diff --git a/app.py b/app.py
index d6384f4..96e82d5 100644
--- a/app.py
+++ b/app.py
@@ -5,7 +5,7 @@ from flask import Flask, request, jsonify, make_response
 
 from presidio_analyzer import AnalyzerEngine, RecognizerRegistry, PatternRecognizer, Pattern
 from presidio_analyzer.nlp_engine import NlpEngineProvider
-# On importe les recognizers prédéfinis qu'on veut pouvoir utiliser
+# On importe les classes des détecteurs prédéfinis que l'on veut pouvoir utiliser depuis le YAML
 from presidio_analyzer.predefined_recognizers import (
     CreditCardRecognizer, CryptoRecognizer, DateRecognizer, IpRecognizer,
     MedicalLicenseRecognizer, UrlRecognizer, SpacyRecognizer
@@ -19,7 +19,6 @@ logger = logging.getLogger(__name__)
 app = Flask(__name__)
 
 # --- Dictionnaire pour mapper les noms du YAML aux classes Python ---
-# C'est ce qui nous permet de lire la liste 'recognizer_registry' du YAML
 PREDEFINED_RECOGNIZERS_MAP = {
     "SpacyRecognizer": SpacyRecognizer,
     "CreditCardRecognizer": CreditCardRecognizer,
@@ -30,7 +29,6 @@ PREDEFINED_RECOGNIZERS_MAP = {
     "UrlRecognizer": UrlRecognizer,
 }
 
-
 # --- Initialisation Globale de l'Analyseur ---
 analyzer = None
 try:
@@ -50,23 +48,14 @@ try:
     # 3. Créer le registre de recognizers EN SUIVANT LE YAML
     logger.info("Creating and populating recognizer registry from config file...")
     registry = RecognizerRegistry()
+    supported_languages = config.get("supported_languages", ["en"])
 
     # === DÉBUT DE LA CORRECTION MAJEURE ===
 
-    # A) Charger les recognizers PRÉDÉFINIS listés dans le YAML
-    supported_languages = config.get("supported_languages", ["en"])
-    for recognizer_name in config.get("recognizer_registry", []):
-        if recognizer_name in PREDEFINED_RECOGNIZERS_MAP:
-            recognizer_class = PREDEFINED_RECOGNIZERS_MAP[recognizer_name]
-            # On passe les langues supportées à chaque recognizer qu'on instancie
-            registry.add_recognizer(recognizer_class(supported_languages=supported_languages))
-            logger.info(f"Loaded predefined recognizer: {recognizer_name}")
-
-    # B) Charger les recognizers PERSONNALISÉS définis dans le YAML
-    custom_recognizers_conf = config.get("recognizers", [])
-    for recognizer_conf in custom_recognizers_conf:
+    # Étape A: On pré-construit tous les détecteurs personnalisés ("custom") définis dans la section 'recognizers'
+    custom_recognizers = {}
+    for recognizer_conf in config.get("recognizers", []):
         patterns = [Pattern(name=p['name'], regex=p['regex'], score=p['score']) for p in recognizer_conf['patterns']]
-        # On s'assure de ne pas recréer un recognizer prédéfini mais bien un custom
         custom_recognizer = PatternRecognizer(
             supported_entity=recognizer_conf['entity_name'],
             name=recognizer_conf['name'],
@@ -74,8 +63,26 @@ try:
             patterns=patterns,
             context=recognizer_conf.get('context')
         )
-        registry.add_recognizer(custom_recognizer)
-        logger.info(f"Loaded custom recognizer from YAML: {custom_recognizer.name}")
+        custom_recognizers[recognizer_conf['name']] = custom_recognizer
+    
+    # Étape B: On parcourt la liste 'recognizer_registry' pour activer les détecteurs demandés
+    for recognizer_name in config.get("recognizer_registry", []):
+        # Cas 1: Le détecteur est dans notre liste de détecteurs personnalisés
+        if recognizer_name in custom_recognizers:
+            registry.add_recognizer(custom_recognizers[recognizer_name])
+            logger.info(f"Loaded custom recognizer from registry list: {recognizer_name}")
+        
+        # Cas 2: Le détecteur est un détecteur prédéfini connu
+        elif recognizer_name in PREDEFINED_RECOGNIZERS_MAP:
+            recognizer_class = PREDEFINED_RECOGNIZERS_MAP[recognizer_name]
+            # On crée une instance pour chaque langue supportée (en, fr)
+            for lang in supported_languages:
+                # CORRECTION : On utilise le mot-clé au singulier 'supported_language'
+                instance = recognizer_class(supported_language=lang)
+                registry.add_recognizer(instance)
+            logger.info(f"Loaded predefined recognizer '{recognizer_name}' for languages: {supported_languages}")
+        else:
+            logger.warning(f"Recognizer '{recognizer_name}' from registry list was not found in custom or predefined lists.")
 
     # === FIN DE LA CORRECTION MAJEURE ===
 
@@ -86,7 +93,6 @@ try:
         registry=registry,
         supported_languages=supported_languages
     )
-    # L'allow list est chargée automatiquement par l'AnalyzerEngine
     analyzer.set_allow_list(config.get("allow_list", []))
 
     logger.info("--- Presidio Analyzer Service Ready ---")
@@ -95,6 +101,7 @@ except Exception as e:
     logger.exception("FATAL: Error during AnalyzerEngine initialization.")
     analyzer = None 
 
+# Le reste du fichier Flask reste identique...
 @app.route('/analyze', methods=['POST'])
 def analyze_text():
     if not analyzer:
@@ -103,17 +110,13 @@ def analyze_text():
     try:
         data = request.get_json(force=True)
         text_to_analyze = data.get("text", "")
-        # Utiliser la première langue supportée comme langue par défaut si non fournie
         default_lang = analyzer.supported_languages[0] if analyzer.supported_languages else "en"
         language = data.get("language", default_lang)
 
         if not text_to_analyze:
              return jsonify({"error": "text field is missing or empty"}), 400
         
-        results = analyzer.analyze(
-            text=text_to_analyze,
-            language=language
-        )
+        results = analyzer.analyze(text=text_to_analyze, language=language)
         
         response_data = [res.to_dict() for res in results]
         return make_response(jsonify(response_data), 200)