Naming conventions

omnitool/gradio/.gitignore

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+tmp/

omnitool/gradio/agent/anthropic_agent.py

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+"""
+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"""<SYSTEM_CAPABILITY>
+* You are utilizing a Windows system with internet access.
+* The current date is {datetime.today().strftime('%A, %B %d, %Y')}.
+</SYSTEM_CAPABILITY>
+"""
+
+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

omnitool/gradio/agent/llm_utils/groqclient.py

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+from groq import Groq
+import os
+from .utils import is_image_path
+
+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.
+    """
+    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

omnitool/gradio/agent/llm_utils/oaiclient.py

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+import os
+import logging
+import base64
+import requests
+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:
+        for item in messages:
+            contents = []
+            if isinstance(item, dict):
+                for cnt in item["content"]:
+                    if isinstance(cnt, str):
+                        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:
+                            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}]
+
+    payload = {
+        "model": model_name,
+        "messages": final_messages,
+    }
+    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
+    )
+
+
+    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 API key. Please check the response: {response.json()} ")
+        return response.json()

omnitool/gradio/agent/llm_utils/omniparserclient.py

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+import requests
+import base64
+from pathlib import Path
+from tools.screen_capture import get_screenshot
+from agent.llm_utils.utils 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

omnitool/gradio/agent/llm_utils/utils.py

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+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")

omnitool/gradio/agent/vlm_agent.py

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+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.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
+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"
+        elif model == "omniparser + R1":
+            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")
+        
+
+        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.step_count = 0
+
+        self.system = ''
+           
+    def __call__(self, messages: list, parsed_screen: list[str, list, dict]):
+        self.step_count += 1
+        image_base64 = parsed_screen['original_screenshot_base64']
+        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']
+
+        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
+        _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):
+                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")
+
+        start = time.time()
+        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,
+                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}")
+            self.total_token_usage += token_usage
+            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,
+                system=system,
+                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
+        self.output_callback(f"LLM: {latency_vlm:.2f}s, OmniParser: {latency_omniparser:.2f}s", sender="bot")
+
+        print(f"{vlm_response}")
+        
+        if self.print_usage:
+            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)
+
+        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)]
+            img_to_show_data = base64.b64decode(img_to_show_base64)
+            img_to_show = Image.open(BytesIO(img_to_show_data))
+
+            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>Parsed Screen elemetns by OmniParser</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}'
+
+        # 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"] == "None":
+            print("Task paused/completed.")
+        elif vlm_response_json["Next Action"] == "type":
+            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.append(sim_content_block)
+        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 = ""):
+        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).
+
+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: 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 (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
+{{
+    "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" # only provide value field if the action is type, else don't include value key
+}}
+```
+
+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.
+
+"""
+        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, don't complete additional actions. You should say "Next Action": "None" in the json field.
+""" 
+
+        return main_section
+
+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(
+    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
+    """
+    if images_to_keep is None:
+        return messages
+
+    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 msg in messages:
+        msg_content = msg["content"]
+        if isinstance(msg_content, list):
+            new_content = []
+            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
+                # 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

omnitool/gradio/app.py

@@ -0,0 +1,405 @@
+"""
+python app.py --windows_host_url localhost:8006 --omniparser_server_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
+import requests
+from requests.exceptions import RequestException
+
+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.
+
+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():
+
+    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")
+    return parser.parse_args()
+args = parse_arguments()
+
+
+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'] = []
+    if 'stop' not in state:
+        state['stop'] = False
+
+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"
+        )
+        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'<img src="data:image/png;base64,{message.base64_image}">'
+
+        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 valid_params(user_input, state):
+    """Validate all requirements and return a list of error messages."""
+    errors = []
+    
+    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)
+            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):
+    # 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))
+    
+    # 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"],
+        max_tokens=16384,
+        omniparser_url=args.omniparser_server_url
+    ):  
+        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>
+        .no-padding {
+            padding: 0 !important;
+        }
+        .no-padding > div {
+            padding: 0 !important;
+        }
+        </style>
+    """)
+    state = gr.State({})
+    
+    setup_state(state.value)
+    
+    gr.Markdown("# OmniTool")
+
+    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 + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl", "claude-3-5-sonnet-20241022"],
+                    value="omniparser + gpt-4o",
+                    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.Column(scale=1, min_width=50):
+            stop_button = gr.Button(value="Stop", variant="secondary")
+
+    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(
+                f'<iframe src="http://{args.windows_host_url}/vnc.html?view_only=1&autoconnect=1&resize=scale" width="100%" height="580" allow="fullscreen"></iframe>',
+                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 in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini"]):
+            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]
+
+        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)
+    stop_button.click(stop_app, [state], None)
+    
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7888)

omnitool/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'<img src="data:image/png;base64,{msg["content"][0]["content"][-1]["source"]["data"]}">'))  # 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"<system>{result.system}</system>\n{result_text}"
+    return result_text

omnitool/gradio/loop.py

@@ -0,0 +1,114 @@
+"""
+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
+        )
+    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")
+    executor = AnthropicExecutor(
+        output_callback=output_callback,
+        tool_output_callback=tool_output_callback,
+    )
+    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"}
+            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')
+            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 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)
+
+            for message, tool_result_content in executor(tools_use_needed, messages):
+                yield message
+        
+            if not tool_result_content:
+                return messages

omnitool/gradio/tools/__init__.py

@@ -0,0 +1,11 @@
+from .base import ToolResult
+from .collection import ToolCollection
+from .computer import ComputerTool
+from .screen_capture import get_screenshot
+
+__ALL__ = [
+    ComputerTool,
+    ToolCollection,
+    ToolResult,
+    get_screenshot,
+]

omnitool/gradio/tools/base.py

@@ -0,0 +1,65 @@
+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 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

omnitool/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)

omnitool/gradio/tools/computer.py

@@ -0,0 +1,326 @@
+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",
+    "hover",
+    "wait"
+]
+
+
+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})")
+                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}")
+        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):
+        """
+        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(base64_image=base64.b64encode(path.read_bytes()).decode())
+
+    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)}")

omnitool/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)}")