Delete app_old.py

app_old.py

@@ -1,1573 +0,0 @@
-#!/usr/bin/env python3
-"""
-Web Demo v2 pour la base de données d'œuvres d'art - Version Sécurisée et Optimisée
-Interface multi-étapes avec matching basé sur prénom, date, ville et émotions
-Optimisé pour les performances avec caching et indexation
-Version sécurisée avec validation des entrées et gestion d'état propre
-"""
-
-import gradio as gr
-import os
-import sys
-import logging
-from logging.handlers import RotatingFileHandler
-import random
-import re
-import json
-import uuid
-import time
-from datetime import datetime
-from typing import List, Dict, Tuple, Optional, Any, Set
-from collections import Counter, defaultdict
-from functools import lru_cache
-from dataclasses import dataclass, field, asdict
-from pathlib import Path
-import pandas as pd
-
-# Configuration du logging principal
-logging.basicConfig(
-    level=logging.INFO,
-    format="[%(asctime)s] %(levelname)s: %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-logger = logging.getLogger(__name__)
-
-# Import pour la sauvegarde persistante sur HF Spaces
-try:
-    from huggingface_hub import CommitScheduler
-    HF_HUB_AVAILABLE = True
-except ImportError:
-    HF_HUB_AVAILABLE = False
-    logger.warning("huggingface_hub non installé - Les logs ne seront pas sauvegardés dans un dataset HF")
-
-# Configuration du logging des sessions
-SESSION_LOG_FILE = "session_logs.jsonl"
-STATS_LOG_FILE = "statistics.json"
-
-# Configuration du dataset HF pour la persistance (modifiez ces valeurs)
-HF_DATASET_ID = os.environ.get("HF_DATASET_ID", "ClickMons/art-matcher-logs")  # Remplacez par votre dataset
-HF_TOKEN = os.environ.get("HF_TOKEN", None)  # Token HF pour l'authentification
-LOGS_UPLOAD_INTERVAL = 1  # Upload toutes les minutes
-
-# Créer un handler pour le fichier de logs des sessions (local)
-if not os.path.exists("logs"):
-    os.makedirs("logs")
-
-session_file_handler = RotatingFileHandler(
-    filename=os.path.join("logs", SESSION_LOG_FILE),
-    maxBytes=10*1024*1024,  # 10MB
-    backupCount=5,
-    encoding='utf-8'
-)
-session_file_handler.setLevel(logging.INFO)
-session_logger = logging.getLogger('session_logger')
-session_logger.addHandler(session_file_handler)
-session_logger.setLevel(logging.INFO)
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))
-
-from art_pieces_db.database import Database
-from art_pieces_db.query import TargetProfile, WeightedLeximaxOptimizer, Optimizer
-from art_pieces_db.emotions import EmotionWheel
-from art_pieces_db.utils import str_to_date
-
-
-@dataclass
-class ScoringWeights:
-    """Centralise toutes les constantes de scoring pour éviter les magic numbers"""
-
-    PRESELECTION_NAME_WEIGHT: float = 3.0
-    PRESELECTION_DATE_WEIGHT: float = 1.0
-    PRESELECTION_PLACE_WEIGHT: float = 2.0
-    PRESELECTION_EMOTION_WEIGHT: float = 0.0
-
-    MIN_PRESELECTION_COUNT: int = 20
-    MAX_IMAGES_PER_SELECTION: int = 3  # nombre d'images par sélection
-    TOTAL_ROUNDS: int = 3  # nombre de rounds avant la recommandation finale
-
-
-@dataclass
-class SessionState:
-    """Gère l'état de session"""
-
-    firstname: str = ""
-    birthday: str = ""
-    city: str = ""
-
-    current_round: int = 0
-    selected_images: List[str] = field(default_factory=list)
-    current_image_ids: List[str] = field(default_factory=list)
-
-    preselected_pieces: Optional[pd.DataFrame] = None
-    
-    # Nouvelles propriétés pour le tracking
-    session_id: str = field(default_factory=lambda: str(uuid.uuid4()))
-    session_start_time: float = field(default_factory=time.time)
-    recommendation_type: str = ""  # "name_date_place" ou "emotions"
-    final_artwork: str = ""
-
-    def reset(self):
-        """Réinitialise l'état de session"""
-        self.firstname = ""
-        self.birthday = ""
-        self.city = ""
-        self.current_round = 0
-        self.selected_images = []
-        self.current_image_ids = []
-        self.preselected_pieces = None
-        self.session_id = str(uuid.uuid4())
-        self.session_start_time = time.time()
-        self.recommendation_type = ""
-        self.final_artwork = ""
-
-    def is_complete(self) -> bool:
-        """Vérifie si la sélection est complète"""
-        return self.current_round >= ScoringWeights.TOTAL_ROUNDS
-
-
-class SessionLogger:
-    """Gère le logging des sessions et les statistiques avec persistance HF"""
-    
-    def __init__(self):
-        # Détection de l'environnement
-        is_hf_space = os.environ.get('SPACE_ID') or os.environ.get('SPACE_HOST')
-        
-        # Configuration du répertoire de logs
-        if is_hf_space and HF_HUB_AVAILABLE and HF_TOKEN:
-            # Sur HF Spaces avec huggingface_hub installé
-            self.logs_dir = Path("hf_logs_data")
-            self.logs_dir.mkdir(exist_ok=True)
-            
-            # Initialiser le CommitScheduler pour la sauvegarde automatique
-            try:
-                self.scheduler = CommitScheduler(
-                    repo_id=HF_DATASET_ID,
-                    repo_type="dataset",
-                    folder_path=self.logs_dir,
-                    path_in_repo="logs",
-                    every=LOGS_UPLOAD_INTERVAL,
-                    token=HF_TOKEN
-                )
-                logger.info(f"CommitScheduler initialisé - Sauvegarde dans {HF_DATASET_ID} toutes les {LOGS_UPLOAD_INTERVAL} minutes")
-                self.use_hf_dataset = True
-            except Exception as e:
-                logger.error(f"Impossible d'initialiser CommitScheduler: {e}")
-                self.scheduler = None
-                self.use_hf_dataset = False
-        else:
-            # Développement local ou pas de configuration HF
-            self.logs_dir = Path("logs")
-            self.logs_dir.mkdir(exist_ok=True)
-            self.scheduler = None
-            self.use_hf_dataset = False
-            
-            if is_hf_space:
-                logger.warning("Sur HF Spaces mais CommitScheduler non configuré - Les logs seront éphémères")
-                logger.info("Pour activer la persistance, configurez HF_DATASET_ID et HF_TOKEN dans les secrets du Space")
-        
-        # Chemins des fichiers de logs
-        self.session_log_path = self.logs_dir / SESSION_LOG_FILE
-        self.stats_log_path = self.logs_dir / STATS_LOG_FILE
-        
-        # Créer des fichiers uniques pour chaque instance si on utilise HF Dataset
-        if self.use_hf_dataset:
-            # Utiliser des fichiers datés pour éviter les conflits
-            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-            self.session_log_path = self.logs_dir / f"sessions_{timestamp}.jsonl"
-            self.stats_log_path = self.logs_dir / "statistics_latest.json"
-    
-    def log_session(self, state: SessionState, recommendation_system: str):
-        """Enregistre les données d'une session terminée"""
-        session_duration = time.time() - state.session_start_time
-        
-        session_data = {
-            "session_id": state.session_id,
-            "timestamp": datetime.now().isoformat(),
-            "duration_seconds": round(session_duration, 2),
-            "recommended_artwork": state.final_artwork,
-            "recommendation_type": recommendation_system
-        }
-        
-        # Écrire dans le fichier de logs des sessions
-        try:
-            # Si on utilise CommitScheduler, utiliser le lock pour la thread safety
-            if self.scheduler:
-                with self.scheduler.lock:
-                    with open(self.session_log_path, 'a', encoding='utf-8') as f:
