#!/usr/bin/env python3
"""
Script pour reconstruire l'index FAISS à partir des tâches existantes.
"""

import sys
import pickle
import json
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent / "geniusia2"))

from core.embeddings_manager import EmbeddingsManager
from core.logger import Logger
from core.config import get_config


def rebuild_index():
    """Reconstruit l'index FAISS à partir des tâches existantes."""
    print("="*60)
    print("🔨 RECONSTRUCTION DE L'INDEX FAISS")
    print("="*60)
    
    # Initialiser les composants
    config = get_config()
    logger = Logger()
    embeddings_manager = EmbeddingsManager(logger=logger)
    
    profiles_dir = Path("geniusia2/data/user_profiles")
    task_dirs = [d for d in profiles_dir.iterdir() if d.is_dir() and d.name.startswith("task_")]
    
    print(f"\n📁 Nombre de tâches trouvées: {len(task_dirs)}")
    
    total_embeddings = 0
    total_actions = 0
    
    # Parcourir toutes les tâches
    for i, task_dir in enumerate(task_dirs, 1):
        signatures_file = task_dir / "signatures.pkl"
        metadata_file = task_dir / "metadata.json"
        
        if not signatures_file.exists():
            continue
        
        # Charger les métadonnées
        task_name = "Unknown"
        if metadata_file.exists():
            with open(metadata_file, 'r') as f:
                metadata = json.load(f)
                task_name = metadata.get('task_name', 'Unknown')
        
        # Charger les signatures
        with open(signatures_file, 'rb') as f:
            signatures = pickle.load(f)
        
        total_actions += len(signatures)
        
        # Ajouter les embeddings à l'index
        for j, signature in enumerate(signatures):
            embedding = signature.get('embedding')
            
            if embedding is not None:
                # Créer les métadonnées
                meta = {
                    "task_id": task_dir.name,
                    "task_name": task_name,
                    "action_index": j,
                    "action_type": signature.get('action_type', 'unknown'),
                    "description": signature.get('description', ''),
                    "window": signature.get('window', ''),
                    "timestamp": signature.get('timestamp', '')
                }
                
                # Ajouter à l'index
                embeddings_manager.add_to_index(embedding, meta)
                total_embeddings += 1
        
        if i % 10 == 0:
            print(f"   Traité {i}/{len(task_dirs)} tâches...")
    
    print(f"\n✅ Traitement terminé:")
    print(f"   - Tâches traitées: {len(task_dirs)}")
    print(f"   - Actions totales: {total_actions}")
    print(f"   - Embeddings ajoutés: {total_embeddings}")
    
    # Sauvegarder l'index
    print(f"\n💾 Sauvegarde de l'index FAISS...")
    embeddings_manager.save_index()
    
    # Vérifier la sauvegarde
    faiss_dir = Path("geniusia2/data/faiss_index")
    index_file = faiss_dir / "embeddings.index"
    metadata_file = faiss_dir / "metadata.pkl"
    
    if index_file.exists() and metadata_file.exists():
        index_size = index_file.stat().st_size
        meta_size = metadata_file.stat().st_size
        
        print(f"\n✅ Index FAISS créé avec succès!")
        print(f"   - embeddings.index: {index_size:,} bytes")
        print(f"   - metadata.pkl: {meta_size:,} bytes")
        
        # Tester la recherche
        print(f"\n🔍 Test de recherche...")
        if total_embeddings > 0:
            # Prendre le premier embedding pour tester
            for task_dir in task_dirs:
                signatures_file = task_dir / "signatures.pkl"
                if signatures_file.exists():
                    with open(signatures_file, 'rb') as f:
                        signatures = pickle.load(f)
                    for sig in signatures:
                        if sig.get('embedding') is not None:
                            test_embedding = sig['embedding']
                            results = embeddings_manager.search_similar(test_embedding, k=3)
                            
                            print(f"   Résultats de recherche: {len(results)} trouvés")
                            for r in results[:3]:
                                print(f"   - Similarité: {r['similarity']:.3f} | {r['metadata'].get('description', 'N/A')}")
                            break
                    break
    else:
        print(f"\n❌ Erreur: Index non créé")


def main():
    """Fonction principale."""
    print("\n🔧 RECONSTRUCTION DE L'INDEX FAISS\n")
    
    try:
        rebuild_index()
        
        print("\n" + "="*60)
        print("✅ RECONSTRUCTION TERMINÉE")
        print("="*60)
        
        print("\n💡 L'index FAISS est maintenant disponible pour:")
        print("   - Recherche de similarité")
        print("   - Suggestions d'actions")
        print("   - Rejeu intelligent")
    
    except Exception as e:
        print(f"\n❌ Erreur: {e}")
        import traceback
        traceback.print_exc()


if __name__ == "__main__":
    main()