From 46a31119465a7844553b1b0303b1bceafcecdc65 Mon Sep 17 00:00:00 2001 From: Nacim Date: Mon, 23 Jun 2025 15:21:41 +0200 Subject: [PATCH] Update app.py --- app.py | 89 +++++++++++++++------------------------------------------- 1 file changed, 22 insertions(+), 67 deletions(-) diff --git a/app.py b/app.py index d7b45da..3c8173e 100644 --- a/app.py +++ b/app.py @@ -1,81 +1,37 @@ import os import logging -import yaml from flask import Flask, request, jsonify, make_response - -# Import des classes nécessaires de Presidio -from presidio_analyzer import AnalyzerEngine, RecognizerRegistry, PatternRecognizer, Pattern -from presidio_analyzer.nlp_engine import NlpEngineProvider +from presidio_analyzer import AnalyzerEngine # 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 --- -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', encoding='utf-8') as f: - config = yaml.safe_load(f) - logger.info("Configuration file loaded successfully.") -except Exception as e: - logger.exception(f"Could not load or parse configuration file at {CONFIG_FILE_PATH}") - # En cas d'échec, on continue avec une config vide pour ne pas planter, mais le service sera limité. - config = {} - -# On récupère les langues supportées depuis la config pour les utiliser partout -supported_languages_from_config = config.get("supported_languages", ["en"]) -logger.info(f"Languages supported according to config: {supported_languages_from_config}") - -# 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 -logger.info("Creating and populating recognizer registry...") -registry = RecognizerRegistry() -# On initialise le registre avec TOUTES les langues supportées -registry.load_predefined_recognizers(languages=supported_languages_from_config) - -# Ajout des recognizers personnalisés définis dans le YAML -custom_recognizers_conf = config.get("recognizers", []) -for recognizer_conf in custom_recognizers_conf: - patterns = [Pattern(name=p['name'], regex=p['regex'], score=p['score']) for p in recognizer_conf['patterns']] - custom_recognizer = PatternRecognizer( - supported_entity=recognizer_conf['entity_name'], - name=recognizer_conf['name'], - supported_language=recognizer_conf['supported_language'], - patterns=patterns, - context=recognizer_conf.get('context') - ) - registry.add_recognizer(custom_recognizer) - logger.info(f"Loaded custom recognizer: {custom_recognizer.name}") - -# Préparation de l'allow_list (simple liste de mots) -allow_list_config = config.get("allow_list", []) -allow_list_terms = [item if isinstance(item, str) else item.get('text') for item in allow_list_config if item] -if allow_list_terms: - logger.info(f"Prepared {len(allow_list_terms)} terms for the allow list.") - # Initialisation de l'application Flask app = Flask(__name__) -# Initialisation du moteur Presidio Analyzer -logger.info("Initializing AnalyzerEngine with custom configuration...") -analyzer = AnalyzerEngine( - nlp_engine=nlp_engine, - registry=registry, - supported_languages=supported_languages_from_config, # On s'assure de la cohérence ici aussi - default_score_threshold=config.get("ner_model_configuration", {}).get("confidence_threshold", {}).get("default", 0.35) -) -logger.info("AnalyzerEngine initialized successfully.") +# --- LAISSER PRESIDIO GÉRER L'INITIALISATION --- +# L'AnalyzerEngine, lorsqu'il est initialisé sans arguments, va automatiquement : +# 1. Chercher la variable d'environnement PRESIDIO_ANALYZER_CONFIG_FILE. +# 2. Lire le fichier de configuration (votre default.yaml). +# 3. Créer le moteur NLP, le registre de recognizers, et charger les recognizers +# personnalisés et l'allow_list, en s'assurant que les langues sont cohérentes. + +try: + logger.info("Initializing AnalyzerEngine using configuration from environment variable...") + analyzer = AnalyzerEngine() + logger.info("AnalyzerEngine initialized successfully.") + # Pour le débogage, on peut lister les recognizers pour une langue spécifique + logger.info(f"Loaded recognizers for 'fr': {[rec.name for rec in analyzer.get_recognizers(language='fr')]}") +except Exception as e: + logger.exception("FATAL: Error initializing AnalyzerEngine from configuration.") + analyzer = None @app.route('/analyze', methods=['POST']) def analyze_text(): + if not analyzer: + return jsonify({"error": "Analyzer engine not initialized"}), 500 + try: data = request.get_json(force=True) text_to_analyze = data.get("text", "") @@ -84,11 +40,10 @@ def analyze_text(): if not text_to_analyze: return jsonify({"error": "text field is missing or empty"}), 400 - # On passe directement la liste de mots à ignorer au paramètre 'allow_list' + # On n'a plus besoin de passer l'allow_list ici, l'Analyzer l'a déjà chargée results = analyzer.analyze( text=text_to_analyze, - language=language, - allow_list=allow_list_terms + language=language ) response_data = [res.to_dict() for res in results]