Update app.py

app.py

@@ -76,29 +76,29 @@ def load_model_and_encodings():
 def predict_price(model, brand, model_name, year):
     # Create a dictionary with default values for the specified categories
     input_data = {
-        'Year': year,
-        'Odometer': year * 12000,  # Estimate based on year and average annual mileage
-        'Age': datetime.now().year - year,
-        'Age_squared': (datetime.now().year - year) ** 2,
-        'Mileage_per_year': 12000,
-        'Make': brand,  # Use the separated make
-        'Model': model_name,  # Use the separated model
-        'Condition': 'Used',  # Default condition
-        'Fuel': 'Gasoline',  # Default fuel type
-        'Title_status': 'Clean',  # Default title status
-        'Transmission': 'Automatic',  # Default transmission
-        'Drive': 'Fwd',  # Default drive
-        'Size': 'Mid-Size',  # Default size
-        'Type': 'Sedan',  # Default type
-        'Paint_color': 'White'  # Default color
+        'year': year,
+        'odometer': year * 12000,  # Estimate based on year and average annual mileage
+        'age': datetime.now().year - year,
+        'age_squared': (datetime.now().year - year) ** 2,
+        'mileage_per_year': 12000,
+        'Make': brand,  # Note the capital 'M' in Make
+        'model': model_name,  # lowercase 'model'
+        'condition': 'Used',
+        'fuel': 'Gasoline',
+        'title_status': 'Clean',
+        'transmission': 'Automatic',
+        'drive': 'Fwd',
+        'size': 'Mid-Size',
+        'type': 'Sedan',
+        'paint_color': 'White'
     }
     
     # Prepare the input for the model
     input_df = pd.DataFrame([input_data])
     
-    # Make sure to only include the specified columns
-    model_columns = [col for col in model.feature_names_in_ if col in input_data.keys()]
-    input_df = input_df[model_columns]
+    # Ensure all expected columns are present and in the correct order
+    expected_columns = ['year', 'odometer', 'age', 'age_squared', 'mileage_per_year', 'Make', 'model', 'condition', 'fuel', 'title_status', 'transmission', 'drive', 'size', 'type', 'paint_color']
+    input_df = input_df[expected_columns]
     
     # Predict the price
     predicted_price = model.predict(input_df)