-                        f.write(json.dumps(session_data, ensure_ascii=False) + '\n')
-            else:
-                # Sauvegarde locale simple
-                with open(self.session_log_path, 'a', encoding='utf-8') as f:
-                    f.write(json.dumps(session_data, ensure_ascii=False) + '\n')
-            
-            logger.info(f"Session {state.session_id} logged successfully")
-            session_logger.info(json.dumps(session_data, ensure_ascii=False))
-            
-            # Mettre à jour les statistiques globales
-            self.update_statistics(session_data)
-            
-            if self.use_hf_dataset:
-                logger.info(f"Session sauvegardée - Upload automatique vers {HF_DATASET_ID} dans max {LOGS_UPLOAD_INTERVAL} minutes")
-            
-        except Exception as e:
-            logger.error(f"Error logging session: {e}")
-    
-    def update_statistics(self, session_data: dict):
-        """Met à jour les statistiques globales"""
-        try:
-            # Charger les statistiques existantes
-            if os.path.exists(self.stats_log_path):
-                with open(self.stats_log_path, 'r', encoding='utf-8') as f:
-                    stats = json.load(f)
-            else:
-                stats = {
-                    "total_sessions": 0,
-                    "total_duration_seconds": 0,
-                    "average_duration_seconds": 0,
-                    "recommendation_systems_usage": {
-                        "name_date_place": 0,
-                        "emotions": 0
-                    },
-                    "artworks_recommended": {},
-                    "last_updated": None
-                }
-            
-            # Mettre à jour les statistiques
-            stats["total_sessions"] += 1
-            stats["total_duration_seconds"] += session_data["duration_seconds"]
-            stats["average_duration_seconds"] = stats["total_duration_seconds"] / stats["total_sessions"]
-            
-            # Compter l'utilisation des systèmes de recommandation
-            rec_type = session_data["recommendation_type"]
-            if rec_type in stats["recommendation_systems_usage"]:
-                stats["recommendation_systems_usage"][rec_type] += 1
-            
-            # Compter les œuvres recommandées
-            artwork = session_data["recommended_artwork"]
-            if artwork:
-                if artwork not in stats["artworks_recommended"]:
-                    stats["artworks_recommended"][artwork] = 0
-                stats["artworks_recommended"][artwork] += 1
-            
-            # Trouver l'œuvre la plus recommandée
-            if stats["artworks_recommended"]:
-                most_recommended = max(stats["artworks_recommended"].items(), key=lambda x: x[1])
-                stats["most_recommended_artwork"] = {
-                    "title": most_recommended[0],
-                    "count": most_recommended[1]
-                }
-            
-            # Calculer l'utilité de chaque système (pourcentage d'utilisation)
-            total_recs = sum(stats["recommendation_systems_usage"].values())
-            if total_recs > 0:
-                stats["system_utility_percentage"] = {
-                    system: (count / total_recs * 100) 
-                    for system, count in stats["recommendation_systems_usage"].items()
-                }
-            
-            stats["last_updated"] = datetime.now().isoformat()
-            
-            # Sauvegarder les statistiques mises à jour
-            if self.scheduler:
-                with self.scheduler.lock:
-                    with open(self.stats_log_path, 'w', encoding='utf-8') as f:
-                        json.dump(stats, f, indent=2, ensure_ascii=False)
-            else:
-                with open(self.stats_log_path, 'w', encoding='utf-8') as f:
-                    json.dump(stats, f, indent=2, ensure_ascii=False)
-            
-            logger.info("Global statistics updated")
-            
-        except Exception as e:
-            logger.error(f"Error updating statistics: {e}")
-    
-    def get_statistics(self) -> dict:
-        """Retourne les statistiques globales"""
-        try:
-            if os.path.exists(self.stats_log_path):
-                with open(self.stats_log_path, 'r', encoding='utf-8') as f:
-                    return json.load(f)
-            return {}
-        except Exception as e:
-            logger.error(f"Error reading statistics: {e}")
-            return {}
-
-
-# Initialiser le logger de sessions
-session_tracker = SessionLogger()
-
-
-class SecurityValidator:
-    """Classe pour centraliser les validations de sécurité"""
-
-    PATH_TRAVERSAL_PATTERN = re.compile(r"\.\.|\.\/")
-    VALID_FILENAME_PATTERN = re.compile(r"^[\w\-\.\s]+$")
-    VALID_INPUT_PATTERN = re.compile(
-        r"^[\w\-\s\'\.,àâäéèêëïîôûùüÿæœçÀÂÄÉÈÊËÏÎÔÛÙÜŸÆŒÇ]+$", re.UNICODE
-    )
-    DATE_PATTERN = re.compile(r"^\d{1,2}/\d{1,2}$")
-
-    @classmethod
-    def validate_filename(cls, filename: str) -> bool:
-        """Valide qu'un nom de fichier est sécurisé"""
-        if not filename:
-            return False
-
-        # Vérifier les tentatives de path traversal
-        if cls.PATH_TRAVERSAL_PATTERN.search(filename):
-            logger.warning(f"Tentative de path traversal détectée: {filename}")
-            return False
-
-        # Vérifier que le nom ne contient que des caractères autorisés
-        base_name = os.path.basename(filename)
-        if not cls.VALID_FILENAME_PATTERN.match(base_name):
-            logger.warning(f"Nom de fichier invalide: {filename}")
-            return False
-
-        return True
-
-    @classmethod
-    def sanitize_input(cls, input_str: str, max_length: int = 100) -> str:
-        """Nettoie et valide une entrée utilisateur"""
-        if not input_str:
-            return ""
-
-        # Tronquer si trop long
-        input_str = input_str[:max_length].strip()
-
-        if not cls.VALID_INPUT_PATTERN.match(input_str):
-            # Garder seulement les caractères valides
-            cleaned = "".join(c for c in input_str if cls.VALID_INPUT_PATTERN.match(c))
-            logger.info(f"Input sanitized: '{input_str}' -> '{cleaned}'")
-            return cleaned
-
-        return input_str
-
-    @classmethod
-    def validate_date(cls, date_str: str) -> Tuple[bool, Optional[datetime]]:
-        """Valide et parse une date au format JJ/MM"""
-        if not date_str:
-            return False, None
-
-        if not cls.DATE_PATTERN.match(date_str):
-            return False, None
-
-        try:
-            day, month = map(int, date_str.split("/"))
-            if not (1 <= day <= 31 and 1 <= month <= 12):
-                return False, None
-
-            date_obj = datetime(year=2000, month=month, day=day)
-            return True, date_obj
-        except (ValueError, Exception) as e:
-            logger.error(f"Erreur de parsing de date: {e}")
-            return False, None
-
-
-class ImageIndexer:
-    """Classe pour indexer et mapper les images depuis la base de données CSV"""
-
-    # Constants for better maintainability
-    IMAGE_EXTENSIONS = (".jpg", ".png")
-    COMMON_SUFFIXES = [".jpg", ".png", "_medium"]
-    MAR_BVM_TEST_SUFFIXES = ["-001", "-002", "-003"]
-
-    def __init__(self, images_dir: str):
-        self.images_dir = os.path.abspath(images_dir)
-        self.available_files = set()
-        self.image_lookup = {}  # normalized_name -> filename
-        self.mar_bvm_lookup = {}  # Special handling for MAR-BVM files
-        self._build_index()
-
-    def _strip_file_extensions(self, filename: str) -> str:
