Update app.py
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Nacim
76
app.py
76
app.py
@@ -5,100 +5,74 @@ from flask import Flask, request, jsonify, make_response
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from presidio_analyzer import AnalyzerEngine, RecognizerRegistry, PatternRecognizer, Pattern
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from presidio_analyzer.nlp_engine import NlpEngineProvider
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from presidio_analyzer.predefined_recognizers import (
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CreditCardRecognizer, CryptoRecognizer, DateRecognizer, IpRecognizer,
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MedicalLicenseRecognizer, UrlRecognizer, SpacyRecognizer
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)
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from presidio_analyzer.predefined_recognizers import SpacyRecognizer
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# Configuration du logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# Initialisation de l'application Flask
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app = Flask(__name__)
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PREDEFINED_RECOGNIZERS_MAP = {
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"SpacyRecognizer": SpacyRecognizer,
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"CreditCardRecognizer": CreditCardRecognizer,
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"CryptoRecognizer": CryptoRecognizer,
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"DateRecognizer": DateRecognizer,
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"IpRecognizer": IpRecognizer,
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"MedicalLicenseRecognizer": MedicalLicenseRecognizer,
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"UrlRecognizer": UrlRecognizer,
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}
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# --- Initialisation Globale de l'Analyseur ---
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analyzer = None
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try:
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logger.info("--- Presidio Analyzer Service Starting ---")
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# 1. Charger la configuration depuis le fichier YAML
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CONFIG_FILE_PATH = os.environ.get("PRESIDIO_ANALYZER_CONFIG_FILE", "conf/default.yaml")
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logger.info(f"Loading configuration from: {CONFIG_FILE_PATH}")
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with open(CONFIG_FILE_PATH, 'r', encoding='utf-8') as f:
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config = yaml.safe_load(f)
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logger.info("Configuration file loaded successfully.")
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# 2. Créer le fournisseur de moteur NLP
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logger.info("Creating NLP engine provider...")
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provider = NlpEngineProvider(nlp_configuration=config)
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logger.info("Creating and populating recognizer registry from config file...")
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# 3. Créer un registre de recognizers VIDE
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logger.info("Creating a clean RecognizerRegistry...")
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registry = RecognizerRegistry()
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# === CORRECTION DÉFINITIVE : ASSURER UN REGISTRE PROPRE ===
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logger.info("Removing any default recognizers to ensure a clean slate...")
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registry.remove_all_recognizers()
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supported_languages = config.get("supported_languages", ["en"])
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# Construire les détecteurs personnalisés
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custom_recognizers = {}
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for recognizer_conf in config.get("recognizers", []):
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# 4. Charger les recognizers personnalisés (définis sous la clé 'recognizers')
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logger.info("Loading custom recognizers from YAML...")
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custom_recognizers_conf = config.get("recognizers", [])
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for recognizer_conf in custom_recognizers_conf:
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patterns = [Pattern(name=p['name'], regex=p['regex'], score=p['score']) for p in recognizer_conf['patterns']]
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custom_recognizers[recognizer_conf['name']] = PatternRecognizer(
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custom_recognizer = PatternRecognizer(
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supported_entity=recognizer_conf['entity_name'],
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name=recognizer_conf['name'],
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supported_language=recognizer_conf['supported_language'],
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patterns=patterns,
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context=recognizer_conf.get('context')
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)
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# Activer les détecteurs listés dans la configuration
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for recognizer_name in config.get("recognizer_registry", []):
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if recognizer_name in custom_recognizers:
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registry.add_recognizer(custom_recognizers[recognizer_name])
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logger.info(f"Loaded CUSTOM recognizer: {recognizer_name}")
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elif recognizer_name in PREDEFINED_RECOGNIZERS_MAP:
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recognizer_class = PREDEFINED_RECOGNIZERS_MAP[recognizer_name]
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for lang in supported_languages:
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# Le SpacyRecognizer est un cas spécial, il n'a pas de paramètre de langue
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if recognizer_class == SpacyRecognizer:
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if 'SpacyRecognizer_added' not in locals(): # Pour ne l'ajouter qu'une seule fois
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registry.add_recognizer(recognizer_class(supported_entities=config.get("spacy_entities", [])))
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logger.info(f"Loaded PREDEFINED singleton recognizer: {recognizer_name}")
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SpacyRecognizer_added = True
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else:
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instance = recognizer_class(supported_language=lang)
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registry.add_recognizer(instance)
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if recognizer_class != SpacyRecognizer:
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logger.info(f"Loaded PREDEFINED recognizer '{recognizer_name}' for languages: {supported_languages}")
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else:
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logger.warning(f"Recognizer '{recognizer_name}' from registry list was not found.")
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# On ajoute UNIQUEMENT les détecteurs customs définis pour le français
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if recognizer_conf['supported_language'] == 'fr':
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registry.add_recognizer(custom_recognizer)
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logger.info(f"Loaded and registered custom recognizer: {custom_recognizer.name}")
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# 5. Ajouter le SpacyRecognizer, qui est un cas spécial pour les entités de base
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registry.add_recognizer(SpacyRecognizer(supported_language="en"))
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registry.add_recognizer(SpacyRecognizer(supported_language="fr"))
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logger.info("Registered SpacyRecognizer for 'en' and 'fr'.")
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# 6. Créer l'AnalyzerEngine
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logger.info("Initializing AnalyzerEngine with custom components...")
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analyzer = AnalyzerEngine(
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nlp_engine=provider.create_engine(),
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registry=registry,
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supported_languages=supported_languages
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supported_languages=config.get("supported_languages", ["en", "fr"])
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)
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analyzer.set_allow_list(config.get("allow_list", []))
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logger.info("--- Presidio Analyzer Service Ready ---")
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logger.info(f"Final supported languages in registry: {registry.supported_languages}")
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except Exception as e:
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logger.exception("FATAL: Error during AnalyzerEngine initialization.")
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analyzer = None
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# Le reste du fichier Flask est identique
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# Le reste du fichier Flask reste identique...
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@app.route('/analyze', methods=['POST'])
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def analyze_text():
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if not analyzer: return jsonify({"error": "Analyzer engine is not available."}), 500
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