncated)") + +def process_input(user_input, state): + # Append the user message to state["messages"] + state["messages"].append( + { + "role": Sender.USER, + "content": [TextBlock(type="text", text=user_input)], + } + ) + + # Append the user's message to chatbot_messages with None for the assistant's reply + state['chatbot_messages'].append((user_input, None)) + yield state['chatbot_messages'] # Yield to update the chatbot UI with the user's message + + print("state") + print(state) + + # Run sampling_loop_sync with the chatbot_output_callback + for loop_msg in sampling_loop_sync( + model=state["model"], + provider=state["provider"], + messages=state["messages"], + output_callback=partial(chatbot_output_callback, chatbot_state=state['chatbot_messages'], hide_images=False), + tool_output_callback=partial(_tool_output_callback, tool_state=state["tools"]), + api_response_callback=partial(_api_response_callback, response_state=state["responses"]), + api_key=state["api_key"], + only_n_most_recent_images=state["only_n_most_recent_images"], + omniparser_url=omniparser_host_url + ): + if loop_msg is None: + yield state['chatbot_messages'] + print("End of task. Close the loop.") + break + + yield state['chatbot_messages'] # Yield the updated chatbot_messages to update the chatbot UI + +with gr.Blocks(theme=gr.themes.Default()) as demo: + gr.HTML(""" + + """) + state = gr.State({}) # Use Gradio's state management + + setup_state(state.value) # Initialize the state + + # Retrieve screen details + gr.Markdown("# OmniParser + โœ–๏ธ Demo") + + if not os.getenv("HIDE_WARNING", False): + gr.Markdown(INTRO_TEXT) + + with gr.Accordion("Settings", open=True): + with gr.Row(): + with gr.Column(): + model = gr.Dropdown( + label="Model", + choices=["omniparser + gpt-4o", "omniparser + phi35v", "claude-3-5-sonnet-20241022"], + value="omniparser + gpt-4o", # Set to one of the choices + interactive=True, + ) + with gr.Column(): + only_n_images = gr.Slider( + label="N most recent screenshots", + minimum=0, + maximum=10, + step=1, + value=2, + interactive=True + ) + with gr.Row(): + with gr.Column(1): + provider = gr.Dropdown( + label="API Provider", + choices=[option.value for option in APIProvider], + value="openai", + interactive=False, + ) + with gr.Column(2): + api_key = gr.Textbox( + label="API Key", + type="password", + value=state.value.get("api_key", ""), + placeholder="Paste your API key here", + interactive=True, + ) + + with gr.Row(): + with gr.Column(scale=8): + chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Omniparser + X ...", container=False) + with gr.Column(scale=1, min_width=50): + submit_button = gr.Button(value="Send", variant="primary") + + with gr.Row(): + with gr.Column(scale=1): + chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) + with gr.Column(scale=3): + if not windows_host_url: + iframe = gr.HTML( + f'', + container=False, + elem_classes="no-padding" + ) + else: + # machine_fqdn = socket.getfqdn() + # print('machine_fqdn:', machine_fqdn) + iframe = gr.HTML( + f'', + container=False, + elem_classes="no-padding" + ) + + def update_model(model_selection, state): + state["model"] = model_selection + print(f"Model updated to: {state['model']}") + + if model_selection == "claude-3-5-sonnet-20241022": + provider_choices = [option.value for option in APIProvider if option.value != "openai"] + elif model_selection == "omniparser + gpt-4o" or model_selection == "omniparser + phi35v": + provider_choices = ["openai"] + else: + provider_choices = [option.value for option in APIProvider] + default_provider_value = provider_choices[0] + provider_interactive = len(provider_choices) > 1 + api_key_placeholder = f"{default_provider_value.title()} API Key" + + # Update state + state["provider"] = default_provider_value + state["api_key"] = state.get(f"{default_provider_value}_api_key", "") + + # Calls to update other components UI + provider_update = gr.update( + choices=provider_choices, + value=default_provider_value, + interactive=provider_interactive + ) + api_key_update = gr.update( + placeholder=api_key_placeholder, + value=state["api_key"] + ) + + return provider_update, api_key_update + + def update_only_n_images(only_n_images_value, state): + state["only_n_most_recent_images"] = only_n_images_value + + def update_provider(provider_value, state): + # Update state + state["provider"] = provider_value + state["api_key"] = state.get(f"{provider_value}_api_key", "") + + # Calls to update other components UI + api_key_update = gr.update( + placeholder=f"{provider_value.title()} API Key", + value=state["api_key"] + ) + return api_key_update + + def update_api_key(api_key_value, state): + state["api_key"] = api_key_value + state[f'{state["provider"]}_api_key'] = api_key_value + + def clear_chat(state): + # Reset message-related state + state["messages"] = [] + state["responses"] = {} + state["tools"] = {} + state['chatbot_messages'] = [] + return state['chatbot_messages'] + + model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key]) + only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) + provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key) + api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None) + chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot]) + + submit_button.click(process_input, [chat_input, state], chatbot) + +if __name__ == "__main__": + demo.launch(server_name="0.0.0.0", server_port=7888) \ No newline at end of file diff --git a/computer_use_demo/gradio/colorful_text.py b/computer_use_demo/gradio/colorful_text.py new file mode 100644 index 0000000..28d772b --- /dev/null +++ b/computer_use_demo/gradio/colorful_text.py @@ -0,0 +1,17 @@ +""" +Define some colorful stuffs for better visualization in the chat. +""" + +# Define the RGB colors for each letter +colors = { + 'S': 'rgb(106, 158, 210)', + 'h': 'rgb(111, 163, 82)', + 'o': 'rgb(209, 100, 94)', + 'w': 'rgb(238, 171, 106)', + 'U': 'rgb(0, 0, 0)', + 'I': 'rgb(0, 0, 0)', +} + +colorful_text_vlm = "**OmniParser Agent**" + +colorful_text_user = "**User**" \ No newline at end of file diff --git a/computer_use_demo/gradio/executor/anthropic_executor.py b/computer_use_demo/gradio/executor/anthropic_executor.py new file mode 100644 index 0000000..02d9ce6 --- /dev/null +++ b/computer_use_demo/gradio/executor/anthropic_executor.py @@ -0,0 +1,132 @@ +import asyncio +from typing import Any, Dict, cast +from collections.abc import Callable +from anthropic.types.beta import ( + BetaContentBlock, + BetaContentBlockParam, + BetaImageBlockParam, + BetaMessage, + BetaMessageParam, + BetaTextBlockParam, + BetaToolResultBlockParam, +) +from anthropic.types import TextBlock +from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock +from tools import ComputerTool, ToolCollection, ToolResult + + +class AnthropicExecutor: + def __init__( + self, + output_callback: Callable[[BetaContentBlockParam], None], + tool_output_callback: Callable[[Any, str], None], + ): + self.tool_collection = ToolCollection( + ComputerTool() + ) + self.output_callback = output_callback + self.tool_output_callback = tool_output_callback + + def __call__(self, response: BetaMessage, messages: list[BetaMessageParam]): + new_message = { + "role": "assistant", + "content": cast(list[BetaContentBlockParam], response.content), + } + if new_message not in messages: + messages.append(new_message) + else: + print("new_message already in messages, there are duplicates.") + + tool_result_content: list[BetaToolResultBlockParam] = [] + for content_block in cast(list[BetaContentBlock], response.content): + self.output_callback(content_block, sender="bot") + # Execute the tool + if content_block.type == "tool_use": + # Run the asynchronous tool execution in a synchronous context + result = asyncio.run(self.tool_collection.run( + name=content_block.name, + tool_input=cast(dict[str, Any], content_block.input), + )) + + self.output_callback(result, sender="bot") + + tool_result_content.append( + _make_api_tool_result(result, content_block.id) + ) + self.tool_output_callback(result, content_block.id) + + # Craft messages based on the content_block + # Note: to display the messages in the gradio, you should organize the messages in the following way (user message, bot message) + + display_messages = _message_display_callback(messages) + # display_messages = [] + + # Send the messages to the gradio + for user_msg, bot_msg in display_messages: + # yield [user_msg, bot_msg], tool_result_content + yield [None, None], tool_result_content + + if not tool_result_content: + return messages + + return tool_result_content + +def _message_display_callback(messages): + display_messages = [] + for msg in messages: + try: + if isinstance(msg["content"][0], TextBlock): + display_messages.append((msg["content"][0].text, None)) # User message + elif isinstance(msg["content"][0], BetaTextBlock): + display_messages.append((None, msg["content"][0].text)) # Bot message + elif isinstance(msg["content"][0], BetaToolUseBlock): + display_messages.append((None, f"Tool Use: {msg['content'][0].name}\nInput: {msg['content'][0].input}")) # Bot message + elif isinstance(msg["content"][0], Dict) and msg["content"][0]["content"][-1]["type"] == "image": + display_messages.append((None, f'')) # Bot message + else: + print(msg["content"][0]) + except Exception as e: + print("error", e) + pass + return display_messages + +def _make_api_tool_result( + result: ToolResult, tool_use_id: str +) -> BetaToolResultBlockParam: + """Convert an agent ToolResult to an API ToolResultBlockParam.""" + tool_result_content: list[BetaTextBlockParam | BetaImageBlockParam] | str = [] + is_error = False + if result.error: + is_error = True + tool_result_content = _maybe_prepend_system_tool_result(result, result.error) + else: + if result.output: + tool_result_content.append( + { + "type": "text", + "text": _maybe_prepend_system_tool_result(result, result.output), + } + ) + if result.base64_image: + tool_result_content.append( + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": result.base64_image, + }, + } + ) + return { + "type": "tool_result", + "content": tool_result_content, + "tool_use_id": tool_use_id, + "is_error": is_error, + } + + +def _maybe_prepend_system_tool_result(result: ToolResult, result_text: str): + if result.system: + result_text = f"{result.system}\n{result_text}" + return result_text \ No newline at end of file diff --git a/computer_use_demo/gradio/loop.py b/computer_use_demo/gradio/loop.py new file mode 100644 index 0000000..f989ef8 --- /dev/null +++ b/computer_use_demo/gradio/loop.py @@ -0,0 +1,129 @@ +""" +Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. +""" +from collections.abc import Callable +from enum import StrEnum + +from anthropic import APIResponse +from anthropic.types import ( + TextBlock, +) +from anthropic.types.beta import ( + BetaContentBlock, + BetaMessage, + BetaMessageParam +) +from tools import ToolResult + +from agent.llm_utils.omniparserclient import OmniParserClient +from agent.anthropic_agent import AnthropicActor +from agent.vlm_agent import VLMAgent +from executor.anthropic_executor import AnthropicExecutor + +BETA_FLAG = "computer-use-2024-10-22" + +class APIProvider(StrEnum): + ANTHROPIC = "anthropic" + BEDROCK = "bedrock" + VERTEX = "vertex" + OPENAI = "openai" + + +PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { + APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", + APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", + APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", + APIProvider.OPENAI: "gpt-4o", +} + +def sampling_loop_sync( + *, + model: str, + provider: APIProvider | None, + messages: list[BetaMessageParam], + output_callback: Callable[[BetaContentBlock], None], + tool_output_callback: Callable[[ToolResult, str], None], + api_response_callback: Callable[[APIResponse[BetaMessage]], None], + api_key: str, + only_n_most_recent_images: int | None = 2, + max_tokens: int = 4096, + omniparser_url: str +): + """ + Synchronous agentic sampling loop for the assistant/tool interaction of computer use. + """ + print('in sampling_loop_sync, model:', model) + omniparser_client = OmniParserClient(url=f"http://{omniparser_url}/parse/") + if model == "claude-3-5-sonnet-20241022": + # Register Actor and Executor + actor = AnthropicActor( + model=model, + provider=provider, + api_key=api_key, + api_response_callback=api_response_callback, + max_tokens=max_tokens, + only_n_most_recent_images=only_n_most_recent_images + ) + + # from IPython.core.debugger import Pdb; Pdb().set_trace() + executor = AnthropicExecutor( + output_callback=output_callback, + tool_output_callback=tool_output_callback + ) + + elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": + actor = VLMAgent( + model=model, + provider=provider, + api_key=api_key, + api_response_callback=api_response_callback, + output_callback=output_callback, + ) + + executor = AnthropicExecutor( + output_callback=output_callback, + tool_output_callback=tool_output_callback, + ) + + else: + raise ValueError(f"Model {model} not supported") + print(f"Model Inited: {model}, Provider: {provider}") + + tool_result_content = None + + print(f"Start the message loop. User messages: {messages}") + + if model == "claude-3-5-sonnet-20241022": # Anthropic loop + while True: + parsed_screen = omniparser_client() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} + import pdb; pdb.set_trace() + screen_info_block = TextBlock(text='Below is the structured accessibility information of the current UI screen, which includes text and icons you can operate on, take these information into account when you are making the prediction for the next action. Note you will still need to take screenshot to get the image: \n' + parsed_screen['screen_info'], type='text') + # # messages[-1]['content'].append(screen_info_block) + screen_info_dict = {"role": "user", "content": [screen_info_block]} + messages.append(screen_info_dict) + tools_use_needed = actor(messages=messages) + + for message, tool_result_content in executor(tools_use_needed, messages): + yield message + + if not tool_result_content: + return messages + + messages.append({"content": tool_result_content, "role": "user"}) + + elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": + while True: + parsed_screen = omniparser_client() + tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) + + for message, tool_result_content in executor(tools_use_needed, messages): + yield message + + if not tool_result_content: + return messages + + # import pdb; pdb.set_trace() + # messages.append({"role": "user", + # "content": ["History plan:\n" + str(vlm_response_json['Reasoning'])]}) + + # messages.append({"content": tool_result_content, "role": "user"}) \ No newline at end of file diff --git a/computer_use_demo/gradio/tools/__init__.py b/computer_use_demo/gradio/tools/__init__.py new file mode 100644 index 0000000..bb051ae --- /dev/null +++ b/computer_use_demo/gradio/tools/__init__.py @@ -0,0 +1,12 @@ +from .base import CLIResult, ToolResult +from .collection import ToolCollection +from .computer import ComputerTool +from .screen_capture import get_screenshot + +__ALL__ = [ + CLIResult, + ComputerTool, + ToolCollection, + ToolResult, + get_screenshot, +] diff --git a/computer_use_demo/gradio/tools/base.py b/computer_use_demo/gradio/tools/base.py new file mode 100644 index 0000000..d6f1371 --- /dev/null +++ b/computer_use_demo/gradio/tools/base.py @@ -0,0 +1,69 @@ +from abc import ABCMeta, abstractmethod +from dataclasses import dataclass, fields, replace +from typing import Any + +from anthropic.types.beta import BetaToolUnionParam + + +class BaseAnthropicTool(metaclass=ABCMeta): + """Abstract base class for Anthropic-defined tools.""" + + @abstractmethod + def __call__(self, **kwargs) -> Any: + """Executes the tool with the given arguments.""" + ... + + @abstractmethod + def to_params( + self, + ) -> BetaToolUnionParam: + raise NotImplementedError + + +@dataclass(kw_only=True, frozen=True) +class ToolResult: + """Represents the result of a tool execution.""" + + output: str | None = None + error: str | None = None + base64_image: str | None = None + system: str | None = None + + def __bool__(self): + return any(getattr(self, field.name) for field in fields(self)) + + def __add__(self, other: "ToolResult"): + def combine_fields( + field: str | None, other_field: str | None, concatenate: bool = True + ): + if field and other_field: + if concatenate: + return field + other_field + raise ValueError("Cannot combine tool results") + return field or other_field + + return ToolResult( + output=combine_fields(self.output, other.output), + error=combine_fields(self.error, other.error), + base64_image=combine_fields(self.base64_image, other.base64_image, False), + system=combine_fields(self.system, other.system), + ) + + def replace(self, **kwargs): + """Returns a new ToolResult with the given fields replaced.""" + return replace(self, **kwargs) + + +class CLIResult(ToolResult): + """A ToolResult that can be rendered as a CLI output.""" + + +class ToolFailure(ToolResult): + """A ToolResult that represents a failure.""" + + +class ToolError(Exception): + """Raised when a tool encounters an error.""" + + def __init__(self, message): + self.message = message diff --git a/computer_use_demo/gradio/tools/collection.py b/computer_use_demo/gradio/tools/collection.py new file mode 100644 index 0000000..c4e8c95 --- /dev/null +++ b/computer_use_demo/gradio/tools/collection.py @@ -0,0 +1,34 @@ +"""Collection classes for managing multiple tools.""" + +from typing import Any + +from anthropic.types.beta import BetaToolUnionParam + +from .base import ( + BaseAnthropicTool, + ToolError, + ToolFailure, + ToolResult, +) + + +class ToolCollection: + """A collection of anthropic-defined tools.""" + + def __init__(self, *tools: BaseAnthropicTool): + self.tools = tools + self.tool_map = {tool.to_params()["name"]: tool for tool in tools} + + def to_params( + self, + ) -> list[BetaToolUnionParam]: + return [tool.to_params() for tool in self.tools] + + async def run(self, *, name: str, tool_input: dict[str, Any]) -> ToolResult: + tool = self.tool_map.get(name) + if not tool: + return ToolFailure(error=f"Tool {name} is invalid") + try: + return await tool(**tool_input) + except ToolError as e: + return ToolFailure(error=e.message) diff --git a/computer_use_demo/gradio/tools/computer.py b/computer_use_demo/gradio/tools/computer.py new file mode 100644 index 0000000..adad9a2 --- /dev/null +++ b/computer_use_demo/gradio/tools/computer.py @@ -0,0 +1,324 @@ +import base64 +import time +from enum import StrEnum +from typing import Literal, TypedDict + +from PIL import Image + +from anthropic.types.beta import BetaToolComputerUse20241022Param + +from .base import BaseAnthropicTool, ToolError, ToolResult +from .screen_capture import get_screenshot +import requests +import re + +OUTPUT_DIR = "./tmp/outputs" + +TYPING_DELAY_MS = 12 +TYPING_GROUP_SIZE = 50 + +Action = Literal[ + "key", + "type", + "mouse_move", + "left_click", + "left_click_drag", + "right_click", + "middle_click", + "double_click", + "screenshot", + "cursor_position", +] + + +class Resolution(TypedDict): + width: int + height: int + + +MAX_SCALING_TARGETS: dict[str, Resolution] = { + "XGA": Resolution(width=1024, height=768), # 4:3 + "WXGA": Resolution(width=1280, height=800), # 16:10 + "FWXGA": Resolution(width=1366, height=768), # ~16:9 +} + + +class ScalingSource(StrEnum): + COMPUTER = "computer" + API = "api" + + +class ComputerToolOptions(TypedDict): + display_height_px: int + display_width_px: int + display_number: int | None + + +def chunks(s: str, chunk_size: int) -> list[str]: + return [s[i : i + chunk_size] for i in range(0, len(s), chunk_size)] + +class ComputerTool(BaseAnthropicTool): + """ + A tool that allows the agent to interact with the screen, keyboard, and mouse of the current computer. + Adapted for Windows using 'pyautogui'. + """ + + name: Literal["computer"] = "computer" + api_type: Literal["computer_20241022"] = "computer_20241022" + width: int + height: int + display_num: int | None + + _screenshot_delay = 2.0 + _scaling_enabled = True + + @property + def options(self) -> ComputerToolOptions: + width, height = self.scale_coordinates( + ScalingSource.COMPUTER, self.width, self.height + ) + return { + "display_width_px": width, + "display_height_px": height, + "display_number": self.display_num, + } + + def to_params(self) -> BetaToolComputerUse20241022Param: + return {"name": self.name, "type": self.api_type, **self.options} + + def __init__(self, is_scaling: bool = False): + super().__init__() + + # Get screen width and height using Windows command + self.display_num = None + self.offset_x = 0 + self.offset_y = 0 + self.is_scaling = is_scaling + self.width, self.height = self.get_screen_size() + print(f"screen size: {self.width}, {self.height}") + + self.key_conversion = {"Page_Down": "pagedown", + "Page_Up": "pageup", + "Super_L": "win", + "Escape": "esc"} + + + async def __call__( + self, + *, + action: Action, + text: str | None = None, + coordinate: tuple[int, int] | None = None, + **kwargs, + ): + print(f"action: {action}, text: {text}, coordinate: {coordinate}, is_scaling: {self.is_scaling}") + if action in ("mouse_move", "left_click_drag"): + if coordinate is None: + raise ToolError(f"coordinate is required for {action}") + if text is not None: + raise ToolError(f"text is not accepted for {action}") + if not isinstance(coordinate, (list, tuple)) or len(coordinate) != 2: + raise ToolError(f"{coordinate} must be a tuple of length 2") + # if not all(isinstance(i, int) and i >= 0 for i in coordinate): + if not all(isinstance(i, int) for i in coordinate): + raise ToolError(f"{coordinate} must be a tuple of non-negative ints") + + if self.is_scaling: + x, y = self.scale_coordinates( + ScalingSource.API, coordinate[0], coordinate[1] + ) + else: + x, y = coordinate + + # print(f"scaled_coordinates: {x}, {y}") + # print(f"offset: {self.offset_x}, {self.offset_y}") + + # x += self.offset_x # TODO - check if this is needed + # y += self.offset_y + + print(f"mouse move to {x}, {y}") + + if action == "mouse_move": + self.send_to_vm(f"pyautogui.moveTo({x}, {y})") + return ToolResult(output=f"Moved mouse to ({x}, {y})") + elif action == "left_click_drag": + current_x, current_y = self.send_to_vm("pyautogui.position()") + self.send_to_vm(f"pyautogui.dragTo({x}, {y}, duration=0.5)") + return ToolResult(output=f"Dragged mouse from ({current_x}, {current_y}) to ({x}, {y})") + + if action in ("key", "type"): + if text is None: + raise ToolError(f"text is required for {action}") + if coordinate is not None: + raise ToolError(f"coordinate is not accepted for {action}") + if not isinstance(text, str): + raise ToolError(output=f"{text} must be a string") + + if action == "key": + # Handle key combinations + keys = text.split('+') + for key in keys: + key = self.key_conversion.get(key.strip(), key.strip()) + key = key.lower() + self.send_to_vm(f"pyautogui.keyDown('{key}')") # Press down each key + for key in reversed(keys): + key = self.key_conversion.get(key.strip(), key.strip()) + key = key.lower() + self.send_to_vm(f"pyautogui.keyUp('{key}')") # Release each key in reverse order + return ToolResult(output=f"Pressed keys: {text}") + + elif action == "type": + self.send_to_vm(f"pyautogui.typewrite('{text}', interval={TYPING_DELAY_MS / 1000})") # Convert ms to seconds + self.send_to_vm("pyautogui.press('enter')") + screenshot_base64 = (await self.screenshot()).base64_image + return ToolResult(output=text, base64_image=screenshot_base64) + + if action in ( + "left_click", + "right_click", + "double_click", + "middle_click", + "screenshot", + "cursor_position", + "left_press", + ): + if text is not None: + raise ToolError(f"text is not accepted for {action}") + if coordinate is not None: + raise ToolError(f"coordinate is not accepted for {action}") + + if action == "screenshot": + return await self.screenshot() + elif action == "cursor_position": + x, y = self.send_to_vm("pyautogui.position()") + x, y = self.scale_coordinates(ScalingSource.COMPUTER, x, y) + return ToolResult(output=f"X={x},Y={y}") + else: + if action == "left_click": + self.send_to_vm("pyautogui.click()") + elif action == "right_click": + self.send_to_vm("pyautogui.rightClick()") + elif action == "middle_click": + self.send_to_vm("pyautogui.middleClick()") + elif action == "double_click": + self.send_to_vm("pyautogui.doubleClick()") + elif action == "left_press": + self.send_to_vm("pyautogui.mouseDown()") + time.sleep(1) + self.send_to_vm("pyautogui.mouseUp()") + return ToolResult(output=f"Performed {action}") + if action in ("scroll_up", "scroll_down"): + if action == "scroll_up": + self.send_to_vm("pyautogui.scroll(100)") + elif action == "scroll_down": + self.send_to_vm("pyautogui.scroll(-100)") + return ToolResult(output=f"Performed {action}") + + raise ToolError(f"Invalid action: {action}") + + def send_to_vm(self, action: str): + """ + Executes a python command on the server. Only return tuple of x,y when action is "pyautogui.position()" + """ + prefix = "import pyautogui; pyautogui.FAILSAFE = False;" + command_list = ["python", "-c", f"{prefix} {action}"] + parse = action == "pyautogui.position()" + if parse: + command_list[-1] = f"{prefix} print({action})" + + try: + print(f"sending to vm: {command_list}") + response = requests.post( + f"http://localhost:5000/execute", + headers={'Content-Type': 'application/json'}, + json={"command": command_list}, + timeout=90 + ) + time.sleep(0.7) # avoid async error as actions take time to complete + print(f"action executed") + if response.status_code != 200: + raise ToolError(f"Failed to execute command. Status code: {response.status_code}") + if parse: + output = response.json()['output'].strip() + match = re.search(r'Point\(x=(\d+),\s*y=(\d+)\)', output) + if not match: + raise ToolError(f"Could not parse coordinates from output: {output}") + x, y = map(int, match.groups()) + return x, y + except requests.exceptions.RequestException as e: + raise ToolError(f"An error occurred while trying to execute the command: {str(e)}") + + async def screenshot(self): + if not hasattr(self, 'target_dimension'): + screenshot = self.padding_image(screenshot) + self.target_dimension = MAX_SCALING_TARGETS["WXGA"] + width, height = self.target_dimension["width"], self.target_dimension["height"] + screenshot, path = get_screenshot(resize=True, target_width=width, target_height=height) + time.sleep(0.7) # avoid async error as actions take time to complete + # return ToolResult() + return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode()) + + raise ToolError(f"Failed to take screenshot: {path} does not exist.") + + def padding_image(self, screenshot): + """Pad the screenshot to 16:10 aspect ratio, when the aspect ratio is not 16:10.""" + _, height = screenshot.size + new_width = height * 16 // 10 + + padding_image = Image.new("RGB", (new_width, height), (255, 255, 255)) + # padding to top left + padding_image.paste(screenshot, (0, 0)) + return padding_image + + def scale_coordinates(self, source: ScalingSource, x: int, y: int): + """Scale coordinates to a target maximum resolution.""" + if not self._scaling_enabled: + return x, y + ratio = self.width / self.height + target_dimension = None + + for target_name, dimension in MAX_SCALING_TARGETS.items(): + # allow some error in the aspect ratio - not ratios are exactly 16:9 + if abs(dimension["width"] / dimension["height"] - ratio) < 0.02: + if dimension["width"] < self.width: + target_dimension = dimension + self.target_dimension = target_dimension + # print(f"target_dimension: {target_dimension}") + break + + if target_dimension is None: + # TODO: currently we force the target to be WXGA (16:10), when it cannot find a match + target_dimension = MAX_SCALING_TARGETS["WXGA"] + self.target_dimension = MAX_SCALING_TARGETS["WXGA"] + + # should be less than 1 + x_scaling_factor = target_dimension["width"] / self.width + y_scaling_factor = target_dimension["height"] / self.height + if source == ScalingSource.API: + if x > self.width or y > self.height: + raise ToolError(f"Coordinates {x}, {y} are out of bounds") + # scale up + return round(x / x_scaling_factor), round(y / y_scaling_factor) + # scale down + return round(x * x_scaling_factor), round(y * y_scaling_factor) + + def get_screen_size(self): + """Return width and height of the screen""" + try: + response = requests.post( + f"http://localhost:5000/execute", + headers={'Content-Type': 'application/json'}, + json={"command": ["python", "-c", "import pyautogui; print(pyautogui.size())"]}, + timeout=90 + ) + if response.status_code != 200: + raise ToolError(f"Failed to get screen size. Status code: {response.status_code}") + + output = response.json()['output'].strip() + match = re.search(r'Size\(width=(\d+),\s*height=(\d+)\)', output) + if not match: + raise ToolError(f"Could not parse screen size from output: {output}") + width, height = map(int, match.groups()) + return width, height + except requests.exceptions.RequestException as e: + raise ToolError(f"An error occurred while trying to get screen size: {str(e)}") \ No newline at end of file diff --git a/computer_use_demo/gradio/tools/screen_capture.py b/computer_use_demo/gradio/tools/screen_capture.py new file mode 100644 index 0000000..1c1ad04 --- /dev/null +++ b/computer_use_demo/gradio/tools/screen_capture.py @@ -0,0 +1,29 @@ +from pathlib import Path +from uuid import uuid4 +import requests +from PIL import Image +from .base import BaseAnthropicTool, ToolError +from io import BytesIO + +OUTPUT_DIR = "./tmp/outputs" + +def get_screenshot(resize: bool = False, target_width: int = 1920, target_height: int = 1080): + """Capture screenshot by requesting from HTTP endpoint - returns native resolution unless resized""" + output_dir = Path(OUTPUT_DIR) + output_dir.mkdir(parents=True, exist_ok=True) + path = output_dir / f"screenshot_{uuid4().hex}.png" + + try: + response = requests.get('http://localhost:5000/screenshot') + if response.status_code != 200: + raise ToolError(f"Failed to capture screenshot: HTTP {response.status_code}") + + # (1280, 800) + screenshot = Image.open(BytesIO(response.content)) + + if resize and screenshot.size != (target_width, target_height): + screenshot = screenshot.resize((target_width, target_height)) + screenshot.save(path) + return screenshot, path + except Exception as e: + raise ToolError(f"Failed to capture screenshot: {str(e)}") \ No newline at end of file From 7800a24b27298ff05696831cbf591bc428a23598 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Wed, 29 Jan 2025 22:44:23 -0800 Subject: [PATCH 27/57] Rename folder + remove coloring on omniparseragent printouts --- {demo => computer_use_demo}/gradio/.gitignore | 0 computer_use_demo/gradio/colorful_text.py | 17 - .../omniparserserver/omniparserserver.py | 0 demo/gradio/app.py | 374 ------------------ demo/gradio/computer_use_demo/__init__.py | 0 .../agent/anthropic_agent.py | 182 --------- .../computer_use_demo/agent/llm_utils/oai.py | 107 ----- .../agent/llm_utils/omniparserclient.py | 44 --- .../computer_use_demo/agent/vlm_agent.py | 302 -------------- .../gradio/computer_use_demo/colorful_text.py | 17 - .../executor/anthropic_executor.py | 132 ------- demo/gradio/computer_use_demo/loop.py | 129 ------ .../computer_use_demo/tools/__init__.py | 12 - demo/gradio/computer_use_demo/tools/base.py | 69 ---- .../computer_use_demo/tools/collection.py | 34 -- .../computer_use_demo/tools/computer.py | 324 --------------- .../computer_use_demo/tools/screen_capture.py | 29 -- 17 files changed, 1772 deletions(-) rename {demo => computer_use_demo}/gradio/.gitignore (100%) delete mode 100644 computer_use_demo/gradio/colorful_text.py rename {demo => computer_use_demo}/omniparserserver/omniparserserver.py (100%) delete mode 100644 demo/gradio/app.py delete mode 100644 demo/gradio/computer_use_demo/__init__.py delete mode 100644 demo/gradio/computer_use_demo/agent/anthropic_agent.py delete mode 100644 demo/gradio/computer_use_demo/agent/llm_utils/oai.py delete mode 100644 demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py delete mode 100644 demo/gradio/computer_use_demo/agent/vlm_agent.py delete mode 100644 demo/gradio/computer_use_demo/colorful_text.py delete mode 100644 demo/gradio/computer_use_demo/executor/anthropic_executor.py delete mode 100644 demo/gradio/computer_use_demo/loop.py delete mode 100644 demo/gradio/computer_use_demo/tools/__init__.py delete mode 100644 demo/gradio/computer_use_demo/tools/base.py delete mode 100644 demo/gradio/computer_use_demo/tools/collection.py delete mode 100644 demo/gradio/computer_use_demo/tools/computer.py delete mode 100644 demo/gradio/computer_use_demo/tools/screen_capture.py diff --git a/demo/gradio/.gitignore b/computer_use_demo/gradio/.gitignore similarity index 100% rename from demo/gradio/.gitignore rename to computer_use_demo/gradio/.gitignore diff --git a/computer_use_demo/gradio/colorful_text.py b/computer_use_demo/gradio/colorful_text.py deleted file mode 100644 index 28d772b..0000000 --- a/computer_use_demo/gradio/colorful_text.py +++ /dev/null @@ -1,17 +0,0 @@ -""" -Define some colorful stuffs for better visualization in the chat. -""" - -# Define the RGB colors for each letter -colors = { - 'S': 'rgb(106, 158, 210)', - 'h': 'rgb(111, 163, 82)', - 'o': 'rgb(209, 100, 94)', - 'w': 'rgb(238, 171, 106)', - 'U': 'rgb(0, 0, 0)', - 'I': 'rgb(0, 0, 0)', -} - -colorful_text_vlm = "**OmniParser Agent**" - -colorful_text_user = "**User**" \ No newline at end of file diff --git a/demo/omniparserserver/omniparserserver.py b/computer_use_demo/omniparserserver/omniparserserver.py similarity index 100% rename from demo/omniparserserver/omniparserserver.py rename to computer_use_demo/omniparserserver/omniparserserver.py diff --git a/demo/gradio/app.py b/demo/gradio/app.py deleted file mode 100644 index 068d17c..0000000 --- a/demo/gradio/app.py +++ /dev/null @@ -1,374 +0,0 @@ -""" -Entrypoint for Gradio, see https://gradio.app/ -python app.py --windows_host_url xxxx:8006/ --omniparser_host_url localhost:8000 -""" - -import os -from datetime import datetime -from enum import StrEnum -from functools import partial -from pathlib import Path -from typing import cast -import argparse -import gradio as gr -from anthropic import APIResponse -from anthropic.types import TextBlock -from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock -from anthropic.types.tool_use_block import ToolUseBlock -from computer_use_demo.loop import ( - APIProvider, - sampling_loop_sync, -) -from computer_use_demo.tools import ToolResult - -CONFIG_DIR = Path("~/.anthropic").expanduser() -API_KEY_FILE = CONFIG_DIR / "api_key" - -INTRO_TEXT = ''' -๐Ÿš€๐Ÿค–โœจ It's Play Time! - -Welcome to the OmniParser+X Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. - -Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history. -''' - -def parse_arguments(): - parser = argparse.ArgumentParser(description="Gradio App") - parser.add_argument("--windows_host_url", type=str, default='localhost:8006') - parser.add_argument("--omniparser_host_url", type=str, default="localhost:8000") - return parser.parse_args() -args = parse_arguments() -windows_host_url = args.windows_host_url -omniparser_host_url = args.omniparser_host_url -print(f"Windows host URL: {windows_host_url}") -print(f"OmniParser host URL: {omniparser_host_url}") - - -class Sender(StrEnum): - USER = "user" - BOT = "assistant" - TOOL = "tool" - - -def setup_state(state): - if "messages" not in state: - state["messages"] = [] - if "model" not in state: - state["model"] = "omniparser + gpt-4o" - if "provider" not in state: - state["provider"] = "openai" - if "openai_api_key" not in state: # Fetch API keys from environment variables - state["openai_api_key"] = os.getenv("OPENAI_API_KEY", "") - if "anthropic_api_key" not in state: - state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "") - if "api_key" not in state: - state["api_key"] = "" - if "auth_validated" not in state: - state["auth_validated"] = False - if "responses" not in state: - state["responses"] = {} - if "tools" not in state: - state["tools"] = {} - if "only_n_most_recent_images" not in state: - state["only_n_most_recent_images"] = 2 - if 'chatbot_messages' not in state: - state['chatbot_messages'] = [] - -async def main(state): - """Render loop for Gradio""" - setup_state(state) - return "Setup completed" - -def validate_auth(provider: APIProvider, api_key: str | None): - if provider == APIProvider.ANTHROPIC: - if not api_key: - return "Enter your Anthropic API key to continue." - if provider == APIProvider.BEDROCK: - import boto3 - - if not boto3.Session().get_credentials(): - return "You must have AWS credentials set up to use the Bedrock API." - if provider == APIProvider.VERTEX: - import google.auth - from google.auth.exceptions import DefaultCredentialsError - - if not os.environ.get("CLOUD_ML_REGION"): - return "Set the CLOUD_ML_REGION environment variable to use the Vertex API." - try: - google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) - except DefaultCredentialsError: - return "Your google cloud credentials are not set up correctly." - -def load_from_storage(filename: str) -> str | None: - """Load data from a file in the storage directory.""" - try: - file_path = CONFIG_DIR / filename - if file_path.exists(): - data = file_path.read_text().strip() - if data: - return data - except Exception as e: - print(f"Debug: Error loading {filename}: {e}") - return None - -def save_to_storage(filename: str, data: str) -> None: - """Save data to a file in the storage directory.""" - try: - CONFIG_DIR.mkdir(parents=True, exist_ok=True) - file_path = CONFIG_DIR / filename - file_path.write_text(data) - # Ensure only user can read/write the file - file_path.chmod(0o600) - except Exception as e: - print(f"Debug: Error saving {filename}: {e}") - -def _api_response_callback(response: APIResponse[BetaMessage], response_state: dict): - response_id = datetime.now().isoformat() - response_state[response_id] = response - -def _tool_output_callback(tool_output: ToolResult, tool_id: str, tool_state: dict): - tool_state[tool_id] = tool_output - -def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="bot"): - def _render_message(message: str | BetaTextBlock | BetaToolUseBlock | ToolResult, hide_images=False): - - print(f"_render_message: {str(message)[:100]}") - - if isinstance(message, str): - return message - - is_tool_result = not isinstance(message, str) and ( - isinstance(message, ToolResult) - or message.__class__.__name__ == "ToolResult" - or message.__class__.