-        """Remove file extensions from filename"""
-        base_name = filename.lower()
-        if base_name.endswith("_medium.jpg"):
-            return base_name[:-11]
-        elif base_name.endswith((".jpg", ".png")):
-            return base_name[:-4]
-        return base_name
-
-    def _normalize_basic_patterns(self, name: str) -> str:
-        """Apply basic normalization patterns"""
-        # Remove trailing comma and normalize whitespace
-        normalized = name.lower().strip().rstrip(",")
-
-        # Remove common suffixes
-        for suffix in self.COMMON_SUFFIXES:
-            if normalized.endswith(suffix):
-                normalized = normalized[: -len(suffix)]
-
-        # Normalize spaces and underscores to dashes
-        return re.sub(r"[\s_]+", "-", normalized)
-
-    def _normalize_mar_bvm_format(self, name: str) -> str:
-        """Handle MAR-BVM specific normalization"""
-        if "mar-bvm" not in name:
-            return name
-
-        # Replace .0. with -0- and remaining dots with dashes
-        return name.replace(".0.", "-0-").replace(".", "-")
-
-    def _normalize_name(self, name: str) -> str:
-        """Normalise un nom pour la comparaison"""
-        normalized = self._normalize_basic_patterns(name)
-
-        # Special handling for MAR-BVM format
-        if "mar-bvm" in normalized:
-            normalized = self._normalize_mar_bvm_format(normalized)
-        # For files starting with year (like 2022.0.86), keep dots
-        elif not normalized.startswith("20"):
-            normalized = normalized.replace(".", "-")
-
-        return normalized
-
-    def _create_mar_bvm_lookups(self, normalized: str, filename: str):
-        """Create additional lookup entries for MAR-BVM files"""
-        if "mar-bvm" not in normalized:
-            return
-
-        parts = normalized.split("-")
-        for i, part in enumerate(parts):
-            if part.isdigit() and i >= 5:  # After mar-bvm-7-2022-0
-                base_key = "-".join(parts[:6])  # mar-bvm-7-2022-0-22
-                if base_key not in self.mar_bvm_lookup:
-                    self.mar_bvm_lookup[base_key] = []
-                self.mar_bvm_lookup[base_key].append(filename)
-                break
-
-    def _process_image_file(self, filename: str):
-        """Process a single image file for indexing"""
-        if not SecurityValidator.validate_filename(filename):
-            logger.warning(f"Fichier ignoré pour raison de sécurité: {filename}")
-            return
-
-        if not filename.lower().endswith(self.IMAGE_EXTENSIONS):
-            return
-
-        self.available_files.add(filename)
-
-        base_name = self._strip_file_extensions(filename)
-        normalized = self._normalize_name(base_name)
-        self.image_lookup[normalized] = filename
-        self._create_mar_bvm_lookups(normalized, filename)
-
-    def _build_index(self):
-        """Construit un index des images disponibles"""
-        try:
-            all_files = os.listdir(self.images_dir)
-            for filename in all_files:
-                self._process_image_file(filename)
-
-            logger.info(
-                f"Index des images construit: {len(self.available_files)} fichiers disponibles, "
-                f"{len(self.image_lookup)} entrées normalisées"
-            )
-        except Exception as e:
-            logger.error(f"Erreur lors de la construction de l'index: {e}")
-            self.available_files = set()
-
-    def _clean_input_name(self, image_name: str) -> str:
-        """Clean and prepare input name for processing"""
-        # Basic cleaning
-        cleaned = image_name.strip().rstrip(",").rstrip("-").strip()
-        # Remove spaces before -001, -002, etc.
-        return re.sub(r"\s+(-\d)", r"\1", cleaned)
-
-    def _normalize_mar_bvm_input(self, image_name: str) -> str:
-        """Handle MAR-BVM specific input normalization"""
-        if "MAR-BVM" not in image_name:
-            return image_name
-
-        # Handle missing "7-" in MAR-BVM-2022-0-153
-        if "MAR-BVM-2022-0-" in image_name:
-            image_name = image_name.replace("MAR-BVM-2022-0-", "MAR-BVM-7-2022-0-")
-
-        # Convert .0. to -0-
-        if ".0." in image_name:
-            image_name = image_name.replace(".0.", "-0-")
-
-        # Handle .001, .002 at the end (convert to -001, -002)
-        image_name = re.sub(r"\.(\d{3})$", r"-\1", image_name)
-
-        # Handle .1 or .2 suffix
-        if image_name.endswith(".1"):
-            image_name = image_name[:-2] + "-1"
-        elif image_name.endswith(".2"):
-            image_name = image_name[:-2] + "-2"
-
-        # Replace any remaining dots with dashes (but be careful not to mess up already processed parts)
-        return image_name.replace(".", "-")
-
-    def _try_mar_bvm_lookups(self, normalized: str) -> Optional[str]:
-        """Try various MAR-BVM specific lookup strategies"""
-        # Check special MAR-BVM lookup
-        if normalized in self.mar_bvm_lookup and self.mar_bvm_lookup[normalized]:
-            return self.mar_bvm_lookup[normalized][0]
-
-        # Try with suffix variations
-        for suffix in self.MAR_BVM_TEST_SUFFIXES:
-            test_pattern = f"{normalized}{suffix}"
-            if test_pattern in self.image_lookup:
-                return self.image_lookup[test_pattern]
-
-        return None
-
-    def _try_year_format_lookup(self, image_name: str) -> Optional[str]:
-        """Handle special case for files starting with year"""
-        if not image_name.startswith("20"):
-            return None
-
-        test_name = image_name.lower().replace(" ", "-")
-        return self.image_lookup.get(test_name)
-
-    def _try_partial_matching(self, normalized: str) -> Optional[str]:
-        """Try partial matching as last resort"""
-        for key, filename in self.image_lookup.items():
-            if key.startswith(normalized) or normalized in key:
-                return filename
-        return None
-
-    def _split_multiple_names(self, image_name: str) -> List[str]:
-        """Split image names that contain multiple names separated by commas or slashes"""
-        # First try comma separation
-        if "," in image_name:
-            return [name.strip() for name in image_name.split(",") if name.strip()]
-
-        # Then try slash separation
-        if "/" in image_name:
-            return [name.strip() for name in image_name.split("/") if name.strip()]
-
-        # Handle " - " separation (for cases like "MAR-BVM-7-2022.0.81 - 2022.0.81")
-        if " - " in image_name and image_name.count(" - ") == 1:
-            parts = [name.strip() for name in image_name.split(" - ")]
-            # Only use the first part if they look like duplicates
-            if len(parts) == 2:
-                first, second = parts
-                # Check if second part is a suffix of the first (like duplicate year)
-                if first.endswith(second) or second in first:
-                    return [first]
-                return parts
-
-        return [image_name]
-
-    def find_image(self, image_name: str) -> Optional[str]:
-        """Trouve un fichier image correspondant au nom donné"""
-        if not image_name:
-            return None
-
-        # Handle multiple image names in one field
-        possible_names = self._split_multiple_names(image_name)
-
-        # Try each name individually
-        for name in possible_names:
-            result = self._find_single_image(name)
-            if result:
-                return result
-
-        return None
-
