95 lines
3.8 KiB
Python
95 lines
3.8 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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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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# --- 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
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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 et VIDER le registre pour garantir une base saine
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logger.info("Creating a new RecognizerRegistry...")
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registry = RecognizerRegistry()
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logger.info(f"Initial registry state supports: {registry.supported_languages}")
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# === CORRECTION DÉFINITIVE : VIDER LE REGISTRE ===
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registry.recognizers.clear()
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logger.info(f"Registry cleared. Now supports: {registry.supported_languages}")
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# 4. Ajouter les détecteurs requis
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# Ajouter les détecteurs personnalisés pour le français
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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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registry.add_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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logger.info(f"Added custom recognizer: {recognizer_conf['name']}")
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# Ajouter le support des entités de base (PERSON, LOC) pour les deux langues
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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("Added SpacyRecognizer for 'en' and 'fr'.")
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logger.info(f"Final registry state. Now supports: {registry.supported_languages}")
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# 5. Créer l'AnalyzerEngine
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logger.info("Initializing AnalyzerEngine...")
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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=config.get("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 est 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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try:
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data = request.get_json(force=True)
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text = data.get("text", "")
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lang = data.get("language", "fr")
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if not text: return jsonify({"error": "text field is missing"}), 400
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results = analyzer.analyze(text=text, language=lang)
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return make_response(jsonify([res.to_dict() for res in results]), 200)
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except Exception as e:
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logger.exception("Error during analysis request.")
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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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