Update app.py

app.py

@@ -13,8 +13,9 @@ app.add_middleware(
     allow_headers=["*"],  # Allows all headers
 )
 
-# Loading the  model
+# Loading the  model and label encoder
 model = joblib.load("soil_npk_joblib_model.joblib")
+le = joblib.load("label_encoder.joblib")
 
 class InputData(BaseModel):
     crop_name: str
@@ -30,16 +31,21 @@ class InputData(BaseModel):
 @app.post("/predict")
 async def predict(data: InputData):
     try:
-        input_data = np.array([[
-            data.target_yield,
-            data.field_size,
-            data.ph,
-            data.organic_carbon,
-            data.nitrogen,
-            data.phosphorus,
-            data.potassium,
-            data.soil_moisture
-        ]])
+        input_data = pd.DataFrame({
+            'crop_name': [data.crop_name],
+            'target_yield': [data.target_yield],
+            'field_size': [data.field_size],
+            'ph': [data.ph],
+            'organic_carbon': [data.organic_carbon],
+            'nitrogen': [data.nitrogen],
+            'phosphorus': [data.phosphorus],
+            'potassium': [data.potassium],
+            'soil_moisture': [data.soil_moisture]
+        })
+        
+        # Use the encoder to transform the crop_name
+        input_data['crop_name'] = le.transform(input_data['crop_name'])
+        
         prediction = model.predict(input_data)
         return {
             "nitrogen_need": float(prediction[0][0]),