-    def _find_single_image(self, image_name: str) -> Optional[str]:
-        """Find a single image by name"""
-        # Clean and normalize the input
-        cleaned_name = self._clean_input_name(image_name)
-        processed_name = self._normalize_mar_bvm_input(cleaned_name)
-        normalized = self._normalize_name(processed_name)
-
-        # Try direct lookup first
-        if normalized in self.image_lookup:
-            return self.image_lookup[normalized]
-
-        # Try MAR-BVM specific lookups
-        if "mar-bvm" in normalized:
-            result = self._try_mar_bvm_lookups(normalized)
-            if result:
-                return result
-
-        # Try year format lookup
-        result = self._try_year_format_lookup(image_name)
-        if result:
-            return result
-
-        # Try partial matching as last resort
-        return self._try_partial_matching(normalized)
-
-    def get_all_files(self) -> Set[str]:
-        """Retourne tous les fichiers disponibles"""
-        return self.available_files.copy()
-
-
-class ArtMatcherV2:
-    """Classe principale pour le matching d'œuvres d'art"""
-
-    def __init__(self, csv_path: str, images_dir: str):
-        """Initialise le système avec la base de données et le répertoire d'images"""
-        self.db = Database(csv_path)
-        self.images_dir = os.path.abspath(images_dir)
-        self.emotion_wheel = EmotionWheel()
-        self.weights = ScoringWeights()
-
-        self.optimizer_helper = WeightedLeximaxOptimizer(TargetProfile(), {})
-
-        self.image_indexer = ImageIndexer(images_dir)
-
-        df = self.db.get_dataframe()
-        self.df_with_images = df[
-            df["name_image"].notna()
-            & (df["name_image"] != "")
-            & (df["name_image"].str.strip() != "")
-        ].copy()
-
-        self.df_with_images["database_id_str"] = self.df_with_images[
-            "database_id"
-        ].astype(str)
-        self.id_to_index = {
-            str(row["database_id"]): idx for idx, row in self.df_with_images.iterrows()
-        }
-
-        self.artwork_images = self._build_artwork_image_index()
-
-        self.temp_db_with_images = Database.__new__(Database)
-        self.temp_db_with_images.dataframe = self.df_with_images
-
-        logger.info(f"Base de données chargée: {self.db.n_pieces()} œuvres")
-        logger.info(f"Œuvres avec images: {len(self.df_with_images)}")
-        logger.info(f"Index des images: {len(self.artwork_images)} œuvres mappées")
-
-    def _sanitize_input(self, input_str: str) -> str:
-        """Nettoie et valide une entrée utilisateur"""
-        return SecurityValidator.sanitize_input(input_str)
-
-    def _parse_date(self, date_str: str) -> Optional[datetime]:
-        """Parse une date avec validation"""
-        is_valid, date_obj = SecurityValidator.validate_date(date_str)
-        return date_obj if is_valid else None
-
-    def _build_artwork_image_index(self) -> Dict[str, List[str]]:
-        """Construit un index artwork_id -> [image_paths] au démarrage"""
-        artwork_images = {}
-
-        for idx, row in self.df_with_images.iterrows():
-            artwork_id = str(row["database_id"])
-            image_paths = []
-
-            if row["name_image"] and str(row["name_image"]).strip():
-                # Parse the image names - handle special separators
-                image_string = str(row["name_image"]).strip().strip('"')
-
-                # Handle cases with " / " or " - " separators
-                if " / " in image_string:
-                    # Take first part before the slash
-                    image_string = image_string.split(" / ")[0].strip()
-
-                # Special case: if it has " - 2022" it's a separator, not part of the name
-                if " - 2022" in image_string:
-                    # Take the part before " - 2022"
-                    image_string = image_string.split(" - 2022")[0].strip()
-                elif " - " in image_string and "MAR-BVM-7-2022-0-" not in image_string:
-                    # For other MAR-BVM formats with " - " separator
-                    parts = image_string.split(" - ")
-                    if "MAR-BVM" in parts[0]:
-                        image_string = parts[0].strip()
-
-                # Clean up trailing " -" or spaces before "-001"
-                image_string = re.sub(
-                    r"\s+-\s*$", "", image_string
-                )  # Remove trailing " -"
-                image_string = re.sub(
-                    r"\s+(-\d)", r"\1", image_string
-                )  # Remove spaces before -001
-
-                # Parse comma-separated list
-                images = [
-                    img.strip()
-                    for img in re.split(r"[,/]", image_string)
-                    if img.strip()
-                ]
-
-                for img_name in images:
-                    # Find the actual file for this image name
-                    matched_file = self.image_indexer.find_image(img_name)
-                    if matched_file:
-                        img_path = os.path.join(self.images_dir, matched_file)
-                        image_paths.append(img_path)
-
-            if image_paths:
-                artwork_images[artwork_id] = image_paths
-
-        return artwork_images
-
-    def preselect_artworks(
-        self, firstname: str, birthday: str, city: str
-    ) -> pd.DataFrame:
-        """
-        Pré-sélectionne les œuvres selon la hiérarchie: prénom > date > ville
-        """
-        logger.info("=== DÉBUT PRÉ-SÉLECTION ===")
-
-        # Nettoyer les entrées
-        firstname = self._sanitize_input(firstname)
-        city = self._sanitize_input(city)
-
-        logger.info(
-            f"Critères de pré-sélection: prénom='{firstname}', date='{birthday}', ville='{city}'"
-        )
-
-        birth_date = self._parse_date(birthday)
-        if birth_date:
-            logger.info(f"Date convertie: {birth_date.strftime('%d/%m')}")
-
-        profile = TargetProfile()
-        profile.set_target_name(firstname)
-        profile.set_target_date(birth_date)
-        profile.set_target_place(city)
-
-        weights = {
-            "related_names": self.weights.PRESELECTION_NAME_WEIGHT,
-            "related_dates": self.weights.PRESELECTION_DATE_WEIGHT,
-            "related_places": self.weights.PRESELECTION_PLACE_WEIGHT,
-            "related_emotions": self.weights.PRESELECTION_EMOTION_WEIGHT,
-        }
-
-        logger.info(
-            f"Poids utilisés: nom={weights['related_names']}, date={weights['related_dates']}, lieu={weights['related_places']}, émotions={weights['related_emotions']}"
-        )
-
-        optimizer = WeightedLeximaxOptimizer(profile, weights)
-        result = optimizer.optimize_max(self.temp_db_with_images)
-
-        preselected = result[result["score"] > (0, 0, 0)]
-        logger.info(f"Œuvres avec score > 0: {len(preselected)}")
-
-        if len(preselected) < self.weights.MIN_PRESELECTION_COUNT:
-            preselected = result.head(self.weights.MIN_PRESELECTION_COUNT)
-            logger.info(f"Ajustement au minimum requis: {len(preselected)} œuvres")
-
-        logger.info("Top 5 pré-sélections:")
-        for i, (idx, piece) in enumerate(preselected.head(5).iterrows()):
-            logger.info(
-                f"  {i+1}. Œuvre #{piece['database_id']} - Score: {piece['score']}"
-            )
-            if firstname and piece["related_names"]:
-                name_score = Optimizer.name_similarity(
-                    firstname, piece["related_names"]
-                )
-                if name_score > 0:
-                    logger.info(