__name__ == "CLIResult" - ) - if not message or ( - is_tool_result - and hide_images - and not hasattr(message, "error") - and not hasattr(message, "output") - ): # return None if hide_images is True - return - # render tool result - if is_tool_result: - message = cast(ToolResult, message) - if message.output: - return message.output - if message.error: - return f"Error: {message.error}" - if message.base64_image and not hide_images: - # somehow can't display via gr.Image - # image_data = base64.b64decode(message.base64_image) - # return gr.Image(value=Image.open(io.BytesIO(image_data))) - return f'' - - elif isinstance(message, BetaTextBlock) or isinstance(message, TextBlock): - return f"Analysis: {message.text}" - elif isinstance(message, BetaToolUseBlock) or isinstance(message, ToolUseBlock): - # return f"Tool Use: {message.name}\nInput: {message.input}" - return f"Next I will perform the following action: {message.input}" - else: - return message - - def _truncate_string(s, max_length=500): - """Truncate long strings for concise printing.""" - if isinstance(s, str) and len(s) > max_length: - return s[:max_length] + "..." - return s - # processing Anthropic messages - message = _render_message(message, hide_images) - - if sender == "bot": - chatbot_state.append((None, message)) - else: - chatbot_state.append((message, None)) - - # Create a concise version of the chatbot state for printing - concise_state = [(_truncate_string(user_msg), _truncate_string(bot_msg)) - for user_msg, bot_msg in chatbot_state] - # print(f"chatbot_output_callback chatbot_state: {concise_state} (truncated)") - -def process_input(user_input, state): - # Append the user message to state["messages"] - state["messages"].append( - { - "role": Sender.USER, - "content": [TextBlock(type="text", text=user_input)], - } - ) - - # Append the user's message to chatbot_messages with None for the assistant's reply - state['chatbot_messages'].append((user_input, None)) - yield state['chatbot_messages'] # Yield to update the chatbot UI with the user's message - - print("state") - print(state) - - # Run sampling_loop_sync with the chatbot_output_callback - for loop_msg in sampling_loop_sync( - model=state["model"], - provider=state["provider"], - messages=state["messages"], - output_callback=partial(chatbot_output_callback, chatbot_state=state['chatbot_messages'], hide_images=False), - tool_output_callback=partial(_tool_output_callback, tool_state=state["tools"]), - api_response_callback=partial(_api_response_callback, response_state=state["responses"]), - api_key=state["api_key"], - only_n_most_recent_images=state["only_n_most_recent_images"], - omniparser_url=omniparser_host_url - ): - if loop_msg is None: - yield state['chatbot_messages'] - print("End of task. Close the loop.") - break - - yield state['chatbot_messages'] # Yield the updated chatbot_messages to update the chatbot UI - -with gr.Blocks(theme=gr.themes.Default()) as demo: - gr.HTML(""" - - """) - state = gr.State({}) # Use Gradio's state management - - setup_state(state.value) # Initialize the state - - # Retrieve screen details - gr.Markdown("# OmniParser + โœ–๏ธ Demo") - - if not os.getenv("HIDE_WARNING", False): - gr.Markdown(INTRO_TEXT) - - with gr.Accordion("Settings", open=True): - with gr.Row(): - with gr.Column(): - model = gr.Dropdown( - label="Model", - choices=["omniparser + gpt-4o", "omniparser + phi35v", "claude-3-5-sonnet-20241022"], - value="omniparser + gpt-4o", # Set to one of the choices - interactive=True, - ) - with gr.Column(): - only_n_images = gr.Slider( - label="N most recent screenshots", - minimum=0, - maximum=10, - step=1, - value=2, - interactive=True - ) - with gr.Row(): - with gr.Column(1): - provider = gr.Dropdown( - label="API Provider", - choices=[option.value for option in APIProvider], - value="openai", - interactive=False, - ) - with gr.Column(2): - api_key = gr.Textbox( - label="API Key", - type="password", - value=state.value.get("api_key", ""), - placeholder="Paste your API key here", - interactive=True, - ) - - with gr.Row(): - with gr.Column(scale=8): - chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Omniparser + X ...", container=False) - with gr.Column(scale=1, min_width=50): - submit_button = gr.Button(value="Send", variant="primary") - - with gr.Row(): - with gr.Column(scale=1): - chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) - with gr.Column(scale=3): - if not windows_host_url: - iframe = gr.HTML( - f'', - container=False, - elem_classes="no-padding" - ) - else: - # machine_fqdn = socket.getfqdn() - # print('machine_fqdn:', machine_fqdn) - iframe = gr.HTML( - f'', - container=False, - elem_classes="no-padding" - ) - - def update_model(model_selection, state): - state["model"] = model_selection - print(f"Model updated to: {state['model']}") - - if model_selection == "claude-3-5-sonnet-20241022": - provider_choices = [option.value for option in APIProvider if option.value != "openai"] - elif model_selection == "omniparser + gpt-4o" or model_selection == "omniparser + phi35v": - provider_choices = ["openai"] - else: - provider_choices = [option.value for option in APIProvider] - default_provider_value = provider_choices[0] - provider_interactive = len(provider_choices) > 1 - api_key_placeholder = f"{default_provider_value.title()} API Key" - - # Update state - state["provider"] = default_provider_value - state["api_key"] = state.get(f"{default_provider_value}_api_key", "") - - # Calls to update other components UI - provider_update = gr.update( - choices=provider_choices, - value=default_provider_value, - interactive=provider_interactive - ) - api_key_update = gr.update( - placeholder=api_key_placeholder, - value=state["api_key"] - ) - - return provider_update, api_key_update - - def update_only_n_images(only_n_images_value, state): - state["only_n_most_recent_images"] = only_n_images_value - - def update_provider(provider_value, state): - # Update state - state["provider"] = provider_value - state["api_key"] = state.get(f"{provider_value}_api_key", "") - - # Calls to update other components UI - api_key_update = gr.update( - placeholder=f"{provider_value.title()} API Key", - value=state["api_key"] - ) - return api_key_update - - def update_api_key(api_key_value, state): - state["api_key"] = api_key_value - state[f'{state["provider"]}_api_key'] = api_key_value - - def clear_chat(state): - # Reset message-related state - state["messages"] = [] - state["responses"] = {} - state["tools"] = {} - state['chatbot_messages'] = [] - return state['chatbot_messages'] - - model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key]) - only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None) - provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key) - api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None) - chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot]) - - submit_button.click(process_input, [chat_input, state], chatbot) - -if __name__ == "__main__": - demo.launch(server_name="0.0.0.0", server_port=7888) \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/__init__.py b/demo/gradio/computer_use_demo/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/demo/gradio/computer_use_demo/agent/anthropic_agent.py b/demo/gradio/computer_use_demo/agent/anthropic_agent.py deleted file mode 100644 index b1a40c0..0000000 --- a/demo/gradio/computer_use_demo/agent/anthropic_agent.py +++ /dev/null @@ -1,182 +0,0 @@ -""" -Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. -""" -import asyncio -import platform -from collections.abc import Callable -from datetime import datetime -from enum import StrEnum -from typing import Any, cast - -from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse -from anthropic.types import ( - ToolResultBlockParam, -) -from anthropic.types.beta import ( - BetaContentBlock, - BetaContentBlockParam, - BetaImageBlockParam, - BetaMessage, - BetaMessageParam, - BetaTextBlockParam, - BetaToolResultBlockParam, -) -from anthropic.types import TextBlock -from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock - -from computer_use_demo.tools import ComputerTool, ToolCollection, ToolResult - -from PIL import Image -from io import BytesIO -import gradio as gr -from typing import Dict - -BETA_FLAG = "computer-use-2024-10-22" - -class APIProvider(StrEnum): - ANTHROPIC = "anthropic" - BEDROCK = "bedrock" - VERTEX = "vertex" - -SYSTEM_PROMPT = f""" -* You are utilizing a Windows system with internet access. -* The current date is {datetime.today().strftime('%A, %B %d, %Y')}. - -""" - -class AnthropicActor: - def __init__( - self, - model: str, - provider: APIProvider, - api_key: str, - api_response_callback: Callable[[APIResponse[BetaMessage]], None], - max_tokens: int = 4096, - only_n_most_recent_images: int | None = None, - print_usage: bool = True, - ): - self.model = model - self.provider = provider - self.api_key = api_key - self.api_response_callback = api_response_callback - self.max_tokens = max_tokens - self.only_n_most_recent_images = only_n_most_recent_images - - self.tool_collection = ToolCollection(ComputerTool()) - - self.system = SYSTEM_PROMPT - - self.total_token_usage = 0 - self.total_cost = 0 - self.print_usage = print_usage - - # Instantiate the appropriate API client based on the provider - if provider == APIProvider.ANTHROPIC: - self.client = Anthropic(api_key=api_key) - elif provider == APIProvider.VERTEX: - self.client = AnthropicVertex() - elif provider == APIProvider.BEDROCK: - self.client = AnthropicBedrock() - - def __call__( - self, - *, - messages: list[BetaMessageParam] - ): - """ - Generate a response given history messages. - """ - if self.only_n_most_recent_images: - _maybe_filter_to_n_most_recent_images(messages, self.only_n_most_recent_images) - - # Call the API synchronously - raw_response = self.client.beta.messages.with_raw_response.create( - max_tokens=self.max_tokens, - messages=messages, - model=self.model, - system=self.system, - tools=self.tool_collection.to_params(), - betas=["computer-use-2024-10-22"], - ) - - self.api_response_callback(cast(APIResponse[BetaMessage], raw_response)) - - response = raw_response.parse() - print(f"AnthropicActor response: {response}") - - self.total_token_usage += response.usage.input_tokens + response.usage.output_tokens - self.total_cost += (response.usage.input_tokens * 3 / 1000000 + response.usage.output_tokens * 15 / 1000000) - - if self.print_usage: - print(f"Claude total token usage so far: {self.total_token_usage}, total cost so far: $USD{self.total_cost}") - - return response - - -def _maybe_filter_to_n_most_recent_images( - messages: list[BetaMessageParam], - images_to_keep: int, - min_removal_threshold: int = 10, -): - """ - With the assumption that images are screenshots that are of diminishing value as - the conversation progresses, remove all but the final `images_to_keep` tool_result - images in place, with a chunk of min_removal_threshold to reduce the amount we - break the implicit prompt cache. - """ - if images_to_keep is None: - return messages - - tool_result_blocks = cast( - list[ToolResultBlockParam], - [ - item - for message in messages - for item in ( - message["content"] if isinstance(message["content"], list) else [] - ) - if isinstance(item, dict) and item.get("type") == "tool_result" - ], - ) - - total_images = sum( - 1 - for tool_result in tool_result_blocks - for content in tool_result.get("content", []) - if isinstance(content, dict) and content.get("type") == "image" - ) - - images_to_remove = total_images - images_to_keep - # for better cache behavior, we want to remove in chunks - images_to_remove -= images_to_remove % min_removal_threshold - - for tool_result in tool_result_blocks: - if isinstance(tool_result.get("content"), list): - new_content = [] - for content in tool_result.get("content", []): - if isinstance(content, dict) and content.get("type") == "image": - if images_to_remove > 0: - images_to_remove -= 1 - continue - new_content.append(content) - tool_result["content"] = new_content - - - -if __name__ == "__main__": - pass - # client = Anthropic(api_key="") - # response = client.beta.messages.with_raw_response.create( - # max_tokens=4096, - # model="claude-3-5-sonnet-20241022", - # system=SYSTEM_PROMPT, - # # tools=ToolCollection( - # # ComputerTool(), - # # ).to_params(), - # betas=["computer-use-2024-10-22"], - # messages=[ - # {"role": "user", "content": "click on (199, 199)."} - # ], - # ) - - # print(f"AnthropicActor response: {response.parse().usage.input_tokens+response.parse().usage.output_tokens}") \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/agent/llm_utils/oai.py b/demo/gradio/computer_use_demo/agent/llm_utils/oai.py deleted file mode 100644 index 0ab3760..0000000 --- a/demo/gradio/computer_use_demo/agent/llm_utils/oai.py +++ /dev/null @@ -1,107 +0,0 @@ - -import os -import logging -import base64 -import requests - -def is_image_path(text): - # Checking if the input text ends with typical image file extensions - image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") - if text.endswith(image_extensions): - return True - else: - return False - -def encode_image(image_path): - """Encode image file to base64.""" - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode("utf-8") - -def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): - - api_key = api_key or os.environ.get("OPENAI_API_KEY") - if not api_key: - raise ValueError("OPENAI_API_KEY is not set") - - headers = {"Content-Type": "application/json", - "Authorization": f"Bearer {api_key}"} - - final_messages = [{"role": "system", "content": system}] - - # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" - if type(messages) == list: - for item in messages: - contents = [] - if isinstance(item, dict): - for cnt in item["content"]: - if isinstance(cnt, str): - if is_image_path(cnt): - base64_image = encode_image(cnt) - content = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}} - else: - content = {"type": "text", "text": cnt} - else: - # in this case it is a text block from anthropic - content = {"type": "text", "text": str(cnt)} - - contents.append(content) - - message = {"role": 'user', "content": contents} - else: # str - contents.append({"type": "text", "text": item}) - message = {"role": "user", "content": contents} - - final_messages.append(message) - - - elif isinstance(messages, str): - final_messages = [{"role": "user", "content": messages}] - # import pdb; pdb.set_trace() - - print("[oai] sending messages:", {"role": "user", "content": messages}) - - payload = { - "model": llm, - "messages": final_messages, - "max_tokens": max_tokens, - "temperature": temperature, - # "stop": stop, - } - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - - response = requests.post( - "https://api.openai.com/v1/chat/completions", headers=headers, json=payload - ) - - try: - text = response.json()['choices'][0]['message']['content'] - token_usage = int(response.json()['usage']['total_tokens']) - return text, token_usage - - # return error message if the response is not successful - except Exception as e: - print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") - return response.json() - - -if __name__ == "__main__": - - api_key = os.environ.get("OPENAI_API_KEY") - if not api_key: - raise ValueError("OPENAI_API_KEY is not set") - - text, token_usage = run_oai_interleaved( - messages= [{"content": [ - "What is in the screenshot?", - "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"], - "role": "user" - }], - llm="gpt-4o-mini", - system="You are a helpful assistant", - api_key=api_key, - max_tokens=256, - temperature=0) - - print(text, token_usage) - # There is an introduction describing the Calyx... 36986 diff --git a/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py b/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py deleted file mode 100644 index 010e82a..0000000 --- a/demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py +++ /dev/null @@ -1,44 +0,0 @@ -import requests -import base64 -from pathlib import Path -from computer_use_demo.tools.screen_capture import get_screenshot -from computer_use_demo.agent.llm_utils.oai import encode_image - -OUTPUT_DIR = "./tmp/outputs" - -class OmniParserClient: - def __init__(self, - url: str) -> None: - self.url = url - - def __call__(self,): - screenshot, screenshot_path = get_screenshot() - screenshot_path = str(screenshot_path) - image_base64 = encode_image(screenshot_path) - response = requests.post(self.url, json={"base64_image": image_base64}) - response_json = response.json() - print('omniparser latency:', response_json['latency']) - - som_image_data = base64.b64decode(response_json['som_image_base64']) - screenshot_path_uuid = Path(screenshot_path).stem.replace("screenshot_", "") - som_screenshot_path = f"{OUTPUT_DIR}/screenshot_som_{screenshot_path_uuid}.png" - with open(som_screenshot_path, "wb") as f: - f.write(som_image_data) - - response_json['width'] = screenshot.size[0] - response_json['height'] = screenshot.size[1] - response_json['original_screenshot_base64'] = image_base64 - response_json['screenshot_uuid'] = screenshot_path_uuid - response_json = self.reformat_messages(response_json) - return response_json - - def reformat_messages(self, response_json: dict): - screen_info = "" - for idx, element in enumerate(response_json["parsed_content_list"]): - element['idx'] = idx - if element['type'] == 'text': - screen_info += f'ID: {idx}, Text: {element["content"]}\n' - elif element['type'] == 'icon': - screen_info += f'ID: {idx}, Icon: {element["content"]}\n' - response_json['screen_info'] = screen_info - return response_json \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/agent/vlm_agent.py b/demo/gradio/computer_use_demo/agent/vlm_agent.py deleted file mode 100644 index 58e243e..0000000 --- a/demo/gradio/computer_use_demo/agent/vlm_agent.py +++ /dev/null @@ -1,302 +0,0 @@ -import json -from collections.abc import Callable -from typing import cast, Callable -import uuid -from PIL import Image, ImageDraw -import base64 -from io import BytesIO - -from anthropic import APIResponse -from anthropic.types import ToolResultBlockParam -from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage - -from computer_use_demo.agent.llm_utils.oai import run_oai_interleaved -from computer_use_demo.colorful_text import colorful_text_vlm -import time -import re - -OUTPUT_DIR = "./tmp/outputs" - -def extract_data(input_string, data_type): - # Regular expression to extract content starting from '```python' until the end if there are no closing backticks - pattern = f"```{data_type}" + r"(.*?)(```|$)" - # Extract content - # re.DOTALL allows '.' to match newlines as well - matches = re.findall(pattern, input_string, re.DOTALL) - # Return the first match if exists, trimming whitespace and ignoring potential closing backticks - return matches[0][0].strip() if matches else input_string - -class VLMAgent: - def __init__( - self, - model: str, - provider: str, - api_key: str, - output_callback: Callable, - api_response_callback: Callable, - max_tokens: int = 4096, - only_n_most_recent_images: int | None = None, - print_usage: bool = True, - ): - if model == "omniparser + gpt-4o": - self.model = "gpt-4o-2024-11-20" - else: - raise ValueError(f"Model {model} not supported") - - self.provider = provider - self.api_key = api_key - self.api_response_callback = api_response_callback - self.max_tokens = max_tokens - self.only_n_most_recent_images = only_n_most_recent_images - self.output_callback = output_callback - - self.print_usage = print_usage - self.total_token_usage = 0 - self.total_cost = 0 - - self.system = '' - - def __call__(self, messages: list, parsed_screen: list[str, list, dict]): - # Show results of Omniparser - image_base64 = parsed_screen['original_screenshot_base64'] - latency_omniparser = parsed_screen['latency'] - self.output_callback(f'Screenshot for {colorful_text_vlm}:\n', - sender="bot") - self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n', sender="bot") - screen_info = str(parsed_screen['screen_info']) - # self.output_callback(f'Screen Info for {colorful_text_vlm}:\n{screen_info}', sender="bot") - self.output_callback( - f'
' - f' Screen Info for {colorful_text_vlm}' - f' 
{screen_info}
' - f'
', - sender="bot" - ) - - - screenshot_uuid = parsed_screen['screenshot_uuid'] - screen_width, screen_height = parsed_screen['width'], parsed_screen['height'] - - # example parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} - boxids_and_labels = parsed_screen["screen_info"] - system = self._get_system_prompt(boxids_and_labels) - - # drop looping actions msg, byte image etc - planner_messages = messages - # import pdb; pdb.set_trace() - planner_messages = _keep_latest_images(planner_messages) - # if self.only_n_most_recent_images: - # _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) - # print(f"filtered_messages: {planner_messages}\n\n", "full messages:", messages) - - if isinstance(planner_messages[-1], dict): - if not isinstance(planner_messages[-1]["content"], list): - planner_messages[-1]["content"] = [planner_messages[-1]["content"]] - planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_{screenshot_uuid}.png") - planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_som_{screenshot_uuid}.png") - - # print(f"Sending messages to VLMPlanner : {planner_messages}") - start = time.time() - if "gpt" in self.model: - vlm_response, token_usage = run_oai_interleaved( - messages=planner_messages, - system=system, - llm=self.model, - api_key=self.api_key, - max_tokens=self.max_tokens, - temperature=0, - ) - print(f"oai token usage: {token_usage}") - self.total_token_usage += token_usage - self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ - elif "phi" in self.model: - pass # TODO - else: - raise ValueError(f"Model {self.model} not supported") - latency_vlm = time.time() - start - self.output_callback(f"VLMPlanner latency: {latency_vlm}, Omniparser latency: {latency_omniparser}", sender="bot") - - print(f"VLMPlanner response: {vlm_response}") - - if self.print_usage: - print(f"VLMPlanner total token usage so far: {self.total_token_usage}. Total cost so far: $USD{self.total_cost:.5f}") - - vlm_response_json = extract_data(vlm_response, "json") - vlm_response_json = json.loads(vlm_response_json) - - # map "box_id" to "idx" in parsed_screen, and output the xy coordinate of bbox - try: - bbox = parsed_screen["parsed_content_list"][int(vlm_response_json["Box ID"])]["bbox"] - vlm_response_json["box_centroid_coordinate"] = [int((bbox[0] + bbox[2]) / 2 * screen_width), int((bbox[1] + bbox[3]) / 2 * screen_height)] - # draw a circle on the screenshot image to indicate the action - self.draw_action(vlm_response_json, image_base64) - except: - print("No Box ID in the response.") - - # Convert the VLM output to a string for printing in chat - vlm_plan_str = "" - for key, value in vlm_response_json.items(): - if key == "Reasoning": - vlm_plan_str += f'{value}' - else: - vlm_plan_str += f'\n{key}: {value}' - # self.output_callback(f"{colorful_text_vlm}:\n{vlm_plan_str}", sender="bot") - - # construct the response so that anthropicExcutor can execute the tool - response_content = [BetaTextBlock(text=vlm_plan_str, type='text')] - if 'box_centroid_coordinate' in vlm_response_json: - move_cursor_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', - input={'action': 'mouse_move', 'coordinate': vlm_response_json["box_centroid_coordinate"]}, - name='computer', type='tool_use') - response_content.append(move_cursor_block) - if vlm_response_json["Next Action"] == "type": - click_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': 'left_click'}, name='computer', type='tool_use') - sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', - input={'action': vlm_response_json["Next Action"], 'text': vlm_response_json["value"]}, - name='computer', type='tool_use') - response_content.extend([click_block, sim_content_block]) - elif vlm_response_json["Next Action"] == "None": - print("Task paused/completed.") - else: - sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', - input={'action': vlm_response_json["Next Action"]}, - name='computer', type='tool_use') - response_content.append(sim_content_block) - response_message = BetaMessage(id=f'toolu_{uuid.uuid4()}', content=response_content, model='', role='assistant', type='message', stop_reason='tool_use', usage=BetaUsage(input_tokens=0, output_tokens=0)) - return response_message, vlm_response_json - - def _api_response_callback(self, response: APIResponse): - self.api_response_callback(response) - - def _get_system_prompt(self, screen_info: str = ""): - return f""" -You are using a Windows device. -You are able to use a mouse and keyboard to interact with the computer based on the given task and screenshot. -You can only interact with the desktop GUI (no terminal or application menu access). - -You may be given some history plan and actions, this is the response from the previous loop. -You should carefully consider your plan base on the task, screenshot, and history actions. - -Here is the list of all detected bounding boxes by IDs on the screen and their description:{screen_info} - -Your available "Next Action" only include: -- type: type a string of text. -- left_click: Describe the ui element to be clicked. -- double_click: Describe the ui element to be double clicked. -- right_click: Describe the ui element to be right clicked. -- escape: Press an ESCAPE key. -- hover: Describe the ui element to be hovered. -- scroll_up: Scroll the screen up. -- scroll_down: Scroll the screen down. -- press: Describe the ui element to be pressed. - -Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. - -Output format: -```json -{{ - "Reasoning": str, # describe what is in the current screen, taking into account the history, then describe your step-by-step thoughts on how to achieve the task, choose one action from available actions at a time. - "Next Action": "action_type, action description" | "None" # one action at a time, describe it in short and precisely. - 'Box ID': n, - 'value': "xxx" # if the action is type, you should provide the text to type. -}} -``` - -One Example: -```json -{{ - "Reasoning": "The current screen shows google result of amazon, in previous action I have searched amazon on google. Then I need to click on the first search results to go to amazon.com.", - "Next Action": "left_click", - 'Box ID': m, -}} -``` - -Another Example: -```json -{{ - "Reasoning": "The current screen shows the front page of amazon. There is no previous action. Therefore I need to type "Apple watch" in the search bar.", - "Next Action": "type", - 'Box ID': n, - 'value': "Apple watch" -}} -``` - -IMPORTANT NOTES: -1. You should only give a single action at a time. -2. You should give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. -3. Attach the next action prediction in the "Next Action". -4. You should not include other actions, such as keyboard shortcuts. -5. When the task is completed, you should say "Next Action": "None" in the json field. -""" - def draw_action(self, vlm_response_json, image_base64): - # draw a circle using the coordinate in parsed_screen['som_image_base64'] - image_data = base64.b64decode(image_base64) - image = Image.open(BytesIO(image_data)) - - draw = ImageDraw.Draw(image) - x, y = vlm_response_json["box_centroid_coordinate"] - radius = 30 - draw.ellipse((x - radius, y - radius, x + radius, y + radius), fill='red') - buffered = BytesIO() - image.save(buffered, format="PNG") - image_with_circle_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") - self.output_callback(f'Action performed on the red circle with centroid ({x}, {y}), for {colorful_text_vlm}:\n', sender="bot") - - -def _keep_latest_images(messages): - for i in range(len(messages)-1): - if isinstance(messages[i]["content"], list): - for cnt in messages[i]["content"]: - if isinstance(cnt, str): - if cnt.endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")): - messages[i]["content"].remove(cnt) - return messages - - -def _maybe_filter_to_n_most_recent_images( - messages: list[BetaMessageParam], - images_to_keep: int, - min_removal_threshold: int = 10, -): - """ - With the assumption that images are screenshots that are of diminishing value as - the conversation progresses, remove all but the final `images_to_keep` tool_result - images in place, with a chunk of min_removal_threshold to reduce the amount we - break the implicit prompt cache. - """ - if images_to_keep is None: - return messages - - tool_result_blocks = cast( - list[ToolResultBlockParam], - [ - item - for message in messages - for item in ( - message["content"] if isinstance(message["content"], list) else [] - ) - if isinstance(item, dict) and item.get("type") == "tool_result" - ], - ) - - total_images = sum( - 1 - for tool_result in tool_result_blocks - for content in tool_result.get("content", []) - if isinstance(content, dict) and content.get("type") == "image" - ) - - images_to_remove = total_images - images_to_keep - # for better cache behavior, we want to remove in chunks - images_to_remove -= images_to_remove % min_removal_threshold - - for tool_result in tool_result_blocks: - if isinstance(tool_result.get("content"), list): - new_content = [] - for content in tool_result.get("content", []): - if isinstance(content, dict) and content.get("type") == "image": - if images_to_remove > 0: - images_to_remove -= 1 - continue - new_content.append(content) - tool_result["content"] = new_content \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/colorful_text.py b/demo/gradio/computer_use_demo/colorful_text.py deleted file mode 100644 index 28d772b..0000000 --- a/demo/gradio/computer_use_demo/colorful_text.py +++ /dev/null @@ -1,17 +0,0 @@ -""" -Define some colorful stuffs for better visualization in the chat. -""" - -# Define the RGB colors for each letter -colors = { - 'S': 'rgb(106, 158, 210)', - 'h': 'rgb(111, 163, 82)', - 'o': 'rgb(209, 100, 94)', - 'w': 'rgb(238, 171, 106)', - 'U': 'rgb(0, 0, 0)', - 'I': 'rgb(0, 0, 0)', -} - -colorful_text_vlm = "**OmniParser Agent**" - -colorful_text_user = "**User**" \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/executor/anthropic_executor.py b/demo/gradio/computer_use_demo/executor/anthropic_executor.py deleted file mode 100644 index a5d5837..0000000 --- a/demo/gradio/computer_use_demo/executor/anthropic_executor.py +++ /dev/null @@ -1,132 +0,0 @@ -import asyncio -from typing import Any, Dict, cast -from collections.abc import Callable -from anthropic.types.beta import ( - BetaContentBlock, - BetaContentBlockParam, - BetaImageBlockParam, - BetaMessage, - BetaMessageParam, - BetaTextBlockParam, - BetaToolResultBlockParam, -) -from anthropic.types import TextBlock -from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock -from ..tools import ComputerTool, ToolCollection, ToolResult - - -class AnthropicExecutor: - def __init__( - self, - output_callback: Callable[[BetaContentBlockParam], None], - tool_output_callback: Callable[[Any, str], None], - ): - self.tool_collection = ToolCollection( - ComputerTool() - ) - self.output_callback = output_callback - self.tool_output_callback = tool_output_callback - - def __call__(self, response: BetaMessage, messages: list[BetaMessageParam]): - new_message = { - "role": "assistant", - "content": cast(list[BetaContentBlockParam], response.content), - } - if new_message not in messages: - messages.append(new_message) - else: - print("new_message already in messages, there are duplicates.") - - tool_result_content: list[BetaToolResultBlockParam] = [] - for content_block in cast(list[BetaContentBlock], response.content): - self.output_callback(content_block, sender="bot") - # Execute the tool - if content_block.type == "tool_use": - # Run the asynchronous tool execution in a synchronous context - result = asyncio.run(self.tool_collection.run( - name=content_block.name, - tool_input=cast(dict[str, Any], content_block.input), - )) - - self.output_callback(result, sender="bot") - - tool_result_content.append( - _make_api_tool_result(result, content_block.id) - ) - self.tool_output_callback(result, content_block.id) - - # Craft messages based on the content_block - # Note: to display the messages in the gradio, you should organize the messages in the following way (user message, bot message) - - display_messages = _message_display_callback(messages) - # display_messages = [] - - # Send the messages to the gradio - for user_msg, bot_msg in display_messages: - # yield [user_msg, bot_msg], tool_result_content - yield [None, None], tool_result_content - - if not tool_result_content: - return messages - - return tool_result_content - -def _message_display_callback(messages): - display_messages = [] - for msg in messages: - try: - if isinstance(msg["content"][0], TextBlock): - display_messages.append((msg["content"][0].text, None)) # User message - elif isinstance(msg["content"][0], BetaTextBlock): - display_messages.append((None, msg["content"][0].text)) # Bot message - elif isinstance(msg["content"][0], BetaToolUseBlock): - display_messages.append((None, f"Tool Use: {msg['content'][0].name}\nInput: {msg['content'][0].input}")) # Bot message - elif isinstance(msg["content"][0], Dict) and msg["content"][0]["content"][-1]["type"] == "image": - display_messages.append((None, f'')) # Bot message - else: - print(msg["content"][0]) - except Exception as e: - print("error", e) - pass - return display_messages - -def _make_api_tool_result( - result: ToolResult, tool_use_id: str -) -> BetaToolResultBlockParam: - """Convert an agent ToolResult to an API ToolResultBlockParam.""" - tool_result_content: list[BetaTextBlockParam | BetaImageBlockParam] | str = [] - is_error = False - if result.error: - is_error = True - tool_result_content = _maybe_prepend_system_tool_result(result, result.error) - else: - if result.output: - tool_result_content.append( - { - "type": "text", - "text": _maybe_prepend_system_tool_result(result, result.output), - } - ) - if result.base64_image: - tool_result_content.append( - { - "type": "image", - "source": { - "type": "base64", - "media_type": "image/png", - "data": result.base64_image, - }, - } - ) - return { - "type": "tool_result", - "content": tool_result_content, - "tool_use_id": tool_use_id, - "is_error": is_error, - } - - -def _maybe_prepend_system_tool_result(result: ToolResult, result_text: str): - if result.system: - result_text = f"{result.system}\n{result_text}" - return result_text \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/loop.py b/demo/gradio/computer_use_demo/loop.py deleted file mode 100644 index e702b10..0000000 --- a/demo/gradio/computer_use_demo/loop.py +++ /dev/null @@ -1,129 +0,0 @@ -""" -Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools. -""" -from collections.abc import Callable -from enum import StrEnum - -from anthropic import APIResponse -from anthropic.types import ( - TextBlock, -) -from anthropic.types.beta import ( - BetaContentBlock, - BetaMessage, - BetaMessageParam -) -from computer_use_demo.tools import ToolResult - -from computer_use_demo.agent.llm_utils.omniparserclient import OmniParserClient -from computer_use_demo.agent.anthropic_agent import AnthropicActor -from computer_use_demo.agent.vlm_agent import VLMAgent -from computer_use_demo.executor.anthropic_executor import AnthropicExecutor - -BETA_FLAG = "computer-use-2024-10-22" - -class APIProvider(StrEnum): - ANTHROPIC = "anthropic" - BEDROCK = "bedrock" - VERTEX = "vertex" - OPENAI = "openai" - - -PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = { - APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", - APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0", - APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022", - APIProvider.OPENAI: "gpt-4o", -} - -def sampling_loop_sync( - *, - model: str, - provider: APIProvider | None, - messages: list[BetaMessageParam], - output_callback: Callable[[BetaContentBlock], None], - tool_output_callback: Callable[[ToolResult, str], None], - api_response_callback: Callable[[APIResponse[BetaMessage]], None], - api_key: str, - only_n_most_recent_images: int | None = 2, - max_tokens: int = 4096, - omniparser_url: str -): - """ - Synchronous agentic sampling loop for the assistant/tool interaction of computer use. - """ - print('in sampling_loop_sync, model:', model) - omniparser_client = OmniParserClient(url=f"http://{omniparser_url}/parse/") - if model == "claude-3-5-sonnet-20241022": - # Register Actor and Executor - actor = AnthropicActor( - model=model, - provider=provider, - api_key=api_key, - api_response_callback=api_response_callback, - max_tokens=max_tokens, - only_n_most_recent_images=only_n_most_recent_images - ) - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - executor = AnthropicExecutor( - output_callback=output_callback, - tool_output_callback=tool_output_callback - ) - - elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": - actor = VLMAgent( - model=model, - provider=provider, - api_key=api_key, - api_response_callback=api_response_callback, - output_callback=output_callback, - ) - - executor = AnthropicExecutor( - output_callback=output_callback, - tool_output_callback=tool_output_callback, - ) - - else: - raise ValueError(f"Model {model} not supported") - print(f"Model Inited: {model}, Provider: {provider}") - - tool_result_content = None - - print(f"Start the message loop. User messages: {messages}") - - if model == "claude-3-5-sonnet-20241022": # Anthropic loop - while True: - parsed_screen = omniparser_client() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} - import pdb; pdb.set_trace() - screen_info_block = TextBlock(text='Below is the structured accessibility information of the current UI screen, which includes text and icons you can operate on, take these information into account when you are making the prediction for the next action. Note you will still need to take screenshot to get the image: \n' + parsed_screen['screen_info'], type='text') - # # messages[-1]['content'].append(screen_info_block) - screen_info_dict = {"role": "user", "content": [screen_info_block]} - messages.append(screen_info_dict) - tools_use_needed = actor(messages=messages) - - for message, tool_result_content in executor(tools_use_needed, messages): - yield message - - if not tool_result_content: - return messages - - messages.append({"content": tool_result_content, "role": "user"}) - - elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": - while True: - parsed_screen = omniparser_client() - tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) - - for message, tool_result_content in executor(tools_use_needed, messages): - yield message - - if not tool_result_content: - return messages - - # import pdb; pdb.set_trace() - # messages.append({"role": "user", - # "content": ["History