Update app.py

app.py

@@ -3,37 +3,37 @@ import logging
 import yaml
 from flask import Flask, request, jsonify, make_response
 
-from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
+# Import des classes nécessaires de Presidio
+from presidio_analyzer import AnalyzerEngine, RecognizerRegistry, PatternRecognizer, Pattern
 from presidio_analyzer.nlp_engine import NlpEngineProvider
+from presidio_analyzer.recognizer_registry.recognizer_registry import RecognizerRegistry
+from presidio_analyzer.recognizer_registry.deny_list_recognizer import DenyListRecognizer
 
-# Configuration du logging pour un meilleur débogage
+# Configuration du logging
 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 ---
-
-# Chemin vers le fichier de configuration, défini par la variable d'environnement
 CONFIG_FILE_PATH = os.environ.get("PRESIDIO_ANALYZER_CONFIG_FILE", "conf/default.yaml")
 logger.info(f"Loading configuration from: {CONFIG_FILE_PATH}")
 
+config = {}
 try:
-    with open(CONFIG_FILE_PATH, 'r') as f:
+    with open(CONFIG_FILE_PATH, 'r', encoding='utf-8') as f:
         config = yaml.safe_load(f)
     logger.info("Configuration file loaded successfully.")
 except FileNotFoundError:
-    logger.error(f"Configuration file not found at {CONFIG_FILE_PATH}. Exiting.")
+    logger.error(f"Configuration file not found at {CONFIG_FILE_PATH}. Using default empty config.")
-    config = {}
 except yaml.YAMLError as e:
-    logger.error(f"Error parsing YAML configuration file: {e}. Exiting.")
+    logger.error(f"Error parsing YAML configuration file: {e}. Using default empty config.")
-    config = {}
 
-# Création du fournisseur de moteur NLP basé sur la configuration
+# Création du fournisseur de moteur NLP
 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 basé sur la configuration
+# Création du registre de recognizers
 logger.info("Creating and populating recognizer registry...")
 registry = RecognizerRegistry()
 registry.load_predefined_recognizers(languages=config.get("supported_languages", ["en"]))
@@ -41,34 +41,41 @@ registry.load_predefined_recognizers(languages=config.get("supported_languages",
 # Ajout des recognizers personnalisés définis dans le YAML
 custom_recognizers_conf = config.get("recognizers", [])
 for recognizer_conf in custom_recognizers_conf:
-    registry.add_pattern_recognizer(
+    patterns = [Pattern(name=p['name'], regex=p['regex'], score=p['score']) for p in recognizer_conf['patterns']]
+    
+    # On crée une instance de PatternRecognizer
+    custom_recognizer = PatternRecognizer(
+        supported_entity=recognizer_conf['entity_name'],
         name=recognizer_conf['name'],
-        patterns=recognizer_conf['patterns'],
-        context=recognizer_conf.get('context'),
         supported_language=recognizer_conf['supported_language'],
-        supported_entity=recognizer_conf['entity_name']
+        patterns=patterns,
+        context=recognizer_conf.get('context')
     )
-    logger.info(f"Loaded custom recognizer: {recognizer_conf['name']}")
+    
+    # --- CORRECTION DE LA LIGNE D'ERREUR ---
+    # La méthode correcte est 'add_recognizer', pas 'add_pattern_recognizer'
+    registry.add_recognizer(custom_recognizer)
+    logger.info(f"Loaded custom recognizer: {custom_recognizer.name}")
 
-# --- FIN DU CHARGEMENT DE LA CONFIGURATION ---
+
+# Ajout de l'allow_list (DenyListRecognizer)
+allow_list_terms = [item['text'] for item in config.get("allow_list", []) if isinstance(item, dict) and 'text' in item]
+if allow_list_terms:
+    deny_list_recognizer = DenyListRecognizer(supported_entity="GENERIC_PII", deny_list=allow_list_terms)
+    registry.add_recognizer(deny_list_recognizer)
+    logger.info(f"Loaded {len(allow_list_terms)} terms into the allow list (as a deny list recognizer).")
 
 # Initialisation de l'application Flask
 app = Flask(__name__)
 
-# Initialisation du moteur Presidio Analyzer avec les composants que nous avons créés
+# Initialisation du moteur Presidio Analyzer avec nos composants créés
 logger.info("Initializing AnalyzerEngine with custom configuration...")
 analyzer = AnalyzerEngine(
     nlp_engine=nlp_engine,
     registry=registry,
-    supported_languages=config.get("supported_languages", ["en"])
+    supported_languages=config.get("supported_languages", ["en"]),
+    default_score_threshold=config.get("ner_model_configuration", {}).get("confidence_threshold", {}).get("default", 0.35)
 )
-
-# On ajoute l'allow_list manuellement
-allow_list = config.get("allow_list", [])
-if allow_list:
-    registry.add_recognizer(DenyListRecognizer(supported_entity="GENERIC_PII", deny_list=allow_list))
-    logger.info(f"Loaded {len(allow_list)} terms into the allow list (deny list recognizer).")
-
 logger.info("AnalyzerEngine initialized successfully.")
 
 @app.route('/analyze', methods=['POST'])
@@ -81,16 +88,7 @@ def analyze_text():
         if not text_to_analyze:
              return jsonify({"error": "text field is missing or empty"}), 400
         
-        # Le seuil de confiance est appliqué ici, à la volée
+        results = analyzer.analyze(text=text_to_analyze, language=language)
-        score_threshold = data.get("score_threshold", config.get("ner_model_configuration", {}).get("confidence_threshold", {}).get("default", 0.35))
-        
-        results = analyzer.analyze(
-            text=text_to_analyze,
-            language=language,
-            score_threshold=score_threshold,
-            allow_list=allow_list # On passe la allow list ici aussi
-        )
-        
         response_data = [res.to_dict() for res in results]
         return make_response(jsonify(response_data), 200)
     except Exception as e: