Update app.py

app.py

@@ -1,26 +1,39 @@
 import os
+import warnings
 import joblib
 import pandas as pd
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 
-# Load the trained model
-script_dir = os.path.dirname(os.path.abspath(__file__))
-model_path = os.path.join(script_dir, "crop_yield_pipeline.pkl")
+# Suppress scikit-learn warnings globally (as backup; not needed with version match)
+warnings.filterwarnings("ignore", category=UserWarning, module="sklearn")
 
-model_loaded = False
+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
 model = None
+model_loaded = False
 
-if os.path.exists(model_path):
-    try:
-        model = joblib.load(model_path)
-        model_loaded = True
-        print("Model loaded successfully!")
-    except Exception as e:
-        print(f"Error loading model file: {e}")
-else:
-    print(f"Model file not found at: {model_path}")
-    print(f"Available files: {os.listdir(script_dir)}")
+@app.on_event("startup")
+async def load_model():
+    global model, model_loaded
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    model_path = os.path.join(script_dir, "district_yield_pipeline.pkl")
+    
+    if os.path.exists(model_path):
+        try:
+            model = joblib.load(model_path)
+            model_loaded = True
+            print("Model loaded successfully!")
+        except Exception as e:
+            print(f"Error loading model file: {e}")
+    else:
+        print(f"Model file not found at: {model_path}")
+        print(f"Available files: {os.listdir(script_dir)}")
 
 class PredictionInput(BaseModel):
     crop: str
@@ -36,12 +49,6 @@ class PredictionOutput(BaseModel):
     prediction: str
     insights: str
 
-app = FastAPI(
-    title="Crop Yield Prediction API",
-    description="API for predicting crop yields based on agricultural parameters",
-    version="1.0.0"
-)
-
 @app.get("/health")
 def health_check():
     return {"status": "healthy", "model_loaded": model_loaded}
@@ -64,8 +71,7 @@ def predict(input_data: PredictionInput):
             'Year': [input_data.year]
         })
         
-        # Make prediction (import numpy here if needed, but not required)
-        import numpy as np
+        # Make prediction
         prediction = model.predict(df)[0]
         
         # Format output