plan:\n" + str(vlm_response_json['Reasoning'])]}) - - # messages.append({"content": tool_result_content, "role": "user"}) \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/tools/__init__.py b/demo/gradio/computer_use_demo/tools/__init__.py deleted file mode 100644 index bb051ae..0000000 --- a/demo/gradio/computer_use_demo/tools/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from .base import CLIResult, ToolResult -from .collection import ToolCollection -from .computer import ComputerTool -from .screen_capture import get_screenshot - -__ALL__ = [ - CLIResult, - ComputerTool, - ToolCollection, - ToolResult, - get_screenshot, -] diff --git a/demo/gradio/computer_use_demo/tools/base.py b/demo/gradio/computer_use_demo/tools/base.py deleted file mode 100644 index d6f1371..0000000 --- a/demo/gradio/computer_use_demo/tools/base.py +++ /dev/null @@ -1,69 +0,0 @@ -from abc import ABCMeta, abstractmethod -from dataclasses import dataclass, fields, replace -from typing import Any - -from anthropic.types.beta import BetaToolUnionParam - - -class BaseAnthropicTool(metaclass=ABCMeta): - """Abstract base class for Anthropic-defined tools.""" - - @abstractmethod - def __call__(self, **kwargs) -> Any: - """Executes the tool with the given arguments.""" - ... - - @abstractmethod - def to_params( - self, - ) -> BetaToolUnionParam: - raise NotImplementedError - - -@dataclass(kw_only=True, frozen=True) -class ToolResult: - """Represents the result of a tool execution.""" - - output: str | None = None - error: str | None = None - base64_image: str | None = None - system: str | None = None - - def __bool__(self): - return any(getattr(self, field.name) for field in fields(self)) - - def __add__(self, other: "ToolResult"): - def combine_fields( - field: str | None, other_field: str | None, concatenate: bool = True - ): - if field and other_field: - if concatenate: - return field + other_field - raise ValueError("Cannot combine tool results") - return field or other_field - - return ToolResult( - output=combine_fields(self.output, other.output), - error=combine_fields(self.error, other.error), - base64_image=combine_fields(self.base64_image, other.base64_image, False), - system=combine_fields(self.system, other.system), - ) - - def replace(self, **kwargs): - """Returns a new ToolResult with the given fields replaced.""" - return replace(self, **kwargs) - - -class CLIResult(ToolResult): - """A ToolResult that can be rendered as a CLI output.""" - - -class ToolFailure(ToolResult): - """A ToolResult that represents a failure.""" - - -class ToolError(Exception): - """Raised when a tool encounters an error.""" - - def __init__(self, message): - self.message = message diff --git a/demo/gradio/computer_use_demo/tools/collection.py b/demo/gradio/computer_use_demo/tools/collection.py deleted file mode 100644 index c4e8c95..0000000 --- a/demo/gradio/computer_use_demo/tools/collection.py +++ /dev/null @@ -1,34 +0,0 @@ -"""Collection classes for managing multiple tools.""" - -from typing import Any - -from anthropic.types.beta import BetaToolUnionParam - -from .base import ( - BaseAnthropicTool, - ToolError, - ToolFailure, - ToolResult, -) - - -class ToolCollection: - """A collection of anthropic-defined tools.""" - - def __init__(self, *tools: BaseAnthropicTool): - self.tools = tools - self.tool_map = {tool.to_params()["name"]: tool for tool in tools} - - def to_params( - self, - ) -> list[BetaToolUnionParam]: - return [tool.to_params() for tool in self.tools] - - async def run(self, *, name: str, tool_input: dict[str, Any]) -> ToolResult: - tool = self.tool_map.get(name) - if not tool: - return ToolFailure(error=f"Tool {name} is invalid") - try: - return await tool(**tool_input) - except ToolError as e: - return ToolFailure(error=e.message) diff --git a/demo/gradio/computer_use_demo/tools/computer.py b/demo/gradio/computer_use_demo/tools/computer.py deleted file mode 100644 index adad9a2..0000000 --- a/demo/gradio/computer_use_demo/tools/computer.py +++ /dev/null @@ -1,324 +0,0 @@ -import base64 -import time -from enum import StrEnum -from typing import Literal, TypedDict - -from PIL import Image - -from anthropic.types.beta import BetaToolComputerUse20241022Param - -from .base import BaseAnthropicTool, ToolError, ToolResult -from .screen_capture import get_screenshot -import requests -import re - -OUTPUT_DIR = "./tmp/outputs" - -TYPING_DELAY_MS = 12 -TYPING_GROUP_SIZE = 50 - -Action = Literal[ - "key", - "type", - "mouse_move", - "left_click", - "left_click_drag", - "right_click", - "middle_click", - "double_click", - "screenshot", - "cursor_position", -] - - -class Resolution(TypedDict): - width: int - height: int - - -MAX_SCALING_TARGETS: dict[str, Resolution] = { - "XGA": Resolution(width=1024, height=768), # 4:3 - "WXGA": Resolution(width=1280, height=800), # 16:10 - "FWXGA": Resolution(width=1366, height=768), # ~16:9 -} - - -class ScalingSource(StrEnum): - COMPUTER = "computer" - API = "api" - - -class ComputerToolOptions(TypedDict): - display_height_px: int - display_width_px: int - display_number: int | None - - -def chunks(s: str, chunk_size: int) -> list[str]: - return [s[i : i + chunk_size] for i in range(0, len(s), chunk_size)] - -class ComputerTool(BaseAnthropicTool): - """ - A tool that allows the agent to interact with the screen, keyboard, and mouse of the current computer. - Adapted for Windows using 'pyautogui'. - """ - - name: Literal["computer"] = "computer" - api_type: Literal["computer_20241022"] = "computer_20241022" - width: int - height: int - display_num: int | None - - _screenshot_delay = 2.0 - _scaling_enabled = True - - @property - def options(self) -> ComputerToolOptions: - width, height = self.scale_coordinates( - ScalingSource.COMPUTER, self.width, self.height - ) - return { - "display_width_px": width, - "display_height_px": height, - "display_number": self.display_num, - } - - def to_params(self) -> BetaToolComputerUse20241022Param: - return {"name": self.name, "type": self.api_type, **self.options} - - def __init__(self, is_scaling: bool = False): - super().__init__() - - # Get screen width and height using Windows command - self.display_num = None - self.offset_x = 0 - self.offset_y = 0 - self.is_scaling = is_scaling - self.width, self.height = self.get_screen_size() - print(f"screen size: {self.width}, {self.height}") - - self.key_conversion = {"Page_Down": "pagedown", - "Page_Up": "pageup", - "Super_L": "win", - "Escape": "esc"} - - - async def __call__( - self, - *, - action: Action, - text: str | None = None, - coordinate: tuple[int, int] | None = None, - **kwargs, - ): - print(f"action: {action}, text: {text}, coordinate: {coordinate}, is_scaling: {self.is_scaling}") - if action in ("mouse_move", "left_click_drag"): - if coordinate is None: - raise ToolError(f"coordinate is required for {action}") - if text is not None: - raise ToolError(f"text is not accepted for {action}") - if not isinstance(coordinate, (list, tuple)) or len(coordinate) != 2: - raise ToolError(f"{coordinate} must be a tuple of length 2") - # if not all(isinstance(i, int) and i >= 0 for i in coordinate): - if not all(isinstance(i, int) for i in coordinate): - raise ToolError(f"{coordinate} must be a tuple of non-negative ints") - - if self.is_scaling: - x, y = self.scale_coordinates( - ScalingSource.API, coordinate[0], coordinate[1] - ) - else: - x, y = coordinate - - # print(f"scaled_coordinates: {x}, {y}") - # print(f"offset: {self.offset_x}, {self.offset_y}") - - # x += self.offset_x # TODO - check if this is needed - # y += self.offset_y - - print(f"mouse move to {x}, {y}") - - if action == "mouse_move": - self.send_to_vm(f"pyautogui.moveTo({x}, {y})") - return ToolResult(output=f"Moved mouse to ({x}, {y})") - elif action == "left_click_drag": - current_x, current_y = self.send_to_vm("pyautogui.position()") - self.send_to_vm(f"pyautogui.dragTo({x}, {y}, duration=0.5)") - return ToolResult(output=f"Dragged mouse from ({current_x}, {current_y}) to ({x}, {y})") - - if action in ("key", "type"): - if text is None: - raise ToolError(f"text is required for {action}") - if coordinate is not None: - raise ToolError(f"coordinate is not accepted for {action}") - if not isinstance(text, str): - raise ToolError(output=f"{text} must be a string") - - if action == "key": - # Handle key combinations - keys = text.split('+') - for key in keys: - key = self.key_conversion.get(key.strip(), key.strip()) - key = key.lower() - self.send_to_vm(f"pyautogui.keyDown('{key}')") # Press down each key - for key in reversed(keys): - key = self.key_conversion.get(key.strip(), key.strip()) - key = key.lower() - self.send_to_vm(f"pyautogui.keyUp('{key}')") # Release each key in reverse order - return ToolResult(output=f"Pressed keys: {text}") - - elif action == "type": - self.send_to_vm(f"pyautogui.typewrite('{text}', interval={TYPING_DELAY_MS / 1000})") # Convert ms to seconds - self.send_to_vm("pyautogui.press('enter')") - screenshot_base64 = (await self.screenshot()).base64_image - return ToolResult(output=text, base64_image=screenshot_base64) - - if action in ( - "left_click", - "right_click", - "double_click", - "middle_click", - "screenshot", - "cursor_position", - "left_press", - ): - if text is not None: - raise ToolError(f"text is not accepted for {action}") - if coordinate is not None: - raise ToolError(f"coordinate is not accepted for {action}") - - if action == "screenshot": - return await self.screenshot() - elif action == "cursor_position": - x, y = self.send_to_vm("pyautogui.position()") - x, y = self.scale_coordinates(ScalingSource.COMPUTER, x, y) - return ToolResult(output=f"X={x},Y={y}") - else: - if action == "left_click": - self.send_to_vm("pyautogui.click()") - elif action == "right_click": - self.send_to_vm("pyautogui.rightClick()") - elif action == "middle_click": - self.send_to_vm("pyautogui.middleClick()") - elif action == "double_click": - self.send_to_vm("pyautogui.doubleClick()") - elif action == "left_press": - self.send_to_vm("pyautogui.mouseDown()") - time.sleep(1) - self.send_to_vm("pyautogui.mouseUp()") - return ToolResult(output=f"Performed {action}") - if action in ("scroll_up", "scroll_down"): - if action == "scroll_up": - self.send_to_vm("pyautogui.scroll(100)") - elif action == "scroll_down": - self.send_to_vm("pyautogui.scroll(-100)") - return ToolResult(output=f"Performed {action}") - - raise ToolError(f"Invalid action: {action}") - - def send_to_vm(self, action: str): - """ - Executes a python command on the server. Only return tuple of x,y when action is "pyautogui.position()" - """ - prefix = "import pyautogui; pyautogui.FAILSAFE = False;" - command_list = ["python", "-c", f"{prefix} {action}"] - parse = action == "pyautogui.position()" - if parse: - command_list[-1] = f"{prefix} print({action})" - - try: - print(f"sending to vm: {command_list}") - response = requests.post( - f"http://localhost:5000/execute", - headers={'Content-Type': 'application/json'}, - json={"command": command_list}, - timeout=90 - ) - time.sleep(0.7) # avoid async error as actions take time to complete - print(f"action executed") - if response.status_code != 200: - raise ToolError(f"Failed to execute command. Status code: {response.status_code}") - if parse: - output = response.json()['output'].strip() - match = re.search(r'Point\(x=(\d+),\s*y=(\d+)\)', output) - if not match: - raise ToolError(f"Could not parse coordinates from output: {output}") - x, y = map(int, match.groups()) - return x, y - except requests.exceptions.RequestException as e: - raise ToolError(f"An error occurred while trying to execute the command: {str(e)}") - - async def screenshot(self): - if not hasattr(self, 'target_dimension'): - screenshot = self.padding_image(screenshot) - self.target_dimension = MAX_SCALING_TARGETS["WXGA"] - width, height = self.target_dimension["width"], self.target_dimension["height"] - screenshot, path = get_screenshot(resize=True, target_width=width, target_height=height) - time.sleep(0.7) # avoid async error as actions take time to complete - # return ToolResult() - return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode()) - - raise ToolError(f"Failed to take screenshot: {path} does not exist.") - - def padding_image(self, screenshot): - """Pad the screenshot to 16:10 aspect ratio, when the aspect ratio is not 16:10.""" - _, height = screenshot.size - new_width = height * 16 // 10 - - padding_image = Image.new("RGB", (new_width, height), (255, 255, 255)) - # padding to top left - padding_image.paste(screenshot, (0, 0)) - return padding_image - - def scale_coordinates(self, source: ScalingSource, x: int, y: int): - """Scale coordinates to a target maximum resolution.""" - if not self._scaling_enabled: - return x, y - ratio = self.width / self.height - target_dimension = None - - for target_name, dimension in MAX_SCALING_TARGETS.items(): - # allow some error in the aspect ratio - not ratios are exactly 16:9 - if abs(dimension["width"] / dimension["height"] - ratio) < 0.02: - if dimension["width"] < self.width: - target_dimension = dimension - self.target_dimension = target_dimension - # print(f"target_dimension: {target_dimension}") - break - - if target_dimension is None: - # TODO: currently we force the target to be WXGA (16:10), when it cannot find a match - target_dimension = MAX_SCALING_TARGETS["WXGA"] - self.target_dimension = MAX_SCALING_TARGETS["WXGA"] - - # should be less than 1 - x_scaling_factor = target_dimension["width"] / self.width - y_scaling_factor = target_dimension["height"] / self.height - if source == ScalingSource.API: - if x > self.width or y > self.height: - raise ToolError(f"Coordinates {x}, {y} are out of bounds") - # scale up - return round(x / x_scaling_factor), round(y / y_scaling_factor) - # scale down - return round(x * x_scaling_factor), round(y * y_scaling_factor) - - def get_screen_size(self): - """Return width and height of the screen""" - try: - response = requests.post( - f"http://localhost:5000/execute", - headers={'Content-Type': 'application/json'}, - json={"command": ["python", "-c", "import pyautogui; print(pyautogui.size())"]}, - timeout=90 - ) - if response.status_code != 200: - raise ToolError(f"Failed to get screen size. Status code: {response.status_code}") - - output = response.json()['output'].strip() - match = re.search(r'Size\(width=(\d+),\s*height=(\d+)\)', output) - if not match: - raise ToolError(f"Could not parse screen size from output: {output}") - width, height = map(int, match.groups()) - return width, height - except requests.exceptions.RequestException as e: - raise ToolError(f"An error occurred while trying to get screen size: {str(e)}") \ No newline at end of file diff --git a/demo/gradio/computer_use_demo/tools/screen_capture.py b/demo/gradio/computer_use_demo/tools/screen_capture.py deleted file mode 100644 index 1c1ad04..0000000 --- a/demo/gradio/computer_use_demo/tools/screen_capture.py +++ /dev/null @@ -1,29 +0,0 @@ -from pathlib import Path -from uuid import uuid4 -import requests -from PIL import Image -from .base import BaseAnthropicTool, ToolError -from io import BytesIO - -OUTPUT_DIR = "./tmp/outputs" - -def get_screenshot(resize: bool = False, target_width: int = 1920, target_height: int = 1080): - """Capture screenshot by requesting from HTTP endpoint - returns native resolution unless resized""" - output_dir = Path(OUTPUT_DIR) - output_dir.mkdir(parents=True, exist_ok=True) - path = output_dir / f"screenshot_{uuid4().hex}.png" - - try: - response = requests.get('http://localhost:5000/screenshot') - if response.status_code != 200: - raise ToolError(f"Failed to capture screenshot: HTTP {response.status_code}") - - # (1280, 800) - screenshot = Image.open(BytesIO(response.content)) - - if resize and screenshot.size != (target_width, target_height): - screenshot = screenshot.resize((target_width, target_height)) - screenshot.save(path) - return screenshot, path - except Exception as e: - raise ToolError(f"Failed to capture screenshot: {str(e)}") \ No newline at end of file From f6029344c53d742d2632cbd08e37a8113fda6e03 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Wed, 29 Jan 2025 22:45:26 -0800 Subject: [PATCH 28/57] further clean colored text --- computer_use_demo/gradio/agent/vlm_agent.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py index 6703ee7..76815a6 100644 --- a/computer_use_demo/gradio/agent/vlm_agent.py +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -11,7 +11,6 @@ from anthropic.types import ToolResultBlockParam from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage from agent.llm_utils.oai import run_oai_interleaved -from colorful_text import colorful_text_vlm import time import re @@ -60,14 +59,14 @@ class VLMAgent: # Show results of Omniparser image_base64 = parsed_screen['original_screenshot_base64'] latency_omniparser = parsed_screen['latency'] - self.output_callback(f'Screenshot for {colorful_text_vlm}:\n', + self.output_callback(f'Screenshot for OmniParser Agent:\n', sender="bot") - self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n', sender="bot") + self.output_callback(f'Set of Marks Screenshot for OmniParser Agent:\n', sender="bot") screen_info = str(parsed_screen['screen_info']) - # self.output_callback(f'Screen Info for {colorful_text_vlm}:\n{screen_info}', sender="bot") + # self.output_callback(f'Screen Info for OmniParser Agent:\n{screen_info}', sender="bot") self.output_callback( f'
' - f' Screen Info for {colorful_text_vlm}' + f' Screen Info for OmniParser Agent' f' 
{screen_info}
' f'
', sender="bot" @@ -140,7 +139,7 @@ class VLMAgent: vlm_plan_str += f'{value}' else: vlm_plan_str += f'\n{key}: {value}' - # self.output_callback(f"{colorful_text_vlm}:\n{vlm_plan_str}", sender="bot") + # self.output_callback(f"OmniParser Agent:\n{vlm_plan_str}", sender="bot") # construct the response so that anthropicExcutor can execute the tool response_content = [BetaTextBlock(text=vlm_plan_str, type='text')] @@ -240,7 +239,7 @@ IMPORTANT NOTES: buffered = BytesIO() image.save(buffered, format="PNG") image_with_circle_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") - self.output_callback(f'Action performed on the red circle with centroid ({x}, {y}), for {colorful_text_vlm}:\n', sender="bot") + self.output_callback(f'Action performed on the red circle with centroid ({x}, {y}), for OmniParser Agent:\n', sender="bot") def _keep_latest_images(messages): From b2d6bc5c3e237e4850ad2c8cb5dc9e880703232c Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Wed, 29 Jan 2025 23:01:14 -0800 Subject: [PATCH 29/57] code cleanup --- .../gradio/agent/anthropic_agent.py | 22 +----------- .../gradio/agent/llm_utils/oai.py | 35 ++----------------- computer_use_demo/gradio/app.py | 31 +++++----------- computer_use_demo/gradio/loop.py | 10 +----- computer_use_demo/gradio/tools/__init__.py | 3 +- computer_use_demo/gradio/tools/base.py | 4 --- 6 files changed, 13 insertions(+), 92 deletions(-) diff --git a/computer_use_demo/gradio/agent/anthropic_agent.py b/computer_use_demo/gradio/agent/anthropic_agent.py index d891f1f..b1c744e 100644 --- a/computer_use_demo/gradio/agent/anthropic_agent.py +++ b/computer_use_demo/gradio/agent/anthropic_agent.py @@ -159,24 +159,4 @@ def _maybe_filter_to_n_most_recent_images( images_to_remove -= 1 continue new_content.append(content) - tool_result["content"] = new_content - - - -if __name__ == "__main__": - pass - # client = Anthropic(api_key="") - # response = client.beta.messages.with_raw_response.create( - # max_tokens=4096, - # model="claude-3-5-sonnet-20241022", - # system=SYSTEM_PROMPT, - # # tools=ToolCollection( - # # ComputerTool(), - # # ).to_params(), - # betas=["computer-use-2024-10-22"], - # messages=[ - # {"role": "user", "content": "click on (199, 199)."} - # ], - # ) - - # print(f"AnthropicActor response: {response.parse().usage.input_tokens+response.parse().usage.output_tokens}") \ No newline at end of file + tool_result["content"] = new_content \ No newline at end of file diff --git a/computer_use_demo/gradio/agent/llm_utils/oai.py b/computer_use_demo/gradio/agent/llm_utils/oai.py index 0ab3760..2d613f1 100644 --- a/computer_use_demo/gradio/agent/llm_utils/oai.py +++ b/computer_use_demo/gradio/agent/llm_utils/oai.py @@ -1,11 +1,9 @@ - import os import logging import base64 import requests def is_image_path(text): - # Checking if the input text ends with typical image file extensions image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") if text.endswith(image_extensions): return True @@ -28,7 +26,6 @@ def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max final_messages = [{"role": "system", "content": system}] - # image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" if type(messages) == list: for item in messages: contents = [] @@ -56,7 +53,6 @@ def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max elif isinstance(messages, str): final_messages = [{"role": "user", "content": messages}] - # import pdb; pdb.set_trace() print("[oai] sending messages:", {"role": "user", "content": messages}) @@ -64,12 +60,9 @@ def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max "model": llm, "messages": final_messages, "max_tokens": max_tokens, - "temperature": temperature, - # "stop": stop, + "temperature": temperature } - # from IPython.core.debugger import Pdb; Pdb().set_trace() - response = requests.post( "https://api.openai.com/v1/chat/completions", headers=headers, json=payload ) @@ -78,30 +71,6 @@ def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max text = response.json()['choices'][0]['message']['content'] token_usage = int(response.json()['usage']['total_tokens']) return text, token_usage - - # return error message if the response is not successful except Exception as e: print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") - return response.json() - - -if __name__ == "__main__": - - api_key = os.environ.get("OPENAI_API_KEY") - if not api_key: - raise ValueError("OPENAI_API_KEY is not set") - - text, token_usage = run_oai_interleaved( - messages= [{"content": [ - "What is in the screenshot?", - "./tmp/outputs/screenshot_0b04acbb783d4706bc93873d17ba8c05.png"], - "role": "user" - }], - llm="gpt-4o-mini", - system="You are a helpful assistant", - api_key=api_key, - max_tokens=256, - temperature=0) - - print(text, token_usage) - # There is an introduction describing the Calyx... 36986 + return response.json() \ No newline at end of file diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py index 5e216c6..69c4efa 100644 --- a/computer_use_demo/gradio/app.py +++ b/computer_use_demo/gradio/app.py @@ -1,6 +1,5 @@ """ -Entrypoint for Gradio, see https://gradio.app/ -python app.py --windows_host_url xxxx:8006/ --omniparser_host_url localhost:8000 +python app.py --windows_host_url localhost:8006/ --omniparser_server_url localhost:8000 """ import os @@ -35,13 +34,9 @@ Type a message and press submit to start OmniParser+X. Press the trash icon in t def parse_arguments(): parser = argparse.ArgumentParser(description="Gradio App") parser.add_argument("--windows_host_url", type=str, default='localhost:8006') - parser.add_argument("--omniparser_host_url", type=str, default="localhost:8000") + parser.add_argument("--omniparser_server_url", type=str, default="localhost:8000") return parser.parse_args() args = parse_arguments() -windows_host_url = args.windows_host_url -omniparser_host_url = args.omniparser_host_url -print(f"Windows host URL: {windows_host_url}") -print(f"OmniParser host URL: {omniparser_host_url}") class Sender(StrEnum): @@ -140,7 +135,6 @@ def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="b is_tool_result = not isinstance(message, str) and ( isinstance(message, ToolResult) or message.__class__.__name__ == "ToolResult" - or message.__class__.__name__ == "CLIResult" ) if not message or ( is_tool_result @@ -214,7 +208,7 @@ def process_input(user_input, state): api_response_callback=partial(_api_response_callback, response_state=state["responses"]), api_key=state["api_key"], only_n_most_recent_images=state["only_n_most_recent_images"], - omniparser_url=omniparser_host_url + omniparser_url=args.omniparser_server_url ): if loop_msg is None: yield state['chatbot_messages'] @@ -289,20 +283,11 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: with gr.Column(scale=1): chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580) with gr.Column(scale=3): - if not windows_host_url: - iframe = gr.HTML( - f'', - container=False, - elem_classes="no-padding" - ) - else: - # machine_fqdn = socket.getfqdn() - # print('machine_fqdn:', machine_fqdn) - iframe = gr.HTML( - f'', - container=False, - elem_classes="no-padding" - ) + iframe = gr.HTML( + f'', + container=False, + elem_classes="no-padding" + ) def update_model(model_selection, state): state["model"] = model_selection diff --git a/computer_use_demo/gradio/loop.py b/computer_use_demo/gradio/loop.py index f989ef8..f809d3e 100644 --- a/computer_use_demo/gradio/loop.py +++ b/computer_use_demo/gradio/loop.py @@ -96,9 +96,7 @@ def sampling_loop_sync( if model == "claude-3-5-sonnet-20241022": # Anthropic loop while True: parsed_screen = omniparser_client() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} - import pdb; pdb.set_trace() screen_info_block = TextBlock(text='Below is the structured accessibility information of the current UI screen, which includes text and icons you can operate on, take these information into account when you are making the prediction for the next action. Note you will still need to take screenshot to get the image: \n' + parsed_screen['screen_info'], type='text') - # # messages[-1]['content'].append(screen_info_block) screen_info_dict = {"role": "user", "content": [screen_info_block]} messages.append(screen_info_dict) tools_use_needed = actor(messages=messages) @@ -120,10 +118,4 @@ def sampling_loop_sync( yield message if not tool_result_content: - return messages - - # import pdb; pdb.set_trace() - # messages.append({"role": "user", - # "content": ["History plan:\n" + str(vlm_response_json['Reasoning'])]}) - - # messages.append({"content": tool_result_content, "role": "user"}) \ No newline at end of file + return messages \ No newline at end of file diff --git a/computer_use_demo/gradio/tools/__init__.py b/computer_use_demo/gradio/tools/__init__.py index bb051ae..2726f25 100644 --- a/computer_use_demo/gradio/tools/__init__.py +++ b/computer_use_demo/gradio/tools/__init__.py @@ -1,10 +1,9 @@ -from .base import CLIResult, ToolResult +from .base import ToolResult from .collection import ToolCollection from .computer import ComputerTool from .screen_capture import get_screenshot __ALL__ = [ - CLIResult, ComputerTool, ToolCollection, ToolResult, diff --git a/computer_use_demo/gradio/tools/base.py b/computer_use_demo/gradio/tools/base.py index d6f1371..8a82c1b 100644 --- a/computer_use_demo/gradio/tools/base.py +++ b/computer_use_demo/gradio/tools/base.py @@ -54,10 +54,6 @@ class ToolResult: return replace(self, **kwargs) -class CLIResult(ToolResult): - """A ToolResult that can be rendered as a CLI output.""" - - class ToolFailure(ToolResult): """A ToolResult that represents a failure.""" From e8882d84846b1ede5fb07c71bb9762bbaf4a52d2 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova Date: Wed, 29 Jan 2025 23:36:38 -0800 Subject: [PATCH 30/57] init windowshost --- computer_use_demo/windowshost/.gitignore | 4 + computer_use_demo/windowshost/Dockerfile | 48 + computer_use_demo/windowshost/compose.yml | 23 + .../windowshost/scripts/manage_vm.ps1 | 70 ++ .../windowshost/scripts/manage_vm.sh | 77 ++ .../windowshost/vm/buildcontainer/define.sh | 410 +++++++ .../windowshost/vm/buildcontainer/entry.sh | 38 + .../windowshost/vm/buildcontainer/install.sh | 1024 +++++++++++++++++ .../windowshost/vm/buildcontainer/power.sh | 223 ++++ .../windowshost/vm/buildcontainer/samba.sh | 109 ++ .../vm/win11def/win11x64-enterprise-eval.xml | 462 ++++++++ .../windowshost/vm/win11iso/README.md | 1 + .../vm/win11setup/firstboot/install.bat | 31 + .../vm/win11setup/setupscripts/on-logon.ps1 | 7 + .../win11setup/setupscripts/server/cursor.png | Bin 0 -> 3207 bytes .../vm/win11setup/setupscripts/server/main.py | 81 ++ .../setupscripts/server/requirements.txt | 2 + .../win11setup/setupscripts/setup-tools.psm1 | 197 ++++ .../vm/win11setup/setupscripts/setup.ps1 | 392 +++++++ .../win11setup/setupscripts/tools_config.json | 71 ++ 20 files changed, 3270 insertions(+) create mode 100644 computer_use_demo/windowshost/.gitignore create mode 100644 computer_use_demo/windowshost/Dockerfile create mode 100644 computer_use_demo/windowshost/compose.yml create mode 100644 computer_use_demo/windowshost/scripts/manage_vm.ps1 create mode 100755 computer_use_demo/windowshost/scripts/manage_vm.sh create mode 100644 computer_use_demo/windowshost/vm/buildcontainer/define.sh create mode 100644 computer_use_demo/windowshost/vm/buildcontainer/entry.sh create mode 100644 computer_use_demo/windowshost/vm/buildcontainer/install.sh create mode 100644 computer_use_demo/windowshost/vm/buildcontainer/power.sh create mode 100644 computer_use_demo/windowshost/vm/buildcontainer/samba.sh create mode 100644 computer_use_demo/windowshost/vm/win11def/win11x64-enterprise-eval.xml create mode 100644 computer_use_demo/windowshost/vm/win11iso/README.md create mode 100644 computer_use_demo/windowshost/vm/win11setup/firstboot/install.bat create mode 100644 computer_use_demo/windowshost/vm/win11setup/setupscripts/on-logon.ps1 create mode 100644 computer_use_demo/windowshost/vm/win11setup/setupscripts/server/cursor.png create mode 100644 computer_use_demo/windowshost/vm/win11setup/setupscripts/server/main.py create mode 100644 computer_use_demo/windowshost/vm/win11setup/setupscripts/server/requirements.txt create mode 100644 computer_use_demo/windowshost/vm/win11setup/setupscripts/setup-tools.psm1 create mode 100644 computer_use_demo/windowshost/vm/win11setup/setupscripts/setup.ps1 create mode 100644 computer_use_demo/windowshost/vm/win11setup/setupscripts/tools_config.json diff --git a/computer_use_demo/windowshost/.gitignore b/computer_use_demo/windowshost/.gitignore new file mode 100644 index 0000000..ed5deb8 --- /dev/null +++ b/computer_use_demo/windowshost/.gitignore @@ -0,0 +1,4 @@ +vm/win11iso/custom.iso +vm/win11storage +vm/win11setup/setupscripts/firstboot_log.txt +vm/win11setup/setupscripts/server/server.log diff --git a/computer_use_demo/windowshost/Dockerfile b/computer_use_demo/windowshost/Dockerfile new file mode 100644 index 0000000..b464714 --- /dev/null +++ b/computer_use_demo/windowshost/Dockerfile @@ -0,0 +1,48 @@ +ARG VERSION_ARG="latest" +FROM scratch AS build-amd64 + +COPY --from=qemux/qemu-docker:6.08 / / + +ARG DEBCONF_NOWARNINGS="yes" +ARG DEBIAN_FRONTEND="noninteractive" +ARG DEBCONF_NONINTERACTIVE_SEEN="true" + +RUN set -eu && \ + apt-get update && \ + apt-get --no-install-recommends -y install \ + bc \ + jq \ + curl \ + 7zip \ + wsdd \ + samba \ + xz-utils \ + wimtools \ + dos2unix \ + cabextract \ + genisoimage \ + libxml2-utils \ + libarchive-tools && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* + +COPY --chmod=755 ./vm/buildcontainer /run/ +RUN dos2unix /run/* + +COPY --chmod=755 ./vm/win11def /run/assets +RUN dos2unix /run/assets/* + +ADD --chmod=755 https://raw.githubusercontent.com/christgau/wsdd/v0.8/src/wsdd.py /usr/sbin/wsdd +ADD --chmod=664 https://github.com/qemus/virtiso-whql/releases/download/v1.9.43-0/virtio-win-1.9.43.tar.xz /drivers.txz + +FROM dockurr/windows-arm:${VERSION_ARG} AS build-arm64 +FROM build-${TARGETARCH} + +ARG VERSION_ARG="0.00" +RUN echo "$VERSION_ARG" > /run/version + +EXPOSE 8006 3389 + +ENV VERSION="win11e" + +ENTRYPOINT ["/usr/bin/tini", "-s", "/run/entry.sh"] \ No newline at end of file diff --git a/computer_use_demo/windowshost/compose.yml b/computer_use_demo/windowshost/compose.yml new file mode 100644 index 0000000..9ad6b90 --- /dev/null +++ b/computer_use_demo/windowshost/compose.yml @@ -0,0 +1,23 @@ +services: + windows: + image: windows-local + container_name: omni-windows + privileged: true + environment: + RAM_SIZE: "8G" + CPU_CORES: "4" + DISK_SIZE: "20G" + devices: + - /dev/kvm + - /dev/net/tun + cap_add: + - NET_ADMIN + ports: + - 8006:8006 # Web Viewer access + - 5000:5000 # Computer control server + volumes: + - ./vm/win11iso/custom.iso:/custom.iso + - ./vm/win11setup/firstboot:/oem + - ./vm/win11setup/setupscripts:/data + - ./vm/win11storage:/storage + \ No newline at end of file diff --git a/computer_use_demo/windowshost/scripts/manage_vm.ps1 b/computer_use_demo/windowshost/scripts/manage_vm.ps1 new file mode 100644 index 0000000..958c095 --- /dev/null +++ b/computer_use_demo/windowshost/scripts/manage_vm.ps1 @@ -0,0 +1,70 @@ +function Create-VM { + if (-not (docker images windows-local -q)) { + Write-Host "Image not found locally. Building..." + docker build -t windows-local .. + } else { + Write-Host "Image found locally. Skipping build." + } + + docker compose -f ../compose.yml up -d + + while ($true) { + try { + $response = Invoke-WebRequest -Uri "http://localhost:5000/probe" -Method GET -UseBasicParsing + if ($response.StatusCode -eq 200) { + break + } + } catch { + Write-Host "Waiting for a response from the computer control server. When first building the VM storage folder this can take a while..." + Start-Sleep -Seconds 5 + } + } + + Write-Host "VM + server is up and running!" +} + +function Start-LocalVM { + Write-Host "Starting VM..." + docker compose -f ../compose.yml start + while ($true) { + try { + $response = Invoke-WebRequest -Uri "http://localhost:5000/probe" -Method GET -UseBasicParsing + if ($response.StatusCode -eq 200) { + break + } + } catch { + Write-Host "Waiting for a response from the computer control server" + Start-Sleep -Seconds 5 + } + } + Write-Host "VM started" +} + +function Stop-LocalVM { + Write-Host "Stopping VM..." + docker compose -f ../compose.yml stop + Write-Host "VM stopped" +} + +function Remove-VM { + Write-Host "Removing VM and associated containers..." + docker compose -f ../compose.yml down + Write-Host "VM removed" +} + +if (-not $args[0]) { + Write-Host "Usage: $($MyInvocation.MyCommand.Name) [create|start|stop|delete]" + exit 1 +} + +switch ($args[0]) { + "create" { Create-VM } + "start" { Start-LocalVM } + "stop" { Stop-LocalVM } + "delete" { Remove-VM } + default { + Write-Host "Invalid option: $($args[0])" + Write-Host "Usage: $($MyInvocation.MyCommand.Name) [create|start|stop|delete]" + exit 1 + } +} \ No newline at end of file diff --git a/computer_use_demo/windowshost/scripts/manage_vm.sh b/computer_use_demo/windowshost/scripts/manage_vm.sh new file mode 100755 index 0000000..6acf45a --- /dev/null +++ b/computer_use_demo/windowshost/scripts/manage_vm.sh @@ -0,0 +1,77 @@ +#!/bin/bash + +create_vm() { + if ! docker images windows-local -q | grep -q .; then + echo "Image not found locally. Building..." + docker build -t windows-local .. + else + echo "Image found locally. Skipping build." + fi + + docker compose -f ../compose.yml up -d + + # Wait for the VM to start up + while true; do + response=$(curl --write-out '%{http_code}' --silent --output /dev/null localhost:5000/probe) + if [ $response -eq 200 ]; then + break + fi + echo "Waiting for a response from the computer control server. When first building the VM storage folder this can take a while..." + sleep 5 + done + + echo "VM + server is up and running!" +} + +start_vm() { + echo "Starting VM..." + docker compose -f ../compose.yml start + while true; do + response=$(curl --write-out '%{http_code}' --silent --output /dev/null localhost:5000/probe) + if [ $response -eq 200 ]; then + break + fi + echo "Waiting for a response from the computer control server" + sleep 5 + done + echo "VM started" +} + +stop_vm() { + echo "Stopping VM..." + docker compose -f ../compose.yml stop + echo "VM stopped" +} + +delete_vm() { + echo "Removing VM and associated containers..." + docker compose -f ../compose.yml down + echo "VM removed" +} + +# Check if control parameter is provided +if [ -z "$1" ]; then + echo "Usage: $0 [create|start|stop|delete]" + exit 1 +fi + +# Execute the appropriate function based on the control parameter +case "$1" in + "create") + create_vm + ;; + "start") + start_vm + ;; + "stop") + stop_vm + ;; + "delete") + delete_vm + ;; + *) + echo "Invalid option: $1" + echo "Usage: $0 [create|start|stop|delete]" + exit 1 + ;; +esac \ No newline at end of file diff --git a/computer_use_demo/windowshost/vm/buildcontainer/define.sh b/computer_use_demo/windowshost/vm/buildcontainer/define.sh new file mode 100644 index 0000000..f5641ed --- /dev/null +++ b/computer_use_demo/windowshost/vm/buildcontainer/define.sh @@ -0,0 +1,410 @@ +#!