Update app.py

app.py

@@ -3,6 +3,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 import joblib
 import numpy as np
+import pandas as pd
 
 app = FastAPI()
 app.add_middleware(
@@ -31,6 +32,9 @@ class InputData(BaseModel):
 @app.post("/predict")
 async def predict(data: InputData):
     try:
+        # Validating crop_name
+        if data.crop_name not in le.classes_:
+            raise ValueError(f"Invalid crop_name: {data.crop_name}")
         input_data = pd.DataFrame({
             'crop_name': [data.crop_name],
             'target_yield': [data.target_yield],
@@ -45,6 +49,11 @@ async def predict(data: InputData):
         
         # Use the encoder to transform the crop_name
         input_data['crop_name'] = le.transform(input_data['crop_name'])
+          # Validating the  input shape
+        expected_shape = model.n_features_in_
+        if input_data.shape[1] != expected_shape:
+            raise ValueError(f"Input shape mismatch. Expected {expected_shape} features, got {input_data.shape[1]}")
+
         
         prediction = model.predict(input_data)
         return {
@@ -55,6 +64,7 @@ async def predict(data: InputData):
             "lime_need": float(prediction[0][4])
         }
     except Exception as e:
+        logging.error(f"Error in predict function: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.get("/")