-                        f"     → Nom: {piece['related_names']} (score: {name_score:.2f})"
-                    )
-            if birth_date and piece["related_dates"]:
-                date_score = Optimizer.date_similarity(
-                    birth_date, piece["related_dates"]
-                )
-                if date_score > 0:
-                    logger.info(
-                        f"     → Dates: {[d.strftime('%d/%m') for d in piece['related_dates']]} (score: {date_score:.2f})"
-                    )
-            if city and piece["related_places"]:
-                place_score = self.optimizer_helper.place_similarity(
-                    city, piece["related_places"]
-                )
-                if place_score > 0:
-                    logger.info(
-                        f"     → Lieux: {piece['related_places']} (score: {place_score:.2f})"
-                    )
-
-        logger.info("=== FIN PRÉ-SÉLECTION ===")
-        return preselected
-
-    def get_random_images_for_selection(
-        self, round_num: int, already_selected: List[str] = None
-    ) -> List[Tuple[str, str]]:
-        """
-        Retourne 3 images aléatoires depuis l'index pré-construit
-        Exclut les œuvres déjà sélectionnées dans les tours précédents
-        """
-        logger.info(f"=== SÉLECTION D'IMAGES POUR LE TOUR {round_num} ===")
-
-        if already_selected:
-            logger.info(f"Œuvres déjà sélectionnées à exclure: {already_selected}")
-
-        available_artworks = list(self.artwork_images.keys())
-
-        # Exclure les œuvres déjà sélectionnées
-        if already_selected:
-            already_selected_set = set(already_selected)
-            available_artworks = [
-                a for a in available_artworks if a not in already_selected_set
-            ]
-
-        logger.info(
-            f"Nombre total d'œuvres avec images disponibles: {len(available_artworks)}"
-        )
-
-        if len(available_artworks) < self.weights.MAX_IMAGES_PER_SELECTION:
-            logger.warning(
-                f"Seulement {len(available_artworks)} œuvres avec images disponibles"
-            )
-            direct_images = []
-            for filename in list(self.image_indexer.get_all_files())[:10]:
-                if filename.endswith(".jpg"):
-                    img_path = os.path.join(self.images_dir, filename)
-                    direct_images.append((img_path, "0"))
-            return direct_images[: self.weights.MAX_IMAGES_PER_SELECTION]
-
-        num_to_select = min(
-            self.weights.MAX_IMAGES_PER_SELECTION, len(available_artworks)
-        )
-        selected_artworks = random.sample(available_artworks, num_to_select)
-
-        logger.info(f"Œuvres sélectionnées aléatoirement: {selected_artworks}")
-
-        selected = []
-        for artwork_id in selected_artworks:
-            img_path = random.choice(self.artwork_images[artwork_id])
-            selected.append((img_path, artwork_id))
-            if artwork_id in self.id_to_index:
-                idx = self.id_to_index[artwork_id]
-                artwork = self.df_with_images.loc[idx]
-                logger.info(f"  Image {len(selected)}: Œuvre #{artwork_id}")
-                logger.info(f"    Type: {artwork['art_piece_type']}")
-                logger.info(f"    Émotions: {artwork['related_emotions']}")
-
-        logger.info(f"=== FIN SÉLECTION IMAGES TOUR {round_num} ===")
-        return selected
-
-    def extract_emotions_from_image_id(self, database_id: str) -> List[str]:
-        """
-        Extrait les émotions associées à une œuvre via son ID
-        Utilise l'index pré-calculé pour éviter les conversions répétées
-        """
-        if database_id in self.id_to_index:
-            idx = self.id_to_index[database_id]
-            emotions = self.df_with_images.loc[idx, "related_emotions"]
-            if isinstance(emotions, list):
-                return emotions
-        return []
-
-    @lru_cache(maxsize=1024)
-    def _cached_emotion_similarity(self, emotion1: str, emotion2: str) -> float:
-        """Cache les calculs de similarité émotionnelle"""
-        return self.emotion_wheel.calculate_emotion_similarity(emotion1, emotion2)
-
-    def calculate_emotion_profile(self, selected_ids: List[str]) -> Dict[str, float]:
-        """
-        Calcule le profil émotionnel basé sur les images sélectionnées
-        """
-        logger.info("=== CALCUL DU PROFIL ÉMOTIONNEL ===")
-        logger.info(f"Images sélectionnées: {selected_ids}")
-
-        emotion_counter = Counter()
-
-        for db_id in selected_ids:
-            emotions = self.extract_emotions_from_image_id(db_id)
-            logger.info(f"  Image {db_id}: émotions = {emotions}")
-            emotion_counter.update(emotions)
-
-        total = sum(emotion_counter.values())
-        if total > 0:
-            emotion_profile = {
-                emotion: count / total for emotion, count in emotion_counter.items()
-            }
-            logger.info(f"Profil émotionnel calculé: {emotion_profile}")
-        else:
-            emotion_profile = {}
-            logger.info("Aucune émotion trouvée dans les images sélectionnées")
-
-        logger.info("=== FIN CALCUL PROFIL ÉMOTIONNEL ===")
-        return emotion_profile
-
-    def _get_artwork_image(self, artwork) -> Optional[str]:
-        """Retourne le chemin de l'image pour une œuvre d'art"""
-        artwork_id = str(artwork["database_id"])
-
-        # Simply return the first image from our pre-built index
-        if artwork_id in self.artwork_images:
-            return self.artwork_images[artwork_id][0]
-
-        return None
-
-    def find_best_match(
-        self, firstname: str, birthday: str, city: str, selected_image_ids: List[str]
-    ) -> Tuple[Optional[str], str, Dict]:
-        """
-        Trouve la meilleure correspondance selon la hiérarchie du scénario:
-        1. Match exact (name/date/city) = gagnant automatique
-        2. Si pré-sélection existe: utiliser émotions pour départager
-        3. Si aucune pré-sélection: utiliser émotions seules
-        4. Type d'objet comme critère de départage final
-        """
-        firstname = self._sanitize_input(firstname)
-        city = self._sanitize_input(city)
-        birth_date = self._parse_date(birthday)
-
-        logger.info(
-            f"Recherche de correspondance pour: {firstname}, {birthday}, {city}"
-        )
-
-        preselected = self.preselect_artworks(firstname, birthday, city)
-
-        logger.info("=== DÉTECTION DE MATCH EXACT ===")
-        for idx, piece in preselected.iterrows():
-            if firstname and piece["related_names"]:
-                name_score = Optimizer.name_similarity(
-                    firstname, piece["related_names"]
-                )
-                if name_score >= 0.95:
-                    logger.info(
-                        f"🎯 MATCH EXACT TROUVÉ: prénom '{firstname}' → œuvre #{piece['database_id']} (score: {name_score:.2f})"
-                    )
-                    logger.info(f"   Noms dans l'œuvre: {piece['related_names']}")
-                    match_image = self._get_artwork_image(piece)
-                    match_info = {
-                        "title": f"Œuvre #{piece['database_id']}",
-                        "type": piece["art_piece_type"],
-                        "place": piece["art_piece_place"],
-                        "emotions": piece["related_emotions"],
-                        "explanation": piece["explanation"],
-                    }
-                    return (
-                        match_image,
