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Clean up folder structure

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This commit is contained in:
Thomas Dhome Casanova (from Dev Box)
2025-01-29 21:54:35 -08:00
parent 53900f8411
commit 124d9f6fb6
19 changed files with 141 additions and 1952 deletions
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182
demo/gradio/computer_use_demo/agent/anthropic_agent.py Normal file
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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 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"""<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
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}")

107
demo/gradio/computer_use_demo/agent/llm_utils/oai.py Normal file
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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

44
demo/gradio/computer_use_demo/agent/llm_utils/omniparserclient.py Normal file
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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

302
demo/gradio/computer_use_demo/agent/vlm_agent.py Normal file
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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 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
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<img src="data:image/png;base64,{image_base64}">',
sender="bot")
self.output_callback(f'Set of Marks Screenshot for {colorful_text_vlm}:\n<img src="data:image/png;base64,{parsed_screen["som_image_base64"]}">', 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'<details>'
f' <summary>Screen Info for {colorful_text_vlm}</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)
# 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<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):
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