4c05ef0ba8
- Updated logging configuration in `run_agent.py` to suppress debug messages from additional third-party libraries, reducing noise in logs. - Enhanced shell scripts for terminal tasks to utilize Singularity for containerized execution, including pre-build SIF image logic and improved logging. - Refactored tool initialization in `mixture_of_agents_tool.py`, `vision_tools.py`, and `web_tools.py` to implement lazy loading of API clients, optimizing resource usage and error handling. - Updated ephemeral system prompts in shell scripts to provide clearer guidance on task execution and resource usage.
509 lines
17 KiB
Python
509 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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Vision Tools Module
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This module provides vision analysis tools that work with image URLs.
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Uses Gemini 3 Flash Preview via OpenRouter API for intelligent image understanding.
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Available tools:
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- vision_analyze_tool: Analyze images from URLs with custom prompts
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Features:
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- Downloads images from URLs and converts to base64 for API compatibility
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- Comprehensive image description
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- Context-aware analysis based on user queries
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- Automatic temporary file cleanup
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- Proper error handling and validation
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- Debug logging support
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Usage:
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from vision_tools import vision_analyze_tool
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import asyncio
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# Analyze an image
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result = await vision_analyze_tool(
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image_url="https://example.com/image.jpg",
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user_prompt="What architectural style is this building?"
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)
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"""
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import json
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import os
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import asyncio
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import uuid
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import datetime
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import base64
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from pathlib import Path
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from typing import Dict, Any, Optional
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from openai import AsyncOpenAI
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import httpx # Use httpx for async HTTP requests
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# Initialize OpenRouter API client lazily (only when needed)
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_openrouter_client = None
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def _get_openrouter_client():
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"""Get or create the OpenRouter client (lazy initialization)."""
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global _openrouter_client
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if _openrouter_client is None:
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api_key = os.getenv("OPENROUTER_API_KEY")
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if not api_key:
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raise ValueError("OPENROUTER_API_KEY environment variable not set")
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_openrouter_client = AsyncOpenAI(
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api_key=api_key,
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base_url="https://openrouter.ai/api/v1"
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)
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return _openrouter_client
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# Configuration for vision processing
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DEFAULT_VISION_MODEL = "google/gemini-3-flash-preview"
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# Debug mode configuration
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DEBUG_MODE = os.getenv("VISION_TOOLS_DEBUG", "false").lower() == "true"
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DEBUG_SESSION_ID = str(uuid.uuid4())
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DEBUG_LOG_PATH = Path("./logs")
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DEBUG_DATA = {
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"session_id": DEBUG_SESSION_ID,
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"start_time": datetime.datetime.now().isoformat(),
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"debug_enabled": DEBUG_MODE,
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"tool_calls": []
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} if DEBUG_MODE else None
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# Create logs directory if debug mode is enabled
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if DEBUG_MODE:
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DEBUG_LOG_PATH.mkdir(exist_ok=True)
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print(f"🐛 Vision debug mode enabled - Session ID: {DEBUG_SESSION_ID}")
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def _log_debug_call(tool_name: str, call_data: Dict[str, Any]) -> None:
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"""
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Log a debug call entry to the global debug data structure.
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Args:
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tool_name (str): Name of the tool being called
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call_data (Dict[str, Any]): Data about the call including parameters and results
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"""
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if not DEBUG_MODE or not DEBUG_DATA:
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return
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call_entry = {
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"timestamp": datetime.datetime.now().isoformat(),
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"tool_name": tool_name,
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**call_data
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}
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DEBUG_DATA["tool_calls"].append(call_entry)
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def _save_debug_log() -> None:
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"""
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Save the current debug data to a JSON file in the logs directory.
