102 lines
4.0 KiB
Python
102 lines
4.0 KiB
Python
import os
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import logging
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import yaml
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from flask import Flask, request, jsonify, make_response
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from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
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from presidio_analyzer.nlp_engine import NlpEngineProvider
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# Configuration du logging pour un meilleur débogage
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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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# --- CHARGEMENT MANUEL ET EXPLICITE DE LA CONFIGURATION ---
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# Chemin vers le fichier de configuration, défini par la variable d'environnement
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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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try:
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with open(CONFIG_FILE_PATH, 'r') 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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except FileNotFoundError:
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logger.error(f"Configuration file not found at {CONFIG_FILE_PATH}. Exiting.")
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config = {}
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except yaml.YAMLError as e:
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logger.error(f"Error parsing YAML configuration file: {e}. Exiting.")
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config = {}
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# Création du fournisseur de moteur NLP basé sur la configuration
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logger.info("Creating NLP engine provider...")
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nlp_engine_provider = NlpEngineProvider(nlp_configuration=config.get("nlp_engine_configuration"))
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nlp_engine = nlp_engine_provider.create_engine()
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logger.info(f"NLP engine created with models for: {nlp_engine.get_supported_languages()}")
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# Création du registre de recognizers basé sur la configuration
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logger.info("Creating and populating recognizer registry...")
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registry = RecognizerRegistry()
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registry.load_predefined_recognizers(languages=config.get("supported_languages", ["en"]))
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# Ajout des recognizers personnalisés définis dans le 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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registry.add_pattern_recognizer(
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name=recognizer_conf['name'],
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patterns=recognizer_conf['patterns'],
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context=recognizer_conf.get('context'),
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supported_language=recognizer_conf['supported_language'],
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supported_entity=recognizer_conf['entity_name']
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)
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logger.info(f"Loaded custom recognizer: {recognizer_conf['name']}")
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# --- FIN DU CHARGEMENT DE LA CONFIGURATION ---
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# Initialisation de l'application Flask
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app = Flask(__name__)
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# Initialisation du moteur Presidio Analyzer avec les composants que nous avons créés
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logger.info("Initializing AnalyzerEngine with custom configuration...")
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analyzer = AnalyzerEngine(
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nlp_engine=nlp_engine,
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registry=registry,
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supported_languages=config.get("supported_languages", ["en"])
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)
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# On ajoute l'allow_list manuellement
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allow_list = config.get("allow_list", [])
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if allow_list:
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registry.add_recognizer(DenyListRecognizer(supported_entity="GENERIC_PII", deny_list=allow_list))
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logger.info(f"Loaded {len(allow_list)} terms into the allow list (deny list recognizer).")
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logger.info("AnalyzerEngine initialized successfully.")
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@app.route('/analyze', methods=['POST'])
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def analyze_text():
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try:
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data = request.get_json(force=True)
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text_to_analyze = data.get("text", "")
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language = data.get("language", "en")
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if not text_to_analyze:
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return jsonify({"error": "text field is missing or empty"}), 400
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# Le seuil de confiance est appliqué ici, à la volée
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score_threshold = data.get("score_threshold", config.get("ner_model_configuration", {}).get("confidence_threshold", {}).get("default", 0.35))
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results = analyzer.analyze(
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text=text_to_analyze,
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language=language,
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score_threshold=score_threshold,
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allow_list=allow_list # On passe la allow list ici aussi
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)
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response_data = [res.to_dict() for res in results]
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return make_response(jsonify(response_data), 200)
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except Exception as e:
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logger.exception("Error during analysis.")
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return jsonify({"error": str(e)}), 500
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5001)
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