mirror of
https://github.com/microsoft/OmniParser.git
synced 2025-02-18 03:18:33 +03:00
426 lines
16 KiB
Python
426 lines
16 KiB
Python
"""
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python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000
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"""
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import os
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from datetime import datetime
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from enum import StrEnum
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from functools import partial
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from pathlib import Path
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from typing import cast
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import argparse
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import gradio as gr
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from anthropic import APIResponse
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from anthropic.types import TextBlock
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from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock
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from anthropic.types.tool_use_block import ToolUseBlock
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from loop import (
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APIProvider,
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sampling_loop_sync,
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)
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from tools import ToolResult
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import requests
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from requests.exceptions import RequestException
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import base64
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CONFIG_DIR = Path("~/.anthropic").expanduser()
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API_KEY_FILE = CONFIG_DIR / "api_key"
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INTRO_TEXT = '''
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OmniParser lets you turn any vision-langauge model into an AI agent. We currently support **OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use (Sonnet).**
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Type a message and press submit to start OmniTool. Press stop to pause, and press the trash icon in the chat to clear the message history.
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'''
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def parse_arguments():
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parser = argparse.ArgumentParser(description="Gradio App")
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parser.add_argument("--windows_host_url", type=str, default='localhost:8006')
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parser.add_argument("--omniparser_server_url", type=str, default="localhost:8000")
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return parser.parse_args()
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args = parse_arguments()
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class Sender(StrEnum):
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USER = "user"
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BOT = "assistant"
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TOOL = "tool"
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def setup_state(state):
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if "messages" not in state:
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state["messages"] = []
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if "model" not in state:
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state["model"] = "omniparser + gpt-4o"
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if "provider" not in state:
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state["provider"] = "openai"
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if "openai_api_key" not in state: # Fetch API keys from environment variables
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state["openai_api_key"] = os.getenv("OPENAI_API_KEY", "")
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if "anthropic_api_key" not in state:
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state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "")
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if "api_key" not in state:
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state["api_key"] = ""
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if "auth_validated" not in state:
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state["auth_validated"] = False
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if "responses" not in state:
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state["responses"] = {}
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if "tools" not in state:
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state["tools"] = {}
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if "only_n_most_recent_images" not in state:
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state["only_n_most_recent_images"] = 2
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if 'chatbot_messages' not in state:
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state['chatbot_messages'] = []
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if 'stop' not in state:
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state['stop'] = False
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async def main(state):
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"""Render loop for Gradio"""
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setup_state(state)
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return "Setup completed"
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def validate_auth(provider: APIProvider, api_key: str | None):
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if provider == APIProvider.ANTHROPIC:
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if not api_key:
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return "Enter your Anthropic API key to continue."
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if provider == APIProvider.BEDROCK:
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import boto3
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if not boto3.Session().get_credentials():
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return "You must have AWS credentials set up to use the Bedrock API."
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if provider == APIProvider.VERTEX:
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import google.auth
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from google.auth.exceptions import DefaultCredentialsError
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if not os.environ.get("CLOUD_ML_REGION"):
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return "Set the CLOUD_ML_REGION environment variable to use the Vertex API."
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try:
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google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
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except DefaultCredentialsError:
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return "Your google cloud credentials are not set up correctly."
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def load_from_storage(filename: str) -> str | None:
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"""Load data from a file in the storage directory."""
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try:
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file_path = CONFIG_DIR / filename
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if file_path.exists():
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data = file_path.read_text().strip()
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if data:
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return data
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except Exception as e:
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print(f"Debug: Error loading {filename}: {e}")
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return None
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def save_to_storage(filename: str, data: str) -> None:
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"""Save data to a file in the storage directory."""
