removing db connexion on HF

App/database.py

@@ -3,13 +3,20 @@ from dotenv import load_dotenv
 from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker, declarative_base
 
+# Tentative d'import SQLAlchemy uniquement si disponible 
+try: 
+    SQLALCHEMY_AVAILABLE = True 
+except ModuleNotFoundError: 
+    SQLALCHEMY_AVAILABLE = False
+
 load_dotenv()
 
 # Détection si on est en CI (GitHub Actions) ou en test 
 IS_CI = os.getenv("CI") == "true"
 IS_PYTEST = "pytest" in os.getenv("PYTHONPATH", "") or os.getenv("PYTEST_CURRENT_TEST") is not None 
+IS_HF = os.getenv("SPACE_ID") is not None # Hugging Face 
 
-SKIP_DB = IS_CI or IS_PYTEST
+SKIP_DB = IS_CI or IS_PYTEST or IS_HF or not SQLALCHEMY_AVAILABLE
 
 DB_USER = os.getenv("DB_USER", "postgres")
 DB_PASSWORD = os.getenv("DB_PASSWORD", "password")
@@ -19,7 +26,7 @@ DB_NAME = os.getenv("DB_NAME", "test_db")
 
 DATABASE_URL = (f"postgresql+psycopg2://{DB_USER}:{DB_PASSWORD}"f"@{DB_HOST}:{DB_PORT}/{DB_NAME}")
 
-Base = declarative_base()
+Base = declarative_base() if SQLALCHEMY_AVAILABLE else None
 
 if not SKIP_DB:
     engine = create_engine(DATABASE_URL)

App/predict.py

@@ -4,8 +4,16 @@ from App.schemas import EmployeeFeatures
 import json
 from pathlib import Path
 from huggingface_hub import hf_hub_download
-from sqlalchemy.orm import Session
-from App.database import SessionLocal
+
+
+# Import SQLAlchemy uniquement si disponible 
+try: 
+    from sqlalchemy.orm import Session 
+    SQLALCHEMY_AVAILABLE = True 
+except ModuleNotFoundError: 
+    SQLALCHEMY_AVAILABLE = False 
+    
+from App.database import SessionLocal 
 from App.model import Input, Predictions
 
 MODEL_REPO = "Diaure/xgb_model"
@@ -16,7 +24,6 @@ classes_mapping = None
 Features = list(EmployeeFeatures.model_fields.keys())
 
 
-
 # Chargement des fichiers: fonction pour charger le modèle, le mapping afin de permettre à l'API de démarrer m^me si les éléments ne sont pas présents
 def files_load():
     global model, classes_mapping
@@ -46,7 +53,10 @@ def predict_employee(data: dict):
     pred = model.predict(df)[0]
     proba = model.predict_proba(df)[0][1]
 
-    db: Session = SessionLocal() if SessionLocal is not None else None
+    # DB désactivée si SQLAlchemy indisponible ou SessionLocal = None 
+    if SQLALCHEMY_AVAILABLE and SessionLocal is not None:
+        db: Session = SessionLocal() 
+    else: db = None
 
     if db is not None:
         try:
@@ -62,7 +72,7 @@ def predict_employee(data: dict):
             db.commit()
         
         except Exception as e: 
-            print("🔥 ERREUR DB :", e) 
+            print("ERREUR DB:", e) 
             raise e
 
         finally:

requirements.txt

@@ -12,5 +12,4 @@ joblib==1.4.2
 pandas==2.2.2
 scikit-learn==1.4.2
 xgboost ==2.0.3
-huggingface-hub ==1.3.1
 python-dotenv ==1.2.1

scripts/create_tables.py

@@ -1,6 +1,9 @@
-from App.database import engine
-from App.database import Base
+from App.database import engine, Base
 
-Base.metadata.create_all(bind=engine)
 
-print("Tables créées avec succès")
+Base.metadata.create_all(bind=engine)
+if engine is None: 
+    print("DB désactivée : aucune table créée.") 
+else: 
+    Base.metadata.create_all(bind=engine) 
+    print("Tables créées avec succès")