/usr/bin/env bash +set -Eeuo pipefail + +: "${WIDTH:=""}" +: "${HEIGHT:=""}" +: "${VERIFY:=""}" +: "${REGION:=""}" +: "${MANUAL:=""}" +: "${REMOVE:=""}" +: "${VERSION:=""}" +: "${DETECTED:=""}" +: "${KEYBOARD:=""}" +: "${LANGUAGE:=""}" +: "${USERNAME:=""}" +: "${PASSWORD:=""}" + +MIRRORS=4 +PLATFORM="x64" + +parseVersion() { + + if [[ "${VERSION}" == \"*\" || "${VERSION}" == \'*\' ]]; then + VERSION="${VERSION:1:-1}" + fi + + [ -z "$VERSION" ] && VERSION="win11" + + case "${VERSION,,}" in + "11" | "11p" | "win11" | "pro11" | "win11p" | "windows11" | "windows 11" ) + VERSION="win11x64" + ;; + "11e" | "win11e" | "windows11e" | "windows 11e" | "win11x64-enterprise-eval" ) + VERSION="win11x64-enterprise-eval" + ;; + esac + + return 0 +} + +getLanguage() { + + local id="$1" + local ret="$2" + local lang="" + local desc="" + local culture="" + + case "${id,,}" in + "ar" | "ar-"* ) + lang="Arabic" + desc="$lang" + culture="ar-SA" ;; + "bg" | "bg-"* ) + lang="Bulgarian" + desc="$lang" + culture="bg-BG" ;; + "cs" | "cs-"* | "cz" | "cz-"* ) + lang="Czech" + desc="$lang" + culture="cs-CZ" ;; + "da" | "da-"* | "dk" | "dk-"* ) + lang="Danish" + desc="$lang" + culture="da-DK" ;; + "de" | "de-"* ) + lang="German" + desc="$lang" + culture="de-DE" ;; + "el" | "el-"* | "gr" | "gr-"* ) + lang="Greek" + desc="$lang" + culture="el-GR" ;; + "gb" | "en-gb" ) + lang="English International" + desc="English" + culture="en-GB" ;; + "en" | "en-"* ) + lang="English" + desc="English" + culture="en-US" ;; + "mx" | "es-mx" ) + lang="Spanish (Mexico)" + desc="Spanish" + culture="es-MX" ;; + "es" | "es-"* ) + lang="Spanish" + desc="$lang" + culture="es-ES" ;; + "et" | "et-"* ) + lang="Estonian" + desc="$lang" + culture="et-EE" ;; + "fi" | "fi-"* ) + lang="Finnish" + desc="$lang" + culture="fi-FI" ;; + "ca" | "fr-ca" ) + lang="French Canadian" + desc="French" + culture="fr-CA" ;; + "fr" | "fr-"* ) + lang="French" + desc="$lang" + culture="fr-FR" ;; + "he" | "he-"* | "il" | "il-"* ) + lang="Hebrew" + desc="$lang" + culture="he-IL" ;; + "hr" | "hr-"* | "cr" | "cr-"* ) + lang="Croatian" + desc="$lang" + culture="hr-HR" ;; + "hu" | "hu-"* ) + lang="Hungarian" + desc="$lang" + culture="hu-HU" ;; + "it" | "it-"* ) + lang="Italian" + desc="$lang" + culture="it-IT" ;; + "ja" | "ja-"* | "jp" | "jp-"* ) + lang="Japanese" + desc="$lang" + culture="ja-JP" ;; + "ko" | "ko-"* | "kr" | "kr-"* ) + lang="Korean" + desc="$lang" + culture="ko-KR" ;; + "lt" | "lt-"* ) + lang="Lithuanian" + desc="$lang" + culture="lv-LV" ;; + "lv" | "lv-"* ) + lang="Latvian" + desc="$lang" + culture="lt-LT" ;; + "nb" | "nb-"* |"nn" | "nn-"* | "no" | "no-"* ) + lang="Norwegian" + desc="$lang" + culture="nb-NO" ;; + "nl" | "nl-"* ) + lang="Dutch" + desc="$lang" + culture="nl-NL" ;; + "pl" | "pl-"* ) + lang="Polish" + desc="$lang" + culture="pl-PL" ;; + "br" | "pt-br" ) + lang="Brazilian Portuguese" + desc="Portuguese" + culture="pt-BR" ;; + "pt" | "pt-"* ) + lang="Portuguese" + desc="$lang" + culture="pt-BR" ;; + "ro" | "ro-"* ) + lang="Romanian" + desc="$lang" + culture="ro-RO" ;; + "ru" | "ru-"* ) + lang="Russian" + desc="$lang" + culture="ru-RU" ;; + "sk" | "sk-"* ) + lang="Slovak" + desc="$lang" + culture="sk-SK" ;; + "sl" | "sl-"* | "si" | "si-"* ) + lang="Slovenian" + desc="$lang" + culture="sl-SI" ;; + "sr" | "sr-"* ) + lang="Serbian Latin" + desc="Serbian" + culture="sr-Latn-RS" ;; + "sv" | "sv-"* | "se" | "se-"* ) + lang="Swedish" + desc="$lang" + culture="sv-SE" ;; + "th" | "th-"* ) + lang="Thai" + desc="$lang" + culture="th-TH" ;; + "tr" | "tr-"* ) + lang="Turkish" + desc="$lang" + culture="tr-TR" ;; + "ua" | "ua-"* | "uk" | "uk-"* ) + lang="Ukrainian" + desc="$lang" + culture="uk-UA" ;; + "hk" | "zh-hk" | "cn-hk" ) + lang="Chinese (Traditional)" + desc="Chinese HK" + culture="zh-TW" ;; + "tw" | "zh-tw" | "cn-tw" ) + lang="Chinese (Traditional)" + desc="Chinese TW" + culture="zh-TW" ;; + "zh" | "zh-"* | "cn" | "cn-"* ) + lang="Chinese (Simplified)" + desc="Chinese" + culture="zh-CN" ;; + esac + + case "${ret,,}" in + "desc" ) echo "$desc" ;; + "name" ) echo "$lang" ;; + "culture" ) echo "$culture" ;; + *) echo "$desc";; + esac + + return 0 +} + +parseLanguage() { + + REGION="${REGION//_/-/}" + KEYBOARD="${KEYBOARD//_/-/}" + LANGUAGE="${LANGUAGE//_/-/}" + + [ -z "$LANGUAGE" ] && LANGUAGE="en" + + case "${LANGUAGE,,}" in + "arabic" | "arab" ) LANGUAGE="ar" ;; + "bulgarian" | "bu" ) LANGUAGE="bg" ;; + "chinese" | "cn" ) LANGUAGE="zh" ;; + "croatian" | "cr" | "hrvatski" ) LANGUAGE="hr" ;; + "czech" | "cz" | "cesky" ) LANGUAGE="cs" ;; + "danish" | "dk" | "danske" ) LANGUAGE="da" ;; + "dutch" | "nederlands" ) LANGUAGE="nl" ;; + "english" | "gb" | "british" ) LANGUAGE="en" ;; + "estonian" | "eesti" ) LANGUAGE="et" ;; + "finnish" | "suomi" ) LANGUAGE="fi" ;; + "french" | "franรงais" | "francais" ) LANGUAGE="fr" ;; + "german" | "deutsch" ) LANGUAGE="de" ;; + "greek" | "gr" ) LANGUAGE="el" ;; + "hebrew" | "il" ) LANGUAGE="he" ;; + "hungarian" | "magyar" ) LANGUAGE="hu" ;; + "italian" | "italiano" ) LANGUAGE="it" ;; + "japanese" | "jp" ) LANGUAGE="ja" ;; + "korean" | "kr" ) LANGUAGE="ko" ;; + "latvian" | "latvijas" ) LANGUAGE="lv" ;; + "lithuanian" | "lietuvos" ) LANGUAGE="lt" ;; + "norwegian" | "no" | "nb" | "norsk" ) LANGUAGE="nn" ;; + "polish" | "polski" ) LANGUAGE="pl" ;; + "portuguese" | "pt" | "br" ) LANGUAGE="pt-br" ;; + "portuguรชs" | "portugues" ) LANGUAGE="pt-br" ;; + "romanian" | "romรขnฤƒ" | "romana" ) LANGUAGE="ro" ;; + "russian" | "ruski" ) LANGUAGE="ru" ;; + "serbian" | "serbian latin" ) LANGUAGE="sr" ;; + "slovak" | "slovenskรฝ" | "slovensky" ) LANGUAGE="sk" ;; + "slovenian" | "si" | "slovenski" ) LANGUAGE="sl" ;; + "spanish" | "espanol" | "espaรฑol" ) LANGUAGE="es" ;; + "swedish" | "se" | "svenska" ) LANGUAGE="sv" ;; + "turkish" | "tรผrk" | "turk" ) LANGUAGE="tr" ;; + "thai" ) LANGUAGE="th" ;; + "ukrainian" | "ua" ) LANGUAGE="uk" ;; + esac + + local culture + culture=$(getLanguage "$LANGUAGE" "culture") + [ -n "$culture" ] && return 0 + + error "Invalid LANGUAGE specified, value \"$LANGUAGE\" is not recognized!" + return 1 +} + +printVersion() { + + local id="$1" + local desc="$2" + + case "${id,,}" in + "win11"* ) desc="Windows 11" ;; + esac + + if [ -z "$desc" ]; then + desc="Windows" + [[ "${PLATFORM,,}" != "x64" ]] && desc+=" for ${PLATFORM}" + fi + + echo "$desc" + return 0 +} + +printEdition() { + + local id="$1" + local desc="$2" + local result="" + local edition="" + + result=$(printVersion "$id" "x") + [[ "$result" == "x" ]] && echo "$desc" && return 0 + + case "${id,,}" in + *"-enterprise" ) + edition="Enterprise" + ;; + *"-enterprise-eval" ) + edition="Enterprise (Evaluation)" + ;; + esac + + [ -n "$edition" ] && result+=" $edition" + + echo "$result" + return 0 +} + +fromName() { + + local id="" + local name="$1" + local arch="$2" + + local add="" + [[ "$arch" != "x64" ]] && add="$arch" + + case "${name,,}" in + *"windows 11"* ) id="win11${arch}" ;; + esac + + echo "$id" + return 0 +} + +getVersion() { + + local id + local name="$1" + local arch="$2" + + id=$(fromName "$name" "$arch") + + case "${id,,}" in + "win11"* ) + case "${name,,}" in + *" enterprise evaluation"* ) id="$id-enterprise-eval" ;; + *" enterprise"* ) id="$id-enterprise" ;; + esac + ;; + esac + + echo "$id" + return 0 +} + +addFolder() { + + local src="$1" + local folder="/oem" + + [ ! -d "$folder" ] && folder="/OEM" + [ ! -d "$folder" ] && folder="$STORAGE/oem" + [ ! -d "$folder" ] && folder="$STORAGE/OEM" + [ ! -d "$folder" ] && return 0 + + local msg="Adding OEM folder to image..." + info "$msg" && html "$msg" + + local dest="$src/\$OEM\$/\$1/OEM" + mkdir -p "$dest" || return 1 + cp -Lr "$folder/." "$dest" || return 1 + + local file + file=$(find "$dest" -maxdepth 1 -type f -iname install.bat | head -n 1) + [ -f "$file" ] && unix2dos -q "$file" + + return 0 +} + +# migrateFiles() { + +# local base="$1" +# local version="$2" +# local file="" + +# [ -f "$base" ] && return 0 + +# [[ "${version,,}" == "tiny10" ]] && file="tiny10_x64_23h2.iso" +# [[ "${version,,}" == "tiny11" ]] && file="tiny11_2311_x64.iso" +# [[ "${version,,}" == "core11" ]] && file="tiny11_core_x64_beta_1.iso" +# [[ "${version,,}" == "winxpx86" ]] && file="en_windows_xp_professional_with_service_pack_3_x86_cd_x14-80428.iso" +# [[ "${version,,}" == "winvistax64" ]] && file="en_windows_vista_sp2_x64_dvd_342267.iso" +# [[ "${version,,}" == "win7x64" ]] && file="en_windows_7_enterprise_with_sp1_x64_dvd_u_677651.iso" + +# [ ! -f "$STORAGE/$file" ] && return 0 +# mv -f "$STORAGE/$file" "$base" || return 1 + +# return 0 +# } + +migrateFiles() { + + local base="$1" + local version="$2" + local file="" + + [ -f "$base" ] && return 0 + + [ ! -f "$STORAGE/$file" ] && return 0 + mv -f "$STORAGE/$file" "$base" || return 1 + + return 0 +} + +return 0 diff --git a/computer_use_demo/windowshost/vm/buildcontainer/entry.sh b/computer_use_demo/windowshost/vm/buildcontainer/entry.sh new file mode 100644 index 0000000..7cfbe70 --- /dev/null +++ b/computer_use_demo/windowshost/vm/buildcontainer/entry.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +set -Eeuo pipefail + +: "${BOOT_MODE:="windows"}" + +APP="OmniParser Windows" +SUPPORT="https://github.com/microsoft/OmniParser" + +cd /run + +. reset.sh # Initialize system +. define.sh # Define versions +. install.sh # Run installation +. disk.sh # Initialize disks +. display.sh # Initialize graphics +. network.sh # Initialize network +. samba.sh # Configure samba +. boot.sh # Configure boot +. proc.sh # Initialize processor +. power.sh # Configure shutdown +. config.sh # Configure arguments + +trap - ERR + +version=$(qemu-system-x86_64 --version | head -n 1 | cut -d '(' -f 1 | awk '{ print $NF }') +info "Booting ${APP}${BOOT_DESC} using QEMU v$version..." + +{ qemu-system-x86_64 ${ARGS:+ $ARGS} >"$QEMU_OUT" 2>"$QEMU_LOG"; rc=$?; } || : +(( rc != 0 )) && error "$(<"$QEMU_LOG")" && exit 15 + +terminal +( sleep 30; boot ) & +tail -fn +0 "$QEMU_LOG" 2>/dev/null & +cat "$QEMU_TERM" 2> /dev/null | tee "$QEMU_PTY" & +wait $! || : + +sleep 1 & wait $! +[ ! -f "$QEMU_END" ] && finish 0 diff --git a/computer_use_demo/windowshost/vm/buildcontainer/install.sh b/computer_use_demo/windowshost/vm/buildcontainer/install.sh new file mode 100644 index 0000000..d65ae6b --- /dev/null +++ b/computer_use_demo/windowshost/vm/buildcontainer/install.sh @@ -0,0 +1,1024 @@ +#!/usr/bin/env bash +set -Eeuo pipefail + +TMP="$STORAGE/tmp" +DIR="$TMP/unpack" +FB="falling back to manual installation!" +ETFS="boot/etfsboot.com" +EFISYS="efi/microsoft/boot/efisys_noprompt.bin" + +skipInstall() { + + local iso="$1" + local magic byte + local boot="$STORAGE/windows.boot" + local previous="$STORAGE/windows.base" + + if [ -f "$previous" ]; then + previous=$(<"$previous") + if [ -n "$previous" ]; then + previous="$STORAGE/$previous" + if [[ "${previous,,}" != "${iso,,}" ]]; then + if [ -f "$boot" ] && hasDisk; then + info "Detected that the version was changed, but ignoring this because Windows is already installed." + info "Please start with an empty /storage folder, if you want to install a different version of Windows." + return 0 + fi + [ -f "$previous" ] && rm -f "$previous" + return 1 + fi + fi + fi + + [ -f "$boot" ] && hasDisk && return 0 + + [ ! -f "$iso" ] && return 1 + [ ! -s "$iso" ] && return 1 + + # Check if the ISO was already processed by our script + magic=$(dd if="$iso" seek=0 bs=1 count=1 status=none | tr -d '\000') + magic="$(printf '%s' "$magic" | od -A n -t x1 -v | tr -d ' \n')" + byte="16" && [[ "$MANUAL" == [Yy1]* ]] && byte="17" + + if [[ "$magic" != "$byte" ]]; then + info "The ISO will be processed again because the configuration was changed..." + return 1 + fi + + return 0 +} + +startInstall() { + + html "Starting $APP..." + + if [ -z "$CUSTOM" ]; then + + local file="${VERSION//\//}.iso" + + if [[ "${VERSION,,}" == "http"* ]]; then + + file=$(basename "${VERSION%%\?*}") + : "${file//+/ }"; printf -v file '%b' "${_//%/\\x}" + file=$(echo "$file" | sed -e 's/[^A-Za-z0-9._-]/_/g') + + else + + local language + language=$(getLanguage "$LANGUAGE" "culture") + language="${language%%-*}" + + if [ -n "$language" ] && [[ "${language,,}" != "en" ]]; then + file="${VERSION//\//}_${language,,}.iso" + fi + + fi + + BOOT="$STORAGE/$file" + + ! migrateFiles "$BOOT" "$VERSION" && error "Migration failed!" && exit 57 + + fi + + skipInstall "$BOOT" && return 1 + + rm -rf "$TMP" + mkdir -p "$TMP" + + if [ -z "$CUSTOM" ]; then + + ISO=$(basename "$BOOT") + ISO="$TMP/$ISO" + + if [ -f "$BOOT" ] && [ -s "$BOOT" ]; then + mv -f "$BOOT" "$ISO" + fi + + fi + + rm -f "$BOOT" + return 0 +} + +finishInstall() { + + local iso="$1" + local aborted="$2" + local base byte + + if [ ! -s "$iso" ] || [ ! -f "$iso" ]; then + error "Failed to find ISO file: $iso" && return 1 + fi + + if [[ "$aborted" != [Yy1]* ]]; then + # Mark ISO as prepared via magic byte + byte="16" && [[ "$MANUAL" == [Yy1]* ]] && byte="17" + if ! printf '%b' "\x$byte" | dd of="$iso" bs=1 seek=0 count=1 conv=notrunc status=none; then + warn "failed to set magic byte in ISO file: $iso" + fi + fi + + rm -f "$STORAGE/windows.old" + rm -f "$STORAGE/windows.vga" + rm -f "$STORAGE/windows.args" + rm -f "$STORAGE/windows.base" + rm -f "$STORAGE/windows.boot" + rm -f "$STORAGE/windows.mode" + rm -f "$STORAGE/windows.type" + + cp -f /run/version "$STORAGE/windows.ver" + + if [[ "$iso" == "$STORAGE/"* ]]; then + if [[ "$aborted" != [Yy1]* ]] || [ -z "$CUSTOM" ]; then + base=$(basename "$iso") + echo "$base" > "$STORAGE/windows.base" + fi + fi + + if [[ "${PLATFORM,,}" == "x64" ]]; then + if [[ "${BOOT_MODE,,}" == "windows_legacy" ]]; then + echo "$BOOT_MODE" > "$STORAGE/windows.mode" + if [[ "${MACHINE,,}" != "q35" ]]; then + echo "$MACHINE" > "$STORAGE/windows.old" + fi + else + # Enable secure boot + TPM on manual installs as Win11 requires + if [[ "$MANUAL" == [Yy1]* ]] || [[ "$aborted" == [Yy1]* ]]; then + if [[ "${DETECTED,,}" == "win11"* ]]; then + BOOT_MODE="windows_secure" + echo "$BOOT_MODE" > "$STORAGE/windows.mode" + fi + fi + # Enable secure boot on multi-socket systems to workaround freeze + if [ -n "$SOCKETS" ] && [[ "$SOCKETS" != "1" ]]; then + BOOT_MODE="windows_secure" + echo "$BOOT_MODE" > "$STORAGE/windows.mode" + fi + fi + fi + + if [ -n "${ARGS:-}" ]; then + ARGUMENTS="$ARGS ${ARGUMENTS:-}" + echo "$ARGS" > "$STORAGE/windows.args" + fi + + if [ -n "${DISK_TYPE:-}" ] && [[ "${DISK_TYPE:-}" != "scsi" ]]; then + echo "$DISK_TYPE" > "$STORAGE/windows.type" + fi + + rm -rf "$TMP" + return 0 +} + +abortInstall() { + + local dir="$1" + local iso="$2" + local efi + + [[ "${iso,,}" == *".esd" ]] && exit 60 + + efi=$(find "$dir" -maxdepth 1 -type d -iname efi | head -n 1) + + if [ -z "$efi" ]; then + [[ "${PLATFORM,,}" == "x64" ]] && BOOT_MODE="windows_legacy" + fi + + if [ -n "$CUSTOM" ]; then + BOOT="$iso" + REMOVE="N" + else + if [[ "$iso" != "$BOOT" ]]; then + if ! mv -f "$iso" "$BOOT"; then + error "Failed to move ISO file: $iso" && return 1 + fi + fi + fi + + finishInstall "$BOOT" "Y" && return 0 + return 1 +} + +detectCustom() { + + local file base + CUSTOM="" + + file=$(find / -maxdepth 1 -type f -iname custom.iso | head -n 1) + [ ! -s "$file" ] && file=$(find "$STORAGE" -maxdepth 1 -type f -iname custom.iso | head -n 1) + + if [ ! -s "$file" ] && [[ "${VERSION,,}" != "http"* ]]; then + base=$(basename "$VERSION") + file="$STORAGE/$base" + fi + + if [ ! -f "$file" ] || [ ! -s "$file" ]; then + return 0 + fi + + local size + size="$(stat -c%s "$file")" + [ -z "$size" ] || [[ "$size" == "0" ]] && return 0 + + ISO="$file" + CUSTOM="$ISO" + BOOT="$STORAGE/windows.$size.iso" + + return 0 +} + +extractESD() { + + local iso="$1" + local dir="$2" + local version="$3" + local desc="$4" + local size size_gb space space_gb desc + + local msg="Extracting $desc bootdisk..." + info "$msg" && html "$msg" + + if [ "$(stat -c%s "$iso")" -lt 100000000 ]; then + error "Invalid ESD file: Size is smaller than 100 MB" && return 1 + fi + + rm -rf "$dir" + mkdir -p "$dir" + + size=16106127360 + size_gb=$(( (size + 1073741823)/1073741824 )) + space=$(df --output=avail -B 1 "$dir" | tail -n 1) + space_gb=$(( (space + 1073741823)/1073741824 )) + + if (( size > space )); then + error "Not enough free space in $STORAGE, have $space_gb GB available but need at least $size_gb GB." && return 1 + fi + + local esdImageCount + esdImageCount=$(wimlib-imagex info "$iso" | awk '/Image Count:/ {print $3}') + + wimlib-imagex apply "$iso" 1 "$dir" --quiet 2>/dev/null || { + retVal=$? + error "Extracting $desc bootdisk failed" && return $retVal + } + + local bootWimFile="$dir/sources/boot.wim" + local installWimFile="$dir/sources/install.wim" + + local msg="Extracting $desc environment..." + info "$msg" && html "$msg" + + wimlib-imagex export "$iso" 2 "$bootWimFile" --compress=none --quiet || { + retVal=$? + error "Adding WinPE failed" && return ${retVal} + } + + local msg="Extracting $desc setup..." + info "$msg" && html "$msg" + + wimlib-imagex export "$iso" 3 "$bootWimFile" --compress=none --boot --quiet || { + retVal=$? + error "Adding Windows Setup failed" && return ${retVal} + } + + if [[ "${PLATFORM,,}" == "x64" ]]; then + LABEL="CCCOMA_X64FRE_EN-US_DV9" + else + LABEL="CPBA_A64FRE_EN-US_DV9" + fi + + local msg="Extracting $desc image..." + info "$msg" && html "$msg" + + local edition imageIndex imageEdition + edition=$(getCatalog "$version" "name") + + if [ -z "$edition" ]; then + error "Invalid VERSION specified, value \"$version\" is not recognized!" && return 1 + fi + + for (( imageIndex=4; imageIndex<=esdImageCount; imageIndex++ )); do + imageEdition=$(wimlib-imagex info "$iso" ${imageIndex} | grep '^Description:' | sed 's/Description:[ \t]*//') + [[ "${imageEdition,,}" != "${edition,,}" ]] && continue + wimlib-imagex export "$iso" ${imageIndex} "$installWimFile" --compress=LZMS --chunk-size 128K --quiet || { + retVal=$? + error "Addition of $imageIndex to the $desc image failed" && return $retVal + } + return 0 + done + + error "Failed to find product '$edition' in install.wim!" && return 1 +} + +extractImage() { + + local iso="$1" + local dir="$2" + local version="$3" + local desc="local ISO" + local size size_gb space space_gb + + if [ -z "$CUSTOM" ]; then + desc="downloaded ISO" + if [[ "$version" != "http"* ]]; then + desc=$(printVersion "$version" "$desc") + fi + fi + + if [[ "${iso,,}" == *".esd" ]]; then + extractESD "$iso" "$dir" "$version" "$desc" && return 0 + return 1 + fi + + local msg="Extracting $desc image..." + info "$msg" && html "$msg" + + rm -rf "$dir" + mkdir -p "$dir" + + size=$(stat -c%s "$iso") + size_gb=$(( (size + 1073741823)/1073741824 )) + space=$(df --output=avail -B 1 "$dir" | tail -n 1) + space_gb=$(( (space + 1073741823)/1073741824 )) + + if ((size<100000000)); then + error "Invalid ISO file: Size is smaller than 100 MB" && return 1 + fi + + if (( size > space )); then + error "Not enough free space in $STORAGE, have $space_gb GB available but need at least $size_gb GB." && return 1 + fi + + rm -rf "$dir" + + if ! 7z x "$iso" -o"$dir" > /dev/null; then + error "Failed to extract ISO file: $iso" && return 1 + fi + + LABEL=$(isoinfo -d -i "$iso" | sed -n 's/Volume id: //p') + + return 0 +} + +getPlatform() { + + local xml="$1" + local tag="ARCH" + local platform="x64" + local arch + + arch=$(sed -n "/$tag/{s/.*<$tag>\(.*\)<\/$tag>.*/\1/;p}" <<< "$xml") + + case "${arch,,}" in + "0" ) platform="x86" ;; + "9" ) platform="x64" ;; + "12" )platform="arm64" ;; + esac + + echo "$platform" + return 0 +} + +checkPlatform() { + + local xml="$1" + local platform compat + + platform=$(getPlatform "$xml") + + case "${platform,,}" in + "x86" ) compat="x64" ;; + "x64" ) compat="$platform" ;; + "arm64" ) compat="$platform" ;; + * ) compat="${PLATFORM,,}" ;; + esac + + [[ "${compat,,}" == "${PLATFORM,,}" ]] && return 0 + + error "You cannot boot ${platform^^} images on a $PLATFORM CPU!" + return 1 +} + +hasVersion() { + + local id="$1" + local tag="$2" + local xml="$3" + local edition + + [ ! -f "/run/assets/$id.xml" ] && return 1 + + edition=$(printEdition "$id" "") + [ -z "$edition" ] && return 1 + [[ "${xml,,}" != *"<${tag,,}>${edition,,}"* ]] && return 1 + + return 0 +} + +selectVersion() { + + local tag="$1" + local xml="$2" + local platform="$3" + local id name prefer + + name=$(sed -n "/$tag/{s/.*<$tag>\(.*\)<\/$tag>.*/\1/;p}" <<< "$xml") + [[ "$name" == *"Operating System"* ]] && name="" + [ -z "$name" ] && return 0 + + id=$(fromName "$name" "$platform") + [ -z "$id" ] && warn "Unknown ${tag,,}: '$name'" && return 0 + + prefer="$id-enterprise" + hasVersion "$prefer" "$tag" "$xml" && echo "$prefer" && return 0 + + prefer="$id" + hasVersion "$prefer" "$tag" "$xml" && echo "$prefer" && return 0 + + prefer=$(getVersion "$name" "$platform") + + echo "$prefer" + return 0 +} + +detectVersion() { + + local xml="$1" + local id platform + + platform=$(getPlatform "$xml") + id=$(selectVersion "DISPLAYNAME" "$xml" "$platform") + [ -z "$id" ] && id=$(selectVersion "PRODUCTNAME" "$xml" "$platform") + [ -z "$id" ] && id=$(selectVersion "NAME" "$xml" "$platform") + + echo "$id" + return 0 +} + +detectLanguage() { + + local xml="$1" + local lang="" + + if [[ "$xml" == *"LANGUAGE>"* ]]; then + lang="${xml#*LANGUAGE>}" + lang="${lang%%<*}" + else + if [[ "$xml" == *"FALLBACK>"* ]]; then + lang="${xml#*FALLBACK>}" + lang="${lang%%<*}" + fi + fi + + if [ -z "$lang" ]; then + warn "Language could not be detected from ISO!" && return 0 + fi + + local culture + culture=$(getLanguage "$lang" "culture") + [ -n "$culture" ] && LANGUAGE="$lang" && return 0 + + warn "Invalid language detected: \"$lang\"" + return 0 +} + +setXML() { + + local file="/custom.xml" + + [ ! -f "$file" ] || [ ! -s "$file" ] && file="$STORAGE/custom.xml" + [ ! -f "$file" ] || [ ! -s "$file" ] && file="/run/assets/custom.xml" + [ ! -f "$file" ] || [ ! -s "$file" ] && file="$1" + [ ! -f "$file" ] || [ ! -s "$file" ] && file="/run/assets/$DETECTED.xml" + [ ! -f "$file" ] || [ ! -s "$file" ] && return 1 + + XML="$file" + return 0 +} + +detectImage() { + + local dir="$1" + local version="$2" + local desc msg find language + + XML="" + + if [ -z "$DETECTED" ] && [ -z "$CUSTOM" ]; then + [[ "${version,,}" != "http"* ]] && DETECTED="$version" + fi + + if [ -n "$DETECTED" ]; then + + if ! setXML "" && [[ "$MANUAL" != [Yy1]* ]]; then + MANUAL="Y" + desc=$(printEdition "$DETECTED" "this version") + warn "the answer file for $desc was not found ($DETECTED.xml), $FB." + fi + + return 0 + fi + + info "Detecting version from ISO image..." + + local src wim info + src=$(find "$dir" -maxdepth 1 -type d -iname sources | head -n 1) + + if [ ! -d "$src" ]; then + warn "failed to locate 'sources' folder in ISO image, $FB" && return 1 + fi + + wim=$(find "$src" -maxdepth 1 -type f -iname install.wim | head -n 1) + [ ! -f "$wim" ] && wim=$(find "$src" -maxdepth 1 -type f -iname install.esd | head -n 1) + + if [ ! -f "$wim" ]; then + warn "failed to locate 'install.wim' or 'install.esd' in ISO image, $FB" && return 1 + fi + + info=$(wimlib-imagex info -xml "$wim" | tr -d '\000') + checkPlatform "$info" || exit 67 + + DETECTED=$(detectVersion "$info") + + if [ -z "$DETECTED" ]; then + msg="Failed to determine Windows version from image" + if setXML "" || [[ "$MANUAL" == [Yy1]* ]]; then + info "${msg}!" + else + MANUAL="Y" + warn "${msg}, $FB." + fi + return 0 + fi + + desc=$(printEdition "$DETECTED" "$DETECTED") + detectLanguage "$info" + + if [[ "${LANGUAGE,,}" != "en" ]] && [[ "${LANGUAGE,,}" != "en-"* ]]; then + language=$(getLanguage "$LANGUAGE" "desc") + desc+=" ($language)" + fi + + info "Detected: $desc" + setXML "" && return 0 + + msg="the answer file for $desc was not found ($DETECTED.xml)" + local fallback="/run/assets/${DETECTED%%-*}.xml" + + if setXML "$fallback" || [[ "$MANUAL" == [Yy1]* ]]; then + [[ "$MANUAL" != [Yy1]* ]] && warn "${msg}." + else + MANUAL="Y" + warn "${msg}, $FB." + fi + + return 0 +} + +prepareImage() { + + local iso="$1" + local dir="$2" + local desc missing + + desc=$(printVersion "$DETECTED" "$DETECTED") + + if [[ "${BOOT_MODE,,}" != "windows_legacy" ]]; then + + [ -f "$dir/$ETFS" ] && [ -f "$dir/$EFISYS" ] && return 0 + + missing=$(basename "$dir/$EFISYS") + [ ! -f "$dir/$ETFS" ] && missing=$(basename "$dir/$ETFS") + + error "Failed to locate file \"${missing,,}\" in ISO image!" + return 1 + fi + + prepareLegacy "$iso" "$dir" "$desc" && return 0 + + error "Failed to extract boot image from ISO image!" + return 1 +} + +updateXML() { + + local asset="$1" + local language="$2" + local culture region user admin pass keyboard + + [ -z "$HEIGHT" ] && HEIGHT="800" + [ -z "$WIDTH" ] && WIDTH="1280" + + sed -i "s/1080<\/VerticalResolution>/$HEIGHT<\/VerticalResolution>/g" "$asset" + sed -i "s/1920<\/HorizontalResolution>/$WIDTH<\/HorizontalResolution>/g" "$asset" + + culture=$(getLanguage "$language" "culture") + + if [ -n "$culture" ] && [[ "${culture,,}" != "en-us" ]]; then + sed -i "s/en-US<\/UILanguage>/$culture<\/UILanguage>/g" "$asset" + fi + + region="$REGION" + [ -z "$region" ] && region="$culture" + + if [ -n "$region" ] && [[ "${region,,}" != "en-us" ]]; then + sed -i "s/en-US<\/UserLocale>/$region<\/UserLocale>/g" "$asset" + sed -i "s/en-US<\/SystemLocale>/$region<\/SystemLocale>/g" "$asset" + fi + + keyboard="$KEYBOARD" + [ -z "$keyboard" ] && keyboard="$culture" + + if [ -n "$keyboard" ] && [[ "${keyboard,,}" != "en-us" ]]; then + sed -i "s/en-US<\/InputLocale>/$keyboard<\/InputLocale>/g" "$asset" + sed -i "s/0409:00000409<\/InputLocale>/$keyboard<\/InputLocale>/g" "$asset" + fi + + user=$(echo "$USERNAME" | sed 's/[^[:alnum:]@!._-]//g') + + if [ -n "$user" ]; then + sed -i "s/Docker<\/Name>/$user<\/Name>/g" "$asset" + sed -i "s/where name=\"Docker\"/where name=\"$user\"/g" "$asset" + sed -i "s/Docker<\/FullName>/$user<\/FullName>/g" "$asset" + sed -i "s/Docker<\/Username>/$user<\/Username>/g" "$asset" + fi + + if [ -n "$PASSWORD" ]; then + pass=$(printf '%s' "${PASSWORD}Password" | iconv -f utf-8 -t utf-16le | base64 -w 0) + admin=$(printf '%s' "${PASSWORD}AdministratorPassword" | iconv -f utf-8 -t utf-16le | base64 -w 0) + sed -i "s/password<\/Value>/$admin<\/Value>/g" "$asset" + sed -i "s/true<\/PlainText>/<PlainText>false<\/PlainText>/g" "$asset" + sed -z "s/<Password>...........<Value \/>/<Password>\n <Value>$pass<\/Value>/g" -i "$asset" + sed -z "s/<Password>...............<Value \/>/<Password>\n <Value>$pass<\/Value>/g" -i "$asset" + sed -z "s/<AdministratorPassword>...........<Value \/>/<AdministratorPassword>\n <Value>$admin<\/Value>/g" -i "$asset" + sed -z "s/<AdministratorPassword>...............<Value \/>/<AdministratorPassword>\n <Value>$admin<\/Value>/g" -i "$asset" + fi + + return 0 +} + +addDriver() { + + local id="$1" + local path="$2" + local target="$3" + local driver="$4" + local folder="" + + case "${id,,}" in + "win11x64"* ) folder="w11/amd64" ;; + esac + + if [ -z "$folder" ]; then + warn "no \"$driver\" driver found for \"$DETECTED\" !" && return 0 + fi + + [ ! -d "$path/$driver/$folder" ] && return 0 + + local dest="$path/$target/$driver" + mkdir -p "$dest" || return 1 + cp -Lr "$path/$driver/$folder/." "$dest" || return 1 + + return 0 +} + +addDrivers() { + + local src="$1" + local tmp="$2" + local file="$3" + local index="$4" + local version="$5" + local drivers="$tmp/drivers" + + rm -rf "$drivers" + mkdir -p "$drivers" + + local msg="Adding drivers to image..." + info "$msg" && html "$msg" + + if ! bsdtar -xf /drivers.txz -C "$drivers"; then + error "Failed to extract drivers from archive!" && return 1 + fi + + local target="\$WinPEDriver\$" + local dest="$drivers/$target" + mkdir -p "$dest" || return 1 + + wimlib-imagex update "$file" "$index" --command "delete --force --recursive /$target" >/dev/null || true + + addDriver "$version" "$drivers" "$target" "qxl" || return 1 + addDriver "$version" "$drivers" "$target" "viofs" || return 1 + addDriver "$version" "$drivers" "$target" "sriov" || return 1 + addDriver "$version" "$drivers" "$target" "smbus" || return 1 + addDriver "$version" "$drivers" "$target" "qxldod" || return 1 + addDriver "$version" "$drivers" "$target" "viorng" || return 1 + addDriver "$version" "$drivers" "$target" "viostor" || return 1 + addDriver "$version" "$drivers" "$target" "viomem" || return 1 + addDriver "$version" "$drivers" "$target" "NetKVM" || return 1 + addDriver "$version" "$drivers" "$target" "Balloon" || return 1 + addDriver "$version" "$drivers" "$target" "vioscsi" || return 1 + addDriver "$version" "$drivers" "$target" "pvpanic" || return 1 + addDriver "$version" "$drivers" "$target" "vioinput" || return 1 + addDriver "$version" "$drivers" "$target" "viogpudo" || return 1 + addDriver "$version" "$drivers" "$target" "vioserial" || return 1 + addDriver "$version" "$drivers" "$target" "qemupciserial" || return 1 + + case "${version,,}" in + "win11x64"* | "win2025"* ) + # Workaround Virtio GPU driver bug + local dst="$src/\$OEM\$/\$\$/Drivers" + mkdir -p "$dst" || return 1 + cp -Lr "$dest/." "$dst" || return 1 + rm -rf "$dest/viogpudo" + ;; + esac + + if ! wimlib-imagex update "$file" "$index" --command "add $dest /$target" >/dev/null; then + return 1 + fi + + rm -rf "$drivers" + return 0 +} + +updateImage() { + + local dir="$1" + local asset="$2" + local language="$3" + local tmp="/tmp/install" + local file="autounattend.xml" + local org="${file//.xml/.org}" + local dat="${file//.xml/.dat}" + local desc path src wim xml index result + + if [ ! -s "$asset" ] || [ ! -f "$asset" ]; then + asset="" + if [[ "$MANUAL" != [Yy1]* ]]; then + MANUAL="Y" + warn "no answer file provided, $FB." + fi + fi + + rm -rf "$tmp" + mkdir -p "$tmp" + + src=$(find "$dir" -maxdepth 1 -type d -iname sources | head -n 1) + + if [ ! -d "$src" ]; then + error "failed to locate 'sources' folder in ISO image, $FB" && return 1 + fi + + wim=$(find "$src" -maxdepth 1 -type f -iname boot.wim | head -n 1) + [ ! -f "$wim" ] && wim=$(find "$src" -maxdepth 1 -type f -iname boot.esd | head -n 1) + + if [ ! -f "$wim" ]; then + error "failed to locate 'boot.wim' or 'boot.esd' in ISO image, $FB" && return 1 + fi + + index="1" + result=$(wimlib-imagex info -xml "$wim" | tr -d '\000') + + if [[ "${result^^}" == *"<IMAGE INDEX=\"2\">"* ]]; then + index="2" + fi + + if ! addDrivers "$src" "$tmp" "$wim" "$index" "$DETECTED"; then + error "Failed to add drivers to image!" + fi + + if ! addFolder "$src"; then + error "Failed to add OEM folder to image!" + fi + + if wimlib-imagex extract "$wim" "$index" "/$file" "--dest-dir=$tmp" >/dev/null 2>&1; then + if ! wimlib-imagex extract "$wim" "$index" "/$dat" "--dest-dir=$tmp" >/dev/null 2>&1; then + if ! wimlib-imagex extract "$wim" "$index" "/$org" "--dest-dir=$tmp" >/dev/null 2>&1; then + if ! wimlib-imagex update "$wim" "$index" --command "rename /$file /$org" > /dev/null; then + warn "failed to backup original answer file ($file)." + fi + fi + fi + fi + + if [[ "$MANUAL" != [Yy1]* ]]; then + + xml=$(basename "$asset") + info "Adding $xml for automatic installation..." + + local answer="$tmp/$xml" + cp "$asset" "$answer" + updateXML "$answer" "$language" + + if ! wimlib-imagex update "$wim" "$index" --command "add $answer /$file" > /dev/null; then + MANUAL="Y" + warn "failed to add answer file ($xml) to ISO image, $FB" + else + wimlib-imagex update "$wim" "$index" --command "add $answer /$dat" > /dev/null || true + fi + + fi + + if [[ "$MANUAL" == [Yy1]* ]]; then + + wimlib-imagex update "$wim" "$index" --command "delete --force /$file" > /dev/null || true + + if wimlib-imagex extract "$wim" "$index" "/$org" "--dest-dir=$tmp" >/dev/null 2>&1; then + if ! wimlib-imagex update "$wim" "$index" --command "add $tmp/$org /$file" > /dev/null; then + warn "failed to restore original answer file ($org)." + fi + fi + + fi + + local find="$file" + [[ "$MANUAL" == [Yy1]* ]] && find="$org" + path=$(find "$dir" -maxdepth 1 -type f -iname "$find" | head -n 1) + + if [ -f "$path" ]; then + if [[ "$MANUAL" != [Yy1]* ]]; then + mv -f "$path" "${path%.*}.org" + else + mv -f "$path" "${path%.*}.xml" + fi + fi + + rm -rf "$tmp" + return 0 +} + +removeImage() { + + local iso="$1" + + [ ! -f "$iso" ] && return 0 + [ -n "$CUSTOM" ] && return 0 + + rm -f "$iso" 2> /dev/null || warn "failed to remove $iso !" + + return 0 +} + +buildImage() { + + local dir="$1" + local failed="" + local cat="BOOT.CAT" + local log="/run/shm/iso.log" + local base size size_gb space space_gb desc + + if [ -f "$BOOT" ]; then + error "File $BOOT does already exist?!" && return 1 + fi + + base=$(basename "$BOOT") + local out="$TMP/${base%.*}.tmp" + rm -f "$out" + + desc=$(printVersion "$DETECTED" "ISO") + + local msg="Building $desc image..." + info "$msg" && html "$msg" + + [ -z "$LABEL" ] && LABEL="Windows" + + if [ ! -f "$dir/$ETFS" ]; then + error "Failed to locate file \"$ETFS\" in ISO image!" && return 1 + fi + + size=$(du -h -b --max-depth=0 "$dir" | cut -f1) + size_gb=$(( (size + 1073741823)/1073741824 )) + space=$(df --output=avail -B 1 "$TMP" | tail -n 1) + space_gb=$(( (space + 1073741823)/1073741824 )) + + if (( size > space )); then + error "Not enough free space in $STORAGE, have $space_gb GB available but need at least $size_gb GB." && return 1 + fi + + ! genisoimage -o "$out" -b "$ETFS" -no-emul-boot -c "$cat" -iso-level 4 -J -l -D -N -joliet-long -relaxed-filenames -V "${LABEL::30}" \ + -udf -boot-info-table -eltorito-alt-boot -eltorito-boot "$EFISYS" -no-emul-boot -allow-limited-size -quiet "$dir" 2> "$log" && failed="y" + + if [ -n "$failed" ]; then + [ -s "$log" ] && echo "$(<"$log")" + error "Failed to build image!" && return 1 + fi + + local error="" + local hide="Warning: creating filesystem that does not conform to ISO-9660." + + [ -s "$log" ] && error="$(<"$log")" + [[ "$error" != "$hide" ]] && echo "$error" + + mv -f "$out" "$BOOT" || return 1 + return 0 +} + +bootWindows() { + + rm -rf "$TMP" + + if [ -f "$STORAGE/windows.args" ]; then + ARGS=$(<"$STORAGE/windows.args") + ARGUMENTS="$ARGS ${ARGUMENTS:-}" + fi + + if [ -s "$STORAGE/windows.type" ] && [ -f "$STORAGE/windows.type" ]; then + [ -z "${DISK_TYPE:-}" ] && DISK_TYPE=$(<"$STORAGE/windows.type") + fi + + if [ -s "$STORAGE/windows.mode" ] && [ -f "$STORAGE/windows.mode" ]; then + BOOT_MODE=$(<"$STORAGE/windows.mode") + if [ -s "$STORAGE/windows.old" ] && [ -f "$STORAGE/windows.old" ]; then + [[ "${PLATFORM,,}" == "x64" ]] && MACHINE=$(<"$STORAGE/windows.old") + fi + return 0 + fi + + # Migrations + + [[ "${PLATFORM,,}" != "x64" ]] && return 0 + + if [ -f "$STORAGE/windows.old" ]; then + MACHINE=$(<"$STORAGE/windows.old") + [ -z "$MACHINE" ] && MACHINE="q35" + BOOT_MODE="windows_legacy" + echo "$BOOT_MODE" > "$STORAGE/windows.mode" + return 0 + fi + + local creation="1.10" + local minimal="2.14" + + if [ -f "$STORAGE/windows.ver" ]; then + creation=$(<"$STORAGE/windows.ver") + [[ "${creation}" != *"."* ]] && creation="$minimal" + fi + + # Force secure boot on installs created prior to v2.14 + if (( $(echo "$creation < $minimal" | bc -l) )); then + if [[ "${BOOT_MODE,,}" == "windows" ]]; then + BOOT_MODE="windows_secure" + echo "$BOOT_MODE" > "$STORAGE/windows.mode" + if [ -f "$STORAGE/windows.rom" ] && [ ! -f "$STORAGE/$BOOT_MODE.rom" ]; then + mv -f "$STORAGE/windows.rom" "$STORAGE/$BOOT_MODE.rom" + fi + if [ -f "$STORAGE/windows.vars" ] && [ ! -f "$STORAGE/$BOOT_MODE.vars" ]; then + mv -f "$STORAGE/windows.vars" "$STORAGE/$BOOT_MODE.vars" + fi + fi + fi + + return 0 +} + +###################################### + +! parseVersion && exit 58 +! parseLanguage && exit 56 +! detectCustom && exit 59 + +if ! startInstall; then + bootWindows && return 0 + exit 68 +fi + +if [ ! -s "$ISO" ] || [ ! -f "$ISO" ]; then + echo "Error: ISO file not found or is empty." + exit 61 +fi + +if ! extractImage "$ISO" "$DIR" "$VERSION"; then + rm -f "$ISO" 2> /dev/null || true + exit 62 +fi + +if ! detectImage "$DIR" "$VERSION"; then + abortInstall "$DIR" "$ISO" && return 0 + exit 60 +fi + +if ! prepareImage "$ISO" "$DIR"; then + abortInstall "$DIR" "$ISO" && return 0 + exit 66 +fi + +if ! updateImage "$DIR" "$XML" "$LANGUAGE"; then + abortInstall "$DIR" "$ISO" && return 0 + exit 63 +fi + +if ! removeImage "$ISO"; then + exit 64 +fi + +if ! buildImage "$DIR"; then + exit 65 +fi + +if ! finishInstall "$BOOT" "N"; then + exit 69 +fi + +html "Successfully prepared image for installation..." +return 0 diff --git a/computer_use_demo/windowshost/vm/buildcontainer/power.sh b/computer_use_demo/windowshost/vm/buildcontainer/power.sh new file mode 100644 index 0000000..060f18c --- /dev/null +++ b/computer_use_demo/windowshost/vm/buildcontainer/power.sh @@ -0,0 +1,223 @@ +#!/usr/bin/env bash +set -Eeuo pipefail + +# Configure QEMU for graceful shutdown + +QEMU_TERM="" +QEMU_PORT=7100 +QEMU_TIMEOUT=110 +QEMU_DIR="/run/shm" +QEMU_PID="$QEMU_DIR/qemu.pid" +QEMU_PTY="$QEMU_DIR/qemu.pty" +QEMU_LOG="$QEMU_DIR/qemu.log" +QEMU_OUT="$QEMU_DIR/qemu.out" +QEMU_END="$QEMU_DIR/qemu.end" + +rm -f "$QEMU_DIR/qemu.*" +touch "$QEMU_LOG" + +_trap() { + func="$1" ; shift + for sig ; do + trap "$func $sig" "$sig" + done +} + +boot() { + + [ -f "$QEMU_END" ] && return 0 + + if [ -s "$QEMU_PTY" ]; then + if [ "$(stat -c%s "$QEMU_PTY")" -gt 7 ]; then + local fail="" + if [[ "${BOOT_MODE,,}" == "windows_legacy" ]]; then + grep -Fq "No bootable device." "$QEMU_PTY" && fail="y" + grep -Fq "BOOTMGR is missing" "$QEMU_PTY" && fail="y" + fi + if [ -z "$fail" ]; then + info "Windows has started successfully. You can directly view the VM at http://localhost:8006/vnc.html?view_only=1&autoconnect=1&resize=scale. Wait until setup is complete before interacting manually." + return 0 + fi + fi + fi + + error "Timeout while waiting for QEMU to boot the machine!" + + local pid + pid=$(<"$QEMU_PID") + { kill -15 "$pid" || true; } 2>/dev/null + + return 0 +} + +ready() { + + [ -f "$STORAGE/windows.boot" ] && return 0 + [ ! -s "$QEMU_PTY" ] && return 1 + + if [[ "${BOOT_MODE,,}" == "windows_legacy" ]]; then + local last + local bios="Booting from Hard" + last=$(grep "^Booting.*" "$QEMU_PTY" | tail -1) + [[ "${last,,}" != "${bios,,}"* ]] && return 1 + grep -Fq "No bootable device." "$QEMU_PTY" && return 1 + grep -Fq "BOOTMGR is missing" "$QEMU_PTY" && return 1 + return 0 + fi + + local line="\"Windows Boot Manager\"" + grep -Fq "$line" "$QEMU_PTY" && return 0 + + return 1 +} + +finish() { + + local pid + local reason=$1 + + touch "$QEMU_END" + + if [ -s "$QEMU_PID" ]; then + + pid=$(<"$QEMU_PID") + error "Forcefully terminating Windows, reason: $reason..." + { kill -15 "$pid" || true; } 2>/dev/null + + while isAlive "$pid"; do + sleep 1 + # Workaround for zombie pid + [ ! -s "$QEMU_PID" ] && break + done + fi + + if [ ! -f "$STORAGE/windows.boot" ] && [ -f "$BOOT" ]; then + # Remove CD-ROM ISO after install + if ready; then + touch "$STORAGE/windows.boot" + if [[ "$REMOVE" != [Nn]* ]]; then + rm -f "$BOOT" 2>/dev/null || true + fi + fi + fi + + pid="/var/run/tpm.pid" + [ -s "$pid" ] && pKill "$(<"$pid")" + + pid="/var/run/wsdd.pid" + [ -s "$pid" ] && pKill "$(<"$pid")" + + fKill "smbd" + + closeNetwork + + sleep 0.5 + echo "โฏ Shutdown completed!" + + exit "$reason" +} + +terminal() { + + local dev="" + + if [ -s "$QEMU_OUT" ]; then + + local msg + msg=$(<"$QEMU_OUT") + + if [ -n "$msg" ]; then + + if [[ "${msg,,}" != "char"* || "$msg" != *"serial0)" ]]; then + echo "$msg" + fi + + dev="${msg#*/dev/p}" + dev="/dev/p${dev%% *}" + + fi + fi + + if [ ! -c "$dev" ]; then + dev=$(echo 'info chardev' | nc -q 1 -w 1 localhost "$QEMU_PORT" | tr -d '\000') + dev="${dev#*serial0}" + dev="${dev#*pty:}" + dev="${dev%%$'\n'*}" + dev="${dev%%$'\r'*}" + fi + + if [ ! -c "$dev" ]; then + error "Device '$dev' not found!" + finish 34 && return 34 + fi + + QEMU_TERM="$dev" + return 0 +} + +_graceful_shutdown() { + + local code=$? + + set +e + + if [ -f "$QEMU_END" ]; then + info "Received $1 while already shutting down..." + return + fi + + touch "$QEMU_END" + info "Received $1, sending ACPI shutdown signal..." + + if [ ! -s "$QEMU_PID" ]; then + error "QEMU PID file does not exist?" + finish "$code" && return "$code" + fi + + local pid="" + pid=$(<"$QEMU_PID") + + if ! isAlive "$pid"; then + error "QEMU process does not exist?" + finish "$code" && return "$code" + fi + + if ! ready; then + info "Cannot send ACPI signal during Windows setup, aborting..." + finish "$code" && return "$code" + fi + + # Send ACPI shutdown signal + echo 'system_powerdown' | nc -q 1 -w 1 localhost "${QEMU_PORT}" > /dev/null + + local cnt=0 + while [ "$cnt" -lt "$QEMU_TIMEOUT" ]; do + + sleep 1 + cnt=$((cnt+1)) + + ! isAlive "$pid" && break + # Workaround for zombie pid + [ ! -s "$QEMU_PID" ] && break + + info "Waiting for Windows to shutdown... ($cnt/$QEMU_TIMEOUT)" + + # Send ACPI shutdown signal + echo 'system_powerdown' | nc -q 1 -w 1 localhost "${QEMU_PORT}" > /dev/null + + done + + if [ "$cnt" -ge "$QEMU_TIMEOUT" ]; then + error "Shutdown timeout reached, aborting..." + fi + + finish "$code" && return "$code" +} + +SERIAL="pty" +MONITOR="telnet:localhost:$QEMU_PORT,server,nowait,nodelay" +MONITOR+=" -daemonize -D $QEMU_LOG -pidfile $QEMU_PID" + +_trap _graceful_shutdown SIGTERM SIGHUP SIGINT SIGABRT SIGQUIT + +return 0 diff --git a/computer_use_demo/windowshost/vm/buildcontainer/samba.sh b/computer_use_demo/windowshost/vm/buildcontainer/samba.sh new file mode 100644 index 0000000..07c535d --- /dev/null +++ b/computer_use_demo/windowshost/vm/buildcontainer/samba.sh @@ -0,0 +1,109 @@ +#!