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Merge pull request #52 from Gushroom/master

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clean up agent
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Dongle 2025-03-04 09:30:41 +08:00 committed by GitHub
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**/__pycache__**
weights**
.conda**
.conda**
.venv

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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 gradio_ui.agent.llm_utils.oaiclient import run_oai_interleaved
from gradio_ui.agent.llm_utils.groqclient import run_groq_interleaved
from gradio_ui.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,
base_url: Optional(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,
):
self.api_key = api_key
self.model = model
self.base_url = base_url # Currently could be "", we should consider having None
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()
# OAI. What's the difference
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), # Only Qwen has a cap of 2048
provider_base_url=self.base_url,
temperature=0,
)
if "r1" in self.model: # or if base_url = ""
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"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 'qwen' in self.model:
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
elif 'r1' in self.model:
self.total_cost += (token_usage * 0.99 / 1000000)
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:
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)]
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")
except:
print(f"Error parsing: {vlm_response_json}")
pass
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 to view previous content.
- scroll_down: scrolls the screen down, when the desired button is not visible, or you need to see more content.
- 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 one of 'type', 'hover', 'scroll_up', 'scroll_down', 'wait', there should be no Box ID field), 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"
}}
```
Another Example:
```json
{{
"Reasoning": "The current screen does not show 'submit' button, I need to scroll down to see if the button is available.",
"Next Action": "scroll_down",
}}
```
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.
6. The tasks involve buying multiple products or navigating through multiple pages. You should break it into subgoals and complete each subgoal one by one in the order of the instructions.
7. avoid choosing the same action/elements multiple times in a row, if it happens, reflect to yourself, what may have gone wrong, and predict a different action.
8. If you are prompted with login information page or captcha page, or you think it need user's permission to do the next action, 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

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"""
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 gradio_ui.loop import (
APIProvider,
sampling_loop_sync,
)
from gradio_ui.tools import ToolResult
import requests
from requests.exceptions import RequestException
import base64
# Read API key somehow
INTRO_TEXT = '''
基于 Omniparser 的自动化控制桌面工具！
'''
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"
def setup_state(state):
if "messages" not in state:
state["messages"] = []
if "model" not in state:
state["model"] = "gpt-4o"
if "api_key" not in state:
state["api_key"] = ""
if "base_url" not in state:
state["base_url"] = ""
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 _api_response_callback(response: APIResponse[BetaMessage], response_state: dict):
response_id = datetime.now().isoformat()
response_state[response_id] = response
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"],
messages=state["messages"],
base_url=state["base_url"],
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"
def get_header_image_base64():
try:
# Get the absolute path to the image relative to this script
script_dir = Path(__file__).parent
image_path = script_dir.parent / "imgs" / "header_bar_thin.png"
with open(image_path, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read()).decode()
return f'data:image/png;base64,{encoded_string}'
except Exception as e:
print(f"Failed to load header image: {e}")
return None
def run():
with gr.Blocks(theme=gr.themes.Default()) as demo:
gr.HTML("""
<style>
.no-padding {
padding: 0 !important;
}
.no-padding > div {
padding: 0 !important;
}
.markdown-text p {
font-size: 18px; /* Adjust the font size as needed */
}
</style>
""")
state = gr.State({})
setup_state(state.value)
header_image = get_header_image_base64()
if header_image:
gr.HTML(f'<img src="{header_image}" alt="autoMate Header" width="100%">', elem_classes="no-padding")
gr.HTML('<h1 style="text-align: center; font-weight: normal;">Omni<span style="font-weight: bold;">Tool</span></h1>')
else:
gr.Markdown("# autoMate")
if not os.getenv("HIDE_WARNING", False):
gr.Markdown(INTRO_TEXT, elem_classes="markdown-text")
with gr.Accordion("Settings", open=True):
with gr.Row():
with gr.Column():
model = gr.Textbox(
label="Model",
value="gpt-4o",
placeholder="输入模型名称",
interactive=True,
)
with gr.Column():
base_url = gr.Textbox(
label="Base URL",
value="https://api.openai-next.com/v1",
placeholder="输入基础 URL",
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():
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,
type="messages"
)
def update_model(model_selection, state):
state["model"] = model_selection
print(f"Model updated to: {state['model']}")
api_key_update = gr.update(
placeholder="API Key",
value=state["api_key"]
)
return api_key_update
def update_api_key(api_key_value, state):
state["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=[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)
demo.launch(server_name="0.0.0.0", server_port=7888)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7888)

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from collections.abc import Callable
from anthropic import APIResponse
from gradio_ui.tools import ToolResult
from gradio_ui.agent.llm_utils.omniparserclient import OmniParserClient
from gradio_ui.agent.vlm_agent import VLMAgent
def sampling_loop_sync(
*,
model: str,
messages: list[BetaMessageParam],
output_callback: Callable[[BetaContentBlock], None],
tool_output_callback: Callable[[ToolResult, str], None],
api_response_callback: Callable[[APIResponse[BetaMessage]], None],
api_key: str,
base_url: Optional(str),
only_n_most_recent_images: int | None = 2,
max_tokens: int = 4096,
omniparser_url: str
):
print('in sampling_loop_sync, model:', model)
omniparser_client = OmniParserClient(url=f"http://{omniparser_url}/parse/")
actor = VLMAgent(
model=model,
api_key=api_key,
base_url = base_url,
api_response_callback=api_response_callback,
output_callback=output_callback,
max_tokens=max_tokens,
only_n_most_recent_images=only_n_most_recent_images,
)
print(f"Model Inited: {model}")
print(f"Start the message loop. User messages: {messages}")
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