-                        f"Prénom '{firstname}' correspond exactement",
-                        match_info,
-                    )
-
-            if birth_date and piece["related_dates"]:
-                date_score = Optimizer.date_similarity(
-                    birth_date, piece["related_dates"]
-                )
-                if date_score == 1.0:
-                    logger.info(
-                        f"🎯 MATCH EXACT TROUVÉ: date '{birthday}' → œuvre #{piece['database_id']}"
-                    )
-                    logger.info(
-                        f"   Dates dans l'œuvre: {[d.strftime('%d/%m/%Y') for d in piece['related_dates']]}"
-                    )
-                    match_image = self._get_artwork_image(piece)
-                    match_info = {
-                        "title": f"Œuvre #{piece['database_id']}",
-                        "type": piece["art_piece_type"],
-                        "place": piece["art_piece_place"],
-                        "emotions": piece["related_emotions"],
-                        "explanation": piece["explanation"],
-                    }
-                    return (
-                        match_image,
-                        f"Date d'anniversaire {birthday} correspond exactement",
-                        match_info,
-                    )
-
-            if city and piece["related_places"]:
-                place_score = self.optimizer_helper.place_similarity(
-                    city, piece["related_places"]
-                )
-                if place_score == 1.0:
-                    logger.info(
-                        f"🎯 MATCH EXACT TROUVÉ: ville '{city}' → œuvre #{piece['database_id']}"
-                    )
-                    logger.info(f"   Lieux dans l'œuvre: {piece['related_places']}")
-                    match_image = self._get_artwork_image(piece)
-                    match_info = {
-                        "title": f"Œuvre #{piece['database_id']}",
-                        "type": piece["art_piece_type"],
-                        "place": piece["art_piece_place"],
-                        "emotions": piece["related_emotions"],
-                        "explanation": piece["explanation"],
-                    }
-                    return (
-                        match_image,
-                        f"Ville '{city}' correspond exactement",
-                        match_info,
-                    )
-
-        logger.info("Aucun match exact trouvé, passage à la sélection par émotions")
-
-        emotion_profile = self.calculate_emotion_profile(selected_image_ids)
-
-        logger.info("=== STRATÉGIE DE MATCHING ===")
-        valid_preselection = preselected[preselected["score"] > (0, 0, 0)]
-
-        if len(valid_preselection) > 0:
-            logger.info(
-                f"📋 CAS A: {len(valid_preselection)} œuvres pré-sélectionnées - utilisation des émotions pour départager"
-            )
-            candidates = valid_preselection
-        else:
-            logger.info(
-                f"📋 CAS B: Aucune pré-sélection valide - recherche par émotions sur {len(self.df_with_images)} œuvres"
-            )
-            candidates = self.df_with_images
-
-        # Exclure les œuvres déjà sélectionnées par l'utilisateur
-        selected_artwork_ids = set(selected_image_ids)
-        candidates = candidates[
-            ~candidates["database_id"].astype(str).isin(selected_artwork_ids)
-        ]
-        logger.info(
-            f"Après exclusion des œuvres déjà sélectionnées {selected_artwork_ids}: {len(candidates)} candidats restants"
-        )
-
-        logger.info("=== CALCUL DES SCORES ÉMOTIONNELS ===")
-        best_matches = []
-        best_emotion_score = -1
-
-        for idx, piece in candidates.iterrows():
-            emotion_score = 0
-
-            if emotion_profile and piece["related_emotions"]:
-                for user_emotion, weight in emotion_profile.items():
-                    best_similarity = 0
-                    for piece_emotion in piece["related_emotions"]:
-                        similarity = self._cached_emotion_similarity(
-                            user_emotion, piece_emotion
-                        )
-                        if similarity > best_similarity:
-                            best_similarity = similarity
-                    emotion_score += best_similarity * weight
-
-                if len(piece["related_emotions"]) > 0:
-                    emotion_score /= len(piece["related_emotions"])
-
-            if emotion_score > best_emotion_score:
-                best_emotion_score = emotion_score
-                best_matches = [piece]
-                logger.info(
-                    f"  Nouveau meilleur score émotionnel: {emotion_score:.3f} - Œuvre #{piece['database_id']}"
-                )
-            elif emotion_score == best_emotion_score and emotion_score > 0:
-                best_matches.append(piece)
-                logger.info(
-                    f"  Score égal au meilleur: {emotion_score:.3f} - Œuvre #{piece['database_id']}"
-                )
-
-        logger.info(
-            f"Nombre de meilleures correspondances: {len(best_matches)} avec score {best_emotion_score:.3f}"
-        )
-
-        if len(best_matches) > 1:
-            logger.info("=== DÉPARTAGE PAR TYPE D'OBJET ===")
-            selected_types = []
-            for img_id in selected_image_ids:
-                if img_id in self.id_to_index:
-                    idx = self.id_to_index[img_id]
-                    selected_types.append(
-                        self.df_with_images.loc[idx, "art_piece_type"]
-                    )
-
-            selected_types_counter = Counter(selected_types)
-
-            type_scored_matches = []
-            best_type_score = -1
-
-            for piece in best_matches:
-                type_score = selected_types_counter.get(piece["art_piece_type"], 0)
-                if type_score > best_type_score:
-                    best_type_score = type_score
-                    type_scored_matches = [piece]
-                elif type_score == best_type_score:
-                    type_scored_matches.append(piece)
-
-            if len(type_scored_matches) > 1:
-                logger.info(
-                    f"  {len(type_scored_matches)} œuvres avec le même score de type ({best_type_score}) - sélection aléatoire"
-                )
-                best_match = random.choice(type_scored_matches)
-                match_reason = (
-                    "Sélection aléatoire parmi les meilleures correspondances"
-                )
-            else:
-                best_match = type_scored_matches[0]
-                match_reason = f"Type d'objet '{best_match['art_piece_type']}' préféré"
-                logger.info(
-                    f"  Type '{best_match['art_piece_type']}' sélectionné avec score {best_type_score}"
-                )
-        elif len(best_matches) == 1:
-            best_match = best_matches[0]
-            match_reason = "Meilleure correspondance émotionnelle"
-        else:
-            logger.info("Aucune correspondance trouvée")
-            return None, "Aucune correspondance trouvée", {}
-
-        reasons = []
-        if len(valid_preselection) > 0:
-            if firstname and best_match["related_names"]:
-                name_score = Optimizer.name_similarity(
-                    firstname, best_match["related_names"]
-                )
-                if name_score > 0:
-                    reasons.append(f"prénom '{firstname}' trouvé")
-
-            if birth_date and best_match["related_dates"]:
-                date_score = Optimizer.date_similarity(
