#!/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 = 5  # Upload toutes les 5 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

    # Propriétés pour le tracking
    session_id: str = field(
        default_factory=lambda: str(uuid.uuid4())
    )  # ID unique de session
    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())  # Nouveau ID de session
        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:
    """Version améliorée du logger de sessions avec CommitScheduler simplifié"""

    def __init__(self):
        # Détection de l'environnement HF Spaces
        self.is_hf_space = os.environ.get("SPACE_ID") is not None

        # Sessions pour le dataset HF (seulement les logs de sessions)
        self.sessions_dir = Path("art_matcher_sessions")
        self.sessions_dir.mkdir(parents=True, exist_ok=True)

        # Statistiques locales uniquement
        self.local_stats_dir = Path("art_matcher_stats")
        self.local_stats_dir.mkdir(parents=True, exist_ok=True)

        # Chaque redémarrage crée un nouveau fichier
        self.sessions_file = self.sessions_dir / f"train-{uuid.uuid4()}.json"
        self.stats_file = self.local_stats_dir / "global_statistics.json"

        # Pour compatibilité avec l'ancien code
        self.data_dir = self.sessions_dir

        # Initialiser le CommitScheduler si sur HF Spaces
        self.scheduler = None
        if self.is_hf_space and HF_HUB_AVAILABLE:
            try:
                # Vérifier que le dataset ID est défini
                if not HF_DATASET_ID:
                    raise ValueError("HF_DATASET_ID n'est pas défini")

                logger.info(
                    f"Tentative d'initialisation du CommitScheduler pour {HF_DATASET_ID}..."
                )
                self.scheduler = CommitScheduler(
                    repo_id=HF_DATASET_ID,
                    repo_type="dataset",
                    folder_path=str(self.sessions_dir),  # Seulement les sessions!
                    path_in_repo="data",
                    every=LOGS_UPLOAD_INTERVAL,
                )
                logger.info(
                    f"✅ CommitScheduler initialisé avec succès pour {HF_DATASET_ID}"
                )
                logger.info(
                    f"📁 Dossier des sessions (HF dataset): {self.sessions_dir}"
                )
                logger.info(
                    f"📊 Dossier des stats (local seulement): {self.local_stats_dir}"
                )
                logger.info(f"📝 Fichier de session actuel: {self.sessions_file.name}")
                logger.info(f"⏱️ Upload toutes les {LOGS_UPLOAD_INTERVAL} minutes")
            except Exception as e:
                logger.error(
                    f"❌ Erreur lors de l'initialisation du CommitScheduler: {e}"
                )
                logger.info("Les données seront stockées localement uniquement")
                self.scheduler = None
        else:
            if not self.is_hf_space:
                logger.info(
                    "🏠 Environnement local détecté - pas de synchronisation HF"
                )
            if not HF_HUB_AVAILABLE:
                logger.warning("📦 huggingface_hub n'est pas installé")

    def log_session(self, state: SessionState, recommendation_system: str):
        """Enregistre une session de manière thread-safe"""
        session_duration = time.time() - state.session_start_time

        # Utiliser le session_id unique de l'état, pas l'instance_id
        entry = {
            "session_id": state.session_id,  # ID unique de la session
            "datetime": datetime.now().isoformat(),
            "duration_seconds": round(session_duration, 2),
            "recommended_artwork": state.final_artwork,
            "recommendation_type": recommendation_system,
        }

        # Utiliser le lock du scheduler pour la thread safety
        try:
            if self.scheduler and hasattr(self.scheduler, "lock"):
                with self.scheduler.lock:
                    self._write_session(entry)
                    self._update_stats(entry)
                    logger.info(f"✅ Session écrite avec lock du scheduler")
            else:
                # Sans scheduler, écriture directe
                self._write_session(entry)
                self._update_stats(entry)
                logger.info(f"📝 Session écrite sans scheduler")

            logger.info(
                f"Session enregistrée - ID: {entry['session_id'][:8]}... - Durée: {entry['duration_seconds']}s"
            )
            logger.info(f"📁 Fichier: {self.sessions_file.name}")
            session_logger.info(json.dumps(entry, ensure_ascii=False))

        except Exception as e:
            logger.error(f"❌ Erreur lors de l'enregistrement de la session: {e}")
            # Toujours essayer de logger dans le fichier local
            try:
                session_logger.info(json.dumps(entry, ensure_ascii=False))
            except:
                pass

    def _write_session(self, entry: dict):
        """Écrit une entrée de session dans le fichier JSON (format newline-delimited)"""
        try:
            self.sessions_dir.mkdir(parents=True, exist_ok=True)

