Upload app.py

app.py

@@ -16,21 +16,8 @@ import logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Initialize FastAPI app
-app = FastAPI(
-    title="MediGuard Disease Prediction API",
-    description="AI-powered disease prediction using stacking ensemble",
-    version="1.0.0"
-)
-
-# CORS middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+# Initialize FastAPI app with lifespan (will be defined below)
+# We need to define lifespan first, then create app
 
 # Model directory
 MODEL_DIR = Path(".")
@@ -61,7 +48,7 @@ class PatientData(BaseModel):
     features: List[float] = Field(
         ...,
         description="List of biomarker values in the correct order",
-        example=[13.2, 165, 245, 280, 7.5, 4.8, 42, 88, 28, 33, 18, 32.5, 145, 92, 210, 7.8, 145, 38, 35, 28, 78, 1.1, 0.01, 2.8]
+        json_schema_extra={"example": [13.2, 165, 245, 280, 7.5, 4.8, 42, 88, 28, 33, 18, 32.5, 145, 92, 210, 7.8, 145, 38, 35, 28, 78, 1.1, 0.01, 2.8]}
     )
 
 
@@ -79,11 +66,15 @@ class HealthResponse(BaseModel):
     feature_count: int
 
 
-@app.on_event("startup")
-async def load_models():
-    """Load all trained models on startup"""
+from contextlib import asynccontextmanager
+from typing import AsyncGenerator
+
+@asynccontextmanager
+async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
+    """Lifespan context manager for startup and shutdown events"""
     global rf_model, nn_model, meta_model, scaler, label_encoder, feature_cols
     
+    # Startup
     try:
         logger.info("Loading models...")
         
@@ -108,6 +99,29 @@ async def load_models():
     except Exception as e:
         logger.error(f"❌ Error loading models: {e}")
         raise
+    
+    yield
+    
+    # Shutdown (cleanup if needed)
+    logger.info("Shutting down...")
+
+
+# Initialize FastAPI app with lifespan
+app = FastAPI(
+    title="MediGuard Disease Prediction API",
+    description="AI-powered disease prediction using stacking ensemble",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 
 def predict_disease(patient_features: np.ndarray):
@@ -157,7 +171,7 @@ def predict_disease(patient_features: np.ndarray):
     return disease, confidence, top_3
 
 
-@app.get("/", response_model=Dict[str, str])
+@app.get("/")
 async def root():
     """Root endpoint"""
     return {