Initial commit: skills library
- 70 skills with code and documentation - Add .gitignore (ignore __pycache__, output/, temp/, venv/) - Clean up test intermediates and caches
This commit is contained in:
hmo
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#!/usr/bin/env python3
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"""
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Vision Processor - Integrates with image-service vision analyzer for agent-vision-awareness skill
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This script provides the actual implementation that replaces the problematic
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custom agent delegation approach described in the outdated documentation.
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"""
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import sys
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import os
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from pathlib import Path
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from typing import Dict, Any, Optional, List
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from enum import Enum
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# Add the image-service scripts to path to reuse the vision analyzer
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sys.path.append(str(Path(__file__).parent.parent.parent / "image-service" / "scripts"))
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try:
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from vision_analyzer import VisionAnalyzer
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except ImportError:
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# Fallback: try to import from current directory if vision_analyzer is copied here
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try:
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from .vision_analyzer import VisionAnalyzer
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except ImportError:
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raise ImportError(
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"Cannot find VisionAnalyzer. Please ensure image-service is properly installed."
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)
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class AnalysisMode(Enum):
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"""Available analysis modes."""
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DESCRIBE = "describe"
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OCR = "ocr"
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CHART = "chart"
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FASHION = "fashion"
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PRODUCT = "product"
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SCENE = "scene"
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CUSTOM = "custom"
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class VisionProcessor:
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"""Main vision processing class that integrates detection and analysis."""
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def __init__(self, config: Optional[Dict[str, str]] = None):
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"""
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Initialize the vision processor.
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Args:
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config: Configuration dictionary for the VisionAnalyzer
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"""
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self.analyzer = VisionAnalyzer(config)
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self.detector = None # Will be created when needed
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def process_visual_content(
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self, user_input: str, user_request: str = ""
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) -> Dict[str, Any]:
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"""
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Process visual content in user input.
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Args:
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user_input: The user's input text that may contain visual content references
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user_request: The original user request/context
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Returns:
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Dictionary containing analysis results and metadata
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"""
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from .vision_detector import VisionContentDetector, DetectionConfidence
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# Initialize detector if not already done
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if self.detector is None:
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self.detector = VisionContentDetector()
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# Detect visual content
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confidence, detected_items = self.detector.detect_visual_content(user_input)
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result = {
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"confidence": confidence.value,
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"detected_items": detected_items,
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"analysis_results": [],
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"errors": [],
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}
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if confidence == DetectionConfidence.NONE:
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result["message"] = "No visual content detected"
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return result
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# Extract image paths
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image_paths = self.detector.extract_image_paths(user_input)
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if not image_paths:
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result["message"] = "Visual content detected but no valid image paths found"
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result["errors"].append("No valid image paths found")
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return result
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# Determine analysis mode based on user request
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analysis_mode = self._determine_analysis_mode(user_request, user_input)
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# Process each image
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for image_path in image_paths:
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try:
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# Handle URLs by downloading first (simplified - in practice would need download logic)
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if image_path.startswith(("http://", "https://")):
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# In a real implementation, you'd download the URL to a temp file
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# For now, we'll assume local paths only
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result["errors"].append(
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f"URL handling not implemented: {image_path}"
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)
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continue
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# Ensure path is absolute
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if not os.path.isabs(image_path):
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# Try to resolve relative to current working directory
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image_path = os.path.join(os.getcwd(), image_path)
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# Analyze the image
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if analysis_mode == AnalysisMode.CUSTOM:
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# Use the user request as the custom question
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analysis_result = self.analyzer.analyze_with_mode(
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Path(image_path),
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"custom",
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user_request or "Please analyze this image.",
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)
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else:
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analysis_result = self.analyzer.analyze_with_mode(
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Path(image_path), analysis_mode.value
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)
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result["analysis_results"].append(
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{
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"image_path": image_path,
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"analysis_mode": analysis_mode.value,
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"result": analysis_result,
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}
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)
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except Exception as e:
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error_msg = f"Failed to analyze {image_path}: {str(e)}"
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result["errors"].append(error_msg)
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print(f"Error: {error_msg}", file=sys.stderr)
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return result
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def _determine_analysis_mode(
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self, user_request: str, user_input: str
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) -> AnalysisMode:
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"""
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Determine the appropriate analysis mode based on user context.
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Args:
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user_request: The user's original request
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user_input: The full input containing visual content references
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Returns:
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AnalysisMode enum value
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"""
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combined_text = (user_request + " " + user_input).lower()
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# Check for specific keywords to determine mode
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if any(
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word in combined_text for word in ["text", "文字", "ocr", "read", "识别"]
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):
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return AnalysisMode.OCR
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elif any(
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word in combined_text
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for word in ["chart", "graph", "plot", "图表", "数据", "趋势"]
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):
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return AnalysisMode.CHART
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elif any(
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word in combined_text
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for word in ["fashion", "服装", "穿搭", "style", "style"]
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):
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return AnalysisMode.FASHION
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elif any(word in combined_text for word in ["product", "产品", "商品", "item"]):
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return AnalysisMode.PRODUCT
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elif any(
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word in combined_text
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for word in ["scene", "场景", "环境", "location", "place"]
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):
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return AnalysisMode.SCENE
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else:
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return AnalysisMode.DESCRIBE
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def main():
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"""Command line interface for testing."""
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import argparse
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parser = argparse.ArgumentParser(description="Process visual content in user input")
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parser.add_argument("input", help="User input containing visual content references")
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parser.add_argument("--request", "-r", help="Original user request/context")
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parser.add_argument("--output", "-o", help="Output file for results")
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args = parser.parse_args()
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try:
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processor = VisionProcessor()
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result = processor.process_visual_content(args.input, args.request or "")
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import json
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output = json.dumps(result, indent=2, ensure_ascii=False)
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if args.output:
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with open(args.output, "w", encoding="utf-8") as f:
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f.write(output)
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print(f"Results saved to: {args.output}")
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else:
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print(output)
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except Exception as e:
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print(f"Error: {e}", file=sys.stderr)
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sys.exit(1)
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if __name__ == "__main__":
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main()
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