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try:
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CONFIG_DIR.mkdir(parents=True, exist_ok=True)
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file_path = CONFIG_DIR / filename
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file_path.write_text(data)
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# Ensure only user can read/write the file
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file_path.chmod(0o600)
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except Exception as e:
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print(f"Debug: Error saving {filename}: {e}")
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def _api_response_callback(response: APIResponse[BetaMessage], response_state: dict):
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response_id = datetime.now().isoformat()
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response_state[response_id] = response
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def _tool_output_callback(tool_output: ToolResult, tool_id: str, tool_state: dict):
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tool_state[tool_id] = tool_output
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def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="bot"):
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def _render_message(message: str | BetaTextBlock | BetaToolUseBlock | ToolResult, hide_images=False):
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print(f"_render_message: {str(message)[:100]}")
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if isinstance(message, str):
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return message
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is_tool_result = not isinstance(message, str) and (
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isinstance(message, ToolResult)
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or message.__class__.__name__ == "ToolResult"
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)
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if not message or (
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is_tool_result
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and hide_images
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and not hasattr(message, "error")
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and not hasattr(message, "output")
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): # return None if hide_images is True
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return
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# render tool result
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if is_tool_result:
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message = cast(ToolResult, message)
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if message.output:
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return message.output
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if message.error:
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return f"Error: {message.error}"
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if message.base64_image and not hide_images:
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# somehow can't display via gr.Image
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# image_data = base64.b64decode(message.base64_image)
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# return gr.Image(value=Image.open(io.BytesIO(image_data)))
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return f'<img src="data:image/png;base64,{message.base64_image}">'
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elif isinstance(message, BetaTextBlock) or isinstance(message, TextBlock):
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return f"Analysis: {message.text}"
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elif isinstance(message, BetaToolUseBlock) or isinstance(message, ToolUseBlock):
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# return f"Tool Use: {message.name}\nInput: {message.input}"
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return f"Next I will perform the following action: {message.input}"
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else:
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return message
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def _truncate_string(s, max_length=500):
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"""Truncate long strings for concise printing."""
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if isinstance(s, str) and len(s) > max_length:
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return s[:max_length] + "..."
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return s
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# processing Anthropic messages
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message = _render_message(message, hide_images)
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if sender == "bot":
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chatbot_state.append((None, message))
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else:
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chatbot_state.append((message, None))
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# Create a concise version of the chatbot state for printing
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concise_state = [(_truncate_string(user_msg), _truncate_string(bot_msg))
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for user_msg, bot_msg in chatbot_state]
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# print(f"chatbot_output_callback chatbot_state: {concise_state} (truncated)")
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def valid_params(user_input, state):
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"""Validate all requirements and return a list of error messages."""
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errors = []
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for server_name, url in [('Windows Host', 'localhost:5000'), ('OmniParser Server', args.omniparser_server_url)]:
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try:
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url = f'http://{url}/probe'
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response = requests.get(url, timeout=3)
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if response.status_code != 200:
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errors.append(f"{server_name} is not responding")
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except RequestException as e:
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errors.append(f"{server_name} is not responding")
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if not state["api_key"].strip():
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errors.append("LLM API Key is not set")
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if not user_input:
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errors.append("no computer use request provided")
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return errors
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def process_input(user_input, state):
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# Reset the stop flag
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if state["stop"]:
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state["stop"] = False
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errors = valid_params(user_input, state)
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if errors:
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raise gr.Error("Validation errors: " + ", ".join(errors))
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# Append the user message to state["messages"]
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state["messages"].append(
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{
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"role": Sender.USER,
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"content": [TextBlock(type="text", text=user_input)],
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}
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)
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# Append the user's message to chatbot_messages with None for the assistant's reply
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state['chatbot_messages'].append((user_input, None))
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yield state['chatbot_messages'] # Yield to update the chatbot UI with the user's message
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print("state")
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print(state)
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# Run sampling_loop_sync with the chatbot_output_callback
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for loop_msg in sampling_loop_sync(
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model=state["model"],
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provider=state["provider"],
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messages=state["messages"],
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output_callback=partial(chatbot_output_callback, chatbot_state=state['chatbot_messages'], hide_images=False),
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tool_output_callback=partial(_tool_output_callback, tool_state=state["tools"]),
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api_response_callback=partial(_api_response_callback, response_state=state["responses"]),
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api_key=state["api_key"],
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only_n_most_recent_images=state["only_n_most_recent_images"],
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max_tokens=16384,
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omniparser_url=args.omniparser_server_url
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):
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if loop_msg is None or state.get("stop"):
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yield state['chatbot_messages']
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print("End of task. Close the loop.")
