131 lines
5.8 KiB
Python
131 lines
5.8 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, PatternRecognizer, Pattern
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from presidio_analyzer.nlp_engine import NlpEngineProvider
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# On importe les classes des détecteurs prédéfinis que l'on veut pouvoir utiliser depuis le YAML
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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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# 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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# --- Dictionnaire pour mapper les noms du YAML aux classes Python ---
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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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# 3. Créer le registre de recognizers EN SUIVANT LE YAML
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logger.info("Creating and populating recognizer registry from config file...")
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registry = RecognizerRegistry()
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supported_languages = config.get("supported_languages", ["en"])
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# === DÉBUT DE LA CORRECTION MAJEURE ===
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# Étape A: On pré-construit tous les détecteurs personnalisés ("custom") définis dans la section 'recognizers'
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custom_recognizers = {}
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for recognizer_conf in config.get("recognizers", []):
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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_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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custom_recognizers[recognizer_conf['name']] = custom_recognizer
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# Étape B: On parcourt la liste 'recognizer_registry' pour activer les détecteurs demandés
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for recognizer_name in config.get("recognizer_registry", []):
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# Cas 1: Le détecteur est dans notre liste de détecteurs personnalisés
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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 from registry list: {recognizer_name}")
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# Cas 2: Le détecteur est un détecteur prédéfini connu
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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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# On crée une instance pour chaque langue supportée (en, fr)
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for lang in supported_languages:
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# CORRECTION : On utilise le mot-clé au singulier 'supported_language'
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instance = recognizer_class(supported_language=lang)
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registry.add_recognizer(instance)
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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 in custom or predefined lists.")
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# === FIN DE LA CORRECTION MAJEURE ===
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# 4. Créer l'AnalyzerEngine avec tous les composants
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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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)
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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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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 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:
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return jsonify({"error": "Analyzer engine is not available. Check startup logs for errors."}), 500
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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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default_lang = analyzer.supported_languages[0] if analyzer.supported_languages else "en"
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language = data.get("language", default_lang)
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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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results = analyzer.analyze(text=text_to_analyze, language=language)
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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(f"Error during analysis request for language '{language}'.")
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if "No matching recognizers" in str(e):
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return jsonify({"error": f"No recognizers available for language '{language}'. Please ensure the language model and recognizers are configured."}), 400
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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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