Update app.py

app.py

@@ -76,7 +76,8 @@ def predict_price(model, brand, model_name, year):
         'age': datetime.now().year - year,
         'age_squared': (datetime.now().year - year) ** 2,
         'mileage_per_year': 12000,
-        'model': model_name,
+        'make': brand,  # Use the separated make
+        'model': model_name,  # Use the separated model
         'condition': 'Used',
         'fuel': 'Gasoline',
         'title_status': 'Clean',
@@ -149,15 +150,16 @@ if st.session_state.image is not None:
             st.write(f"Model: {classification['label']}")
             st.write(f"Confidence: {classification['score'] * 100:.2f}%")
         
-        # Use the top prediction for further processing
+        # Separate make and model from the classification result
         top_prediction = car_classifications[0]['label']
-        brand, model_name = top_prediction.split(' ', 1)
+        make_name, model_name = top_prediction.split(' ', 1)
         
-        st.write(f"Identified Car: {brand} {model_name}")
+        st.write(f"Identified Car Make: {make_name}")
+        st.write(f"Identified Car Model: {model_name}")
 
         # Get additional information using GPT-3.5-turbo
         current_year = datetime.now().year
-        overview = get_car_overview(brand, model_name, current_year)
+        overview = get_car_overview(make_name, model_name, current_year)
         st.write("Car Overview:")
         st.write(overview)
 
@@ -170,13 +172,13 @@ if st.session_state.image is not None:
         predicted_prices = []
 
         for year in years:
-            price = predict_price(model, brand, model_name, year)
+            price = predict_price(model, make_name, model_name, year)
             predicted_prices.append(price)
 
         # Plotting the results
         plt.figure(figsize=(10, 5))
         plt.plot(years, predicted_prices, marker='o')
-        plt.title(f"Predicted Price of {brand} {model_name} Over Time")
+        plt.title(f"Predicted Price of {make_name} {model_name} Over Time")
         plt.xlabel("Year")
         plt.ylabel("Predicted Price ($)")
         plt.grid()