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break
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yield state['chatbot_messages'] # Yield the updated chatbot_messages to update the chatbot UI
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def stop_app(state):
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state["stop"] = True
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return "App stopped"
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def get_header_image_base64():
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try:
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# Get the absolute path to the image relative to this script
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script_dir = Path(__file__).parent
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image_path = script_dir.parent.parent / "imgs" / "header_bar_thin.png"
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with open(image_path, "rb") as image_file:
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encoded_string = base64.b64encode(image_file.read()).decode()
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return f'data:image/png;base64,{encoded_string}'
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except Exception as e:
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print(f"Failed to load header image: {e}")
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return None
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with gr.Blocks(theme=gr.themes.Default()) as demo:
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gr.HTML("""
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<style>
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.no-padding {
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padding: 0 !important;
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}
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.no-padding > div {
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padding: 0 !important;
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}
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.markdown-text p {
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font-size: 18px; /* Adjust the font size as needed */
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}
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</style>
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""")
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state = gr.State({})
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setup_state(state.value)
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header_image = get_header_image_base64()
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if header_image:
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gr.HTML(f'<img src="{header_image}" alt="OmniTool Header" width="100%">', elem_classes="no-padding")
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gr.HTML('<h1 style="text-align: center; font-weight: normal;">Omni<span style="font-weight: bold;">Tool</span></h1>')
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else:
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gr.Markdown("# OmniTool")
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if not os.getenv("HIDE_WARNING", False):
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gr.Markdown(INTRO_TEXT, elem_classes="markdown-text")
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with gr.Accordion("Settings", open=True):
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with gr.Row():
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with gr.Column():
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model = gr.Dropdown(
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label="Model",
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choices=["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl", "claude-3-5-sonnet-20241022"],
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value="omniparser + gpt-4o",
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interactive=True,
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)
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with gr.Column():
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only_n_images = gr.Slider(
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label="N most recent screenshots",
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minimum=0,
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maximum=10,
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step=1,
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value=2,
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interactive=True
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)
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with gr.Row():
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with gr.Column(1):
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provider = gr.Dropdown(
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label="API Provider",
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choices=[option.value for option in APIProvider],
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value="openai",
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interactive=False,
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)
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with gr.Column(2):
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api_key = gr.Textbox(
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label="API Key",
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type="password",
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value=state.value.get("api_key", ""),
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placeholder="Paste your API key here",
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interactive=True,
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)
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with gr.Row():
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with gr.Column(scale=8):
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chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Omniparser + X ...", container=False)
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with gr.Column(scale=1, min_width=50):
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submit_button = gr.Button(value="Send", variant="primary")
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with gr.Column(scale=1, min_width=50):
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stop_button = gr.Button(value="Stop", variant="secondary")
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with gr.Row():
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with gr.Column(scale=1):
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chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580)
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with gr.Column(scale=3):
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iframe = gr.HTML(
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f'<iframe src="http://{args.windows_host_url}/vnc.html?view_only=1&autoconnect=1&resize=scale" width="100%" height="580" allow="fullscreen"></iframe>',
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container=False,
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elem_classes="no-padding"
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)
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def update_model(model_selection, state):
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state["model"] = model_selection
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print(f"Model updated to: {state['model']}")
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if model_selection == "claude-3-5-sonnet-20241022":
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provider_choices = [option.value for option in APIProvider if option.value != "openai"]
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elif model_selection in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini"]):
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provider_choices = ["openai"]
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elif model_selection == "omniparser + R1":
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provider_choices = ["groq"]
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elif model_selection == "omniparser + qwen2.5vl":
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provider_choices = ["dashscope"]
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else:
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provider_choices = [option.value for option in APIProvider]
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default_provider_value = provider_choices[0]
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provider_interactive = len(provider_choices) > 1
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api_key_placeholder = f"{default_provider_value.title()} API Key"
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# Update state
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state["provider"] = default_provider_value
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state["api_key"] = state.get(f"{default_provider_value}_api_key", "")
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# Calls to update other components UI
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provider_update = gr.update(
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choices=provider_choices,
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value=default_provider_value,
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interactive=provider_interactive
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)
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api_key_update = gr.update(
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placeholder=api_key_placeholder,
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value=state["api_key"]
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)
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return provider_update, api_key_update
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def update_only_n_images(only_n_images_value, state):
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state["only_n_most_recent_images"] = only_n_images_value
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def update_provider(provider_value, state):
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# Update state
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state["provider"] = provider_value
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state["api_key"] = state.get(f"{provider_value}_api_key", "")
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# Calls to update other components UI
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api_key_update = gr.update(
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placeholder=f"{provider_value.title()} API Key",
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value=state["api_key"]
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)
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return api_key_update
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def update_api_key(api_key_value, state):
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state["api_key"] = api_key_value
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state[f'{state["provider"]}_api_key'] = api_key_value
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def clear_chat(state):
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# Reset message-related state
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state["messages"] = []
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state["responses"] = {}
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state["tools"] = {}
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state['chatbot_messages'] = []
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return state['chatbot_messages']
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model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key])
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only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None)
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provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key)
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api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None)
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chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot])
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submit_button.click(process_input, [chat_input, state], chatbot)
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stop_button.click(stop_app, [state], None)
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0", server_port=7888) |