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"""
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if not DEBUG_MODE or not DEBUG_DATA:
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return
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try:
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debug_filename = f"vision_tools_debug_{DEBUG_SESSION_ID}.json"
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debug_filepath = DEBUG_LOG_PATH / debug_filename
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# Update end time
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DEBUG_DATA["end_time"] = datetime.datetime.now().isoformat()
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DEBUG_DATA["total_calls"] = len(DEBUG_DATA["tool_calls"])
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with open(debug_filepath, 'w', encoding='utf-8') as f:
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json.dump(DEBUG_DATA, f, indent=2, ensure_ascii=False)
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print(f"🐛 Vision debug log saved: {debug_filepath}")
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except Exception as e:
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print(f"❌ Error saving vision debug log: {str(e)}")
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def _validate_image_url(url: str) -> bool:
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"""
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Basic validation of image URL format.
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Args:
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url (str): The URL to validate
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Returns:
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bool: True if URL appears to be valid, False otherwise
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"""
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if not url or not isinstance(url, str):
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return False
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# Check if it's a valid URL format
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if not (url.startswith('http://') or url.startswith('https://')):
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return False
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# Check for common image extensions (optional, as URLs may not have extensions)
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image_extensions = ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp', '.svg']
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return True # Allow all HTTP/HTTPS URLs for flexibility
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async def _download_image(image_url: str, destination: Path, max_retries: int = 3) -> Path:
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"""
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Download an image from a URL to a local destination (async) with retry logic.
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Args:
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image_url (str): The URL of the image to download
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destination (Path): The path where the image should be saved
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max_retries (int): Maximum number of retry attempts (default: 3)
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Returns:
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Path: The path to the downloaded image
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Raises:
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Exception: If download fails after all retries
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"""
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import asyncio
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# Create parent directories if they don't exist
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destination.parent.mkdir(parents=True, exist_ok=True)
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last_error = None
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for attempt in range(max_retries):
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try:
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# Download the image with appropriate headers using async httpx
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# Enable follow_redirects to handle image CDNs that redirect (e.g., Imgur, Picsum)
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async with httpx.AsyncClient(timeout=30.0, follow_redirects=True) as client:
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response = await client.get(
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image_url,
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headers={
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
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"Accept": "image/*,*/*;q=0.8",
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},
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)
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response.raise_for_status()
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# Save the image content
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destination.write_bytes(response.content)
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return destination
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except Exception as e:
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last_error = e
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if attempt < max_retries - 1:
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wait_time = 2 ** (attempt + 1) # 2s, 4s, 8s
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print(f"⚠️ Image download failed (attempt {attempt + 1}/{max_retries}): {str(e)[:50]}")
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print(f" Retrying in {wait_time}s...")
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await asyncio.sleep(wait_time)
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else:
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print(f"❌ Image download failed after {max_retries} attempts: {str(e)[:100]}")
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raise last_error
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def _determine_mime_type(image_path: Path) -> str:
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"""
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Determine the MIME type of an image based on its file extension.
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Args:
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image_path (Path): Path to the image file
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Returns:
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str: The MIME type (defaults to image/jpeg if unknown)
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"""
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extension = image_path.suffix.lower()
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mime_types = {
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'.jpg': 'image/jpeg',
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'.jpeg': 'image/jpeg',
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'.png': 'image/png',
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'.gif': 'image/gif',
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'.bmp': 'image/bmp',
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'.webp': 'image/webp',
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'.svg': 'image/svg+xml'
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}
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return mime_types.get(extension, 'image/jpeg')
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def _image_to_base64_data_url(image_path: Path, mime_type: Optional[str] = None) -> str:
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"""
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Convert an image file to a base64-encoded data URL.
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Args:
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image_path (Path): Path to the image file
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mime_type (Optional[str]): MIME type of the image (auto-detected if None)
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Returns:
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str: Base64-encoded data URL (e.g., "data:image/jpeg;base64,...")
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"""
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# Read the image as bytes
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data = image_path.read_bytes()
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# Encode to base64
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encoded = base64.b64encode(data).decode("ascii")
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# Determine MIME type
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mime = mime_type or _determine_mime_type(image_path)
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# Create data URL
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data_url = f"data:{mime};base64,{encoded}"
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return data_url
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async def vision_analyze_tool(
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image_url: str,
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user_prompt: str,
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model: str = DEFAULT_VISION_MODEL
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) -> str:
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"""
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Analyze an image from a URL using vision AI.
