diff --git a/app.py b/app.py
index 55dadcb..c6523ff 100644
--- a/app.py
+++ b/app.py
@@ -3,15 +3,13 @@ import logging
 import yaml
 from flask import Flask, request, jsonify, make_response
 
-# Import des classes nécessaires de Presidio
 from presidio_analyzer import AnalyzerEngine, RecognizerRegistry, PatternRecognizer, Pattern
 from presidio_analyzer.nlp_engine import NlpEngineProvider
+from presidio_analyzer.deny_list_recognizer import DenyListRecognizer
 
-# Configuration du logging pour un meilleur débogage
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
-# --- CHARGEMENT MANUEL ET EXPLICITE DE LA CONFIGURATION ---
 CONFIG_FILE_PATH = os.environ.get("PRESIDIO_ANALYZER_CONFIG_FILE", "conf/default.yaml")
 logger.info(f"Loading configuration from: {CONFIG_FILE_PATH}")
 
@@ -25,18 +23,22 @@ except FileNotFoundError:
 except yaml.YAMLError as e:
     logger.error(f"Error parsing YAML configuration file: {e}. Using default empty config.")
 
-# Création du fournisseur de moteur NLP
+supported_languages_from_config = config.get("supported_languages", ["en"])
+logger.info(f"Languages supported according to config: {supported_languages_from_config}")
+
 logger.info("Creating NLP engine provider...")
 nlp_engine_provider = NlpEngineProvider(nlp_configuration=config.get("nlp_engine_configuration"))
 nlp_engine = nlp_engine_provider.create_engine()
 logger.info(f"NLP engine created with models for: {nlp_engine.get_supported_languages()}")
 
-# Création du registre de recognizers
+# --- CORRECTION ICI ---
+# On s'assure que le registry est initialisé avec les mêmes langues que le reste
 logger.info("Creating and populating recognizer registry...")
 registry = RecognizerRegistry()
-registry.load_predefined_recognizers(languages=config.get("supported_languages", ["en"]))
+registry.load_predefined_recognizers(languages=supported_languages_from_config) # On utilise la variable
+logger.info(f"Recognizer registry loaded for languages: {supported_languages_from_config}")
 
-# Ajout des recognizers personnalisés définis dans le YAML
+# Ajout des recognizers personnalisés
 custom_recognizers_conf = config.get("recognizers", [])
 for recognizer_conf in custom_recognizers_conf:
     patterns = [Pattern(name=p['name'], regex=p['regex'], score=p['score']) for p in recognizer_conf['patterns']]
@@ -52,18 +54,12 @@ for recognizer_conf in custom_recognizers_conf:
     registry.add_recognizer(custom_recognizer)
     logger.info(f"Loaded custom recognizer: {custom_recognizer.name}")
 
-# --- CORRECTION DE LA GESTION DE L'ALLOW LIST ---
-# On prépare la liste de mots à ignorer. Pas besoin d'importer DenyListRecognizer.
+# Préparation de l'allow_list
 allow_list_config = config.get("allow_list", [])
-# On extrait juste les chaînes de caractères de la liste.
 allow_list_terms = [item if isinstance(item, str) else item.get('text') for item in allow_list_config if item]
 if allow_list_terms:
     logger.info(f"Prepared {len(allow_list_terms)} terms for the allow list.")
-else:
-    logger.info("No allow list terms found in configuration.")
 
-
-# Initialisation de l'application Flask
 app = Flask(__name__)
 
 # Initialisation du moteur Presidio Analyzer
@@ -71,7 +67,7 @@ logger.info("Initializing AnalyzerEngine with custom configuration...")
 analyzer = AnalyzerEngine(
     nlp_engine=nlp_engine,
     registry=registry,
-    supported_languages=config.get("supported_languages", ["en"]),
+    supported_languages=supported_languages_from_config, # On utilise la variable ici aussi
     default_score_threshold=config.get("ner_model_configuration", {}).get("confidence_threshold", {}).get("default", 0.35)
 )
 logger.info("AnalyzerEngine initialized successfully.")
@@ -86,12 +82,10 @@ def analyze_text():
         if not text_to_analyze:
              return jsonify({"error": "text field is missing or empty"}), 400
         
-        # --- CORRECTION DE L'APPEL A ANALYZE ---
-        # On passe directement la liste de mots à ignorer au paramètre 'allow_list'
         results = analyzer.analyze(
             text=text_to_analyze,
             language=language,
-            allow_list=allow_list_terms 
+            allow_list=allow_list_terms
         )
         
         response_data = [res.to_dict() for res in results]