99 lines
4.0 KiB
Python
99 lines
4.0 KiB
Python
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.deny_list_recognizer import DenyListRecognizer
|
|
|
|
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
|
logger = logging.getLogger(__name__)
|
|
|
|
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 FileNotFoundError:
|
|
logger.error(f"Configuration file not found at {CONFIG_FILE_PATH}. Using default empty config.")
|
|
except yaml.YAMLError as e:
|
|
logger.error(f"Error parsing YAML configuration file: {e}. Using default empty config.")
|
|
|
|
supported_languages_from_config = config.get("supported_languages", ["en"])
|
|
logger.info(f"Languages supported according to config: {supported_languages_from_config}")
|
|
|
|
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()}")
|
|
|
|
# --- CORRECTION ICI ---
|
|
# On s'assure que le registry est initialisé avec les mêmes langues que le reste
|
|
logger.info("Creating and populating recognizer registry...")
|
|
registry = RecognizerRegistry()
|
|
registry.load_predefined_recognizers(languages=supported_languages_from_config) # On utilise la variable
|
|
logger.info(f"Recognizer registry loaded for languages: {supported_languages_from_config}")
|
|
|
|
# Ajout des recognizers personnalisés
|
|
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
|
|
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.")
|
|
|
|
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 utilise la variable ici aussi
|
|
default_score_threshold=config.get("ner_model_configuration", {}).get("confidence_threshold", {}).get("default", 0.35)
|
|
)
|
|
logger.info("AnalyzerEngine initialized successfully.")
|
|
|
|
@app.route('/analyze', methods=['POST'])
|
|
def analyze_text():
|
|
try:
|
|
data = request.get_json(force=True)
|
|
text_to_analyze = data.get("text", "")
|
|
language = data.get("language", "en")
|
|
|
|
if not text_to_analyze:
|
|
return jsonify({"error": "text field is missing or empty"}), 400
|
|
|
|
results = analyzer.analyze(
|
|
text=text_to_analyze,
|
|
language=language,
|
|
allow_list=allow_list_terms
|
|
)
|
|
|
|
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.")
|
|
return jsonify({"error": str(e)}), 500
|
|
|
|
if __name__ == '__main__':
|
|
app.run(host='0.0.0.0', port=5001)
|