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app.py

@@ -12,7 +12,16 @@ from datetime import datetime
 def load_model():
     # Use the path where the model was saved in the notebook
     saved_model_path = "deployment_files/car_prediction_model_v1_0.joblib"
-    return joblib.load(saved_model_path)
+    try:
+        model = joblib.load(saved_model_path)
+        return model
+    except FileNotFoundError:
+        st.error(f"Model file not found at {saved_model_path}. Please ensure the model is saved correctly.")
+        return None
+    except Exception as e:
+        st.error(f"An error occurred while loading the model: {e}")
+        return None
+
 
 model = load_model()
 
@@ -40,37 +49,37 @@ def main():
     # -------------------------------
     # User Inputs
     # -------------------------------
-    manufacture_year = st.number_input(
+    manufacture_year_input = st.number_input(
         "Year of Manufacture",
         min_value=1990, max_value=datetime.now().year, value=2015,
         help="Enter the year the car was made"
     )
 
-    kms_driven = st.number_input(
+    kms_driven_input = st.number_input(
         "Total Kilometers Driven",
         min_value=0, value=50000, step=1000,
         help="Enter total kilometers driven"
     )
 
-    owner = st.number_input(
+    owner_input = st.number_input(
         "Number of Previous Owners",
         min_value=0, max_value=10, value=0, step=1,
         help="Enter the number of previous owners"
     )
 
-    fuel_type = st.selectbox(
+    fuel_type_input = st.selectbox(
         "Fuel Type",
         ["Petrol", "Diesel", "CNG"],
         help="Select the fuel type"
     )
 
-    seller_type = st.selectbox(
+    seller_type_input = st.selectbox(
         "Seller Type",
         ["Dealer", "Individual"],
         help="Select the seller type"
     )
 
-    transmission = st.selectbox(
+    transmission_input = st.selectbox(
         "Transmission Type",
         ["Manual", "Automatic"],
         help="Select the transmission type"
@@ -80,52 +89,53 @@ def main():
     # Prediction Logic
     # -------------------------------
     if st.button("Predict Selling Price"):
-        # Create a DataFrame from user inputs
-        # Ensure column order matches the training data used for the model
-        # The model was trained on: ['Year', 'Kms_Driven', 'Owner', 'Fuel_Type_Diesel', 'Fuel_Type_Petrol', 'Seller_Type_Individual', 'Transmission_Manual']
-
-        # Calculate Car Age
-        car_age = datetime.now().year - manufacture_year
-
-        input_data = pd.DataFrame({
-            'Year': [car_age], # Use calculated car age
-            'Kms_Driven': [kms_driven],
-            'Owner': [owner],
-            # One-hot encode Fuel_Type
-            'Fuel_Type_Diesel': [1 if fuel_type == 'Diesel' else 0],
-            'Fuel_Type_Petrol': [1 if fuel_type == 'Petrol' else 0],
-            # One-hot encode Seller_Type
-            'Seller_Type_Individual': [1 if seller_type == 'Individual' else 0],
-            # One-hot encode Transmission
-            'Transmission_Manual': [1 if transmission == 'Manual' else 0]
-        })
-
-
-        # Ensure numerical columns have correct types
-        numerical_cols = ['Year', 'Kms_Driven', 'Owner']
-        for col in numerical_cols:
-             input_data[col] = pd.to_numeric(input_data[col], errors='coerce')
-
-        # Ensure one-hot encoded columns have correct types (int)
-        encoded_cols = ['Fuel_Type_Diesel', 'Fuel_Type_Petrol', 'Seller_Type_Individual', 'Transmission_Manual']
-        for col in encoded_cols:
-             input_data[col] = input_data[col].astype(int)
-
-
-        try:
-            # Make prediction
-            predicted_price = model.predict(input_data)[0]
-
-            # Display prediction
-            st.success(f"Predicted Selling Price: **${predicted_price:,.2f}**")
-
-        except Exception as e:
-            st.error(f"An error occurred during prediction: {e}")
-            st.write("Please check your input values.")
-
-
-# -------------------------------
-# Run the App
-# -------------------------------
-if __name__ == "__main__":
-    main()
+        if model is not None:
+            # Create a DataFrame from user inputs
+            # Ensure column order matches the training data used for the model
+            # The model was trained on: ['Year', 'Kms_Driven', 'Owner', 'Fuel_Type_Diesel', 'Fuel_Type_Petrol', 'Seller_Type_Individual', 'Transmission_Manual']
+
+            # Calculate Car Age
+            current_year = datetime.now().year
+            car_age = current_year - manufacture_year_input
+
+            input_data = pd.DataFrame({
+                'Year': [car_age], # Use calculated car age
+                'Kms_Driven': [kms_driven_input],
+                'Owner': [owner_input],
+                # One-hot encode Fuel_Type
+                'Fuel_Type_Diesel': [1 if fuel_type_input == 'Diesel' else 0],
+                'Fuel_Type_Petrol': [1 if fuel_type_input == 'Petrol' else 0],
+                # One-hot encode Seller_Type
+                'Seller_Type_Individual': [1 if seller_type_input == 'Individual' else 0],
+                # One-hot encode Transmission
+                'Transmission_Manual': [1 if transmission_input == 'Manual' else 0]
+            })
+
+            # Ensure numerical columns have correct types and check for NaNs
+            numerical_cols = ['Year', 'Kms_Driven', 'Owner']
+            for col in numerical_cols:
+                 input_data[col] = pd.to_numeric(input_data[col], errors='coerce')
+
+            if input_data[numerical_cols].isnull().any().any():
+                 st.error("Invalid input detected for numerical fields. Please check your values.")
+                 return # Stop execution if invalid input is found
+
+
+            # Ensure one-hot encoded columns have correct types (int)
+            encoded_cols = ['Fuel_Type_Diesel', 'Fuel_Type_Petrol', 'Seller_Type_Individual', 'Transmission_Manual']
+            for col in encoded_cols:
+                 input_data[col] = input_data[col].astype(int)
+
+
+            try:
+                # Make prediction
+                predicted_price = model.predict(input_data)[0]
+
+                # Display prediction
+                st.success(f"Predicted Selling Price: **${predicted_price:,.2f}**")
+
+            except Exception as e:
+                st.error(f"An error occurred during prediction: {e}")
+                st.write("Please check your input values and ensure the model is loaded correctly.")
+        else:
+            st.warning("Model not loaded. Please check the model file path and try again.")