/usr/bin/env bash +set -Eeuo pipefail + +: "${SAMBA:="Y"}" + +[[ "$SAMBA" == [Nn]* ]] && return 0 +[[ "$NETWORK" == [Nn]* ]] && return 0 + +hostname="host.lan" +interface="dockerbridge" + +if [[ "$DHCP" == [Yy1]* ]]; then + hostname="$IP" + interface="$VM_NET_DEV" +fi + +addShare() { + local dir="$1" + local name="$2" + local comment="$3" + + mkdir -p "$dir" || return 1 + + if [ -z "$(ls -A "$dir")" ]; then + + chmod 777 "$dir" + + { echo "--------------------------------------------------------" + echo " $APP" + echo " For support visit $SUPPORT" + echo "--------------------------------------------------------" + echo "" + echo "Using this folder you can share files with the host machine." + echo "" + echo "To change its location, include the following bind mount in your compose file:" + echo "" + echo " volumes:" + echo " - \"/home/example:/${name,,}\"" + echo "" + echo "Or in your run command:" + echo "" + echo " -v \"/home/example:/${name,,}\"" + echo "" + echo "Replace the example path /home/example with the desired shared folder." + echo "" + } | unix2dos > "$dir/readme.txt" + + fi + + { echo "" + echo "[$name]" + echo " path = $dir" + echo " comment = $comment" + echo " writable = yes" + echo " guest ok = yes" + echo " guest only = yes" + echo " force user = root" + echo " force group = root" + } >> "/etc/samba/smb.conf" + + return 0 +} + +{ echo "[global]" + echo " server string = Dockur" + echo " netbios name = $hostname" + echo " workgroup = WORKGROUP" + echo " interfaces = $interface" + echo " bind interfaces only = yes" + echo " security = user" + echo " guest account = nobody" + echo " map to guest = Bad User" + echo " server min protocol = NT1" + echo "" + echo " # disable printing services" + echo " load printers = no" + echo " printing = bsd" + echo " printcap name = /dev/null" + echo " disable spoolss = yes" +} > "/etc/samba/smb.conf" + +share="/data" +[ ! -d "$share" ] && [ -d "$STORAGE/data" ] && share="$STORAGE/data" +[ ! -d "$share" ] && [ -d "/shared" ] && share="/shared" +[ ! -d "$share" ] && [ -d "$STORAGE/shared" ] && share="$STORAGE/shared" + +addShare "$share" "Data" "Shared" || error "Failed to create shared folder!" + +[ -d "/data2" ] && addShare "/data2" "Data2" "Shared" +[ -d "/data3" ] && addShare "/data3" "Data3" "Shared" + +if ! smbd; then + error "Samba daemon failed to start!" + smbd -i --debug-stdout || true +fi + +if [[ "${BOOT_MODE:-}" == "windows_legacy" ]]; then + # Enable NetBIOS on Windows 7 and lower + if ! nmbd; then + error "NetBIOS daemon failed to start!" + nmbd -i --debug-stdout || true + fi +else + # Enable Web Service Discovery on Vista and up + wsdd -i "$interface" -p -n "$hostname" & + echo "$!" > /var/run/wsdd.pid +fi + +return 0 diff --git a/computer_use_demo/windowshost/vm/win11def/win11x64-enterprise-eval.xml b/computer_use_demo/windowshost/vm/win11def/win11x64-enterprise-eval.xml new file mode 100644 index 0000000..36f13fb --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11def/win11x64-enterprise-eval.xml @@ -0,0 +1,462 @@ +<?xml version="1.0" encoding="UTF-8"?> +<unattend xmlns="urn:schemas-microsoft-com:unattend" xmlns:wcm="http://schemas.microsoft.com/WMIConfig/2002/State"> + <settings pass="windowsPE"> + <component name="Microsoft-Windows-International-Core-WinPE" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <SetupUILanguage> + <UILanguage>en-US</UILanguage> + </SetupUILanguage> + <InputLocale>0409:00000409</InputLocale> + <SystemLocale>en-US</SystemLocale> + <UILanguage>en-US</UILanguage> + <UserLocale>en-US</UserLocale> + </component> + <component name="Microsoft-Windows-Setup" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <DiskConfiguration> + <Disk wcm:action="add"> + <DiskID>0</DiskID> + <WillWipeDisk>true</WillWipeDisk> + <CreatePartitions> + <!-- System partition (ESP) --> + <CreatePartition wcm:action="add"> + <Order>1</Order> + <Type>EFI</Type> + <Size>128</Size> + </CreatePartition> + <!-- Microsoft reserved partition (MSR) --> + <CreatePartition wcm:action="add"> + <Order>2</Order> + <Type>MSR</Type> + <Size>128</Size> + </CreatePartition> + <!-- Windows partition --> + <CreatePartition wcm:action="add"> + <Order>3</Order> + <Type>Primary</Type> + <Extend>true</Extend> + </CreatePartition> + </CreatePartitions> + <ModifyPartitions> + <!-- System partition (ESP) --> + <ModifyPartition wcm:action="add"> + <Order>1</Order> + <PartitionID>1</PartitionID> + <Label>System</Label> + <Format>FAT32</Format> + </ModifyPartition> + <!-- MSR partition does not need to be modified --> + <ModifyPartition wcm:action="add"> + <Order>2</Order> + <PartitionID>2</PartitionID> + </ModifyPartition> + <!-- Windows partition --> + <ModifyPartition wcm:action="add"> + <Order>3</Order> + <PartitionID>3</PartitionID> + <Label>Windows</Label> + <Letter>C</Letter> + <Format>NTFS</Format> + </ModifyPartition> + </ModifyPartitions> + </Disk> + </DiskConfiguration> + <ImageInstall> + <OSImage> + <InstallTo> + <DiskID>0</DiskID> + <PartitionID>3</PartitionID> + </InstallTo> + <InstallToAvailablePartition>false</InstallToAvailablePartition> + </OSImage> + </ImageInstall> + <DynamicUpdate> + <Enable>true</Enable> + <WillShowUI>Never</WillShowUI> + </DynamicUpdate> + <UpgradeData> + <Upgrade>false</Upgrade> + <WillShowUI>Never</WillShowUI> + </UpgradeData> + <UserData> + <AcceptEula>true</AcceptEula> + <FullName>Docker</FullName> + <Organization>Windows for Docker</Organization> + </UserData> + <EnableFirewall>false</EnableFirewall> + <Diagnostics> + <OptIn>false</OptIn> + </Diagnostics> + <RunSynchronous> + <RunSynchronousCommand wcm:action="add"> + <Order>1</Order> + <Path>reg.exe add "HKLM\SYSTEM\Setup\LabConfig" /v BypassTPMCheck /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>2</Order> + <Path>reg.exe add "HKLM\SYSTEM\Setup\LabConfig" /v BypassSecureBootCheck /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>3</Order> + <Path>reg.exe add "HKLM\SYSTEM\Setup\LabConfig" /v BypassRAMCheck /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>4</Order> + <Path>reg.exe add "HKLM\SYSTEM\Setup\MoSetup" /v AllowUpgradesWithUnsupportedTPMOrCPU /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + </RunSynchronous> + </component> + </settings> + <settings pass="offlineServicing"> + <component name="Microsoft-Windows-LUA-Settings" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <EnableLUA>false</EnableLUA> + </component> + </settings> + <settings pass="generalize"> + <component name="Microsoft-Windows-PnPSysprep" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <PersistAllDeviceInstalls>true</PersistAllDeviceInstalls> + </component> + <component name="Microsoft-Windows-Security-SPP" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <SkipRearm>1</SkipRearm> + </component> + </settings> + <settings pass="specialize"> + <component name="Microsoft-Windows-Security-SPP-UX" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <SkipAutoActivation>true</SkipAutoActivation> + </component> + <component name="Microsoft-Windows-Shell-Setup" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <ComputerName>*</ComputerName> + <OEMInformation> + <Manufacturer>Dockur</Manufacturer> + <Model>Windows for Docker</Model> + <SupportHours>24/7</SupportHours> + <SupportPhone /> + <SupportProvider>Dockur</SupportProvider> + <SupportURL>https://github.com/dockur/windows/issues</SupportURL> + </OEMInformation> + <OEMName>Windows for Docker</OEMName> + </component> + <component name="Microsoft-Windows-ErrorReportingCore" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <DisableWER>1</DisableWER> + </component> + <component name="Microsoft-Windows-IE-InternetExplorer" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <DisableAccelerators>true</DisableAccelerators> + <DisableFirstRunWizard>true</DisableFirstRunWizard> + <Home_Page>https://google.com</Home_Page> + <Help_Page>about:blank</Help_Page> + </component> + <component name="Microsoft-Windows-IE-InternetExplorer" processorArchitecture="wow64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <DisableAccelerators>true</DisableAccelerators> + <DisableFirstRunWizard>true</DisableFirstRunWizard> + <Home_Page>https://google.com</Home_Page> + <Help_Page>about:blank</Help_Page> + </component> + <component name="Microsoft-Windows-SQMApi" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <CEIPEnabled>0</CEIPEnabled> + </component> + <component name="Microsoft-Windows-SystemRestore-Main" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <DisableSR>1</DisableSR> + </component> + <component name="Microsoft-Windows-International-Core" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <InputLocale>0409:00000409</InputLocale> + <SystemLocale>en-US</SystemLocale> + <UILanguage>en-US</UILanguage> + <UserLocale>en-US</UserLocale> + </component> + <component name="Microsoft-Windows-Deployment" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <RunSynchronous> + <RunSynchronousCommand wcm:action="add"> + <Order>1</Order> + <Path>reg.exe add "HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\OOBE" /v BypassNRO /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>2</Order> + <Path>reg.exe load "HKU\mount" "C:\Users\Default\NTUSER.DAT"</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>3</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "ContentDeliveryAllowed" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>4</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "FeatureManagementEnabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>5</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "OEMPreInstalledAppsEnabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>6</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "PreInstalledAppsEnabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>7</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "PreInstalledAppsEverEnabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>8</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SilentInstalledAppsEnabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>9</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SoftLandingEnabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>10</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContentEnabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>11</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-310093Enabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>12</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-338387Enabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>13</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-338388Enabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>14</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-338389Enabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>15</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-338393Enabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>16</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-353698Enabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>17</Order> + <Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SystemPaneSuggestionsEnabled" /t REG_DWORD /d 0 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>18</Order> + <Path>reg.exe add "HKU\mount\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableCloudOptimizedContent" /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>19</Order> + <Path>reg.exe add "HKU\mount\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableWindowsConsumerFeatures" /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>20</Order> + <Path>reg.exe add "HKU\mount\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableConsumerAccountStateContent" /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>21</Order> + <Path>reg.exe unload "HKU\mount"</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>22</Order> + <Path>reg.exe add "HKLM\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableCloudOptimizedContent" /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>23</Order> + <Path>reg.exe add "HKLM\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableWindowsConsumerFeatures" /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>24</Order> + <Path>reg.exe add "HKLM\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableConsumerAccountStateContent" /t REG_DWORD /d 1 /f</Path> + </RunSynchronousCommand> + <RunSynchronousCommand wcm:action="add"> + <Order>25</Order> + <Path>reg.exe add "HKLM\SOFTWARE\Policies\Microsoft\Windows NT\CurrentVersion\NetworkList\Signatures\FirstNetwork" /v Category /t REG_DWORD /d 1 /f</Path> + <Description>Set Network Location to Home</Description> + </RunSynchronousCommand> + </RunSynchronous> + </component> + <component name="Microsoft-Windows-TerminalServices-LocalSessionManager" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <fDenyTSConnections>false</fDenyTSConnections> + </component> + <component name="Microsoft-Windows-TerminalServices-RDP-WinStationExtensions" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <UserAuthentication>0</UserAuthentication> + </component> + <component name="Networking-MPSSVC-Svc" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <FirewallGroups> + <FirewallGroup wcm:action="add" wcm:keyValue="RemoteDesktop"> + <Active>true</Active> + <Profile>all</Profile> + <Group>@FirewallAPI.dll,-28752</Group> + </FirewallGroup> + </FirewallGroups> + </component> + </settings> + <settings pass="auditSystem" /> + <settings pass="auditUser" /> + <settings pass="oobeSystem"> + <component name="Microsoft-Windows-SecureStartup-FilterDriver" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <PreventDeviceEncryption>true</PreventDeviceEncryption> + </component> + <component name="Microsoft-Windows-EnhancedStorage-Adm" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <TCGSecurityActivationDisabled>1</TCGSecurityActivationDisabled> + </component> + <component name="Microsoft-Windows-Shell-Setup" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS"> + <UserAccounts> + <LocalAccounts> + <LocalAccount wcm:action="add"> + <Name>Docker</Name> + <Group>Administrators</Group> + <Password> + <Value /> + <PlainText>true</PlainText> + </Password> + </LocalAccount> + </LocalAccounts> + <AdministratorPassword> + <Value>password</Value> + <PlainText>true</PlainText> + </AdministratorPassword> + </UserAccounts> + <AutoLogon> + <Username>Docker</Username> + <Enabled>true</Enabled> + <LogonCount>65432</LogonCount> + <Password> + <Value /> + <PlainText>true</PlainText> + </Password> + </AutoLogon> + <Display> + <ColorDepth>32</ColorDepth> + <HorizontalResolution>1920</HorizontalResolution> + <VerticalResolution>1080</VerticalResolution> + </Display> + <OOBE> + <HideEULAPage>true</HideEULAPage> + <HideLocalAccountScreen>true</HideLocalAccountScreen> + <HideOEMRegistrationScreen>true</HideOEMRegistrationScreen> + <HideOnlineAccountScreens>true</HideOnlineAccountScreens> + <HideWirelessSetupInOOBE>true</HideWirelessSetupInOOBE> + <NetworkLocation>Home</NetworkLocation> + <ProtectYourPC>3</ProtectYourPC> + <SkipUserOOBE>true</SkipUserOOBE> + <SkipMachineOOBE>true</SkipMachineOOBE> + </OOBE> + <RegisteredOrganization>Dockur</RegisteredOrganization> + <RegisteredOwner>Windows for Docker</RegisteredOwner> + <FirstLogonCommands> + <SynchronousCommand wcm:action="add"> + <Order>1</Order> + <CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Services\LanmanWorkstation\Parameters" /v "AllowInsecureGuestAuth" /t REG_DWORD /d 1 /f</CommandLine> + <Description>Allow guest access to network shares</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>2</Order> + <CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Services\LanmanWorkstation\Parameters" /v "RequireSecuritySignature" /t REG_DWORD /d 0 /f</CommandLine> + <Description>Disable SMB signing requirement</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>3</Order> + <CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Control\Lsa" /v LimitBlankPasswordUse /t REG_DWORD /d 0 /f</CommandLine> + <Description>Allow RDP login with blank password</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>4</Order> + <CommandLine>reg.exe add "HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\PasswordLess\Device" /v "DevicePasswordLessBuildVersion" /t REG_DWORD /d 0 /f</CommandLine> + <Description>Enable option for passwordless sign-in</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>5</Order> + <CommandLine>cmd /C wmic useraccount where name="Docker" set PasswordExpires=false</CommandLine> + <Description>Password Never Expires</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>6</Order> + <CommandLine>cmd /C POWERCFG -H OFF</CommandLine> + <Description>Disable Hibernation</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>7</Order> + <CommandLine>cmd /C POWERCFG -X -monitor-timeout-ac 0</CommandLine> + <Description>Disable monitor blanking</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>8</Order> + <CommandLine>reg.exe add "HKLM\SOFTWARE\Policies\Microsoft\Edge" /v "HideFirstRunExperience" /t REG_DWORD /d 1 /f</CommandLine> + <Description>Disable first-run experience in Edge</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>9</Order> + <CommandLine>reg.exe add "HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Advanced" /v "HideFileExt" /t REG_DWORD /d 0 /f</CommandLine> + <Description>Show file extensions in Explorer</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>10</Order> + <CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Control\Power" /v "HibernateFileSizePercent" /t REG_DWORD /d 0 /f</CommandLine> + <Description>Zero Hibernation File</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>11</Order> + <CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Control\Power" /v "HibernateEnabled" /t REG_DWORD /d 0 /f</CommandLine> + <Description>Disable Hibernation</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>12</Order> + <CommandLine>cmd /C POWERCFG -X -standby-timeout-ac 0</CommandLine> + <Description>Disable Sleep</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>13</Order> + <CommandLine>reg.exe add "HKLM\SOFTWARE\Policies\Microsoft\Windows NT\Terminal Services" /v "fAllowUnlistedRemotePrograms" /t REG_DWORD /d 1 /f</CommandLine> + <Description>Enable RemoteAPP to launch unlisted programs</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>14</Order> + <CommandLine>reg.exe add "HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Advanced" /v "ShowTaskViewButton" /t REG_DWORD /d 0 /f</CommandLine> + <Description>Remove Task View from the Taskbar</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>15</Order> + <CommandLine>reg.exe add "HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Advanced" /v "TaskbarDa" /t REG_DWORD /d 0 /f</CommandLine> + <Description>Remove Widgets from the Taskbar</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>16</Order> + <CommandLine>reg.exe add "HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Advanced" /v "TaskbarMn" /t REG_DWORD /d 0 /f</CommandLine> + <Description>Remove Chat from the Taskbar</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>17</Order> + <CommandLine>reg.exe add "HKLM\SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU" /v "NoAutoUpdate" /t REG_DWORD /d 1 /f</CommandLine> + <Description>Turn off Windows Update auto download</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>18</Order> + <CommandLine>netsh advfirewall firewall set rule group="@FirewallAPI.dll,-32752" new enable=Yes</CommandLine> + <Description>Enable Network Discovery</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>19</Order> + <CommandLine>netsh advfirewall firewall set rule group="@FirewallAPI.dll,-28502" new enable=Yes</CommandLine> + <Description>Enable File Sharing</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>20</Order> + <CommandLine>reg.exe add "HKCU\Control Panel\UnsupportedHardwareNotificationCache" /v SV1 /d 0 /t REG_DWORD /f</CommandLine> + <Description>Disable unsupported hardware notifications</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>21</Order> + <CommandLine>reg.exe add "HKCU\Control Panel\UnsupportedHardwareNotificationCache" /v SV2 /d 0 /t REG_DWORD /f</CommandLine> + <Description>Disable unsupported hardware notifications</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>22</Order> + <CommandLine>pnputil -i -a C:\Windows\Drivers\viogpudo\viogpudo.inf</CommandLine> + <Description>Install VirtIO display driver</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>23</Order> + <CommandLine>cmd /C rd /q C:\Windows.old</CommandLine> + <Description>Remove empty Windows.old folder</Description> + </SynchronousCommand> + <SynchronousCommand wcm:action="add"> + <Order>24</Order> + <CommandLine>cmd /C if exist "C:\OEM\install.bat" start "Install" "cmd /C C:\OEM\install.bat"</CommandLine> + <Description>Execute custom script from the OEM folder if exists</Description> + </SynchronousCommand> + </FirstLogonCommands> + </component> + </settings> +</unattend> \ No newline at end of file diff --git a/computer_use_demo/windowshost/vm/win11iso/README.md b/computer_use_demo/windowshost/vm/win11iso/README.md new file mode 100644 index 0000000..23c4741 --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11iso/README.md @@ -0,0 +1 @@ +Add your Win11E setup.iso to this folder \ No newline at end of file diff --git a/computer_use_demo/windowshost/vm/win11setup/firstboot/install.bat b/computer_use_demo/windowshost/vm/win11setup/firstboot/install.bat new file mode 100644 index 0000000..08324f2 --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11setup/firstboot/install.bat @@ -0,0 +1,31 @@ +@echo off + +SET ScriptFolder=\\host.lan\Data +SET LogFile=%ScriptFolder%\firstboot_log.txt + +echo Running PowerShell script... > %LogFile% + +:: Check for PowerShell availability +where powershell >> %LogFile% 2>&1 +if %ERRORLEVEL% neq 0 ( + echo PowerShell is not available! >> %LogFile% + echo PowerShell is not available! + exit /b 1 +) + +:: Add a 30-second delay +echo Waiting for 30 seconds before continuing... >> %LogFile% +timeout /t 30 /nobreak >> %LogFile% 2>&1 + +:: Run PowerShell script with ExecutionPolicy Bypass and log errors +echo Running setup.ps1... >> %LogFile% + +powershell -ExecutionPolicy Bypass -File "%ScriptFolder%\setup.ps1" >> %LogFile% 2>&1 + +if %ERRORLEVEL% neq 0 ( + echo An error occurred. See %LogFile% for details. +) else ( + echo PowerShell script has completed successfully. +) + +echo PowerShell script has completed. \ No newline at end of file diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/on-logon.ps1 b/computer_use_demo/windowshost/vm/win11setup/setupscripts/on-logon.ps1 new file mode 100644 index 0000000..3221ac2 --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11setup/setupscripts/on-logon.ps1 @@ -0,0 +1,7 @@ +$scriptFolder = "\\host.lan\Data" +$pythonScriptFile = "$scriptFolder\server\main.py" +$pythonServerPort = 5000 + +# Start the flask computer use server +Write-Host "Running the server on port $pythonServerPort" +python $pythonScriptFile --port $pythonServerPort diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/cursor.png b/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/cursor.png new file mode 100644 index 0000000000000000000000000000000000000000..d3a3c5bbe1d070b65dc3bb31ab18998823ff54f8 GIT binary patch literal 3207 zcmV;240!X2P)<h;3K|Lk000e1NJLTq000yK001Be1^@s65@J}k000T0X+uL$Nkc;* zP;zf(X>4Tx0C=43nP*T`Sr&lrd#`h#p}T1^G&xC>(17HeBu7z!?k3Yhlfj4#j=%^a zB1sWM6mY=NA;>5yq8Jbb1<cdPAfSwcqlk*i0{)n-+O6H%s;&LAeSW<6)w!qMIrr7A zdSBH8K!%XOPE3LI0Z8Pf@_n7{n86{TOzeGt17tt~RDg+KrzCm$Ir)R-Sg8g75bs9; 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zd~JMu{P1{}x~_{->Q<_POy}9;a=9W02M2$#a5ya8-QC}mQnxeIri~kulamq(g~aRi teo;yd0~h`m1F&eCwyc!84-}s^@iz_-B{HTjtFHh6002ovPDHLkV1i9}Eqwq0 literal 0 HcmV?d00001 diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/main.py b/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/main.py new file mode 100644 index 0000000..5343918 --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/main.py @@ -0,0 +1,81 @@ +import os +import logging +import argparse +import shlex +import subprocess +from flask import Flask, request, jsonify, send_file +import threading +import traceback +import pyautogui +from PIL import Image +from io import BytesIO + +parser = argparse.ArgumentParser() +parser.add_argument("--log_file", help="log file path", type=str, + default=os.path.join(os.path.dirname(__file__), "server.log")) +parser.add_argument("--port", help="port", type=int, default=5000) +args = parser.parse_args() + +logging.basicConfig(filename=args.log_file,level=logging.DEBUG, filemode='w' ) +logger = logging.getLogger('werkzeug') + +app = Flask(__name__) + +computer_control_lock = threading.Lock() + +@app.route('/probe', methods=['GET']) +def probe_endpoint(): + return jsonify({"status": "Probe successful", "message": "Service is operational"}), 200 + +@app.route('/execute', methods=['POST']) +def execute_command(): + # Only execute one command at a time + with computer_control_lock: + data = request.json + # The 'command' key in the JSON request should contain the command to be executed. + shell = data.get('shell', False) + command = data.get('command', "" if shell else []) + + if isinstance(command, str) and not shell: + command = shlex.split(command) + + # Expand user directory + for i, arg in enumerate(command): + if arg.startswith("~/"): + command[i] = os.path.expanduser(arg) + + # Execute the command without any safety checks. + try: + result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell, text=True, timeout=120) + return jsonify({ + 'status': 'success', + 'output': result.stdout, + 'error': result.stderr, + 'returncode': result.returncode + }) + except Exception as e: + logger.error("\n" + traceback.format_exc() + "\n") + return jsonify({ + 'status': 'error', + 'message': str(e) + }), 500 + +@app.route('/screenshot', methods=['GET']) +def capture_screen_with_cursor(): + cursor_path = os.path.join(os.path.dirname(__file__), "cursor.png") + screenshot = pyautogui.screenshot() + cursor_x, cursor_y = pyautogui.position() + cursor = Image.open(cursor_path) + # make the cursor smaller + cursor = cursor.resize((int(cursor.width / 1.5), int(cursor.height / 1.5))) + screenshot.paste(cursor, (cursor_x, cursor_y), cursor) + + + # Convert PIL Image to bytes and send + img_io = BytesIO() + screenshot.save(img_io, 'PNG') + img_io.seek(0) + return send_file(img_io, mimetype='image/png') + +if __name__ == '__main__': + app.run(debug=True, host="0.0.0.0", port=args.port) \ No newline at end of file diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/requirements.txt b/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/requirements.txt new file mode 100644 index 0000000..31b005f --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/requirements.txt @@ -0,0 +1,2 @@ +flask +PyAutoGUI \ No newline at end of file diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/setup-tools.psm1 b/computer_use_demo/windowshost/vm/win11setup/setupscripts/setup-tools.psm1 new file mode 100644 index 0000000..063935d --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11setup/setupscripts/setup-tools.psm1 @@ -0,0 +1,197 @@ +function Get-Tools { + param( + [string]$toolsConfigJson + ) + + # Convert the JSON string to a PowerShell object + $toolsList = $toolsConfigJson | ConvertFrom-Json + + return $toolsList +} + +function Get-ToolDetails { + param( + $toolsList, + [string]$toolName + ) + + # Check if the program exists in the JSON data + if ($toolsList.PSObject.Properties.Name -contains $toolName) { + # Return the program details as a PowerShell object + return $toolsList.$toolName + } else { + # Handle the case where the program is not found + Write-Host "Program '$toolName' not found in the list." + return $null + } +} + +function Invoke-DownloadFileFromAvailableMirrors { + param ( + [string[]]$mirrorUrls, + [string]$outfile + ) + foreach ($url in $mirrorUrls) { + try { + $result = Invoke-DownloadFile -url $url -outfile $outfile + if ($result -eq $true) { + Write-Host "Downloaded using $url" + return $true + } + } catch { + Write-Host "Error downloading from $url. Please check and update the mirrors." + } + } + + Write-Host "Downloading from the provided mirrors failed. Please check and update the mirrors." + return $false +} + +function Invoke-DownloadFile { + param ( + [string]$url, + [string]$outfile + ) + # Makes download faster by disabling progress bar + $ProgressPreference = "SilentlyContinue" + + $retryCount = 0 + $maxRetries = 3 + $sleepSeconds = 2 + $maxSleepSeconds = 10 + $userAgent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36" + + # Ensure directory exists + $directory = Split-Path -Path $outfile -Parent + if (-Not (Test-Path -Path $directory)) { + Write-Host "Creating directory $directory..." + New-Item -Path $directory -ItemType Directory -Force | Out-Null + } + + while ($retryCount -lt $maxRetries) { + try { + Invoke-RestMethod -Uri $url -OutFile $outfile -Headers @{"User-Agent" = $userAgent} + Write-Host "Download successful, file saved to: $outfile" + break + } catch { + $retryCount++ + Write-Host "Attempt $retryCount of $maxRetries failed. Error: $($_.Exception.Message)" + Start-Sleep -Seconds $sleepSeconds + $sleepSeconds = [Math]::Min($sleepSeconds * 2, $maxSleepSeconds) # Exponential backoff with a cap + } + } + + if ($retryCount -eq $maxRetries) { + Write-Host "Failed to download the file after $maxRetries attempts." + return $false + } + + return $true +} + +function Add-ToEnvPath { + param ( + [string]$NewPath + ) + + # Get the current PATH environment variable + $envPath = [Environment]::GetEnvironmentVariable("PATH", "Machine") + + # Append the new path to the existing PATH + $newPath = "$envPath;$NewPath" + + # Set the updated PATH environment variable + [Environment]::SetEnvironmentVariable("PATH", $newPath, "Machine") + + # Fetch updates from the shell + $env:PATH += ";${newPath}" +} + +function Register-LogonTask { + param( + + [parameter(Mandatory = $true, ValueFromPipelineByPropertyName = $true, HelpMessage = "Name of the scheduled task")] + [string] + $TaskName, + + [parameter(Mandatory = $true, ValueFromPipelineByPropertyName = $true, HelpMessage = "Path to the .py script")] + [string] + $ScriptPath, + + [parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "Arguments to the .py script")] + [string] + $Arguments = "", + + [parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "Local Account username")] + [string] + $LocalUser, + + [parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "Local Account password")] + [string] + $LocalPassword, + + [parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "Whether to execute the command as SYSTEM")] + [switch] + $AsSystem = $false, + + [parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "logging file")] + [string] + $LogFilePath + ) + + $scriptDirectory = Split-Path $ScriptPath + + $taskActionArgument = "-ExecutionPolicy Bypass -windowstyle hidden -Command `"try { . '$ScriptPath' $Arguments } catch { Write `$_.Exception.Message | Out-File $($TaskName)_Log.txt } finally { } `"" + $taskAction = New-ScheduledTaskAction -Execute "$PSHome\powershell.exe" -Argument $taskActionArgument -WorkingDirectory $scriptDirectory + + $params = @{ + Force = $True + Action = $taskAction + RunLevel = "Highest" + TaskName = $TaskName + } + + $taskTrigger = New-ScheduledTaskTrigger -AtLogOn + $params.Add("Trigger", $taskTrigger) + + if ($AsSystem) { + $params.Add("User", "NT AUTHORITY\SYSTEM") + } + else { + $params.Add("User", $LocalUser) + if ($LocalPassword) { + $params.Add("Password", $LocalPassword) + } + } + + Write-Host "Registering scheduled task '$TaskName' to run 'powershell.exe $taskActionArgument'..." + Register-ScheduledTask @params +} + +# Function to attempt pip install and handle failures +function Install-PythonPackages { + param ( + [string]$Package = "", + [string]$Arguments = "", + [string]$RequirementsPath = "" + ) + $RetryCount = 3 + $currentAttempt = 0 + while ($currentAttempt -lt $RetryCount) { + if (-not [string]::IsNullOrWhiteSpace($RequirementsPath)) { + & python -m pip install --no-cache-dir -r $RequirementsPath $Arguments + } else { + & python -m pip install --no-cache-dir $Package $Arguments + } + if ($LASTEXITCODE -eq 0) { + Write-Host "Installation successful." + return + } else { + Write-Host "Attempt $($currentAttempt + 1) failed. Retrying..." + Start-Sleep -Seconds 10 + $currentAttempt++ + } + } + Write-Error "Failed to install after $RetryCount attempts." + exit +} \ No newline at end of file diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/setup.ps1 b/computer_use_demo/windowshost/vm/win11setup/setupscripts/setup.ps1 new file mode 100644 index 0000000..013cc2f --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11setup/setupscripts/setup.ps1 @@ -0,0 +1,392 @@ +$ErrorActionPreference = "Continue" # until downloading from mirrors is more stable + +# Section - General Setup +$scriptFolder = "\\host.lan\Data" +$toolsFolder = "C:\Users\$env:USERNAME\Tools" + +# Load the shared setup-tools module +Import-Module (Join-Path $scriptFolder -ChildPath "setup-tools.psm1") + +# Check if profile exists +if (-not (Test-Path $PROFILE)) { + New-Item -ItemType File -Path $PROFILE -Force +} + +# Create a folder where we store all the standalone executables +if (-not (Test-Path $toolsFolder)) { + New-Item -ItemType Directory -Path $toolsFolder -Force + $envPath = [Environment]::GetEnvironmentVariable("PATH", "Machine") + $newPath = "$envPath;$toolsFolder" + [Environment]::SetEnvironmentVariable("PATH", $newPath, "Machine") +} + +# Section - Tools Installation + +# Set TLS version to 1.2 or higher +[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12 -bor [Net.SecurityProtocolType]::Tls13 + +# Load the tools config json listing mirrors and aliases used for installing tools +$toolsConfigJsonPath = Join-Path $scriptFolder -ChildPath "tools_config.json" +$toolsConfigJson = Get-Content -Path $toolsConfigJsonPath -Raw +$toolsList = Get-Tools -toolsConfigJson $toolsConfigJson + +## - Python +$pythonToolName = "Python" +$userPythonPath = "$env:LOCALAPPDATA\Programs\Python" +$pythonDetails = Get-ToolDetails -toolsList $toolsList -toolName $pythonToolName +$pythonAlias = $pythonDetails.alias + +# Check for Python installation +$pythonExecutablePath = Get-ChildItem -Path $userPythonPath -Filter python.exe -Recurse -ErrorAction SilentlyContinue | Select-Object -First 1 -ExpandProperty FullName + +# Force to install Python 3.10 as the pre-installed version on Windows may not work sometimes +Write-Host "Downloading Python $pythonVersion..." +$pythonInstallerFilePath = "$env:TEMP\python_installer.exe" +$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $pythonDetails.mirrors -outfile $pythonInstallerFilePath +if (-not $downloadResult) { + Write-Host "Failed to download Python. Please try again later or install manually." +} else { + Write-Host "Installing Python for current user..." + Start-Process -FilePath $pythonInstallerFilePath -Args "/quiet InstallAllUsers=0 PrependPath=0" -NoNewWindow -Wait + $pythonExecutablePath = "$userPythonPath\Python310\python.exe" + $setAliasExpression = "Set-Alias -Name $pythonAlias -Value `"$pythonExecutablePath`"" + Add-Content -Path $PROFILE -Value $setAliasExpression + Invoke-Expression $setAliasExpression +} + +## - Git +$gitToolName = "git" +$gitToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $gitToolName + +# Check for Git installation +try { + git --version | Out-Null + Write-Host "Git is already installed." +} catch { + Write-Host "Git is not installed. Downloading and installing Git..." + $gitInstallerFilePath = "$env:TEMP\git_installer.exe" + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $gitToolDetails.mirrors -outfile $gitInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download Git. Please try again later or install manually." + } else { + Start-Process -FilePath $gitInstallerFilePath -Args "/VERYSILENT /NORESTART /NOCANCEL /SP-" -Wait + Add-ToEnvPath -NewPath "C:\Program Files\Git\bin" + + Write-Host "Git has been installed." + } +} + +# - 7zip +$7ZipToolName = "7zip" +$7ZipToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $7ZipToolName +Write-Host "$7ZipToolDetails" + +if (Get-Command 7z -ErrorAction SilentlyContinue) { + Write-Host "7-Zip is already installed." +} +else { + Write-Host "Installing 7-Zip..." + + $7ZipInstallerFilePath = "$env:TEMP\7_zip.exe" + Write-Host "$($7ZipToolDetails.mirrors)" + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $7ZipToolDetails.mirrors -outfile $7ZipInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download 7-Zip. Please try again later or install manually." + } else { + Start-Process -FilePath $7ZipInstallerFilePath -Args "/S" -Verb RunAs -Wait + Remove-Item $7ZipInstallerFilePath + + # add 7z to PATH + Add-ToEnvPath -NewPath "${env:ProgramFiles}\7-Zip" + } +} + +# - ffpmeg +$ffpmegToolName = "ffmpeg" +$ffpmegToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $ffpmegToolName + +if (Get-Command ffmpeg -ErrorAction SilentlyContinue) { + Write-Host "ffmpeg is already installed." +} else { + Write-Host "ffmpeg is not installed. Installing it." + $ffpmegInstallerFilePath = "C:\ffmpeg.7z" + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $ffpmegToolDetails.mirrors -outfile $ffpmegInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download ffmpeg. Please try again later or install manually." + } else { + Write-Host "Extracting $ffpmegInstallerFilePath..." + 7z x -y -o"C:\" "C:\ffmpeg.7z" + + $ffmpegFolder = Get-ChildItem -Path "C:\" -Filter "ffmpeg-*" -Directory + $ffmpegFolder = -join ("C:\", $ffmpegFolder) + #remove ffmpeg folder if exists + if (Test-Path "C:\ffmpeg") { + Remove-Item -Path "C:\ffmpeg" -Recurse -Force + } + Rename-Item -Path "$ffmpegFolder" -NewName "ffmpeg" + + Write-Host "Adding ffmpeg to PATH..." + Add-ToEnvPath -NewPath "C:\ffmpeg\bin" + + Write-Host "ffmpeg is installed" + } +} + +# Disable Edge Auto Updates +Stop-Process -Name "MicrosoftEdgeUpdate" -Force -ErrorAction SilentlyContinue +$edgeUpdatePath = "${env:ProgramFiles(x86)}\Microsoft\EdgeUpdate" +Remove-Item -Path $edgeUpdatePath -Recurse -Force -ErrorAction SilentlyContinue +Write-Host "Edge Update processes terminated and directory removed." + +# - Google Chrome +$chromeToolName = "Google Chrome" +$chromeToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $chromeToolName +$chromeExePath = "C:\Program Files\Google\Chrome\Application\chrome.exe" +$chromeAlias = $chromeToolDetails.alias + +# Check if Google Chrome is already installed by its alias +if (Get-Command $chromeAlias -ErrorAction SilentlyContinue) { + Write-Host "Google Chrome is already installed." +} else { + # Download the installer to the Temp directory + $chromeInstallerFilePath = "$env:TEMP\chrome_installer.exe" + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $chromeToolDetails.mirrors -outfile $chromeInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download Google Chrome. Please try again later or install manually." + } else { + # Execute the installer silently with elevated permissions + Start-Process -FilePath $chromeInstallerFilePath -ArgumentList "/silent", "/install" -Verb RunAs -Wait + + # Remove the installer file after installation + Remove-Item -Path $chromeInstallerFilePath + + # Set alias + $setAliasExpression = "Set-Alias -Name $chromeAlias -Value `"$chromeExePath`"" + Add-Content -Path $PROFILE -Value $setAliasExpression + Invoke-Expression $setAliasExpression + + # Add Chrome to the system PATH environment variable + Add-ToEnvPath -NewPath "${env:ProgramFiles}\Google\Chrome\Application" + + # Disable Google Chrome Auto Updates + $chromeRegPath = "HKLM:\SOFTWARE\Policies\Google\Update" + if (-not (Test-Path $chromeRegPath)) { + New-Item -Path $chromeRegPath -Force + } + Set-ItemProperty -Path $chromeRegPath -Name "AutoUpdateCheckPeriodMinutes" -Value 0 + Set-ItemProperty -Path $chromeRegPath -Name "UpdateDefault" -Value 0 + } +} + +# - LibreOffice +$libreOfficeToolName = "LibreOffice" +$libreOfficeToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $libreOfficeToolName + +# Check for LibreOffice installation +$installedVersion = (Get-WmiObject -Query "SELECT * FROM Win32_Product WHERE Name like 'LibreOffice%'").Version +if (-not [string]::IsNullOrWhiteSpace($installedVersion)) { + Write-Host "LibreOffice $version is already installed." +} else { + Write-Host "LibreOffice is not installed. Downloading and installing LibreOffice..." + $libreOfficeInstallerFilePath = "$env:TEMP\libreOffice_installer.exe" + + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $libreOfficeToolDetails.mirrors -outfile $libreOfficeInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download LibreOffice. Please try again later or install manually." + } else { + Start-Process "msiexec.exe" -ArgumentList "/i `"$libreOfficeInstallerFilePath`" /quiet" -Wait -NoNewWindow + Write-Host "LibreOffice has been installed." + + # Add LibreOffice to the system PATH environment variable + Add-ToEnvPath -NewPath "C:\Program Files\LibreOffice\program" + } +} + +# - VLC +$vlcToolName = "VLC" +$vlcToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $vlcToolName +$vlcAlias = $vlcToolDetails.alias +$vlcExecutableFilePath = "C:\Program Files\VideoLAN\VLC\vlc.exe" + +# Check if VLC is already installed by checking the VLC command +if (Test-Path $vlcExecutableFilePath) { + Write-Host "VLC is already installed." +} else { + # Download the installer to the Temp directory + $vlcInstallerFilePath = "$env:TEMP\vlc_installer.exe" + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $vlcToolDetails.mirrors -outfile $vlcInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download VLC. Please try again later or install manually." + } else { + # Execute the installer silently with elevated permissions + Start-Process -FilePath $vlcInstallerFilePath -ArgumentList "/S" -Verb RunAs -Wait + + # Remove the installer file after installation + Remove-Item -Path $vlcInstallerFilePath + + # Set alias + $setAliasExpression = "Set-Alias -Name $vlcAlias -Value `"$vlcExecutableFilePath`"" + Add-Content -Path $PROFILE -Value $setAliasExpression + Invoke-Expression $setAliasExpression + + # Add VLC to the system PATH environment variable + Add-ToEnvPath -NewPath "C:\Program Files\VideoLAN\VLC" + } +} + +# - GIMP +$gimpToolName = "GIMP" +$gimpToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $gimpToolName +$gimpAlias = $gimpToolDetails.alias +$gimpExecutablePath = "C:\Program Files\GIMP 2\bin\gimp-2.10.exe" + +# Check if GIMP is already installed by checking the GIMP executable path +if (Test-Path $gimpExecutablePath) { + Write-Host "GIMP is already installed." +} else { + # Download the installer to the Temp directory + $gimpInstallerFilePath = "$env:TEMP\gimp_installer.exe" + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $gimpToolDetails.mirrors -outfile $gimpInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download GIMP. Please try again later or install manually." + } else { + # Execute the installer silently with elevated permissions + Start-Process -FilePath $gimpInstallerFilePath -ArgumentList "/VERYSILENT /ALLUSERS" -Verb RunAs -Wait + + # Remove the installer file after installation + Remove-Item -Path $gimpInstallerFilePath + + # Set alias + $setAliasExpression = "Set-Alias -Name $gimpAlias -Value `"$gimpExecutablePath`"" + Add-Content -Path $PROFILE -Value $setAliasExpression + Invoke-Expression $setAliasExpression + + # Add GIMP to the system PATH environment variable + Add-ToEnvPath -NewPath "C:\Program Files\GIMP 2\bin" + } +} + +# - VS Code +$vsCodeToolName = "VS Code" +$vsCodeToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $vsCodeToolName +$vsCodeAlias = $gimpToolDetails.alias +$vsCodeExecutablePath = "C:\Users\$env:USERNAME\AppData\Local\Programs\Microsoft VS Code\Code.exe" + +# Check if VS Code is already installed by checking the VS Code executable path +if (Test-Path $vsCodeExecutablePath) { + Write-Host "VS Code is already installed." +} else { + # Download the installer to the Temp directory + $vsCodeInstallerFilePath = "$env:TEMP\VSCodeSetup.exe" + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $vsCodeToolDetails.mirrors -outfile $vsCodeInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download VS Code. Please try again later or install manually." + } else { + # Execute the installer silently with elevated permissions + Start-Process -FilePath $vsCodeInstallerFilePath -ArgumentList "/VERYSILENT", "/mergetasks=!runcode" -Verb RunAs -Wait + + # Remove the installer file after installation + Remove-Item -Path $vsCodeInstallerFilePath + + # Set alias + $setAliasExpression = "Set-Alias -Name $vsCodeAlias -Value `"$vsCodeExecutablePath`"" + Add-Content -Path $PROFILE -Value $setAliasExpression + Invoke-Expression $setAliasExpression + + # Add VS Code to the system PATH environment variable + Add-ToEnvPath -NewPath "C:\Users\$env:USERNAME\AppData\Local\Programs\Microsoft VS Code\bin" + + # Disable Visual Studio Code Auto Updates + $vsCodeSettingsPath = "${env:APPDATA}\Code\User\settings.json" + if (-not (Test-Path $vsCodeSettingsPath)) { + # Create the directory if it doesn't exist + $dirPath = Split-Path -Path $vsCodeSettingsPath -Parent + if (-not (Test-Path $dirPath)) { + New-Item -ItemType Directory -Path $dirPath -Force + } + # Initialize an empty hashtable to act as the JSON object + $settingsObj = @{} + $settingsObj["update.mode"] = "none" # Set update mode to none + $settingsObj | ConvertTo-Json | Set-Content $vsCodeSettingsPath + } else { + # If the file exists, modify it + $settingsObj = Get-Content $vsCodeSettingsPath | ConvertFrom-Json + $settingsObj["update.mode"] = "none" + $settingsObj | ConvertTo-Json | Set-Content $vsCodeSettingsPath + } + } +} + +# - Thunderbird +$thunderbirdToolName = "Thunderbird" +$thunderbirdToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $thunderbirdToolName +$thunderbirdAlias = $thunderbirdToolDetails.alias +$thunderbirdExecutablePath = "C:\Program Files\Mozilla Thunderbird\thunderbird.exe" + +# Check if Thunderbird is already installed by checking the Thunderbird executable path +if (Test-Path $thunderbirdExecutablePath) { + Write-Host "Thunderbird is already installed." +} else { + # Download the installer to the Temp directory + $thunderbirdInstallerFilePath = "$env:TEMP\ThunderbirdSetup.exe" + $downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $thunderbirdToolDetails.mirrors -outfile $thunderbirdInstallerFilePath + if (-not $downloadResult) { + Write-Host "Failed to download Thunderbird. Please try again later or install manually." + } else { + # Execute the installer silently with elevated permissions + Start-Process -FilePath $thunderbirdInstallerFilePath -ArgumentList "/S" -Verb RunAs -Wait + + # Remove the installer file after installation + Remove-Item -Path $thunderbirdInstallerFilePath + + # Set alias + $setAliasExpression = "Set-Alias -Name $thunderbirdAlias -Value `"$thunderbirdExecutablePath`"" + Add-Content -Path $PROFILE -Value $setAliasExpression + Invoke-Expression $setAliasExpression + + # Add Thunderbird to the system PATH environment variable + Add-ToEnvPath -NewPath "C:\Program Files\Mozilla Thunderbird" + } +} + +# - Server Setup + +$pythonServerPort = 5000 +$onLogonTaskName = "Server_OnLogon" +$requirementsFile = "$scriptFolder\server\requirements.txt" + +# Ensure pip is updated to the latest version +Install-PythonPackages -Package "pip" -Arguments "--upgrade" + +Install-PythonPackages -Package "wheel" +Install-PythonPackages -Package "pywinauto" + +# Install Python packages from requirements.txt using Python's pip module +if (Test-Path $requirementsFile) { + Write-Host "Installing required Python packages using pip from requirements file..." + Install-PythonPackages -RequirementsPath $requirementsFile +} else { + Write-Error "Requirements file not found: $requirementsFile" + exit +} + +# Add a firewall rule to allow incoming connections on the specified port for the Python executable +$pythonServerRuleName = "PythonHTTPServer-$pythonServerPort" +if (-not (Get-NetFirewallRule -Name $pythonServerRuleName -ErrorAction SilentlyContinue)) { + New-NetFirewallRule -DisplayName $pythonServerRuleName -Direction Inbound -Program $pythonExecutablePath -Protocol TCP -LocalPort $pythonServerPort -Action Allow -Profile Any + Write-Host "Firewall rule added to allow traffic on port $pythonServerPort for Python" +} else { + Write-Host "Firewall rule already exists. $pythonServerRuleName " +} + +$onLogonScriptPath = "$scriptFolder\on-logon.ps1" +# Check if the scheduled task exists before unregistering it +if (Get-ScheduledTask -TaskName $onLogonTaskName -ErrorAction SilentlyContinue) { + Write-Host "Scheduled task $onLogonTaskName already exists." +} else { + Write-Host "Registering new task $onLogonTaskName..." + Register-LogonTask -TaskName $onLogonTaskName -ScriptPath $onLogonScriptPath -LocalUser "Docker" +} + +Start-Sleep -Seconds 10 +Start-ScheduledTask -TaskName $onLogonTaskName \ No newline at end of file diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/tools_config.json b/computer_use_demo/windowshost/vm/win11setup/setupscripts/tools_config.json new file mode 100644 index 0000000..b78db29 --- /dev/null +++ b/computer_use_demo/windowshost/vm/win11setup/setupscripts/tools_config.json @@ -0,0 +1,71 @@ +{ + "Python": { + "mirrors": [ + "https://www.python.org/ftp/python/3.10.0/python-3.10.0-amd64.exe" + ], + "alias": "python" + }, + "git": { + "mirrors": [ + "https://github.com/git-for-windows/git/releases/download/v2.37.1.windows.1/Git-2.37.1-64-bit.exe" + ] + }, + "7zip": { + "mirrors": [ + "https://www.7-zip.org/a/7z2407-x64.exe" + ] + }, + "ffmpeg": { + "mirrors": [ + "https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-essentials.7z" + ] + }, + "Google Chrome": { + "mirrors": [ + "https://dl.google.com/chrome/install/latest/chrome_installer.exe" + ], + "alias": "google-chrome" + }, + "LibreOffice": { + "mirrors": [ + "https://mirror.raiolanetworks.com/tdf/libreoffice/stable/24.8.4/win/x86_64/LibreOffice_24.8.4_Win_x86-64.msi", + "https://mirrors.iu13.net/tdf/libreoffice/stable/24.8.4/win/x86_64/LibreOffice_24.8.4_Win_x86-64.msi", + "https://download.documentfoundation.org/libreoffice/stable/24.8.4/win/x86_64/LibreOffice_24.8.4_Win_x86-64.msi" + ] + }, + "VLC": { + "mirrors": [ + "https://ftp.free.org/mirrors/videolan/vlc/3.0.21/win64/vlc-3.0.21-win64.exe", + "https://mirror.fcix.net/videolan-ftp/vlc/3.0.21/win64/vlc-3.0.21-win64.exe", + "https://mirror.raiolanetworks.com/videolan/vlc/3.0.21/win64/vlc-3.0.21-win64.exe" + ], + "alias": "vlc" + }, + "GIMP": { + "mirrors": [ + "https://www-ftp.lip6.fr/pub/gimp/gimp/v2.10/windows/gimp-2.10.38-setup.exe", + "https://download.gimp.org/gimp/v2.10/windows/gimp-2.10.38-setup.exe", + "https://www-ftp.lip6.fr/pub/gimp/gimp/v2.10/windows/gimp-2.10.0-setup.exe" + ], + "alias": "gimp" + }, + "VS Code": { + "mirrors": [ + "https://update.code.visualstudio.com/latest/win32-x64-user/stable" + ], + "alias": "code" + }, + "Thunderbird": { + "mirrors": [ + "https://download-installer.cdn.mozilla.net/pub/thunderbird/releases/115.12.1/win64/en-US/Thunderbird%20Setup%20115.12.1.exe", + "https://archive.mozilla.org/pub/thunderbird/releases/115.12.1/win64/en-US/Thunderbird%20Setup%20115.12.1.exe" + ], + "alias": "thunderbird" + }, + "Caddy Proxy": { + "mirrors": [ + "https://caddyserver.com/api/download?os=windows&arch=amd64" + ], + "alias": "caddy" + } + } \ No newline at end of file From 5f699faf1694bd15a656cb372dc172cf52a664f5 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Thu, 30 Jan 2025 00:14:07 -0800 Subject: [PATCH 31/57] readme init --- computer_use_demo/gradio/app.py | 4 +-- computer_use_demo/readme.md | 62 +++++++++++++++++++++++++++++++++ 2 files changed, 64 insertions(+), 2 deletions(-) create mode 100644 computer_use_demo/readme.md diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py index 69c4efa..505f5a4 100644 --- a/computer_use_demo/gradio/app.py +++ b/computer_use_demo/gradio/app.py @@ -1,5 +1,5 @@ """ -python app.py --windows_host_url localhost:8006/ --omniparser_server_url localhost:8000 +python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000 """ import os @@ -26,7 +26,7 @@ API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' ๐Ÿš€๐Ÿค–โœจ It's Play Time! -Welcome to the OmniParser+X Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. +Welcome to the OmniParser+X Computer Use Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history. ''' diff --git a/computer_use_demo/readme.md b/computer_use_demo/readme.md new file mode 100644 index 0000000..df44c59 --- /dev/null +++ b/computer_use_demo/readme.md @@ -0,0 +1,62 @@ +# OmniParser+X Computer Use Demo + +Control a Windows 11 VM with OmniParser+X (X = [GPT-4o/4o-mini, Claude, ...]). + +## Overview + +There are three components: + +1. **windowshost**: A Windows 11 VM running in a Docker container +2. **omniparserserver**: FastAPI server running OmniParser +3. **gradio**: UI where you can provide commands and watch OmniParser+X reasoning and executing on the Windows 11 VM + +Notes: +1. The Windows 11 VM docker is dependent on KVM so can only run quickly on Windows and Linux. This can run on a CPU machine (doesn't need GPU). +2. Though OmniParser can run on a CPU, we have separated this out if you want to run it fast on a GPU machine +3. The Gradio UI can also run on a CPU machine. + +## Setup + +1. **windowshost**: + + a. Install Docker Desktop + + b. Visit [Microsoft Evaluation Center](https://info.microsoft.com/ww-landing-windows-11-enterprise.html), accept the Terms of Service, and download a **Windows 11 Enterprise Evaluation (90-day trial, English, United States)** ISO file [~6GB]. Rename the file to `custom.iso` and copy it to the directory `OmniParser/computer_use_demo/windowshost/vm/win11iso` + + c. Navigate to vm management script directory with`cd OmniParser/computer_use_demo/windowshost/scripts` + + d. Build the docker container [400MB] and install the ISO to a storage folder [20GB] with `./manage_vm.sh create` + + e. After creating the first time it will store a save of the VM state in `vm/win11storage`. You can then manage the VM with `./manage_vm.sh start` and `./manage_vm.sh stop`. To delete the VM, use `./manage_vm.sh delete` and delete the `OmniParser/computer_use_demo/windowshost/vm/win11storage` directory. + +2. **omniparserserver**: + + a. If you already have a conda environment for OmniParser, you can use that. Else follow the following steps to create one + + b. Ensure conda is installed with `conda --version` or install from the [Anaconda website](https://www.anaconda.com/download/success) + + c. Navigate to the root of the repo with `cd OmniParser` + + d. Create a conda python environment with `conda create -n "omni" python==3.12` + + e. Set the python environment to be used with `conda activate omni` + + f. Install the dependencies with `pip install -r requirements.txt` + + g. Continue from here if you already had the conda environment. + + h. Ensure you have the weights downloaded in weights folder. If not download them with `for folder in icon_caption_blip2 icon_caption_florence icon_detect icon_detect_v1_5; do huggingface-cli download microsoft/OmniParser --local-dir weights/"$folder" --repo-type model --include "$folder/*"; done` + + h. Navigate to the server directory with `cd OmniParser/computer_use_demo/omniparserserver` + + i. Start the server with `python -m omniparserserver` + +3. **gradio**: + + a. Navigate to the gradio directory with `cd OmniParser/computer_use_demo/gradio` + + b. Ensure you have activated the conda python environment with `conda activate omni` + + c. Start the server with `python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000` + + d. Open the URL in the terminal output, set your API Key from OpenAI and start playing with the AI agent! From ed7b34621b4be5afb3dc19b58ef0140f1fd2fe86 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Thu, 30 Jan 2025 08:10:38 -0800 Subject: [PATCH 32/57] Add validation on submit --- computer_use_demo/gradio/app.py | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py index 505f5a4..97cc48a 100644 --- a/computer_use_demo/gradio/app.py +++ b/computer_use_demo/gradio/app.py @@ -19,6 +19,8 @@ from loop import ( sampling_loop_sync, ) from tools import ToolResult +import requests +from requests.exceptions import RequestException CONFIG_DIR = Path("~/.anthropic").expanduser() API_KEY_FILE = CONFIG_DIR / "api_key" @@ -182,7 +184,32 @@ def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="b for user_msg, bot_msg in chatbot_state] # print(f"chatbot_output_callback chatbot_state: {concise_state} (truncated)") +def valid_params(user_input, state): + """Validate all requirements and return a list of error messages.""" + errors = [] + + for server_name, url in [('Windows Host', args.windows_host_url), ('OmniParser Server', args.omniparser_server_url)]: + try: + url = f'http://{url}/probe' + response = requests.get(url, timeout=3) + if response.status_code != 200: + errors.append(f"{server_name} is not responding") + except RequestException as e: + errors.append(f"{server_name} is not responding") + + if not state["api_key"].strip(): + errors.append("LLM API Key is not set") + + if not user_input: + errors.append("no computer use request provided") + + return errors + def process_input(user_input, state): + errors = valid_params(user_input, state) + if errors: + raise gr.Error("Validation errors: " + ", ".join(errors)) + # Append the user message to state["messages"] state["messages"].append( { From e268184f8d9d84631cc96556ff4ffbc8c6a2580e Mon Sep 17 00:00:00 2001 From: "Thomas Dhome Casanova (from Dev Box)" <thomasdh@microsoft.com> Date: Fri, 31 Jan 2025 18:37:14 -0800 Subject: [PATCH 33/57] init r1 --- .../gradio/agent/llm_utils/groqclient.py | 59 +++++++++++++++++++ .../gradio/agent/llm_utils/oai.py | 16 +---- .../agent/llm_utils/omniparserclient.py | 2 +- .../gradio/agent/llm_utils/utils.py | 13 ++++ computer_use_demo/gradio/agent/vlm_agent.py | 35 ++++++++++- computer_use_demo/gradio/app.py | 19 +++--- requirements.txt | 3 +- 7 files changed, 119 insertions(+), 28 deletions(-) create mode 100644 computer_use_demo/gradio/agent/llm_utils/groqclient.py create mode 100644 computer_use_demo/gradio/agent/llm_utils/utils.py diff --git a/computer_use_demo/gradio/agent/llm_utils/groqclient.py b/computer_use_demo/gradio/agent/llm_utils/groqclient.py new file mode 100644 index 0000000..812d929 --- /dev/null +++ b/computer_use_demo/gradio/agent/llm_utils/groqclient.py @@ -0,0 +1,59 @@ +from groq import Groq +import os +from .utils import is_image_path + +def run_groq_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0.6): + """ + Run a chat completion through Groq's API, ignoring any images in the messages. + """ + api_key = api_key or os.environ.get("GROQ_API_KEY") + if not api_key: + raise ValueError("GROQ_API_KEY is not set") + + client = Groq(api_key=api_key) + # avoid using system messages for R1 + final_messages = [{"role": "user", "content": system}] + + if isinstance(messages, list): + for item in messages: + if isinstance(item, dict): + # For dict items, concatenate all text content, ignoring images + text_contents = [] + for cnt in item["content"]: + if isinstance(cnt, str): + if not is_image_path(cnt): # Skip image paths + text_contents.append(cnt) + else: + text_contents.append(str(cnt)) + + if text_contents: # Only add if there's text content + message = {"role": "user", "content": " ".join(text_contents)} + final_messages.append(message) + else: # str + message = {"role": "user", "content": item} + final_messages.append(message) + + elif isinstance(messages, str): + final_messages.append({"role": "user", "content": messages}) + + try: + completion = client.chat.completions.create( + model="deepseek-r1-distill-llama-70b", + messages=final_messages, + temperature=0.6, + max_completion_tokens=max_tokens, + top_p=0.95, + stream=False, + reasoning_format="raw" + ) + + response = completion.choices[0].message.content + final_answer = response.split('</think>\n')[-1] if '</think>' in response else response + final_answer = final_answer.replace("<output>", "").replace("</output>", "") + token_usage = completion.usage.total_tokens + + return final_answer, token_usage + except Exception as e: + print(f"Error in interleaved Groq: {e}") + + return str(e), 0 \ No newline at end of file diff --git a/computer_use_demo/gradio/agent/llm_utils/oai.py b/computer_use_demo/gradio/agent/llm_utils/oai.py index 2d613f1..e2daba7 100644 --- a/computer_use_demo/gradio/agent/llm_utils/oai.py +++ b/computer_use_demo/gradio/agent/llm_utils/oai.py @@ -2,21 +2,9 @@ import os import logging import base64 import requests - -def is_image_path(text): - image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") - if text.endswith(image_extensions): - return True - else: - return False - -def encode_image(image_path): - """Encode image file to base64.""" - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode("utf-8") +from .utils import is_image_path, encode_image def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): - api_key = api_key or os.environ.get("OPENAI_API_KEY") if not api_key: raise ValueError("OPENAI_API_KEY is not set") @@ -54,8 +42,6 @@ def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max elif isinstance(messages, str): final_messages = [{"role": "user", "content": messages}] - print("[oai] sending messages:", {"role": "user", "content": messages}) - payload = { "model": llm, "messages": final_messages, diff --git a/computer_use_demo/gradio/agent/llm_utils/omniparserclient.py b/computer_use_demo/gradio/agent/llm_utils/omniparserclient.py index c29eb58..e90ddef 100644 --- a/computer_use_demo/gradio/agent/llm_utils/omniparserclient.py +++ b/computer_use_demo/gradio/agent/llm_utils/omniparserclient.py @@ -2,7 +2,7 @@ import requests import base64 from pathlib import Path from tools.screen_capture import get_screenshot -from agent.llm_utils.oai import encode_image +from agent.llm_utils.utils import encode_image OUTPUT_DIR = "./tmp/outputs" diff --git a/computer_use_demo/gradio/agent/llm_utils/utils.py b/computer_use_demo/gradio/agent/llm_utils/utils.py new file mode 100644 index 0000000..12ab36e --- /dev/null +++ b/computer_use_demo/gradio/agent/llm_utils/utils.py @@ -0,0 +1,13 @@ +import base64 + +def is_image_path(text): + image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif") + if text.endswith(image_extensions): + return True + else: + return False + +def encode_image(image_path): + """Encode image file to base64.""" + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") \ No newline at end of file diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py index 76815a6..ece91c0 100644 --- a/computer_use_demo/gradio/agent/vlm_agent.py +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -11,6 +11,7 @@ from anthropic.types import ToolResultBlockParam from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage from agent.llm_utils.oai import run_oai_interleaved +from agent.llm_utils.groqclient import run_groq_interleaved import time import re @@ -39,9 +40,12 @@ class VLMAgent: ): if model == "omniparser + gpt-4o": self.model = "gpt-4o-2024-11-20" + elif model == "omniparser + R1": + self.model = "deepseek-r1-distill-llama-70b" else: raise ValueError(f"Model {model} not supported") + self.provider = provider self.api_key = api_key self.api_response_callback = api_response_callback @@ -108,8 +112,17 @@ class VLMAgent: print(f"oai token usage: {token_usage}") self.total_token_usage += token_usage self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ - elif "phi" in self.model: - pass # TODO + elif "r1" in self.model: + vlm_response, token_usage = run_groq_interleaved( + messages=planner_messages, + system=system, + llm=self.model, + api_key=self.api_key, + max_tokens=self.max_tokens, + ) + print(f"groq token usage: {token_usage}") + self.total_token_usage += token_usage + self.total_cost += (token_usage * 0.99 / 1000000) else: raise ValueError(f"Model {self.model} not supported") latency_vlm = time.time() - start @@ -168,7 +181,7 @@ class VLMAgent: self.api_response_callback(response) def _get_system_prompt(self, screen_info: str = ""): - return f""" + main_section = f""" You are using a Windows device. You are able to use a mouse and keyboard to interact with the computer based on the given task and screenshot. You can only interact with the desktop GUI (no terminal or application menu access). @@ -222,11 +235,27 @@ Another Example: IMPORTANT NOTES: 1. You should only give a single action at a time. + +""" + thinking_model = "r1" in self.model + if not thinking_model: + main_section += """ 2. You should give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. + +""" + else: + main_section += """ +2. In <think> XML tags give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. In <output> XML tags put the next action prediction JSON. + +""" + main_section += """ 3. Attach the next action prediction in the "Next Action". 4. You should not include other actions, such as keyboard shortcuts. 5. When the task is completed, you should say "Next Action": "None" in the json field. """ + + return main_section + def draw_action(self, vlm_response_json, image_base64): # draw a circle using the coordinate in parsed_screen['som_image_base64'] image_data = base64.b64decode(image_base64) diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py index 97cc48a..7ca2a22 100644 --- a/computer_use_demo/gradio/app.py +++ b/computer_use_demo/gradio/app.py @@ -28,12 +28,13 @@ API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' ๐Ÿš€๐Ÿค–โœจ It's Play Time! -Welcome to the OmniParser+X Computer Use Demo! X = [GPT-4o/4o-mini, Claude, Phi, Llama]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. +Welcome to the OmniParser+X Computer Use Demo! X = [GPT-4o, R1, Claude]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history. ''' def parse_arguments(): + parser = argparse.ArgumentParser(description="Gradio App") parser.add_argument("--windows_host_url", type=str, default='localhost:8006') parser.add_argument("--omniparser_server_url", type=str, default="localhost:8000") @@ -255,11 +256,10 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: } </style> """) - state = gr.State({}) # Use Gradio's state management + state = gr.State({}) - setup_state(state.value) # Initialize the state - - # Retrieve screen details + setup_state(state.value) + gr.Markdown("# OmniParser + โœ–๏ธ Demo") if not os.getenv("HIDE_WARNING", False): @@ -270,8 +270,8 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: with gr.Column(): model = gr.Dropdown( label="Model", - choices=["omniparser + gpt-4o", "omniparser + phi35v", "claude-3-5-sonnet-20241022"], - value="omniparser + gpt-4o", # Set to one of the choices + choices=["omniparser + gpt-4o", "omniparser + R1", "claude-3-5-sonnet-20241022"], + value="omniparser + gpt-4o", interactive=True, ) with gr.Column(): @@ -322,11 +322,14 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: if model_selection == "claude-3-5-sonnet-20241022": provider_choices = [option.value for option in APIProvider if option.value != "openai"] - elif model_selection == "omniparser + gpt-4o" or model_selection == "omniparser + phi35v": + elif model_selection == "omniparser + gpt-4o": provider_choices = ["openai"] + elif model_selection == "omniparser + R1": + provider_choices = ["groq"] else: provider_choices = [option.value for option in APIProvider] default_provider_value = provider_choices[0] + provider_interactive = len(provider_choices) > 1 api_key_placeholder = f"{default_provider_value.title()} API Key" diff --git a/requirements.txt b/requirements.txt index 5631fb6..b55038f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,4 +28,5 @@ boto3>=1.28.57 google-auth<3,>=2 screeninfo uiautomation -dashscope \ No newline at end of file +dashscope +groq \ No newline at end of file From be506b2d096a7941172bdd89cf63ddf6512bc34b Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Fri, 31 Jan 2025 20:02:58 -0800 Subject: [PATCH 34/57] R1 fixes --- computer_use_demo/gradio/agent/vlm_agent.py | 88 +++++++++------------ computer_use_demo/gradio/loop.py | 21 ++--- computer_use_demo/gradio/tools/computer.py | 8 +- 3 files changed, 51 insertions(+), 66 deletions(-) diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py index ece91c0..ef4c044 100644 --- a/computer_use_demo/gradio/agent/vlm_agent.py +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -56,37 +56,27 @@ class VLMAgent: self.print_usage = print_usage self.total_token_usage = 0 self.total_cost = 0 + self.step_count = 0 self.system = '' def __call__(self, messages: list, parsed_screen: list[str, list, dict]): - # Show results of Omniparser + self.step_count += 1 image_base64 = parsed_screen['original_screenshot_base64'] latency_omniparser = parsed_screen['latency'] - self.output_callback(f'Screenshot for OmniParser Agent:\n<img src="data:image/png;base64,{image_base64}">', - sender="bot") - self.output_callback(f'Set of Marks Screenshot for OmniParser Agent:\n<img src="data:image/png;base64,{parsed_screen["som_image_base64"]}">', sender="bot") + self.output_callback(f'-- Step {self.step_count}: --', sender="bot") screen_info = str(parsed_screen['screen_info']) - # self.output_callback(f'Screen Info for OmniParser Agent:\n{screen_info}', sender="bot") - self.output_callback( - f'<details>' - f' <summary>Screen Info for OmniParser Agent</summary>' - f' <pre>{screen_info}</pre>' - f'</details>', - sender="bot" - ) + screenshot_uuid = parsed_screen['screenshot_uuid'] screen_width, screen_height = parsed_screen['width'], parsed_screen['height'] - # example parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"} boxids_and_labels = parsed_screen["screen_info"] system = self._get_system_prompt(boxids_and_labels) # drop looping actions msg, byte image etc planner_messages = messages - # import pdb; pdb.set_trace() planner_messages = _keep_latest_images(planner_messages) # if self.only_n_most_recent_images: # _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) @@ -98,7 +88,6 @@ class VLMAgent: planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_{screenshot_uuid}.png") planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_som_{screenshot_uuid}.png") - # print(f"Sending messages to VLMPlanner : {planner_messages}") start = time.time() if "gpt" in self.model: vlm_response, token_usage = run_oai_interleaved( @@ -113,6 +102,7 @@ class VLMAgent: self.total_token_usage += token_usage self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ elif "r1" in self.model: + print(f"Sending messages to Groq: {planner_messages}") vlm_response, token_usage = run_groq_interleaved( messages=planner_messages, system=system, @@ -126,33 +116,46 @@ class VLMAgent: else: raise ValueError(f"Model {self.model} not supported") latency_vlm = time.time() - start - self.output_callback(f"VLMPlanner latency: {latency_vlm}, Omniparser latency: {latency_omniparser}", sender="bot") + self.output_callback(f"LLM: {latency_vlm:.2f}s, OmniParser: {latency_omniparser:.2f}s", sender="bot") - print(f"VLMPlanner response: {vlm_response}") + print(f"{vlm_response}") if self.print_usage: - print(f"VLMPlanner total token usage so far: {self.total_token_usage}. Total cost so far: $USD{self.total_cost:.5f}") + print(f"Total token so far: {self.total_token_usage}. Total cost so far: $USD{self.total_cost:.5f}") vlm_response_json = extract_data(vlm_response, "json") vlm_response_json = json.loads(vlm_response_json) - # map "box_id" to "idx" in parsed_screen, and output the xy coordinate of bbox - try: + img_to_show_base64 = parsed_screen["som_image_base64"] + if "Box ID" in vlm_response_json: bbox = parsed_screen["parsed_content_list"][int(vlm_response_json["Box ID"])]["bbox"] vlm_response_json["box_centroid_coordinate"] = [int((bbox[0] + bbox[2]) / 2 * screen_width), int((bbox[1] + bbox[3]) / 2 * screen_height)] - # draw a circle on the screenshot image to indicate the action - self.draw_action(vlm_response_json, image_base64) - except: - print("No Box ID in the response.") + img_to_show_data = base64.b64decode(img_to_show_base64) + img_to_show = Image.open(BytesIO(img_to_show_data)) - # Convert the VLM output to a string for printing in chat + draw = ImageDraw.Draw(img_to_show) + x, y = vlm_response_json["box_centroid_coordinate"] + radius = 10 + draw.ellipse((x - radius, y - radius, x + radius, y + radius), fill='red') + draw.ellipse((x - radius*3, y - radius*3, x + radius*3, y + radius*3), fill=None, outline='red', width=2) + + buffered = BytesIO() + img_to_show.save(buffered, format="PNG") + img_to_show_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") + self.output_callback(f'<img src="data:image/png;base64,{img_to_show_base64}">', sender="bot") + self.output_callback( + f'<details>' + f' <summary>Screen Info for OmniParser Agent</summary>' + f' <pre>{screen_info}</pre>' + f'</details>', + sender="bot" + ) vlm_plan_str = "" for key, value in vlm_response_json.items(): if key == "Reasoning": vlm_plan_str += f'{value}' else: vlm_plan_str += f'\n{key}: {value}' - # self.output_callback(f"OmniParser Agent:\n{vlm_plan_str}", sender="bot") # construct the response so that anthropicExcutor can execute the tool response_content = [BetaTextBlock(text=vlm_plan_str, type='text')] @@ -161,6 +164,7 @@ class VLMAgent: input={'action': 'mouse_move', 'coordinate': vlm_response_json["box_centroid_coordinate"]}, name='computer', type='tool_use') response_content.append(move_cursor_block) + if vlm_response_json["Next Action"] == "type": click_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': 'left_click'}, name='computer', type='tool_use') sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', @@ -194,13 +198,12 @@ Here is the list of all detected bounding boxes by IDs on the screen and their d Your available "Next Action" only include: - type: type a string of text. - left_click: Describe the ui element to be clicked. -- double_click: Describe the ui element to be double clicked. - right_click: Describe the ui element to be right clicked. -- escape: Press an ESCAPE key. +- double_click: Describe the ui element to be double clicked. - hover: Describe the ui element to be hovered. - scroll_up: Scroll the screen up. - scroll_down: Scroll the screen down. -- press: Describe the ui element to be pressed. +- wait: Wait for 1 second for the device to load or respond. Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. @@ -209,8 +212,8 @@ Output format: {{ "Reasoning": str, # describe what is in the current screen, taking into account the history, then describe your step-by-step thoughts on how to achieve the task, choose one action from available actions at a time. "Next Action": "action_type, action description" | "None" # one action at a time, describe it in short and precisely. - 'Box ID': n, - 'value': "xxx" # if the action is type, you should provide the text to type. + "Box ID": n, + "value": "xxx" # only provide value field if the action is type, else don't include value key }} ``` @@ -219,7 +222,7 @@ One Example: {{ "Reasoning": "The current screen shows google result of amazon, in previous action I have searched amazon on google. Then I need to click on the first search results to go to amazon.com.", "Next Action": "left_click", - 'Box ID': m, + "Box ID": m }} ``` @@ -228,8 +231,8 @@ Another Example: {{ "Reasoning": "The current screen shows the front page of amazon. There is no previous action. Therefore I need to type "Apple watch" in the search bar.", "Next Action": "type", - 'Box ID': n, - 'value': "Apple watch" + "Box ID": n, + "value": "Apple watch" }} ``` @@ -251,26 +254,11 @@ IMPORTANT NOTES: main_section += """ 3. Attach the next action prediction in the "Next Action". 