fix: replace deprecated @app.on_event('startup') with lifespan context manager in main.py

Author: DHANUSHGCODE

backend/main.py

@@ -1,3 +1,4 @@
+from contextlib import asynccontextmanager
 from fastapi import FastAPI, Depends, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from sqlalchemy.orm import Session
@@ -10,36 +11,18 @@
 
 # Add current directory to path so we can import from backend
 sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
 from database import SecurityLog, SessionLocal, engine, init_db
 import schemas
 
-# Fix #5: Corrected typo "Thread" -> "Threat"
-app = FastAPI(title="AI Security Monitor", description="Real-time Threat Detection API")
-
-# Fix #4: Added Docker service hostname 'frontend' to CORS origins
-# so the frontend container can reach the backend inside Docker network
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=[
-        "http://localhost:3000",
-        "http://frontend:3000",
-    ],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Initialize DB
-init_db()
-
 # Load Model (Global)
 model = None
 encoders = None
+
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 MODEL_PATH = os.path.join(BASE_DIR, "../ai-model/isolation_forest_model.pkl")
 ENCODERS_PATH = os.path.join(BASE_DIR, "../ai-model/encoders.pkl")
 
+
 def load_model():
     global model, encoders
     if os.path.exists(MODEL_PATH):
@@ -52,9 +35,35 @@ def load_model():
     else:
         print("Model file not found. Run: python ai-model/train_model.py")
 
-@app.on_event("startup")
-async def startup_event():
+
+# Fix: Use lifespan context manager instead of deprecated @app.on_event('startup')
+@asynccontextmanager
+async def lifespan(app: FastAPI):
     load_model()
+    init_db()
+    yield
+
+
+# Fix #5: Corrected typo "Thread" -> "Threat"
+app = FastAPI(
+    title="AI Security Monitor",
+    description="Real-time Threat Detection API",
+    lifespan=lifespan,
+)
+
+# Fix #4: Added Docker service hostname 'frontend' to CORS origins
+# so the frontend container can reach the backend inside Docker network
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=[
+        "http://localhost:3000",
+        "http://frontend:3000",
+    ],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 
 # Dependency
 def get_db():
@@ -64,10 +73,12 @@ def get_db():
     finally:
         db.close()
 
+
 @app.get("/")
 def read_root():
     return {"message": "AI Security Monitor API is running"}
 
+
 @app.post("/logs/", response_model=schemas.LogResponse)
 def create_log(log: schemas.LogCreate, db: Session = Depends(get_db)):
     db_log = SecurityLog(**log.dict())
@@ -76,11 +87,13 @@ def create_log(log: schemas.LogCreate, db: Session = Depends(get_db)):
     db.refresh(db_log)
     return db_log
 
+
 @app.get("/logs/", response_model=List[schemas.LogResponse])
 def read_logs(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
     logs = db.query(SecurityLog).order_by(SecurityLog.timestamp.desc()).offset(skip).limit(limit).all()
     return logs
 
+
 @app.post("/predict/", response_model=schemas.PredictionResponse)
 def predict_anomaly(request: schemas.PredictionRequest):
     global model, encoders
@@ -98,10 +111,9 @@ def predict_anomaly(request: schemas.PredictionRequest):
             [[request.bytes_transferred, protocol_encoded]],
             columns=['bytes', 'protocol_encoded']
         )
-        prediction = model.predict(features)[0]   # -1 = anomaly, 1 = normal
+        prediction = model.predict(features)[0]  # -1 = anomaly, 1 = normal
         score = float(model.decision_function(features)[0])
         is_anomaly = prediction == -1
-
         return {"is_anomaly": is_anomaly, "anomaly_score": score}
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))