-                    birth_date, best_match["related_dates"]
-                )
-                if date_score > 0:
-                    reasons.append(
-                        f"date {'exacte' if date_score == 1.0 else 'partielle'}"
-                    )
-
-            if city and best_match["related_places"]:
-                place_score = self.optimizer_helper.place_similarity(
-                    city, best_match["related_places"]
-                )
-                if place_score > 0:
-                    reasons.append(f"ville '{city}' trouvée")
-
-        if best_emotion_score > 0:
-            reasons.append(
-                f"correspondance émotionnelle (score: {best_emotion_score:.2f})"
-            )
-
-        if len(reasons) == 0:
-            reasons.append(match_reason)
-
-        final_reason = " ; ".join(reasons)
-
-        logger.info(f"\n🏆 RÉSULTAT FINAL: Œuvre #{best_match['database_id']}")
-        logger.info(f"   Raison: {final_reason}")
-        logger.info(f"   Type: {best_match['art_piece_type']}")
-        logger.info(f"   Lieu: {best_match['art_piece_place']}")
-
-        match_image = self._get_artwork_image(best_match)
-
-        match_info = {
-            "title": f"Œuvre #{best_match['database_id']}",
-            "type": best_match["art_piece_type"],
-            "place": best_match["art_piece_place"],
-            "emotions": best_match["related_emotions"],
-            "explanation": best_match["explanation"],
-        }
-
-        return match_image, final_reason, match_info
-
-
-csv_path = "PP1-Collection_Database_new-cleaned.csv"
-images_dir = "pictures_data"
-
-if not os.path.exists(csv_path):
-    logger.error(f"Fichier CSV introuvable: {csv_path}")
-if not os.path.exists(images_dir):
-    logger.error(f"Répertoire images introuvable: {images_dir}")
-
-matcher = ArtMatcherV2(csv_path, images_dir)
-
-
-def process_user_info(firstname: str, birthday: str, city: str, state: SessionState):
-    """Traite les informations utilisateur avec validation"""
-    firstname = SecurityValidator.sanitize_input(firstname)
-    city = SecurityValidator.sanitize_input(city)
-
-    state.firstname = firstname
-    state.birthday = birthday
-    state.city = city
-
-    if not firstname or not birthday:
-        return (
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            "Veuillez remplir au moins votre prénom et date de naissance.",
-            state,
-        )
-
-    is_valid, _ = SecurityValidator.validate_date(birthday)
-    if not is_valid:
-        return (
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            "Format de date invalide. Utilisez JJ/MM (ex: 15/03)",
-            state,
-        )
-
-    return (
-        gr.update(visible=False),
-        gr.update(visible=True),
-        gr.update(visible=False),
-        "Informations enregistrées ! Passons à la sélection d'images.",
-        state,
-    )
-
-
-def load_images_for_round(round_num: int, state: SessionState):
-    """Charge 3 images pour un tour de sélection"""
-    images_data = matcher.get_random_images_for_selection(
-        round_num, state.selected_images
-    )
-
-    if len(images_data) < ScoringWeights.MAX_IMAGES_PER_SELECTION:
-        logger.warning(f"Seulement {len(images_data)} images disponibles")
-        return (
-            [None, None, None],
-            [],
-            f"Pas assez d'images disponibles (seulement {len(images_data)} trouvées)",
-            state,
-        )
-
-    images = [img[0] for img in images_data]
-    ids = [img[1] for img in images_data]
-
-    state.current_image_ids = ids
-
-    return (
-        images,
-        ids,
-        f"Tour {round_num + 1}/{ScoringWeights.TOTAL_ROUNDS} : Sélectionnez l'image qui vous attire le plus",
-        state,
-    )
-
-
-def select_image(choice: Optional[int], state: SessionState):
-    """Traite la sélection d'image"""
-    if choice is None:
-        return (
-            gr.update(),
-            gr.update(),
-            gr.update(),
-            gr.update(),
-            "Veuillez sélectionner une image",
-            state,
-        )
-
-    if state.current_image_ids and len(state.current_image_ids) > choice:
-        selected_id = state.current_image_ids[choice]
-    else:
-        return (
-            gr.update(),
-            gr.update(),
-            gr.update(),
-            gr.update(),
-            "Erreur: image non trouvée",
-            state,
-        )
-
-    state.selected_images.append(selected_id)
-    state.current_round += 1
-
-    logger.info(
-        f"Tour {state.current_round}: Image {choice+1} sélectionnée (ID: {selected_id})"
-    )
-
-    if state.current_round < ScoringWeights.TOTAL_ROUNDS:
-        new_images, new_ids, message, state = load_images_for_round(
-            state.current_round, state
-        )
-        return (
-            gr.update(value=new_images[0]),
-            gr.update(value=new_images[1]),
-            gr.update(value=new_images[2]),
-            gr.update(value=None),
-            message,
-            state,
-            gr.update(visible=True),  # keep selection_section visible
-            gr.update(visible=False),  # keep loading_section hidden
-        )
-    else:
-        # Toutes les sélections sont terminées, afficher le loading
-        return (
-            gr.update(),  # img1
-            gr.update(),  # img2
-            gr.update(),  # img3
-            gr.update(),  # image_choice
-            "",  # status_message vide
-            state,
-            gr.update(visible=False),  # hide selection_section
-            gr.update(visible=True),  # show loading_section
-        )
-
-
-def show_results(state: SessionState):
-    """Affiche les résultats finaux"""
-    if not state.is_complete():
-        return (
-            gr.update(visible=False),  # info_section
-            gr.update(visible=True),   # selection_section
-            gr.update(visible=False),  # loading_section
-            gr.update(visible=False),  # results_section
-            None,
-            "",
-            "",
-        )
-
-    match_image, reason, info = matcher.find_best_match(
-        state.firstname,
-        state.birthday,
-        state.city,
-        state.selected_images,
-    )
-
-    if match_image:
-        # Déterminer le type de système de recommandation utilisé
-        if "correspond exactement" in reason.lower():
-            # Match exact sur nom, date ou lieu
-            recommendation_type = "name_date_place"
-        else:
-            # Match basé sur les émotions
-            recommendation_type = "emotions"
-        
-        # Enregistrer l'œuvre finale et le type de recommandation
-        state.final_artwork = info.get("title", "Œuvre inconnue")
-        state.recommendation_type = recommendation_type
-        
-        # Logger la session
-        session_tracker.log_session(state, recommendation_type)
-        
-        explanation = f"""
-**Votre œuvre correspondante a été trouvée !**
-
-**Raison du match :** {reason}
-
-**Détails de l'œuvre :**
-- Type : {info.get('type', 'Non spécifié')}
-- Lieu : {info.get('place', 'Non spécifié')}
-- Émotions : {', '.join(info.get('emotions', [])) if info.get('emotions') else 'Non spécifiées'}
-
-**Description :**
-{info.get('explanation', 'Aucune description disponible')}
-"""
-    else:
-        # Aucune œuvre trouvée - logger quand même
-        state.final_artwork = "Aucune œuvre trouvée"
-        state.recommendation_type = "none"
-        session_tracker.log_session(state, "none")
-        
-        explanation = "Désolé, aucune œuvre correspondante n'a pu être trouvée."