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This tool downloads images from URLs, converts them to base64, and processes
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them using Gemini 3 Flash Preview via OpenRouter API. The image is downloaded to a
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temporary location and automatically cleaned up after processing.
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The user_prompt parameter is expected to be pre-formatted by the calling
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function (typically model_tools.py) to include both full description
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requests and specific questions.
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Args:
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image_url (str): The URL of the image to analyze (must be http:// or https://)
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user_prompt (str): The pre-formatted prompt for the vision model
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model (str): The vision model to use (default: google/gemini-3-flash-preview)
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Returns:
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str: JSON string containing the analysis results with the following structure:
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{
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"success": bool,
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"analysis": str (defaults to error message if None)
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}
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Raises:
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Exception: If download fails, analysis fails, or API key is not set
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Note:
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- Temporary images are stored in ./temp_vision_images/
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- Images are automatically deleted after processing
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- Supports common image formats (JPEG, PNG, GIF, WebP, etc.)
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"""
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debug_call_data = {
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"parameters": {
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"image_url": image_url,
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"user_prompt": user_prompt[:200] + "..." if len(user_prompt) > 200 else user_prompt,
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"model": model
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},
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"error": None,
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"success": False,
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"analysis_length": 0,
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"model_used": model,
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"image_size_bytes": 0
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}
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temp_image_path = None
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try:
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print(f"🔍 Analyzing image from URL: {image_url[:60]}{'...' if len(image_url) > 60 else ''}", flush=True)
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print(f"📝 User prompt: {user_prompt[:100]}{'...' if len(user_prompt) > 100 else ''}", flush=True)
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# Validate image URL
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if not _validate_image_url(image_url):
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raise ValueError("Invalid image URL format. Must start with http:// or https://")
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# Check API key availability
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if not os.getenv("OPENROUTER_API_KEY"):
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raise ValueError("OPENROUTER_API_KEY environment variable not set")
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# Download the image to a temporary location
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print(f"⬇️ Downloading image from URL...", flush=True)
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temp_dir = Path("./temp_vision_images")
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temp_image_path = temp_dir / f"temp_image_{uuid.uuid4()}.jpg"
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await _download_image(image_url, temp_image_path)
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# Get image file size for logging
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image_size_bytes = temp_image_path.stat().st_size
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image_size_kb = image_size_bytes / 1024
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print(f"✅ Image downloaded successfully ({image_size_kb:.1f} KB)", flush=True)
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# Convert image to base64 data URL
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print(f"🔄 Converting image to base64...", flush=True)
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image_data_url = _image_to_base64_data_url(temp_image_path)
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# Calculate size in KB for better readability
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data_size_kb = len(image_data_url) / 1024
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print(f"✅ Image converted to base64 ({data_size_kb:.1f} KB)", flush=True)
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debug_call_data["image_size_bytes"] = image_size_bytes
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# Use the prompt as provided (model_tools.py now handles full description formatting)
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comprehensive_prompt = user_prompt
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# Prepare the message with base64-encoded image
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": comprehensive_prompt
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},
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{
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"type": "image_url",
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"image_url": {
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"url": image_data_url
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}
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}
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]
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}
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]
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print(f"🧠 Processing image with {model}...", flush=True)
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# Call the vision API with reasoning enabled
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response = await _get_openrouter_client().chat.completions.create(
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model=model,
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messages=messages,
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temperature=0.1, # Low temperature for consistent analysis
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max_tokens=2000, # Generous limit for detailed analysis
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extra_body={
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"reasoning": {
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"enabled": True,
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"effort": "xhigh"
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}
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}
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)
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# Extract the analysis
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analysis = response.choices[0].message.content.strip()
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analysis_length = len(analysis)
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print(f"✅ Image analysis completed ({analysis_length} characters)", flush=True)
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# Prepare successful response
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result = {
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"success": True,
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"analysis": analysis or "There was a problem with the request and the image could not be analyzed."
