agents/agent_3_visual_comparator.py

"""
Agent 3: Visual Comparator
Compares Figma and Website screenshots, generates diff overlays.
"""

from typing import Dict, Any
import sys
import os

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from utils.image_differ import ImageDiffer


def agent_3_node(state: Dict[str, Any]) -> Dict[str, Any]:
    """
    Compare screenshots and generate visual diff report.
    
    This agent:
    1. Normalizes image sizes (handles Figma 2x)
    2. Calculates similarity scores
    3. Creates side-by-side comparison with diff overlay
    4. Detects specific differences (color, layout, structure)
    """
    print("\n" + "="*60)
    print("🔍 Agent 3: Visual Comparator - Analyzing Differences")
    print("="*60)
    
    figma_screenshots = state.get("figma_screenshots", {})
    website_screenshots = state.get("website_screenshots", {})
    figma_dims = state.get("figma_dimensions", {})
    website_dims = state.get("website_dimensions", {})
    execution_id = state.get("execution_id", "")
    logs = state.get("logs", [])
    
    if not figma_screenshots or not website_screenshots:
        error_msg = "Missing screenshots for comparison"
        print(f"\n  ❌ {error_msg}")
        return {
            "status": "comparison_failed",
            "error_message": error_msg,
            "logs": logs + [f"❌ {error_msg}"]
        }
    
    try:
        # Initialize differ
        differ = ImageDiffer(output_dir="data/comparisons")
        
        # Run comparison
        results = differ.compare_all_viewports(
            figma_screenshots=figma_screenshots,
            website_screenshots=website_screenshots,
            figma_dims=figma_dims,
            website_dims=website_dims,
            execution_id=execution_id
        )
        
        # Build comparison images dict
        comparison_images = {}
        for viewport, comparison in results["comparisons"].items():
            comparison_images[viewport] = comparison["comparison_image"]
        
        # Log results
        print(f"\n" + "="*60)
        print("📊 COMPARISON RESULTS")
        print("="*60)
        print(f"  Overall Similarity: {results['overall_score']:.1f}%")
        
        for viewport, score in results["viewport_scores"].items():
            status_emoji = "✅" if score >= 90 else "⚠️" if score >= 70 else "❌"
            print(f"  {status_emoji} {viewport.capitalize()}: {score:.1f}%")
            logs.append(f"{status_emoji} {viewport} similarity: {score:.1f}%")
        
        if results["all_differences"]:
            print(f"\n  🔍 Found {len(results['all_differences'])} differences:")
            for diff in results["all_differences"]:
                severity_emoji = "🔴" if diff["severity"] == "High" else "🟡" if diff["severity"] == "Medium" else "🟢"
                print(f"     {severity_emoji} [{diff['category']}] {diff['title']}")
                logs.append(f"{severity_emoji} {diff['title']}")
        else:
            print(f"\n  ✅ No significant differences detected!")
            logs.append("✅ No significant differences detected")
        
        # Convert numpy types to Python native types for serialization
        def convert_to_native(obj):
            """Convert numpy types to Python native types."""
            import numpy as np
            if isinstance(obj, np.floating):
                return float(obj)
            elif isinstance(obj, np.integer):
                return int(obj)
            elif isinstance(obj, np.ndarray):
                return obj.tolist()
            elif isinstance(obj, dict):
                return {k: convert_to_native(v) for k, v in obj.items()}
            elif isinstance(obj, list):
                return [convert_to_native(i) for i in obj]
            return obj
        
        # Convert all results to native Python types
        similarity_scores_native = {k: float(v) for k, v in results["viewport_scores"].items()}
        overall_score_native = float(results["overall_score"])
        differences_native = convert_to_native(results["all_differences"])
        
        return {
            "comparison_images": comparison_images,
            "visual_differences": differences_native,
            "similarity_scores": similarity_scores_native,
            "overall_score": overall_score_native,
            "status": "analysis_complete",
            "logs": logs
        }
        
    except Exception as e:
        import traceback
        error_msg = f"Comparison failed: {str(e)}"
        print(f"\n  ❌ {error_msg}")
        traceback.print_exc()
        return {
            "status": "comparison_failed",
            "error_message": error_msg,
            "logs": logs + [f"❌ {error_msg}"]
        }