            # Écrire en mode append avec une nouvelle ligne pour chaque entrée
            with self.sessions_file.open("a", encoding="utf-8") as f:
                f.write(json.dumps(entry, ensure_ascii=False) + "\n")
                f.flush()  # Forcer l'écriture sur le disque

            # Vérifier que le fichier existe et a du contenu
            if self.sessions_file.exists():
                size = self.sessions_file.stat().st_size
                logger.debug(
                    f"📊 Fichier {self.sessions_file.name} - Taille: {size} octets"
                )

        except Exception as e:
            logger.error(f"❌ Erreur lors de l'écriture dans {self.sessions_file}: {e}")

    def _update_stats(self, session_entry: dict):
        """Met à jour les statistiques globales"""
        # Charger les stats existantes
        stats = {}
        if self.stats_file.exists():
            try:
                with self.stats_file.open("r", encoding="utf-8") as f:
                    stats = json.load(f)
            except json.JSONDecodeError:
                stats = {}

        # Initialiser la structure si nécessaire
        if "total_sessions" not in stats:
            stats = {
                "total_sessions": 0,
                "total_duration_seconds": 0,
                "average_duration_seconds": 0,
                "artworks_recommended": {},
                "recommendation_types": {
                    "name_date_place": 0,
                    "emotions": 0,
                    "none": 0,
                },
                "first_session": session_entry["datetime"],
                "last_session": session_entry["datetime"],
            }

        # Mettre à jour les compteurs
        stats["total_sessions"] += 1
        stats["total_duration_seconds"] += session_entry.get("duration_seconds", 0)
        stats["average_duration_seconds"] = (
            stats["total_duration_seconds"] / stats["total_sessions"]
        )
        stats["last_session"] = session_entry["datetime"]

        # Compter les types de recommandation
        rec_type = session_entry.get("recommendation_type", "none")
        if rec_type in stats["recommendation_types"]:
            stats["recommendation_types"][rec_type] += 1

        # Compter les œuvres recommandées
        artwork = session_entry.get("recommended_artwork")
        if artwork and artwork != "Aucune œuvre trouvée":
            if artwork not in stats["artworks_recommended"]:
                stats["artworks_recommended"][artwork] = 0
            stats["artworks_recommended"][artwork] += 1

        # Trouver l'œuvre la plus populaire
        if stats["artworks_recommended"]:
            most_popular = max(
                stats["artworks_recommended"].items(), key=lambda x: x[1]
            )
            stats["most_popular_artwork"] = {
                "title": most_popular[0],
                "count": most_popular[1],
                "percentage": (most_popular[1] / stats["total_sessions"]) * 100,
            }

        # Calculer les pourcentages d'utilisation
        total = stats["total_sessions"]
        if total > 0:
            stats["recommendation_percentages"] = {
                k: (v / total) * 100 for k, v in stats["recommendation_types"].items()
            }

        stats["last_updated"] = datetime.now().isoformat()

        # Sauvegarder les stats mises à jour
        with self.stats_file.open("w", encoding="utf-8") as f:
            json.dump(stats, f, indent=2, ensure_ascii=False)

    def get_statistics(self) -> dict:
        """Retourne les statistiques globales"""
        if self.stats_file.exists():
            try:
                with self.stats_file.open("r", encoding="utf-8") as f:
                    return json.load(f)
            except Exception as e:
                logger.error(f"Erreur lecture stats: {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")

        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("Terminer", variant="primary")

    status_message = gr.Markdown("")

    def on_info_submit(firstname, birthday, city, state):
        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()