-
-    return (
-        gr.update(visible=False),  # info_section
-        gr.update(visible=False),  # selection_section
-        gr.update(visible=False),  # loading_section
-        gr.update(visible=True),   # results_section
-        match_image,
-        info.get("title", "Œuvre non trouvée") if match_image else "Œuvre non trouvée",
-        explanation,
-    )
-
-
-with gr.Blocks(
-    title="Art Matcher", 
-    theme=gr.themes.Soft(
-        primary_hue="teal", secondary_hue="teal", neutral_hue="zinc"
-    ),
-) as demo:
-    gr.Markdown(
-        """
-        # 🎨 Art Matcher
-        ### Découvrez l'œuvre d'art qui vous correspond !
-        
-        Cette application utilise vos informations personnelles et vos préférences visuelles
-        pour trouver l'œuvre d'art qui vous correspond le mieux dans notre collection.
-        """
-    )
-
-    session_state = gr.State(SessionState())
-
-    with gr.Group(visible=True) as info_section:
-        gr.Markdown("### Étape 1 : Vos informations")
-        with gr.Row():
-            firstname_input = gr.Textbox(
-                label="Prénom", placeholder="Entrez votre prénom", max_lines=1
-            )
-            birthday_input = gr.Textbox(
-                label="Date d'anniversaire (JJ/MM)",
-                placeholder="Ex: 25/12",
-                max_lines=1,
-            )
-            city_input = gr.Textbox(
-                label="Ville de résidence", placeholder="Ex: Paris", max_lines=1
-            )
-
-        submit_info_btn = gr.Button("Valider mes informations", variant="primary")
-
-    with gr.Group(visible=False) as selection_section:
-        selection_title = gr.Markdown("### Étape 2 : Sélection d'images")
-
-        with gr.Row():
-            img1 = gr.Image(label="Image 1", type="filepath", height=300)
-            img2 = gr.Image(label="Image 2", type="filepath", height=300)
-            img3 = gr.Image(label="Image 3", type="filepath", height=300)
-
-        image_choice = gr.Radio(
-            choices=["Image 1", "Image 2", "Image 3"],
-            label="Quelle image vous attire le plus ?",
-            type="index",
-        )
-
-        select_btn = gr.Button("Valider mon choix", variant="primary")
-
-    with gr.Group(visible=False) as loading_section:
-        gr.Markdown("### ⏳ Analyse en cours...")
-        gr.HTML("""
-        <div style="text-align: center; padding: 40px;">
-            <div style="display: inline-block; width: 60px; height: 60px; border: 6px solid #f3f3f3; border-top: 6px solid #14b8a6; border-radius: 50%; animation: spin 1s linear infinite;"></div>
-            <style>
-                @keyframes spin {
-                    0% { transform: rotate(0deg); }
-                    100% { transform: rotate(360deg); }
-                }
-            </style>
-            <p style="margin-top: 20px; font-size: 18px; color: #666;">
-                <strong>Traitement de vos sélections...</strong><br>
-                <span style="font-size: 14px;">Nous analysons votre profil pour trouver l'œuvre parfaite</span>
-            </p>
-        </div>
-        """)
-    
-    with gr.Group(visible=False) as results_section:
-        gr.Markdown("### Votre œuvre correspondante")
-
-        with gr.Row():
-            with gr.Column(scale=1):
-                result_image = gr.Image(label="Votre œuvre", height=400)
-                result_title = gr.Markdown("## Titre de l'œuvre")
-
-            with gr.Column(scale=1):
-                result_explanation = gr.Markdown("")
-
-        restart_btn = gr.Button("Recommencer", variant="secondary")
-
-    status_message = gr.Markdown("")
-
-    def on_info_submit(firstname, birthday, city, state):
-        state.reset()
-
-        info_vis, select_vis, results_vis, message, state = process_user_info(
-            firstname, birthday, city, state
-        )
-
-        if select_vis["visible"]:
-            images, ids, round_message, state = load_images_for_round(0, state)
-            return (
-                info_vis,
-                select_vis,
-                results_vis,
-                images[0] if len(images) > 0 else None,
-                images[1] if len(images) > 1 else None,
-                images[2] if len(images) > 2 else None,
-                round_message,
-                state,
-            )
-        else:
-            return (info_vis, select_vis, results_vis, None, None, None, message, state)
-
-    submit_info_btn.click(
-        fn=on_info_submit,
-        inputs=[firstname_input, birthday_input, city_input, session_state],
-        outputs=[
-            info_section,
-            selection_section,
-            results_section,
-            img1,
-            img2,
-            img3,
-            status_message,
-            session_state,
-        ],
-    )
-
-    def on_image_select(choice, state):
-        result = select_image(choice, state)
-        
-        # La fonction select_image retourne maintenant 8 valeurs
-        if len(result) == 8:
-            (img1_update, img2_update, img3_update, choice_update, message, state, 
-             selection_vis, loading_vis) = result
-            return (
-                gr.update(),  # info_section
-                selection_vis,  # selection_section
-                loading_vis,  # loading_section  
-                gr.update(),  # results_section
-                img1_update,  # img1
-                img2_update,  # img2
-                img3_update,  # img3
-                choice_update,  # image_choice
-                message,  # status_message
-                state,
-            )
-        else:
-            # Format avec 6 valeurs (cas sans loading)
-            (img1_update, img2_update, img3_update, choice_update, message, state) = result
-            return (
-                gr.update(),  # info_section
-                gr.update(),  # selection_section
-                gr.update(),  # loading_section
-                gr.update(),  # results_section
-                img1_update,  # img1
-                img2_update,  # img2
-                img3_update,  # img3
-                choice_update,  # image_choice
-                message,  # status_message
-                state,
-            )
-
-    def handle_final_results(state):
-        if state.is_complete():
-            return show_results(state)
-        else:
-            return gr.update(), gr.update(), gr.update(), gr.update(), None, "", ""
-
-    select_btn.click(
-        fn=on_image_select,
-        inputs=[image_choice, session_state],
-        outputs=[
-            info_section,
-            selection_section,
-            loading_section,
-            results_section,
-            img1,
-            img2,
-            img3,
-            image_choice,
-            status_message,
-            session_state,
-        ],
-    ).then(
-        fn=handle_final_results,
-        inputs=[session_state],
-        outputs=[
-            info_section,
-            selection_section,
-            loading_section,
-            results_section,
-            result_image,
-            result_title,
-            result_explanation,
-        ],
-    )
-
-    def restart_app(state):
-        state.reset()
-
-        return (
-            gr.update(visible=True),   # info_section
-            gr.update(visible=False),  # selection_section
-            gr.update(visible=False),  # loading_section
-            gr.update(visible=False),  # results_section
-            "",  # firstname_input
-            "",  # birthday_input
-            "",  # city_input
-            None,  # image_choice
-            "Application réinitialisée. Veuillez entrer vos informations.",  # status_message
-            state,
-        )
-
-    restart_btn.click(
-        fn=restart_app,
-        inputs=[session_state],
-        outputs=[
-            info_section,
-            selection_section,
-            loading_section,
-            results_section,
-            firstname_input,
-            birthday_input,
-            city_input,
-            image_choice,
-            status_message,
-            session_state,
-        ],
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()