Update app.py

app.py

@@ -71,21 +71,21 @@ 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',
-        'fuel': 'Gasoline',
-        'title_status': 'Clean',
-        'transmission': 'Automatic',
-        'drive': 'Fwd',
-        'size': 'Mid-Size',
-        'type': 'Sedan',
-        'paint_color': 'White'
+        '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',
+        'Fuel': 'Gasoline',
+        'Title_status': 'Clean',
+        'Transmission': 'Automatic',
+        'Drive': 'Fwd',
+        'Size': 'Mid-Size',
+        'Type': 'Sedan',
+        'Paint_color': 'White'
     }
     
     # Prepare the input for the model