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Update app.py

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Nacim
2025-06-23 17:18:35 +02:00
committed by GitHub
parent 46abf1f010
commit e092308998
1 changed files with 34 additions and 56 deletions
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app.py
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@@ -1,11 +1,9 @@
import os
import logging
import yaml
from flask import Flask, request, jsonify, make_response
from presidio_analyzer import AnalyzerEngine, RecognizerRegistry, PatternRecognizer, Pattern
from presidio_analyzer.nlp_engine import NlpEngineProvider
from presidio_analyzer.predefined_recognizers import SpacyRecognizer
# On importe UNIQUEMENT le Provider, c'est lui qui gère tout.
from presidio_analyzer import AnalyzerEngineProvider
# Configuration du logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
@@ -14,60 +12,27 @@ logger = logging.getLogger(__name__)
# Initialisation de l'application Flask
app = Flask(__name__)
# --- Initialisation Globale de l'Analyseur ---
# --- Initialisation Globale de l'Analyseur via le Provider ---
analyzer = None
try:
logger.info("--- Presidio Analyzer Service Starting ---")
# 1. Charger la configuration
# Le chemin vers le fichier de configuration est toujours défini par la variable d'environnement
CONFIG_FILE_PATH = os.environ.get("PRESIDIO_ANALYZER_CONFIG_FILE", "conf/default.yaml")
logger.info(f"Loading configuration from: {CONFIG_FILE_PATH}")
with open(CONFIG_FILE_PATH, 'r', encoding='utf-8') as f:
config = yaml.safe_load(f)
logger.info("Configuration file loaded successfully.")
# 2. Créer le fournisseur de moteur NLP
logger.info("Creating NLP engine provider...")
provider = NlpEngineProvider(nlp_configuration=config)
# On utilise le Provider pour lire le fichier et créer le moteur
# C'est la méthode officielle et robuste.
provider = AnalyzerEngineProvider(analyzer_engine_conf_file=CONFIG_FILE_PATH)
analyzer = provider.create_engine()
# 3. Créer le registre. Il contient déjà les détecteurs anglais par défaut.
logger.info("Creating RecognizerRegistry (with default EN recognizers)...")
registry = RecognizerRegistry()
logger.info(f"Initial registry state supports: {registry.supported_languages}")
# 4. AJOUTER les détecteurs français à ce registre existant
logger.info("Adding French recognizers to the existing registry...")
# Ajouter le support des entités de base (PERSON, LOC) pour le français
registry.add_recognizer(SpacyRecognizer(supported_language="fr"))
logger.info("Added SpacyRecognizer for 'fr'.")
# Ajouter tous vos détecteurs personnalisés (qui sont pour 'fr')
for recognizer_conf in config.get("recognizers", []):
patterns = [Pattern(name=p['name'], regex=p['regex'], score=p['score']) for p in recognizer_conf['patterns']]
registry.add_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')
))
logger.info(f"Added custom recognizer '{recognizer_conf['name']}' for language 'fr'")
logger.info(f"Final registry state. Should now support: {registry.supported_languages}")
# 5. Créer l'AnalyzerEngine
logger.info("Initializing AnalyzerEngine...")
analyzer = AnalyzerEngine(
nlp_engine=provider.create_engine(),
registry=registry,
supported_languages=config.get("supported_languages")
)
analyzer.set_allow_list(config.get("allow_list", []))
# L'allow_list est aussi gérée par le provider, mais on peut la surcharger si besoin
# from presidio_analyzer.store import AllowListStore
# allow_list_store = AllowListStore()
# allow_list_store.set_allow_list(provider.get_configuration().get("allow_list", []))
# analyzer.allow_list_store = allow_list_store
logger.info("--- Presidio Analyzer Service Ready ---")
logger.info(f"SUCCESS: Final analyzer languages are: {analyzer.supported_languages}")
logger.info(f"Analyzer created successfully, supporting languages: {analyzer.supported_languages}")
except Exception as e:
logger.exception("FATAL: Error during AnalyzerEngine initialization.")
@@ -76,16 +41,29 @@ except Exception as e:
# Le reste du fichier Flask est identique
@app.route('/analyze', methods=['POST'])
def analyze_text():
if not analyzer: return jsonify({"error": "Analyzer engine is not available."}), 500
if not analyzer:
return jsonify({"error": "Analyzer engine is not available. Check startup logs."}), 500
try:
data = request.get_json(force=True)
text = data.get("text", "")
lang = data.get("language", "fr")
if not text: return jsonify({"error": "text field is missing"}), 400
results = analyzer.analyze(text=text, language=lang)
return make_response(jsonify([res.to_dict() for res in results]), 200)
text_to_analyze = data.get("text", "")
language = data.get("language", "fr")
if not text_to_analyze:
return jsonify({"error": "text field is missing or empty"}), 400
results = analyzer.analyze(
text=text_to_analyze,
language=language
)
response_data = [res.to_dict() for res in results]
return make_response(jsonify(response_data), 200)
except Exception as e:
logger.exception("Error during analysis request.")
logger.exception(f"Error during analysis for language '{language}'.")
if "No matching recognizers" in str(e):
return jsonify({"error": f"No recognizers available for language '{language}'."}), 400
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':