Update app.py

app.py

@@ -2,24 +2,20 @@ import os
 import warnings
 import joblib
 import pandas as pd
+from contextlib import asynccontextmanager  # Added for lifespan
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 
-# Suppress scikit-learn warnings globally (as backup; not needed with version match)
+# Suppress any residual scikit-learn warnings (backup; not needed if versions match)
 warnings.filterwarnings("ignore", category=UserWarning, module="sklearn")
 
-app = FastAPI(
-    title="Crop Yield Prediction API",
-    description="API for predicting crop yields based on agricultural parameters",
-    version="1.0.0"
-)
-
-# Load model at app startup to ensure single load
+# Global variables for model (loaded in lifespan)
 model = None
 model_loaded = False
 
-@app.on_event("startup")
-async def load_model():
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Startup: Load model here
     global model, model_loaded
     script_dir = os.path.dirname(os.path.abspath(__file__))
     model_path = os.path.join(script_dir, "district_yield_pipeline.pkl")
@@ -34,6 +30,20 @@ async def load_model():
     else:
         print(f"Model file not found at: {model_path}")
         print(f"Available files: {os.listdir(script_dir)}")
+    
+    yield  # App runs after this
+    
+    # Optional shutdown/cleanup (e.g., free resources)
+    model = None
+    model_loaded = False
+    print("App shutdown: Model unloaded.")
+
+app = FastAPI(
+    lifespan=lifespan,  # Use the new lifespan handler
+    title="Crop Yield Prediction API",
+    description="API for predicting crop yields based on agricultural parameters",
+    version="1.0.0"
+)
 
 class PredictionInput(BaseModel):
     crop: str