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}
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debug_call_data["success"] = True
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debug_call_data["analysis_length"] = analysis_length
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# Log debug information
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_log_debug_call("vision_analyze_tool", debug_call_data)
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_save_debug_log()
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return json.dumps(result, indent=2, ensure_ascii=False)
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except Exception as e:
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error_msg = f"Error analyzing image: {str(e)}"
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print(f"❌ {error_msg}", flush=True)
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# Prepare error response
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result = {
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"success": False,
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"analysis": "There was a problem with the request and the image could not be analyzed."
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}
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debug_call_data["error"] = error_msg
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_log_debug_call("vision_analyze_tool", debug_call_data)
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_save_debug_log()
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return json.dumps(result, indent=2, ensure_ascii=False)
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finally:
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# Clean up temporary image file
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if temp_image_path and temp_image_path.exists():
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try:
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temp_image_path.unlink()
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print(f"🧹 Cleaned up temporary image file", flush=True)
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except Exception as cleanup_error:
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print(f"⚠️ Warning: Could not delete temporary file: {cleanup_error}", flush=True)
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def check_openrouter_api_key() -> bool:
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"""
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Check if the OpenRouter API key is available in environment variables.
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Returns:
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bool: True if API key is set, False otherwise
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"""
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return bool(os.getenv("OPENROUTER_API_KEY"))
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def check_vision_requirements() -> bool:
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"""
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Check if all requirements for vision tools are met.
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Returns:
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bool: True if requirements are met, False otherwise
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"""
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return check_openrouter_api_key()
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def get_debug_session_info() -> Dict[str, Any]:
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"""
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Get information about the current debug session.
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Returns:
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Dict[str, Any]: Dictionary containing debug session information
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"""
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if not DEBUG_MODE or not DEBUG_DATA:
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return {
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"enabled": False,
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"session_id": None,
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"log_path": None,
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"total_calls": 0
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}
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return {
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"enabled": True,
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"session_id": DEBUG_SESSION_ID,
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"log_path": str(DEBUG_LOG_PATH / f"vision_tools_debug_{DEBUG_SESSION_ID}.json"),
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"total_calls": len(DEBUG_DATA["tool_calls"])
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}
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if __name__ == "__main__":
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"""
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Simple test/demo when run directly
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"""
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print("👁️ Vision Tools Module")
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print("=" * 40)
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# Check if API key is available
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api_available = check_openrouter_api_key()
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if not api_available:
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print("❌ OPENROUTER_API_KEY environment variable not set")
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print("Please set your API key: export OPENROUTER_API_KEY='your-key-here'")
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print("Get API key at: https://openrouter.ai/")
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exit(1)
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else:
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print("✅ OpenRouter API key found")
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print("🛠️ Vision tools ready for use!")
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print(f"🧠 Using model: {DEFAULT_VISION_MODEL}")
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# Show debug mode status
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if DEBUG_MODE:
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print(f"🐛 Debug mode ENABLED - Session ID: {DEBUG_SESSION_ID}")
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print(f" Debug logs will be saved to: ./logs/vision_tools_debug_{DEBUG_SESSION_ID}.json")
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else:
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print("🐛 Debug mode disabled (set VISION_TOOLS_DEBUG=true to enable)")
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print("\nBasic usage:")
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print(" from vision_tools import vision_analyze_tool")
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print(" import asyncio")
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print("")
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print(" async def main():")
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print(" result = await vision_analyze_tool(")
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print(" image_url='https://example.com/image.jpg',")
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print(" user_prompt='What do you see in this image?'")
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print(" )")
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print(" print(result)")
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print(" asyncio.run(main())")
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print("\nExample prompts:")
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print(" - 'What architectural style is this building?'")
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print(" - 'Describe the emotions and mood in this image'")
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print(" - 'What text can you read in this image?'")
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print(" - 'Identify any safety hazards visible'")
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print(" - 'What products or brands are shown?'")
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print("\nDebug mode:")
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print(" # Enable debug logging")
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print(" export VISION_TOOLS_DEBUG=true")
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print(" # Debug logs capture all vision analysis calls and results")
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print(" # Logs saved to: ./logs/vision_tools_debug_UUID.json")
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