4. You should not include other actions, such as keyboard shortcuts. -5. When the task is completed, you should say "Next Action": "None" in the json field. +5. When the task is completed, don't complete additional actions. You should say "Next Action": "None" in the json field. """ return main_section - def draw_action(self, vlm_response_json, image_base64): - # draw a circle using the coordinate in parsed_screen['som_image_base64'] - image_data = base64.b64decode(image_base64) - image = Image.open(BytesIO(image_data)) - - draw = ImageDraw.Draw(image) - x, y = vlm_response_json["box_centroid_coordinate"] - radius = 30 - draw.ellipse((x - radius, y - radius, x + radius, y + radius), fill='red') - buffered = BytesIO() - image.save(buffered, format="PNG") - image_with_circle_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") - self.output_callback(f'Action performed on the red circle with centroid ({x}, {y}), for OmniParser Agent:\n<img src="data:image/png;base64,{image_with_circle_base64}">', sender="bot") - - def _keep_latest_images(messages): for i in range(len(messages)-1): if isinstance(messages[i]["content"], list): diff --git a/computer_use_demo/gradio/loop.py b/computer_use_demo/gradio/loop.py index f809d3e..15041cf 100644 --- a/computer_use_demo/gradio/loop.py +++ b/computer_use_demo/gradio/loop.py @@ -64,14 +64,7 @@ def sampling_loop_sync( max_tokens=max_tokens, only_n_most_recent_images=only_n_most_recent_images ) - - # from IPython.core.debugger import Pdb; Pdb().set_trace() - executor = AnthropicExecutor( - output_callback=output_callback, - tool_output_callback=tool_output_callback - ) - - elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": + elif model == "omniparser + gpt-4o" or model == "omniparser + R1": actor = VLMAgent( model=model, provider=provider, @@ -79,14 +72,12 @@ def sampling_loop_sync( api_response_callback=api_response_callback, output_callback=output_callback, ) - - executor = AnthropicExecutor( - output_callback=output_callback, - tool_output_callback=tool_output_callback, - ) - else: raise ValueError(f"Model {model} not supported") + executor = AnthropicExecutor( + output_callback=output_callback, + tool_output_callback=tool_output_callback, + ) print(f"Model Inited: {model}, Provider: {provider}") tool_result_content = None @@ -109,7 +100,7 @@ def sampling_loop_sync( messages.append({"content": tool_result_content, "role": "user"}) - elif model == "omniparser + gpt-4o" or model == "omniparser + phi35v": + elif model == "omniparser + gpt-4o" or model == "omniparser + R1": while True: parsed_screen = omniparser_client() tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) diff --git a/computer_use_demo/gradio/tools/computer.py b/computer_use_demo/gradio/tools/computer.py index adad9a2..dc5ad80 100644 --- a/computer_use_demo/gradio/tools/computer.py +++ b/computer_use_demo/gradio/tools/computer.py @@ -28,6 +28,8 @@ Action = Literal[ "double_click", "screenshot", "cursor_position", + "hover", + "wait" ] @@ -213,7 +215,11 @@ class ComputerTool(BaseAnthropicTool): elif action == "scroll_down": self.send_to_vm("pyautogui.scroll(-100)") return ToolResult(output=f"Performed {action}") - + if action == "hover": + return ToolResult(output=f"Performed {action}") + if action == "wait": + time.sleep(1) + return ToolResult(output=f"Performed {action}") raise ToolError(f"Invalid action: {action}") def send_to_vm(self, action: str): From 0a4a9f4d23a62675466a5418f66d5be0f1c8fc62 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Sat, 1 Feb 2025 12:09:27 -0800 Subject: [PATCH 35/57] improve typing perf --- computer_use_demo/gradio/agent/vlm_agent.py | 22 ++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py index ef4c044..0ae68e4 100644 --- a/computer_use_demo/gradio/agent/vlm_agent.py +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -165,14 +165,14 @@ class VLMAgent: name='computer', type='tool_use') response_content.append(move_cursor_block) - if vlm_response_json["Next Action"] == "type": + if vlm_response_json["Next Action"] == "None": + print("Task paused/completed.") + elif vlm_response_json["Next Action"] == "type": click_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': 'left_click'}, name='computer', type='tool_use') sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': vlm_response_json["Next Action"], 'text': vlm_response_json["value"]}, name='computer', type='tool_use') response_content.extend([click_block, sim_content_block]) - elif vlm_response_json["Next Action"] == "None": - print("Task paused/completed.") else: sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': vlm_response_json["Next Action"]}, @@ -196,14 +196,14 @@ You should carefully consider your plan base on the task, screenshot, and histor Here is the list of all detected bounding boxes by IDs on the screen and their description:{screen_info} Your available "Next Action" only include: -- type: type a string of text. -- left_click: Describe the ui element to be clicked. -- right_click: Describe the ui element to be right clicked. -- double_click: Describe the ui element to be double clicked. -- hover: Describe the ui element to be hovered. -- scroll_up: Scroll the screen up. -- scroll_down: Scroll the screen down. -- wait: Wait for 1 second for the device to load or respond. +- type: move mouse to box id, left clicks and types a string of text. +- left_click: move mouse to box id and left clicks +- right_click: move mouse to box id and right clicks +- double_click: move mouse to box id and double clicks +- hover: move mouse to box id +- scroll_up: scrolls the screen up. +- scroll_down: scrolls the screen down. +- wait: waits for 1 second for the device to load or respond. Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. From 80f6be73e110aca8b9337d90dc049b0aef98d493 Mon Sep 17 00:00:00 2001 From: yadonglu <yadonglu@micrsoft.com> Date: Sat, 1 Feb 2025 14:25:55 -0800 Subject: [PATCH 36/57] fix readme download model weight folder structure --- .gitignore | 2 ++ computer_use_demo/readme.md | 3 ++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 40c4d8c..1cbd9d3 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,5 @@ weights/icon_detect_v1_5_2/ __pycache__/ debug.ipynb util/__pycache__/ +index.html?linkid=2289031 +wget-log \ No newline at end of file diff --git a/computer_use_demo/readme.md b/computer_use_demo/readme.md index df44c59..ddf5107 100644 --- a/computer_use_demo/readme.md +++ b/computer_use_demo/readme.md @@ -45,7 +45,8 @@ Notes: g. Continue from here if you already had the conda environment. - h. Ensure you have the weights downloaded in weights folder. If not download them with `for folder in icon_caption_blip2 icon_caption_florence icon_detect icon_detect_v1_5; do huggingface-cli download microsoft/OmniParser --local-dir weights/"$folder" --repo-type model --include "$folder/*"; done` + h. Ensure you have the weights downloaded in weights folder. If not download them with: + `for folder in icon_caption_florence icon_detect icon_detect_v1_5; do huggingface-cli download microsoft/OmniParser --local-dir weights/ --repo-type model --include "$folder/*"; done` h. Navigate to the server directory with `cd OmniParser/computer_use_demo/omniparserserver` From 3d981833e61cebfc5ea2fa3896ace45c4329c5fd Mon Sep 17 00:00:00 2001 From: yadonglu <yadonglu@micrsoft.com> Date: Sat, 1 Feb 2025 14:49:04 -0800 Subject: [PATCH 37/57] fix omniparser server; fix yyolo dependency --- computer_use_demo/omniparserserver/omniparserserver.py | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/computer_use_demo/omniparserserver/omniparserserver.py b/computer_use_demo/omniparserserver/omniparserserver.py index 4cc7030..434d399 100644 --- a/computer_use_demo/omniparserserver/omniparserserver.py +++ b/computer_use_demo/omniparserserver/omniparserserver.py @@ -48,4 +48,4 @@ async def root(): return {"message": "Omniparser API ready"} if __name__ == "__main__": - uvicorn.run("remote_request:app", host=args.host, port=args.port, reload=True) \ No newline at end of file + uvicorn.run("omniparserserver:app", host=args.host, port=args.port, reload=True) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index b55038f..00f43d8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ torchvision supervision==0.18.0 openai==1.3.5 transformers -ultralytics==8.1.24 +ultralytics==8.3.70 azure-identity numpy opencv-python From ba7ed0ac06ea48481683423c024df9139b377c18 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Sat, 1 Feb 2025 17:29:34 -0800 Subject: [PATCH 38/57] qwen2.5vl --- .../gradio/agent/llm_utils/groqclient.py | 2 +- .../gradio/agent/llm_utils/oai.py | 13 +++++------- computer_use_demo/gradio/agent/vlm_agent.py | 20 +++++++++++++++++-- computer_use_demo/gradio/app.py | 8 +++++--- computer_use_demo/gradio/loop.py | 4 ++-- 5 files changed, 31 insertions(+), 16 deletions(-) diff --git a/computer_use_demo/gradio/agent/llm_utils/groqclient.py b/computer_use_demo/gradio/agent/llm_utils/groqclient.py index 812d929..c31a502 100644 --- a/computer_use_demo/gradio/agent/llm_utils/groqclient.py +++ b/computer_use_demo/gradio/agent/llm_utils/groqclient.py @@ -2,7 +2,7 @@ from groq import Groq import os from .utils import is_image_path -def run_groq_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0.6): +def run_groq_interleaved(messages: list, system: str, model_name: str, api_key: str, max_tokens=256, temperature=0.6): """ Run a chat completion through Groq's API, ignoring any images in the messages. """ diff --git a/computer_use_demo/gradio/agent/llm_utils/oai.py b/computer_use_demo/gradio/agent/llm_utils/oai.py index e2daba7..64eb21d 100644 --- a/computer_use_demo/gradio/agent/llm_utils/oai.py +++ b/computer_use_demo/gradio/agent/llm_utils/oai.py @@ -4,11 +4,7 @@ import base64 import requests from .utils import is_image_path, encode_image -def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max_tokens=256, temperature=0): - api_key = api_key or os.environ.get("OPENAI_API_KEY") - if not api_key: - raise ValueError("OPENAI_API_KEY is not set") - +def run_oai_interleaved(messages: list, system: str, model_name: str, api_key: str, max_tokens=256, temperature=0, provider_base_url: str = "https://api.openai.com/v1"): headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"} @@ -43,20 +39,21 @@ def run_oai_interleaved(messages: list, system: str, llm: str, api_key: str, max final_messages = [{"role": "user", "content": messages}] payload = { - "model": llm, + "model": model_name, "messages": final_messages, "max_tokens": max_tokens, "temperature": temperature } response = requests.post( - "https://api.openai.com/v1/chat/completions", headers=headers, json=payload + f"{provider_base_url}/chat/completions", headers=headers, json=payload ) + try: text = response.json()['choices'][0]['message']['content'] token_usage = int(response.json()['usage']['total_tokens']) return text, token_usage except Exception as e: - print(f"Error in interleaved openAI: {e}. This may due to your invalid OPENAI_API_KEY. Please check the response: {response.json()} ") + print(f"Error in interleaved openAI: {e}. This may due to your invalid API key. Please check the response: {response.json()} ") return response.json() \ No newline at end of file diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py index 0ae68e4..3c4ea73 100644 --- a/computer_use_demo/gradio/agent/vlm_agent.py +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -42,6 +42,8 @@ class VLMAgent: self.model = "gpt-4o-2024-11-20" elif model == "omniparser + R1": self.model = "deepseek-r1-distill-llama-70b" + elif model == "omniparser + qwen2.5vl": + self.model = "qwen2.5-vl-72b-instruct" else: raise ValueError(f"Model {model} not supported") @@ -93,9 +95,10 @@ class VLMAgent: vlm_response, token_usage = run_oai_interleaved( messages=planner_messages, system=system, - llm=self.model, + model_name=self.model, api_key=self.api_key, max_tokens=self.max_tokens, + provider_base_url="https://api.openai.com/v1", temperature=0, ) print(f"oai token usage: {token_usage}") @@ -106,13 +109,26 @@ class VLMAgent: vlm_response, token_usage = run_groq_interleaved( messages=planner_messages, system=system, - llm=self.model, + model_name=self.model, api_key=self.api_key, max_tokens=self.max_tokens, ) print(f"groq token usage: {token_usage}") self.total_token_usage += token_usage self.total_cost += (token_usage * 0.99 / 1000000) + elif "qwen" in self.model: + vlm_response, token_usage = run_oai_interleaved( + messages=planner_messages, + system=system, + model_name=self.model, + api_key=self.api_key, + max_tokens=min(2048, self.max_tokens), + provider_base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", + temperature=0, + ) + print(f"qwen token usage: {token_usage}") + self.total_token_usage += token_usage + self.total_cost += (token_usage * 2.2 / 1000000) # https://help.aliyun.com/zh/model-studio/getting-started/models?spm=a2c4g.11186623.0.0.74b04823CGnPv7#fe96cfb1a422a else: raise ValueError(f"Model {self.model} not supported") latency_vlm = time.time() - start diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py index 7ca2a22..9c4ad5c 100644 --- a/computer_use_demo/gradio/app.py +++ b/computer_use_demo/gradio/app.py @@ -28,7 +28,7 @@ API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' ๐Ÿš€๐Ÿค–โœจ It's Play Time! -Welcome to the OmniParser+X Computer Use Demo! X = [GPT-4o, R1, Claude]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. +Welcome to the OmniParser+X Computer Use Demo! X = [GPT-4o, R1, Qwen2.5VL, Claude]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history. ''' @@ -189,7 +189,7 @@ def valid_params(user_input, state): """Validate all requirements and return a list of error messages.""" errors = [] - for server_name, url in [('Windows Host', args.windows_host_url), ('OmniParser Server', args.omniparser_server_url)]: + for server_name, url in [('Windows Host', 'localhost:5000'), ('OmniParser Server', args.omniparser_server_url)]: try: url = f'http://{url}/probe' response = requests.get(url, timeout=3) @@ -270,7 +270,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: with gr.Column(): model = gr.Dropdown( label="Model", - choices=["omniparser + gpt-4o", "omniparser + R1", "claude-3-5-sonnet-20241022"], + choices=["omniparser + gpt-4o", "omniparser + R1", "omniparser + qwen2.5vl", "claude-3-5-sonnet-20241022"], value="omniparser + gpt-4o", interactive=True, ) @@ -326,6 +326,8 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: provider_choices = ["openai"] elif model_selection == "omniparser + R1": provider_choices = ["groq"] + elif model_selection == "omniparser + qwen2.5vl": + provider_choices = ["dashscope"] else: provider_choices = [option.value for option in APIProvider] default_provider_value = provider_choices[0] diff --git a/computer_use_demo/gradio/loop.py b/computer_use_demo/gradio/loop.py index 15041cf..f6d4aa6 100644 --- a/computer_use_demo/gradio/loop.py +++ b/computer_use_demo/gradio/loop.py @@ -64,7 +64,7 @@ def sampling_loop_sync( max_tokens=max_tokens, only_n_most_recent_images=only_n_most_recent_images ) - elif model == "omniparser + gpt-4o" or model == "omniparser + R1": + elif model == "omniparser + gpt-4o" or model == "omniparser + R1" or model == "omniparser + qwen2.5vl": actor = VLMAgent( model=model, provider=provider, @@ -100,7 +100,7 @@ def sampling_loop_sync( messages.append({"content": tool_result_content, "role": "user"}) - elif model == "omniparser + gpt-4o" or model == "omniparser + R1": + elif model == "omniparser + gpt-4o" or model == "omniparser + R1" or model == "omniparser + qwen2.5vl": while True: parsed_screen = omniparser_client() tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) From 2b621c5c0f8a766eba2820c6d3e8decf84b7e3fc Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Sun, 2 Feb 2025 16:09:54 -0800 Subject: [PATCH 39/57] fixes --- computer_use_demo/gradio/agent/vlm_agent.py | 100 ++++++++++---------- computer_use_demo/gradio/tools/computer.py | 6 +- 2 files changed, 50 insertions(+), 56 deletions(-) diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py index 3c4ea73..85177e9 100644 --- a/computer_use_demo/gradio/agent/vlm_agent.py +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -12,6 +12,7 @@ from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, B from agent.llm_utils.oai import run_oai_interleaved from agent.llm_utils.groqclient import run_groq_interleaved +from agent.llm_utils.utils import is_image_path import time import re @@ -79,10 +80,8 @@ class VLMAgent: # drop looping actions msg, byte image etc planner_messages = messages - planner_messages = _keep_latest_images(planner_messages) - # if self.only_n_most_recent_images: - # _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) - # print(f"filtered_messages: {planner_messages}\n\n", "full messages:", messages) + _remove_som_images(planner_messages) + _maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images) if isinstance(planner_messages[-1], dict): if not isinstance(planner_messages[-1]["content"], list): @@ -103,9 +102,8 @@ class VLMAgent: ) print(f"oai token usage: {token_usage}") self.total_token_usage += token_usage - self.total_cost += (token_usage * 0.15 / 1000000) # https://openai.com/api/pricing/ + self.total_cost += (token_usage * 2.5 / 1000000) # https://openai.com/api/pricing/ elif "r1" in self.model: - print(f"Sending messages to Groq: {planner_messages}") vlm_response, token_usage = run_groq_interleaved( messages=planner_messages, system=system, @@ -184,11 +182,10 @@ class VLMAgent: if vlm_response_json["Next Action"] == "None": print("Task paused/completed.") elif vlm_response_json["Next Action"] == "type": - click_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': 'left_click'}, name='computer', type='tool_use') sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': vlm_response_json["Next Action"], 'text': vlm_response_json["value"]}, name='computer', type='tool_use') - response_content.extend([click_block, sim_content_block]) + response_content.append(sim_content_block) else: sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}', input={'action': vlm_response_json["Next Action"]}, @@ -212,16 +209,16 @@ You should carefully consider your plan base on the task, screenshot, and histor Here is the list of all detected bounding boxes by IDs on the screen and their description:{screen_info} Your available "Next Action" only include: -- type: move mouse to box id, left clicks and types a string of text. -- left_click: move mouse to box id and left clicks -- right_click: move mouse to box id and right clicks -- double_click: move mouse to box id and double clicks -- hover: move mouse to box id +- type: types a string of text. +- left_click: move mouse to box id and left clicks. +- right_click: move mouse to box id and right clicks. +- double_click: move mouse to box id and double clicks. +- hover: move mouse to box id. - scroll_up: scrolls the screen up. - scroll_down: scrolls the screen down. - wait: waits for 1 second for the device to load or respond. -Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on, and the value (if the action is 'type') in order to complete the task. +Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on (if action is not 'type', 'hover', 'scroll_up', 'scroll_down', 'wait'), and the value (if the action is 'type') in order to complete the task. Output format: ```json @@ -275,14 +272,14 @@ IMPORTANT NOTES: return main_section -def _keep_latest_images(messages): - for i in range(len(messages)-1): - if isinstance(messages[i]["content"], list): - for cnt in messages[i]["content"]: - if isinstance(cnt, str): - if cnt.endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")): - messages[i]["content"].remove(cnt) - return messages +def _remove_som_images(messages): + for msg in messages: + msg_content = msg["content"] + if isinstance(msg_content, list): + msg["content"] = [ + cnt for cnt in msg_content + if not (isinstance(cnt, str) and 'som' in cnt and is_image_path(cnt)) + ] def _maybe_filter_to_n_most_recent_images( @@ -293,42 +290,43 @@ def _maybe_filter_to_n_most_recent_images( """ With the assumption that images are screenshots that are of diminishing value as the conversation progresses, remove all but the final `images_to_keep` tool_result - images in place, with a chunk of min_removal_threshold to reduce the amount we - break the implicit prompt cache. + images in place """ if images_to_keep is None: return messages - tool_result_blocks = cast( - list[ToolResultBlockParam], - [ - item - for message in messages - for item in ( - message["content"] if isinstance(message["content"], list) else [] - ) - if isinstance(item, dict) and item.get("type") == "tool_result" - ], - ) - - total_images = sum( - 1 - for tool_result in tool_result_blocks - for content in tool_result.get("content", []) - if isinstance(content, dict) and content.get("type") == "image" - ) + total_images = 0 + for msg in messages: + for cnt in msg.get("content", []): + if isinstance(cnt, str) and is_image_path(cnt): + total_images += 1 + elif isinstance(cnt, dict) and cnt.get("type") == "tool_result": + for content in cnt.get("content", []): + if isinstance(content, dict) and content.get("type") == "image": + total_images += 1 images_to_remove = total_images - images_to_keep - # for better cache behavior, we want to remove in chunks - images_to_remove -= images_to_remove % min_removal_threshold - - for tool_result in tool_result_blocks: - if isinstance(tool_result.get("content"), list): + + for msg in messages: + msg_content = msg["content"] + if isinstance(msg_content, list): new_content = [] - for content in tool_result.get("content", []): - if isinstance(content, dict) and content.get("type") == "image": + for cnt in msg_content: + # Remove images from SOM or screenshot as needed + if isinstance(cnt, str) and is_image_path(cnt): if images_to_remove > 0: images_to_remove -= 1 continue - new_content.append(content) - tool_result["content"] = new_content \ No newline at end of file + # VLM shouldn't use anthropic screenshot tool so shouldn't have these but in case it does, remove as needed + elif isinstance(cnt, dict) and cnt.get("type") == "tool_result": + new_tool_result_content = [] + for tool_result_entry in cnt.get("content", []): + if isinstance(tool_result_entry, dict) and tool_result_entry.get("type") == "image": + if images_to_remove > 0: + images_to_remove -= 1 + continue + new_tool_result_content.append(tool_result_entry) + cnt["content"] = new_tool_result_content + # Append fixed content to current message's content list + new_content.append(cnt) + msg["content"] = new_content \ No newline at end of file diff --git a/computer_use_demo/gradio/tools/computer.py b/computer_use_demo/gradio/tools/computer.py index dc5ad80..8386b2b 100644 --- a/computer_use_demo/gradio/tools/computer.py +++ b/computer_use_demo/gradio/tools/computer.py @@ -170,8 +170,7 @@ class ComputerTool(BaseAnthropicTool): return ToolResult(output=f"Pressed keys: {text}") elif action == "type": - self.send_to_vm(f"pyautogui.typewrite('{text}', interval={TYPING_DELAY_MS / 1000})") # Convert ms to seconds - self.send_to_vm("pyautogui.press('enter')") + self.send_to_vm(f"pyautogui.typewrite('{text}', interval={TYPING_DELAY_MS / 1000})") screenshot_base64 = (await self.screenshot()).base64_image return ToolResult(output=text, base64_image=screenshot_base64) @@ -261,10 +260,7 @@ class ComputerTool(BaseAnthropicTool): width, height = self.target_dimension["width"], self.target_dimension["height"] screenshot, path = get_screenshot(resize=True, target_width=width, target_height=height) time.sleep(0.7) # avoid async error as actions take time to complete - # return ToolResult() return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode()) - - raise ToolError(f"Failed to take screenshot: {path} does not exist.") def padding_image(self, screenshot): """Pad the screenshot to 16:10 aspect ratio, when the aspect ratio is not 16:10.""" 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zU_B$cKIgWP`S&q3?A9)^+27@~lM*W{Xo5f?<86(NRnt;^7wZ9aXTi3@7>l~G2wBN{ zjT|T@;hlS(1;>dV>cChY7hNZvB=E~P%W<sZy3y#<{t1HT){9<Xjs1vYkhHk}!F=cj zsr3Lv+f?D?N<~!_nv~KK@@Osn%a<>lJ{jj)>oEI5!{UDeucR|5Y5}Z;N6n0a{%FQO zufE5No7OS!qk2<8H%}hH+-^-;rxsya+;XLI7IPv)v!A6@dMS4#I{!$dmM6zv-j@93 z{_f2MMon)q<zIjk7;fZ%J05v>VGN47!1t-j=%`rB<kjH?DQh>qWKee2WC}VdFk$@Y z72)er(xLB#69pfQw@69senzi8(fvkEtC&c?1)w3D_^NaAJq4a4CO?b4L~iFjnEgRW zweZn6;LC&VZ8oW~M3;sN|M9Y>OJt13=z8CRy{^6I50+o@NO1<4ogJpyO@?$fH?Mo1 zLW<I+Tl$LgYB^F0*K)*|%o}DlyTJ&}eJpE@QzwIj@hQ>_=@ulXb!_6cKJfq-YEzjX z5G?G!{b_7y2&i{QUVc_?KVOT2zoah$R|6`f8f(UH|2Nxqj1>Gy+^>6o{mEa9y6F`3 zLmgNeO98LH*V5)KNp2e1{D!;_-2!3(V_-4@W8tb58(|GQYM<g{OHY(%DXu1u4}4C8 ztntAM3TPT%BJ9Ee_z7zzmAR{bsgHrpUr3QEkvwk>{>Z=Gu)pI>?X*55(q%<jR|4W! zQRF#K>k0wu8VbG}f#D%A=JrVWZNl2m>fGFWi5a~qX1d`Q{)Func1U;ho~6OM;{O~- zzeg9yhMk6}RsN_dGMa%FEo@ailNNt}_Sbp!cR1@EP1r9{mtPig|2YH}s8IepPKF2c z;{WqQQ3|9u85!qA?td3V`^RTd>%Rk{Fa`wdk9YQej_zD0h4s$8Z~32}{{i$)9Vx13 zGu!6G@i&V9FTDPKlA4?pkUM83@}HmRBBhUWP7*}W|BoOf+3z4^;0;m=#eZ1M|7U19 a<Df;nd~@GtPw*V+NA;=Zlj6q~!T$#~ZvzSd literal 0 HcmV?d00001 From 89b0e9a807c5da4dfb899859f9db175e57917369 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Mon, 3 Feb 2025 00:18:36 -0500 Subject: [PATCH 41/57] OmniParser+X Computer Use Demo screenshot --- computer_use_demo/readme.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/computer_use_demo/readme.md b/computer_use_demo/readme.md index ddf5107..5ad3002 100644 --- a/computer_use_demo/readme.md +++ b/computer_use_demo/readme.md @@ -1,6 +1,9 @@ # OmniParser+X Computer Use Demo Control a Windows 11 VM with OmniParser+X (X = [GPT-4o/4o-mini, Claude, ...]). +<p align="center"> + <img src="imgs/som_overlaid_omni.png" alt="OmniParser+X Computer Use Demo screenshot"> +</p> ## Overview From c724a44080a995315424056645cc4f9b1961ef95 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Mon, 3 Feb 2025 00:19:40 -0500 Subject: [PATCH 42/57] One folder up --- computer_use_demo/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/computer_use_demo/readme.md b/computer_use_demo/readme.md index 5ad3002..08f3e7a 100644 --- a/computer_use_demo/readme.md +++ b/computer_use_demo/readme.md @@ -2,7 +2,7 @@ Control a Windows 11 VM with OmniParser+X (X = [GPT-4o/4o-mini, Claude, ...]). <p align="center"> - <img src="imgs/som_overlaid_omni.png" alt="OmniParser+X Computer Use Demo screenshot"> + <img src="../imgs/som_overlaid_omni.png" alt="OmniParser+X Computer Use Demo screenshot"> </p> ## Overview From 3e51ba0da6889d1902cfaf95d3f0837229c7e6a8 Mon Sep 17 00:00:00 2001 From: yadonglu <yadonglu@micrsoft.com> Date: Mon, 3 Feb 2025 14:28:52 -0800 Subject: [PATCH 43/57] add stop button --- computer_use_demo/gradio/app.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py index 9c4ad5c..35847f3 100644 --- a/computer_use_demo/gradio/app.py +++ b/computer_use_demo/gradio/app.py @@ -238,13 +238,17 @@ def process_input(user_input, state): only_n_most_recent_images=state["only_n_most_recent_images"], omniparser_url=args.omniparser_server_url ): - if loop_msg is None: + if loop_msg is None or state.get("stop"): yield state['chatbot_messages'] print("End of task. Close the loop.") break yield state['chatbot_messages'] # Yield the updated chatbot_messages to update the chatbot UI +def stop_app(state): + state["stop"] = True + return "App stopped" + with gr.Blocks(theme=gr.themes.Default()) as demo: gr.HTML(""" <style> @@ -256,7 +260,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: } </style> """) - state = gr.State({}) + state = gr.State({"stop": False}) setup_state(state.value) @@ -305,6 +309,8 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Omniparser + X ...", container=False) with gr.Column(scale=1, min_width=50): submit_button = gr.Button(value="Send", variant="primary") + with gr.Column(scale=1, min_width=50): + stop_button = gr.Button(value="Stop", variant="secondary") with gr.Row(): with gr.Column(scale=1): @@ -386,6 +392,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot]) submit_button.click(process_input, [chat_input, state], chatbot) + stop_button.click(stop_app, [state], None) if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=7888) \ No newline at end of file From 87254458810851adde60de8401ae04fc1c35f213 Mon Sep 17 00:00:00 2001 From: yadonglu <yadonglu@micrsoft.com> Date: Mon, 3 Feb 2025 17:05:17 -0800 Subject: [PATCH 44/57] small fixes --- computer_use_demo/gradio/agent/vlm_agent.py | 2 +- computer_use_demo/gradio/app.py | 14 ++++++++++---- computer_use_demo/readme.md | 2 +- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py index 85177e9..173c664 100644 --- a/computer_use_demo/gradio/agent/vlm_agent.py +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -159,7 +159,7 @@ class VLMAgent: self.output_callback(f'<img src="data:image/png;base64,{img_to_show_base64}">', sender="bot") self.output_callback( f'<details>' - f' <summary>Screen Info for OmniParser Agent</summary>' + f' <summary>Parsed Screen elemetns by OmniParser</summary>' f' <pre>{screen_info}</pre>' f'</details>', sender="bot" diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py index 35847f3..0128471 100644 --- a/computer_use_demo/gradio/app.py +++ b/computer_use_demo/gradio/app.py @@ -28,9 +28,9 @@ API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' ๐Ÿš€๐Ÿค–โœจ It's Play Time! -Welcome to the OmniParser+X Computer Use Demo! X = [GPT-4o, R1, Qwen2.5VL, Claude]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. +Welcome to the OmniParser+X Computer Use Demo! X = [GPT family (4o/o1/o3-mini), Claude, deepseek R1/V3, Qwen-2.5VL]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. -Type a message and press submit to start OmniParser+X. Press the trash icon in the chat to clear the message history. +Type a message and press submit to start OmniParser+X. Press stop to pause, and press the trash icon in the chat to clear the message history. ''' def parse_arguments(): @@ -71,6 +71,8 @@ def setup_state(state): state["only_n_most_recent_images"] = 2 if 'chatbot_messages' not in state: state['chatbot_messages'] = [] + if 'stop' not in state: + state['stop'] = False async def main(state): """Render loop for Gradio""" @@ -207,6 +209,10 @@ def valid_params(user_input, state): return errors def process_input(user_input, state): + # Reset the stop flag + if state["stop"]: + state["stop"] = False + errors = valid_params(user_input, state) if errors: raise gr.Error("Validation errors: " + ", ".join(errors)) @@ -260,8 +266,8 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: } </style> """) - state = gr.State({"stop": False}) - + state = gr.State({}) + setup_state(state.value) gr.Markdown("# OmniParser + โœ–๏ธ Demo") diff --git a/computer_use_demo/readme.md b/computer_use_demo/readme.md index 08f3e7a..b7bb4b8 100644 --- a/computer_use_demo/readme.md +++ b/computer_use_demo/readme.md @@ -1,6 +1,6 @@ # OmniParser+X Computer Use Demo -Control a Windows 11 VM with OmniParser+X (X = [GPT-4o/4o-mini, Claude, ...]). +Control a Windows 11 VM with OmniParser+X (X = [GPT family (4o/o1/o3-mini), Claude, deepseek R1/V3, Qwen-2.5VL]). <p align="center"> <img src="../imgs/som_overlaid_omni.png" alt="OmniParser+X Computer Use Demo screenshot"> </p> From 31d7b1d0960e520d47c9d6726e88307f6e351a2e Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Mon, 3 Feb 2025 23:52:04 -0800 Subject: [PATCH 45/57] o1 (has vision) and o3-mini (no vision) --- .../agent/llm_utils/{oai.py => oaiclient.py} | 11 +++++++---- computer_use_demo/gradio/agent/vlm_agent.py | 18 ++++++++++++------ computer_use_demo/gradio/app.py | 5 +++-- computer_use_demo/gradio/loop.py | 6 ++++-- 4 files changed, 26 insertions(+), 14 deletions(-) rename computer_use_demo/gradio/agent/llm_utils/{oai.py => oaiclient.py} (86%) diff --git a/computer_use_demo/gradio/agent/llm_utils/oai.py b/computer_use_demo/gradio/agent/llm_utils/oaiclient.py similarity index 86% rename from computer_use_demo/gradio/agent/llm_utils/oai.py rename to computer_use_demo/gradio/agent/llm_utils/oaiclient.py index 64eb21d..ad42110 100644 --- a/computer_use_demo/gradio/agent/llm_utils/oai.py +++ b/computer_use_demo/gradio/agent/llm_utils/oaiclient.py @@ -7,7 +7,6 @@ from .utils import is_image_path, encode_image def run_oai_interleaved(messages: list, system: str, model_name: str, api_key: str, max_tokens=256, temperature=0, provider_base_url: str = "https://api.openai.com/v1"): headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"} - final_messages = [{"role": "system", "content": system}] if type(messages) == list: @@ -16,7 +15,8 @@ def run_oai_interleaved(messages: list, system: str, model_name: str, api_key: s if isinstance(item, dict): for cnt in item["content"]: if isinstance(cnt, str): - if is_image_path(cnt): + if is_image_path(cnt) and 'o3-mini' not in model_name: + # 03 mini does not support images base64_image = encode_image(cnt) content = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}} else: @@ -41,9 +41,12 @@ def run_oai_interleaved(messages: list, system: str, model_name: str, api_key: s payload = { "model": model_name, "messages": final_messages, - "max_tokens": max_tokens, - "temperature": temperature } + if 'o1' in model_name or 'o3-mini' in model_name: + payload['reasoning_effort'] = 'low' + payload['max_completion_tokens'] = max_tokens + else: + payload['max_tokens'] = max_tokens response = requests.post( f"{provider_base_url}/chat/completions", headers=headers, json=payload diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/computer_use_demo/gradio/agent/vlm_agent.py index 173c664..7599c39 100644 --- a/computer_use_demo/gradio/agent/vlm_agent.py +++ b/computer_use_demo/gradio/agent/vlm_agent.py @@ -10,7 +10,7 @@ from anthropic import APIResponse from anthropic.types import ToolResultBlockParam from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage -from agent.llm_utils.oai import run_oai_interleaved +from agent.llm_utils.oaiclient import run_oai_interleaved from agent.llm_utils.groqclient import run_groq_interleaved from agent.llm_utils.utils import is_image_path import time @@ -45,6 +45,10 @@ class VLMAgent: self.model = "deepseek-r1-distill-llama-70b" elif model == "omniparser + qwen2.5vl": self.model = "qwen2.5-vl-72b-instruct" + elif model == "omniparser + o1": + self.model = "o1" + elif model == "omniparser + o3-mini": + self.model = "o3-mini" else: raise ValueError(f"Model {model} not supported") @@ -69,9 +73,6 @@ class VLMAgent: latency_omniparser = parsed_screen['latency'] self.output_callback(f'-- Step {self.step_count}: --', sender="bot") screen_info = str(parsed_screen['screen_info']) - - - screenshot_uuid = parsed_screen['screenshot_uuid'] screen_width, screen_height = parsed_screen['width'], parsed_screen['height'] @@ -90,7 +91,7 @@ class VLMAgent: planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_som_{screenshot_uuid}.png") start = time.time() - if "gpt" in self.model: + if "gpt" in self.model or "o1" in self.model or "o3-mini" in self.model: vlm_response, token_usage = run_oai_interleaved( messages=planner_messages, system=system, @@ -102,7 +103,12 @@ class VLMAgent: ) print(f"oai token usage: {token_usage}") self.total_token_usage += token_usage - self.total_cost += (token_usage * 2.5 / 1000000) # https://openai.com/api/pricing/ + if 'gpt' in self.model: + self.total_cost += (token_usage * 2.5 / 1000000) # https://openai.com/api/pricing/ + elif 'o1' in self.model: + self.total_cost += (token_usage * 15 / 1000000) # https://openai.com/api/pricing/ + elif 'o3-mini' in self.model: + self.total_cost += (token_usage * 1.1 / 1000000) # https://openai.com/api/pricing/ elif "r1" in self.model: vlm_response, token_usage = run_groq_interleaved( messages=planner_messages, diff --git a/computer_use_demo/gradio/app.py b/computer_use_demo/gradio/app.py index 0128471..92ea689 100644 --- a/computer_use_demo/gradio/app.py +++ b/computer_use_demo/gradio/app.py @@ -242,6 +242,7 @@ def process_input(user_input, state): api_response_callback=partial(_api_response_callback, response_state=state["responses"]), api_key=state["api_key"], only_n_most_recent_images=state["only_n_most_recent_images"], + max_tokens=16384, omniparser_url=args.omniparser_server_url ): if loop_msg is None or state.get("stop"): @@ -280,7 +281,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: with gr.Column(): model = gr.Dropdown( label="Model", - choices=["omniparser + gpt-4o", "omniparser + R1", "omniparser + qwen2.5vl", "claude-3-5-sonnet-20241022"], + choices=["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl", "claude-3-5-sonnet-20241022"], value="omniparser + gpt-4o", interactive=True, ) @@ -334,7 +335,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: if model_selection == "claude-3-5-sonnet-20241022": provider_choices = [option.value for option in APIProvider if option.value != "openai"] - elif model_selection == "omniparser + gpt-4o": + elif model_selection in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini"]): provider_choices = ["openai"] elif model_selection == "omniparser + R1": provider_choices = ["groq"] diff --git a/computer_use_demo/gradio/loop.py b/computer_use_demo/gradio/loop.py index f6d4aa6..8d7b75f 100644 --- a/computer_use_demo/gradio/loop.py +++ b/computer_use_demo/gradio/loop.py @@ -64,13 +64,15 @@ def sampling_loop_sync( max_tokens=max_tokens, only_n_most_recent_images=only_n_most_recent_images ) - elif model == "omniparser + gpt-4o" or model == "omniparser + R1" or model == "omniparser + qwen2.5vl": + elif model in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl"]): actor = VLMAgent( model=model, provider=provider, api_key=api_key, api_response_callback=api_response_callback, output_callback=output_callback, + max_tokens=max_tokens, + only_n_most_recent_images=only_n_most_recent_images ) else: raise ValueError(f"Model {model} not supported") @@ -100,7 +102,7 @@ def sampling_loop_sync( messages.append({"content": tool_result_content, "role": "user"}) - elif model == "omniparser + gpt-4o" or model == "omniparser + R1" or model == "omniparser + qwen2.5vl": + elif model in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl"]): while True: parsed_screen = omniparser_client() tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen) From fe84a352929ac351a45c1b0756a4a06b01136aac Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <tmd4@princeton.edu> Date: Tue, 4 Feb 2025 11:43:36 -0800 Subject: [PATCH 46/57] Naming conventions --- imgs/header_bar.png | Bin 0 -> 215168 bytes .../gradio/.gitignore | 0 .../gradio/__init__.py | 0 .../gradio/agent/anthropic_agent.py | 0 .../gradio/agent/llm_utils/groqclient.py | 0 .../gradio/agent/llm_utils/oaiclient.py | 0 .../agent/llm_utils/omniparserclient.py | 0 .../gradio/agent/llm_utils/utils.py | 0 .../gradio/agent/vlm_agent.py | 0 {computer_use_demo => omnitool}/gradio/app.py | 8 +-- .../gradio/executor/anthropic_executor.py | 0 .../gradio/loop.py | 0 .../gradio/tools/__init__.py | 0 .../gradio/tools/base.py | 0 .../gradio/tools/collection.py | 0 .../gradio/tools/computer.py | 0 .../gradio/tools/screen_capture.py | 0 .../omnibox}/.gitignore | 0 .../omnibox}/Dockerfile | 0 .../omnibox}/compose.yml | 0 .../omnibox}/scripts/manage_vm.ps1 | 0 .../omnibox}/scripts/manage_vm.sh | 0 .../omnibox}/vm/buildcontainer/define.sh | 0 .../omnibox}/vm/buildcontainer/entry.sh | 0 .../omnibox}/vm/buildcontainer/install.sh | 0 .../omnibox}/vm/buildcontainer/power.sh | 0 .../omnibox}/vm/buildcontainer/samba.sh | 0 .../vm/win11def/win11x64-enterprise-eval.xml | 0 .../omnibox}/vm/win11iso/README.md | 0 .../vm/win11setup/firstboot/install.bat | 0 .../vm/win11setup/setupscripts/on-logon.ps1 | 0 .../win11setup/setupscripts/server/cursor.png | Bin .../vm/win11setup/setupscripts/server/main.py | 0 .../setupscripts/server/requirements.txt | 0 .../win11setup/setupscripts/setup-tools.psm1 | 0 .../vm/win11setup/setupscripts/setup.ps1 | 0 .../win11setup/setupscripts/tools_config.json | 0 .../omniparserserver/omniparserserver.py | 0 {computer_use_demo => omnitool}/readme.md | 60 +++++++++--------- 39 files changed, 35 insertions(+), 33 deletions(-) create mode 100644 imgs/header_bar.png rename {computer_use_demo => omnitool}/gradio/.gitignore (100%) rename {computer_use_demo => omnitool}/gradio/__init__.py (100%) rename {computer_use_demo => omnitool}/gradio/agent/anthropic_agent.py (100%) rename {computer_use_demo => omnitool}/gradio/agent/llm_utils/groqclient.py (100%) rename {computer_use_demo => omnitool}/gradio/agent/llm_utils/oaiclient.py (100%) rename {computer_use_demo => omnitool}/gradio/agent/llm_utils/omniparserclient.py (100%) rename {computer_use_demo => omnitool}/gradio/agent/llm_utils/utils.py (100%) rename {computer_use_demo => omnitool}/gradio/agent/vlm_agent.py (100%) rename {computer_use_demo => omnitool}/gradio/app.py (97%) rename {computer_use_demo => omnitool}/gradio/executor/anthropic_executor.py (100%) rename {computer_use_demo => omnitool}/gradio/loop.py (100%) rename {computer_use_demo => omnitool}/gradio/tools/__init__.py (100%) rename {computer_use_demo => omnitool}/gradio/tools/base.py (100%) rename {computer_use_demo => omnitool}/gradio/tools/collection.py (100%) rename {computer_use_demo => omnitool}/gradio/tools/computer.py (100%) rename {computer_use_demo => omnitool}/gradio/tools/screen_capture.py (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/.gitignore (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/Dockerfile (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/compose.yml (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/scripts/manage_vm.ps1 (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/scripts/manage_vm.sh (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/buildcontainer/define.sh (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/buildcontainer/entry.sh (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/buildcontainer/install.sh (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/buildcontainer/power.sh (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/buildcontainer/samba.sh (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11def/win11x64-enterprise-eval.xml (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11iso/README.md (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11setup/firstboot/install.bat (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11setup/setupscripts/on-logon.ps1 (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11setup/setupscripts/server/cursor.png (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11setup/setupscripts/server/main.py (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11setup/setupscripts/server/requirements.txt (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11setup/setupscripts/setup-tools.psm1 (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11setup/setupscripts/setup.ps1 (100%) rename {computer_use_demo/windowshost => omnitool/omnibox}/vm/win11setup/setupscripts/tools_config.json (100%) rename {computer_use_demo => omnitool}/omniparserserver/omniparserserver.py (100%) rename {computer_use_demo => omnitool}/readme.md (62%) 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a/computer_use_demo/gradio/__init__.py b/omnitool/gradio/__init__.py similarity index 100% rename from computer_use_demo/gradio/__init__.py rename to omnitool/gradio/__init__.py diff --git a/computer_use_demo/gradio/agent/anthropic_agent.py b/omnitool/gradio/agent/anthropic_agent.py similarity index 100% rename from computer_use_demo/gradio/agent/anthropic_agent.py rename to omnitool/gradio/agent/anthropic_agent.py diff --git a/computer_use_demo/gradio/agent/llm_utils/groqclient.py b/omnitool/gradio/agent/llm_utils/groqclient.py similarity index 100% rename from computer_use_demo/gradio/agent/llm_utils/groqclient.py rename to omnitool/gradio/agent/llm_utils/groqclient.py diff --git a/computer_use_demo/gradio/agent/llm_utils/oaiclient.py b/omnitool/gradio/agent/llm_utils/oaiclient.py similarity index 100% rename from computer_use_demo/gradio/agent/llm_utils/oaiclient.py rename to omnitool/gradio/agent/llm_utils/oaiclient.py diff --git a/computer_use_demo/gradio/agent/llm_utils/omniparserclient.py b/omnitool/gradio/agent/llm_utils/omniparserclient.py similarity index 100% rename from computer_use_demo/gradio/agent/llm_utils/omniparserclient.py rename to omnitool/gradio/agent/llm_utils/omniparserclient.py diff --git a/computer_use_demo/gradio/agent/llm_utils/utils.py b/omnitool/gradio/agent/llm_utils/utils.py similarity index 100% rename from computer_use_demo/gradio/agent/llm_utils/utils.py rename to omnitool/gradio/agent/llm_utils/utils.py diff --git a/computer_use_demo/gradio/agent/vlm_agent.py b/omnitool/gradio/agent/vlm_agent.py similarity index 100% rename from computer_use_demo/gradio/agent/vlm_agent.py rename to omnitool/gradio/agent/vlm_agent.py diff --git a/computer_use_demo/gradio/app.py b/omnitool/gradio/app.py similarity index 97% rename from computer_use_demo/gradio/app.py rename to omnitool/gradio/app.py index 92ea689..1b2c459 100644 --- a/computer_use_demo/gradio/app.py +++ b/omnitool/gradio/app.py @@ -26,11 +26,11 @@ CONFIG_DIR = Path("~/.anthropic").expanduser() API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' -๐Ÿš€๐Ÿค–โœจ It's Play Time! +Welcome to OmniTool - the OmniParser+X Computer Use Demo! X = [OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use (Sonnet)]. -Welcome to the OmniParser+X Computer Use Demo! X = [GPT family (4o/o1/o3-mini), Claude, deepseek R1/V3, Qwen-2.5VL]. Let OmniParser turn your general purpose vision-langauge model to an AI agent. +OmniParser lets you turn any vision-langauge model into an AI agent. -Type a message and press submit to start OmniParser+X. Press stop to pause, and press the trash icon in the chat to clear the message history. +Type a message and press submit to start OmniTool. Press stop to pause, and press the trash icon in the chat to clear the message history. ''' def parse_arguments(): @@ -271,7 +271,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: setup_state(state.value) - gr.Markdown("# OmniParser + โœ–๏ธ Demo") + gr.Markdown("# OmniTool") if not os.getenv("HIDE_WARNING", False): gr.Markdown(INTRO_TEXT) diff --git a/computer_use_demo/gradio/executor/anthropic_executor.py b/omnitool/gradio/executor/anthropic_executor.py similarity index 100% rename from computer_use_demo/gradio/executor/anthropic_executor.py rename to omnitool/gradio/executor/anthropic_executor.py diff --git a/computer_use_demo/gradio/loop.py b/omnitool/gradio/loop.py similarity index 100% rename from computer_use_demo/gradio/loop.py rename to omnitool/gradio/loop.py diff --git a/computer_use_demo/gradio/tools/__init__.py b/omnitool/gradio/tools/__init__.py similarity index 100% rename from computer_use_demo/gradio/tools/__init__.py rename to omnitool/gradio/tools/__init__.py diff --git a/computer_use_demo/gradio/tools/base.py b/omnitool/gradio/tools/base.py similarity index 100% rename from computer_use_demo/gradio/tools/base.py rename to omnitool/gradio/tools/base.py diff --git a/computer_use_demo/gradio/tools/collection.py b/omnitool/gradio/tools/collection.py similarity index 100% rename from computer_use_demo/gradio/tools/collection.py rename to omnitool/gradio/tools/collection.py diff --git a/computer_use_demo/gradio/tools/computer.py b/omnitool/gradio/tools/computer.py similarity index 100% rename from computer_use_demo/gradio/tools/computer.py rename to omnitool/gradio/tools/computer.py diff --git a/computer_use_demo/gradio/tools/screen_capture.py b/omnitool/gradio/tools/screen_capture.py similarity index 100% rename from computer_use_demo/gradio/tools/screen_capture.py rename to omnitool/gradio/tools/screen_capture.py diff --git a/computer_use_demo/windowshost/.gitignore b/omnitool/omnibox/.gitignore similarity index 100% rename from computer_use_demo/windowshost/.gitignore rename to omnitool/omnibox/.gitignore diff --git a/computer_use_demo/windowshost/Dockerfile b/omnitool/omnibox/Dockerfile similarity index 100% rename from computer_use_demo/windowshost/Dockerfile rename to omnitool/omnibox/Dockerfile diff --git a/computer_use_demo/windowshost/compose.yml b/omnitool/omnibox/compose.yml similarity index 100% rename from computer_use_demo/windowshost/compose.yml rename to omnitool/omnibox/compose.yml diff --git a/computer_use_demo/windowshost/scripts/manage_vm.ps1 b/omnitool/omnibox/scripts/manage_vm.ps1 similarity index 100% rename from computer_use_demo/windowshost/scripts/manage_vm.ps1 rename to omnitool/omnibox/scripts/manage_vm.ps1 diff --git a/computer_use_demo/windowshost/scripts/manage_vm.sh b/omnitool/omnibox/scripts/manage_vm.sh similarity index 100% rename from computer_use_demo/windowshost/scripts/manage_vm.sh rename to omnitool/omnibox/scripts/manage_vm.sh diff --git a/computer_use_demo/windowshost/vm/buildcontainer/define.sh b/omnitool/omnibox/vm/buildcontainer/define.sh similarity index 100% rename from computer_use_demo/windowshost/vm/buildcontainer/define.sh rename to omnitool/omnibox/vm/buildcontainer/define.sh diff --git a/computer_use_demo/windowshost/vm/buildcontainer/entry.sh b/omnitool/omnibox/vm/buildcontainer/entry.sh similarity index 100% rename from computer_use_demo/windowshost/vm/buildcontainer/entry.sh rename to omnitool/omnibox/vm/buildcontainer/entry.sh diff --git a/computer_use_demo/windowshost/vm/buildcontainer/install.sh b/omnitool/omnibox/vm/buildcontainer/install.sh similarity index 100% rename from computer_use_demo/windowshost/vm/buildcontainer/install.sh rename to omnitool/omnibox/vm/buildcontainer/install.sh diff --git a/computer_use_demo/windowshost/vm/buildcontainer/power.sh b/omnitool/omnibox/vm/buildcontainer/power.sh similarity index 100% rename from computer_use_demo/windowshost/vm/buildcontainer/power.sh rename to omnitool/omnibox/vm/buildcontainer/power.sh diff --git a/computer_use_demo/windowshost/vm/buildcontainer/samba.sh b/omnitool/omnibox/vm/buildcontainer/samba.sh similarity index 100% rename from computer_use_demo/windowshost/vm/buildcontainer/samba.sh rename to omnitool/omnibox/vm/buildcontainer/samba.sh diff --git a/computer_use_demo/windowshost/vm/win11def/win11x64-enterprise-eval.xml b/omnitool/omnibox/vm/win11def/win11x64-enterprise-eval.xml similarity index 100% rename from computer_use_demo/windowshost/vm/win11def/win11x64-enterprise-eval.xml rename to omnitool/omnibox/vm/win11def/win11x64-enterprise-eval.xml diff --git a/computer_use_demo/windowshost/vm/win11iso/README.md b/omnitool/omnibox/vm/win11iso/README.md similarity index 100% rename from computer_use_demo/windowshost/vm/win11iso/README.md rename to omnitool/omnibox/vm/win11iso/README.md diff --git a/computer_use_demo/windowshost/vm/win11setup/firstboot/install.bat b/omnitool/omnibox/vm/win11setup/firstboot/install.bat similarity index 100% rename from computer_use_demo/windowshost/vm/win11setup/firstboot/install.bat rename to omnitool/omnibox/vm/win11setup/firstboot/install.bat diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/on-logon.ps1 b/omnitool/omnibox/vm/win11setup/setupscripts/on-logon.ps1 similarity index 100% rename from computer_use_demo/windowshost/vm/win11setup/setupscripts/on-logon.ps1 rename to omnitool/omnibox/vm/win11setup/setupscripts/on-logon.ps1 diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/cursor.png b/omnitool/omnibox/vm/win11setup/setupscripts/server/cursor.png similarity index 100% rename from computer_use_demo/windowshost/vm/win11setup/setupscripts/server/cursor.png rename to omnitool/omnibox/vm/win11setup/setupscripts/server/cursor.png diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/main.py b/omnitool/omnibox/vm/win11setup/setupscripts/server/main.py similarity index 100% rename from computer_use_demo/windowshost/vm/win11setup/setupscripts/server/main.py rename to omnitool/omnibox/vm/win11setup/setupscripts/server/main.py diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/server/requirements.txt b/omnitool/omnibox/vm/win11setup/setupscripts/server/requirements.txt similarity index 100% rename from computer_use_demo/windowshost/vm/win11setup/setupscripts/server/requirements.txt rename to omnitool/omnibox/vm/win11setup/setupscripts/server/requirements.txt diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/setup-tools.psm1 b/omnitool/omnibox/vm/win11setup/setupscripts/setup-tools.psm1 similarity index 100% rename from computer_use_demo/windowshost/vm/win11setup/setupscripts/setup-tools.psm1 rename to omnitool/omnibox/vm/win11setup/setupscripts/setup-tools.psm1 diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/setup.ps1 b/omnitool/omnibox/vm/win11setup/setupscripts/setup.ps1 similarity index 100% rename from computer_use_demo/windowshost/vm/win11setup/setupscripts/setup.ps1 rename to omnitool/omnibox/vm/win11setup/setupscripts/setup.ps1 diff --git a/computer_use_demo/windowshost/vm/win11setup/setupscripts/tools_config.json b/omnitool/omnibox/vm/win11setup/setupscripts/tools_config.json similarity index 100% rename from computer_use_demo/windowshost/vm/win11setup/setupscripts/tools_config.json rename to omnitool/omnibox/vm/win11setup/setupscripts/tools_config.json diff --git a/computer_use_demo/omniparserserver/omniparserserver.py b/omnitool/omniparserserver/omniparserserver.py similarity index 100% rename from computer_use_demo/omniparserserver/omniparserserver.py rename to omnitool/omniparserserver/omniparserserver.py diff --git a/computer_use_demo/readme.md b/omnitool/readme.md similarity index 62% rename from computer_use_demo/readme.md rename to omnitool/readme.md index b7bb4b8..c572fc0 100644 --- a/computer_use_demo/readme.md +++ b/omnitool/readme.md @@ -1,66 +1,68 @@ -# OmniParser+X Computer Use Demo - -Control a Windows 11 VM with OmniParser+X (X = [GPT family (4o/o1/o3-mini), Claude, deepseek R1/V3, Qwen-2.5VL]). <p align="center"> - <img src="../imgs/som_overlaid_omni.png" alt="OmniParser+X Computer Use Demo screenshot"> + <img src="../imgs/header_bar.png" alt="OmniParser+X Computer Use Demo screenshot"> </p> +# OmniTool + +Control a Windows 11 VM with OmniParser+X (OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL)) or Anthropic Computer Use. + ## Overview There are three components: -1. **windowshost**: A Windows 11 VM running in a Docker container -2. **omniparserserver**: FastAPI server running OmniParser +1. **omnibox**: A Windows 11 VM running in a Docker container +2. **omniparserserver**: FastAPI server running OmniParser V2 3. **gradio**: UI where you can provide commands and watch OmniParser+X reasoning and executing on the Windows 11 VM Notes: + 1. The Windows 11 VM docker is dependent on KVM so can only run quickly on Windows and Linux. This can run on a CPU machine (doesn't need GPU). 2. Though OmniParser can run on a CPU, we have separated this out if you want to run it fast on a GPU machine -3. The Gradio UI can also run on a CPU machine. +3. The Gradio UI can also run on a CPU machine. We suggest running **omnibox** and **gradio** on the same CPU machine and **omniparserserver** on a GPU server. ## Setup -1. **windowshost**: +1. **omnibox**: a. Install Docker Desktop - - b. Visit [Microsoft Evaluation Center](https://info.microsoft.com/ww-landing-windows-11-enterprise.html), accept the Terms of Service, and download a **Windows 11 Enterprise Evaluation (90-day trial, English, United States)** ISO file [~6GB]. Rename the file to `custom.iso` and copy it to the directory `OmniParser/computer_use_demo/windowshost/vm/win11iso` - - c. Navigate to vm management script directory with`cd OmniParser/computer_use_demo/windowshost/scripts` - + + b. Visit [Microsoft Evaluation Center](https://info.microsoft.com/ww-landing-windows-11-enterprise.html), accept the Terms of Service, and download a **Windows 11 Enterprise Evaluation (90-day trial, English, United States)** ISO file [~6GB]. Rename the file to `custom.iso` and copy it to the directory `OmniParser/omnitool/omnibox/vm/win11iso` + + c. Navigate to vm management script directory with`cd OmniParser/omnitool/omnibox/scripts` + d. Build the docker container [400MB] and install the ISO to a storage folder [20GB] with `./manage_vm.sh create` - - e. After creating the first time it will store a save of the VM state in `vm/win11storage`. You can then manage the VM with `./manage_vm.sh start` and `./manage_vm.sh stop`. To delete the VM, use `./manage_vm.sh delete` and delete the `OmniParser/computer_use_demo/windowshost/vm/win11storage` directory. + + e. After creating the first time it will store a save of the VM state in `vm/win11storage`. You can then manage the VM with `./manage_vm.sh start` and `./manage_vm.sh stop`. To delete the VM, use `./manage_vm.sh delete` and delete the `OmniParser/omnitool/omnibox/vm/win11storage` directory. 2. **omniparserserver**: a. If you already have a conda environment for OmniParser, you can use that. Else follow the following steps to create one - + b. Ensure conda is installed with `conda --version` or install from the [Anaconda website](https://www.anaconda.com/download/success) - + c. Navigate to the root of the repo with `cd OmniParser` - + d. Create a conda python environment with `conda create -n "omni" python==3.12` - + e. Set the python environment to be used with `conda activate omni` - + f. Install the dependencies with `pip install -r requirements.txt` - + g. Continue from here if you already had the conda environment. - + h. Ensure you have the weights downloaded in weights folder. If not download them with: `for folder in icon_caption_florence icon_detect icon_detect_v1_5; do huggingface-cli download microsoft/OmniParser --local-dir weights/ --repo-type model --include "$folder/*"; done` - - h. Navigate to the server directory with `cd OmniParser/computer_use_demo/omniparserserver` - + + h. Navigate to the server directory with `cd OmniParser/omnitool/omniparserserver` + i. Start the server with `python -m omniparserserver` 3. **gradio**: - a. Navigate to the gradio directory with `cd OmniParser/computer_use_demo/gradio` + a. Navigate to the gradio directory with `cd OmniParser/omnitool/gradio` - b. Ensure you have activated the conda python environment with `conda activate omni` + b. Ensure you have activated the conda python environment with `conda activate omni` - c. Start the server with `python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000` + c. Start the server with `python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000` - d. Open the URL in the terminal output, set your API Key from OpenAI and start playing with the AI agent! + d. Open the URL in the terminal output, set your API Key from OpenAI and start playing with the AI agent! 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z?y~3cYUEMFdvbW=z-(3>+O8l;Al3><EID|-lL2mx7;lr<$DI4E1^%wAdeUUH%+I%M zk-yF5Oa!w9NV(_FIB<dzBXf9NUPx-@Y}}s%E)jN{D1J=W(8*&^f$&AcRGq}Tb;9c& zu0!sND}@2Bnpw{KWy@$|Py9L{H&k$z5{Y1@j3zS{zsDnREtFf}hmT?j&i7~gCI;1x zUWwy1$-lvs3GP4UaD&&8N|$6uc)Kzo5zcNhZrpQtw3XXM$s&uO&sz|LRkYWM-swFu g^7Bka^4`^Eq}v@C9%)M8{tslJW2#-R>GtaX0EQ0iJOBUy literal 0 HcmV?d00001 diff --git a/omnitool/readme.md b/omnitool/readme.md index c572fc0..a017a8c 100644 --- a/omnitool/readme.md +++ b/omnitool/readme.md @@ -1,6 +1,4 @@ -<p align="center"> - <img src="../imgs/header_bar.png" alt="OmniParser+X Computer Use Demo screenshot"> -</p> +<img src="../imgs/header_bar.png" alt="OmniTool Header" width="100%"> # OmniTool @@ -10,9 +8,27 @@ Control a Windows 11 VM with OmniParser+X (OpenAI (4o/o1/o3-mini), DeepSeek (R1) There are three components: -1. **omnibox**: A Windows 11 VM running in a Docker container +<table style="border-collapse: collapse; border: none;"> + <tr> + <td style="border: none;"><img src="../imgs/omniboxicon.png" width="50"></td> + <td style="border: none;"><strong>omnibox</strong></td> + <td style="border: none;">A Windows 11 VM running in a Docker container.</td> + </tr> + <tr> + <td style="border: none;"><img src="../imgs/omniparsericon.png" width="50"></td> + <td style="border: none;"><strong>omniparserserver</strong></td> + <td style="border: none;">FastAPI server running OmniParser V2.</td> + </tr> + <tr> + <td style="border: none;"><img src="../imgs/gradioicon.png" width="50"></td> + <td style="border: none;"><strong>gradio</strong></td> + <td style="border: none;">UI to provide commands and watch reasoning + execution on OmniBox</td> + </tr> +</table> + +<!-- 1. **omnibox**: A Windows 11 VM running in a Docker container 2. **omniparserserver**: FastAPI server running OmniParser V2 -3. **gradio**: UI where you can provide commands and watch OmniParser+X reasoning and executing on the Windows 11 VM +3. **gradio**: UI where you can provide commands and watch OmniParser+X reasoning and executing on the Windows 11 VM --> Notes: From 07effd1a6817805f186cac16392864d07d91456f Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <tmd4@princeton.edu> Date: Tue, 4 Feb 2025 11:57:52 -0800 Subject: [PATCH 48/57] readme + gradio updates --- omnitool/gradio/app.py | 2 ++ omnitool/readme.md | 46 +++++++++++++++++++----------------------- 2 files changed, 23 insertions(+), 25 deletions(-) diff --git a/omnitool/gradio/app.py b/omnitool/gradio/app.py index 1b2c459..fdbd57c 100644 --- a/omnitool/gradio/app.py +++ b/omnitool/gradio/app.py @@ -26,6 +26,8 @@ CONFIG_DIR = Path("~/.anthropic").expanduser() API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' +<img src="../../imgs/header_bar.png" alt="OmniTool Header" width="100%"> + Welcome to OmniTool - the OmniParser+X Computer Use Demo! X = [OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use (Sonnet)]. OmniParser lets you turn any vision-langauge model into an AI agent. diff --git a/omnitool/readme.md b/omnitool/readme.md index a017a8c..ecda499 100644 --- a/omnitool/readme.md +++ b/omnitool/readme.md @@ -9,16 +9,16 @@ Control a Windows 11 VM with OmniParser+X (OpenAI (4o/o1/o3-mini), DeepSeek (R1) There are three components: <table style="border-collapse: collapse; border: none;"> - <tr> - <td style="border: none;"><img src="../imgs/omniboxicon.png" width="50"></td> - <td style="border: none;"><strong>omnibox</strong></td> - <td style="border: none;">A Windows 11 VM running in a Docker container.</td> - </tr> <tr> <td style="border: none;"><img src="../imgs/omniparsericon.png" width="50"></td> <td style="border: none;"><strong>omniparserserver</strong></td> <td style="border: none;">FastAPI server running OmniParser V2.</td> </tr> + <tr> + <td style="border: none;"><img src="../imgs/omniboxicon.png" width="50"></td> + <td style="border: none;"><strong>omnibox</strong></td> + <td style="border: none;">A Windows 11 VM running in a Docker container.</td> + </tr> <tr> <td style="border: none;"><img src="../imgs/gradioicon.png" width="50"></td> <td style="border: none;"><strong>gradio</strong></td> @@ -26,31 +26,15 @@ There are three components: </tr> </table> -<!-- 1. **omnibox**: A Windows 11 VM running in a Docker container -2. **omniparserserver**: FastAPI server running OmniParser V2 -3. **gradio**: UI where you can provide commands and watch OmniParser+X reasoning and executing on the Windows 11 VM --> - Notes: -1. The Windows 11 VM docker is dependent on KVM so can only run quickly on Windows and Linux. This can run on a CPU machine (doesn't need GPU). -2. Though OmniParser can run on a CPU, we have separated this out if you want to run it fast on a GPU machine +1. Though OmniParser can run on a CPU, we have separated this out if you want to run it fast on a GPU machine +2. The Windows 11 VM docker is dependent on KVM so can only run quickly on Windows and Linux. This can run on a CPU machine (doesn't need GPU). 3. The Gradio UI can also run on a CPU machine. We suggest running **omnibox** and **gradio** on the same CPU machine and **omniparserserver** on a GPU server. ## Setup -1. **omnibox**: - - a. Install Docker Desktop - - b. Visit [Microsoft Evaluation Center](https://info.microsoft.com/ww-landing-windows-11-enterprise.html), accept the Terms of Service, and download a **Windows 11 Enterprise Evaluation (90-day trial, English, United States)** ISO file [~6GB]. Rename the file to `custom.iso` and copy it to the directory `OmniParser/omnitool/omnibox/vm/win11iso` - - c. Navigate to vm management script directory with`cd OmniParser/omnitool/omnibox/scripts` - - d. Build the docker container [400MB] and install the ISO to a storage folder [20GB] with `./manage_vm.sh create` - - e. After creating the first time it will store a save of the VM state in `vm/win11storage`. You can then manage the VM with `./manage_vm.sh start` and `./manage_vm.sh stop`. To delete the VM, use `./manage_vm.sh delete` and delete the `OmniParser/omnitool/omnibox/vm/win11storage` directory. - -2. **omniparserserver**: +1. **omniparserserver**: a. If you already have a conda environment for OmniParser, you can use that. Else follow the following steps to create one @@ -73,6 +57,18 @@ Notes: i. Start the server with `python -m omniparserserver` +2. **omnibox**: + + a. Install Docker Desktop + + b. Visit [Microsoft Evaluation Center](https://info.microsoft.com/ww-landing-windows-11-enterprise.html), accept the Terms of Service, and download a **Windows 11 Enterprise Evaluation (90-day trial, English, United States)** ISO file [~6GB]. Rename the file to `custom.iso` and copy it to the directory `OmniParser/omnitool/omnibox/vm/win11iso` + + c. Navigate to vm management script directory with`cd OmniParser/omnitool/omnibox/scripts` + + d. Build the docker container [400MB] and install the ISO to a storage folder [20GB] with `./manage_vm.sh create` + + e. After creating the first time it will store a save of the VM state in `vm/win11storage`. You can then manage the VM with `./manage_vm.sh start` and `./manage_vm.sh stop`. To delete the VM, use `./manage_vm.sh delete` and delete the `OmniParser/omnitool/omnibox/vm/win11storage` directory. + 3. **gradio**: a. Navigate to the gradio directory with `cd OmniParser/omnitool/gradio` @@ -81,4 +77,4 @@ Notes: c. Start the server with `python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000` - d. Open the URL in the terminal output, set your API Key from OpenAI and start playing with the AI agent! + d. Open the URL in the terminal output, set your API Key and start playing with the AI agent! From bc4931baba0b83aaba43dc6784af4ddb4ff7d1c2 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <thomasdhca@gmail.com> Date: Tue, 4 Feb 2025 13:45:47 -0800 Subject: [PATCH 49/57] fix header for gradio app --- omnitool/gradio/app.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/omnitool/gradio/app.py b/omnitool/gradio/app.py index fdbd57c..52b810c 100644 --- a/omnitool/gradio/app.py +++ b/omnitool/gradio/app.py @@ -21,13 +21,12 @@ from loop import ( from tools import ToolResult import requests from requests.exceptions import RequestException +import base64 CONFIG_DIR = Path("~/.anthropic").expanduser() API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' -<img src="../../imgs/header_bar.png" alt="OmniTool Header" width="100%"> - Welcome to OmniTool - the OmniParser+X Computer Use Demo! X = [OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use (Sonnet)]. OmniParser lets you turn any vision-langauge model into an AI agent. @@ -258,6 +257,19 @@ def stop_app(state): state["stop"] = True return "App stopped" +def get_header_image_base64(): + try: + # Get the absolute path to the image relative to this script + script_dir = Path(__file__).parent + image_path = script_dir.parent.parent / "imgs" / "header_bar.png" + + with open(image_path, "rb") as image_file: + encoded_string = base64.b64encode(image_file.read()).decode() + return f'data:image/png;base64,{encoded_string}' + except Exception as e: + print(f"Failed to load header image: {e}") + return None + with gr.Blocks(theme=gr.themes.Default()) as demo: gr.HTML(""" <style> @@ -273,7 +285,12 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: setup_state(state.value) - gr.Markdown("# OmniTool") + header_image = get_header_image_base64() + if header_image: + gr.HTML(f'<img src="{header_image}" alt="OmniTool Header" width="100%">') + gr.HTML('<h1 style="text-align: center; font-weight: normal;">Omni<span style="font-weight: bold;">Tool</span></h1>') + else: + gr.Markdown("# OmniTool") if not os.getenv("HIDE_WARNING", False): gr.Markdown(INTRO_TEXT) From 75585a65289c48354827f749a66ff89b8b7d13d7 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <tmd4@princeton.edu> Date: Tue, 4 Feb 2025 17:40:30 -0800 Subject: [PATCH 50/57] make header bar thin for gradio --- imgs/header_bar_thin.png | Bin 0 -> 135659 bytes omnitool/gradio/app.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 imgs/header_bar_thin.png diff --git a/imgs/header_bar_thin.png b/imgs/header_bar_thin.png new file mode 100644 index 0000000000000000000000000000000000000000..e4be8ba8b18710dc52507bab08fb155db1330116 GIT binary patch literal 135659 zcmV)CK*GO?P)<h;3K|Lk000e1NJLTq00jU5005#01^@s69tdEy0004iX+uL$b5ch_ zAW20-HZeIiHZ3wPF#rH4k#&*FOF}^uhrhHidSFnC7A+*KRfZXeZGx1rKqAbb*G8|e zz{llYBcg54Rw#(t^jg$kP}`t&5EMlat!rD<rsQ_63QFV*=bUeTGv_dK1}u?`lGU5t 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z<-Q`n)t{k%DtLpGw(p@eV1Y;AzXmM6p}3V?gx9526*rI;!GX6K{apR)e-4Arp*ucN zQd;7_rXBF<dtiv$<Jb`vp~uT^mJgty<*I(F%J@{rTAcF|9Ee2RZMKw{^1CAP+v|V( pZzA%~MdY9S==&cWxB8ya{{<>8;ube~$|3*&002ovPDHLkV1isQ*HHif literal 0 HcmV?d00001 diff --git a/omnitool/gradio/app.py b/omnitool/gradio/app.py index 52b810c..beb0dd7 100644 --- a/omnitool/gradio/app.py +++ b/omnitool/gradio/app.py @@ -261,7 +261,7 @@ def get_header_image_base64(): try: # Get the absolute path to the image relative to this script script_dir = Path(__file__).parent - image_path = script_dir.parent.parent / "imgs" / "header_bar.png" + image_path = script_dir.parent.parent / "imgs" / "header_bar_thin.png" with open(image_path, "rb") as image_file: encoded_string = base64.b64encode(image_file.read()).decode() From 5d2d58bba89a34a1ac3202b4ca2b88957b0a6ec6 Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <tmd4@princeton.edu> Date: Tue, 4 Feb 2025 17:44:40 -0800 Subject: [PATCH 51/57] Simplify init message --- omnitool/gradio/app.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/omnitool/gradio/app.py b/omnitool/gradio/app.py index beb0dd7..a6846be 100644 --- a/omnitool/gradio/app.py +++ b/omnitool/gradio/app.py @@ -27,9 +27,7 @@ CONFIG_DIR = Path("~/.anthropic").expanduser() API_KEY_FILE = CONFIG_DIR / "api_key" INTRO_TEXT = ''' -Welcome to OmniTool - the OmniParser+X Computer Use Demo! X = [OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use (Sonnet)]. - -OmniParser lets you turn any vision-langauge model into an AI agent. +OmniParser lets you turn any vision-langauge model into an AI agent. We currently support OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use (Sonnet). Type a message and press submit to start OmniTool. Press stop to pause, and press the trash icon in the chat to clear the message history. 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extra small header + padding fix --- omnitool/gradio/app.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/omnitool/gradio/app.py b/omnitool/gradio/app.py index a6846be..1ad28d2 100644 --- a/omnitool/gradio/app.py +++ b/omnitool/gradio/app.py @@ -259,7 +259,7 @@ def get_header_image_base64(): try: # Get the absolute path to the image relative to this script script_dir = Path(__file__).parent - image_path = script_dir.parent.parent / "imgs" / "header_bar_thin.png" + image_path = script_dir.parent.parent / "imgs" / "header_bar_small.png" with open(image_path, "rb") as image_file: encoded_string = base64.b64encode(image_file.read()).decode() @@ -285,7 +285,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo: header_image = get_header_image_base64() if header_image: - gr.HTML(f'<img src="{header_image}" alt="OmniTool Header" width="100%">') + gr.HTML(f'<img src="{header_image}" alt="OmniTool Header" width="100%">', elem_classes="no-padding") gr.HTML('<h1 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--git a/omnitool/readme.md b/omnitool/readme.md index ecda499..3a501aa 100644 --- a/omnitool/readme.md +++ b/omnitool/readme.md @@ -26,10 +26,13 @@ There are three components: </tr> </table> -Notes: +## Highlights: +1. **OmniParser V2** is 60% faster than V1 and now understands a wide variety of OS, app and inside app icons! +2. **OmniBox** uses 50% less disk space than other Windows VMs for agent testing, whilst providing the same computer use API -1. Though OmniParser can run on a CPU, we have separated this out if you want to run it fast on a GPU machine -2. The Windows 11 VM docker is dependent on KVM so can only run quickly on Windows and Linux. This can run on a CPU machine (doesn't need GPU). +## Notes: +1. Though **OmniParser V2** can run on a CPU, we have separated this out if you want to run it fast on a GPU machine +2. The **OmniBox** Windows 11 VM docker is dependent on KVM so can only run quickly on Windows and Linux. This can run on a CPU machine (doesn't need GPU). 3. The Gradio UI can also run on a CPU machine. We suggest running **omnibox** and **gradio** on the same CPU machine and **omniparserserver** on a GPU server. ## Setup From cb92e432b34b04a093b3078de5b8bae253d0f31f Mon Sep 17 00:00:00 2001 From: Thomas Dhome-Casanova <tmd4@princeton.edu> Date: Wed, 5 Feb 2025 11:11:08 -0800 Subject: [PATCH 55/57] Tweaking of information order --- omnitool/readme.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/omnitool/readme.md b/omnitool/readme.md index 3a501aa..2a8b529 100644 --- a/omnitool/readme.md +++ b/omnitool/readme.md @@ -2,7 +2,13 @@ # OmniTool -Control a Windows 11 VM with OmniParser+X (OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL)) or Anthropic Computer Use. +Control a Windows 11 VM with OmniParser + your vision model of choice. + +## Highlights: + +1. **OmniParser V2** is 60% faster than V1 and now understands a wide variety of OS, app and inside app icons! +2. **OmniBox** uses 50% less disk space than other Windows VMs for agent testing, whilst providing the same computer use API +3. **OmniTool** supports out of the box the following vision models - OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use ## Overview @@ -26,11 +32,8 @@ There are three components: </tr> </table> -## Highlights: -1. **OmniParser V2** is 60% faster than V1 and now understands a wide variety of OS, app and inside app icons! -2. **OmniBox** uses 50% less disk space than other Windows VMs for agent testing, whilst providing the same computer use API - ## Notes: + 1. Though **OmniParser V2** can run on a CPU, we have separated this out if you want to run it fast on a GPU machine 2. The **OmniBox** Windows 11 VM docker is dependent on KVM so can only run quickly on Windows and Linux. This can run on a CPU machine (doesn't need GPU). 3. The Gradio UI can also run on a CPU machine. We suggest running **omnibox** and **gradio** on the same CPU machine and **omniparserserver** on a GPU server. From dbb86b2f4c690772bd7a9fd18c4a952607992af1 Mon Sep 17 00:00:00 2001 From: yadonglu <yadonglu@micrsoft.com> Date: Wed, 5 Feb 2025 23:37:19 -0800 Subject: [PATCH 56/57] =?UTF-8?q?app=20logo=20file=20name=20fix=EF=BC=9B?= =?UTF-8?q?=20Acknowledgment;=20risk=20mitigation?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 3 ++- omnitool/gradio/app.py | 2 +- omnitool/readme.md | 5 +++++ 3 files changed, 8 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 1cbd9d3..02eae0d 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,5 @@ __pycache__/ debug.ipynb util/__pycache__/ index.html?linkid=2289031 -wget-log \ No newline at end of file +wget-log +weights/omniv2/ \ No newline at end of file diff --git a/omnitool/gradio/app.py b/omnitool/gradio/app.py index 1ad28d2..44a1580 100644 --- a/omnitool/gradio/app.py +++ b/omnitool/gradio/app.py @@ -259,7 +259,7 @@ def get_header_image_base64(): try: # Get the absolute path to the image relative to this script script_dir = Path(__file__).parent - image_path = script_dir.parent.parent / "imgs" / "header_bar_small.png" + image_path = script_dir.parent.parent / "imgs" / "header_bar_thin.png" with open(image_path, "rb") as image_file: encoded_string = base64.b64encode(image_file.read()).decode() diff --git a/omnitool/readme.md b/omnitool/readme.md index 2a8b529..cc101d7 100644 --- a/omnitool/readme.md +++ b/omnitool/readme.md @@ -84,3 +84,8 @@ There are three components: c. Start the server with `python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000` d. Open the URL in the terminal output, set your API Key and start playing with the AI agent! + + +## Acknowledgment +Kudos to the amazing resources that are indispensable in the development of our code: [Claude Computer Use](https://github.com/anthropics/anthropic-quickstarts/blob/main/computer-use-demo/README.md), [OS World](https://github.com/xlang-ai/OSWorld), [Windows Agent Arena](https://github.com/microsoft/WindowsAgentArena), and [computer_use_ootb](https://github.com/showlab/computer_use_ootb). +We are grateful for helpful suggestions and feedbacks provided by Francesco Bonacci, Jianwei Yang, Dillon DuPont, Yue Wu, Anh Nguyen. From 5ba2a7f144bcd712781da7dd38b320d0fb796636 Mon Sep 17 00:00:00 2001 From: yadonglu <yadonglu@micrsoft.com> Date: Wed, 5 Feb 2025 23:37:56 -0800 Subject: [PATCH 57/57] risk miti --- omnitool/readme.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/omnitool/readme.md b/omnitool/readme.md index cc101d7..b2dfbb8 100644 --- a/omnitool/readme.md +++ b/omnitool/readme.md @@ -86,6 +86,10 @@ There are three components: d. Open the URL in the terminal output, set your API Key and start playing with the AI agent! + +## Risks and Mitigations +To align with the Microsoft AI principles and Responsible AI practices, we conduct risk mitigation by training the icon caption model with Responsible AI data, which helps the model avoid inferring sensitive attributes (e.g.race, religion etc.) of the individuals which happen to be in icon images as much as possible. At the same time, we encourage user to apply OmniParser only for screenshot that does not contain harmful/violent content. For the OmniTool, we conduct threat model analysis using Microsoft Threat Modeling Tool overview - Azure | Microsoft Learn. We provide a sandbox docker container, and provide safety guidance and examples in our GitHub Repository. We advise human to stay in the loop in order to minimize risk. + ## Acknowledgment Kudos to the amazing resources that are indispensable in the development of our code: [Claude Computer Use](https://github.com/anthropics/anthropic-quickstarts/blob/main/computer-use-demo/README.md), [OS World](https://github.com/xlang-ai/OSWorld), [Windows Agent Arena](https://github.com/microsoft/WindowsAgentArena), and [computer_use_ootb](https://github.com/showlab/computer_use_ootb). We are grateful for helpful suggestions and feedbacks provided by Francesco Bonacci, Jianwei Yang, Dillon DuPont, Yue Wu, Anh Nguyen.

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