Update app.py

app.py

@@ -123,55 +123,55 @@ if camera_image is not None:
     if car_info:
         st.write(f"Identified Car: {car_info}")
 
-            # Find the closest match in the CSV
-            match = find_closest_match(df, brand, model_name)
-            if match is not None:
-                st.write("Closest Match Found:")
-                st.write(match)
-
-                # Get additional information using GPT-3.5-turbo
-                overview = get_car_overview(match)
-                st.write("Car Overview:")
-                st.write(overview)
-
-                # Interactive Price Prediction
-                st.subheader("Price Prediction Over Time")
-                selected_years = st.slider("Select range of years for price prediction", 
-                                             min_value=2000, max_value=2023, value=(2010, 2023))
-
-                years = np.arange(selected_years[0], selected_years[1] + 1)
-                predicted_prices = []
-
-                for year in years:
-                    user_input = {
-                        'Make': brand,
-                        'Model': model_name,
-                        'Condition': match['Condition'],
-                        'Fuel': match['Fuel'],
-                        'Title_status': match['Title_status'],
-                        'Transmission': match['Transmission'],
-                        'Drive': match['Drive'],
-                        'Size': match['Size'],
-                        'Type': match['Type'],
-                        'Paint_color': match['Paint_color'],
-                        'Year': year
-                    }
-                    
-                    price = predict_price(model, label_encoders, user_input)
-                    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.xlabel("Year")
-                plt.ylabel("Predicted Price ($)")
-                plt.grid()
-                st.pyplot(plt)
-
-            else:
-                st.write("No match found in the database.")
+        # Find the closest match in the CSV
+        match = find_closest_match(df, brand, model_name)
+        if match is not None:
+            st.write("Closest Match Found:")
+            st.write(match)
+
+            # Get additional information using GPT-3.5-turbo
+            overview = get_car_overview(match)
+            st.write("Car Overview:")
+            st.write(overview)
+
+            # Interactive Price Prediction
+            st.subheader("Price Prediction Over Time")
+            selected_years = st.slider("Select range of years for price prediction", 
+                                         min_value=2000, max_value=2023, value=(2010, 2023))
+
+            years = np.arange(selected_years[0], selected_years[1] + 1)
+            predicted_prices = []
+
+            for year in years:
+                user_input = {
+                    'Make': brand,
+                    'Model': model_name,
+                    'Condition': match['Condition'],
+                    'Fuel': match['Fuel'],
+                    'Title_status': match['Title_status'],
+                    'Transmission': match['Transmission'],
+                    'Drive': match['Drive'],
+                    'Size': match['Size'],
+                    'Type': match['Type'],
+                    'Paint_color': match['Paint_color'],
+                    'Year': year
+                }
+                
+                price = predict_price(model, label_encoders, user_input)
+                predicted_prices.append(price)
+
+            # Plotting the results
+            plt.figure(figsize=(10, 5))
+            plt.plot(years, predicted_prices, marker='o')
+            plt.title(f"Predicted Price of {brand} {model_name} Over Time")
+            plt.xlabel("Year")
+            plt.ylabel("Predicted Price ($)")
+            plt.grid()
+            st.pyplot(plt)
+
         else:
-            st.error("Could not identify the brand or model. Please try again.")
+            st.write("No match found in the database.")
+    else:
+        st.error("Could not identify the brand or model. Please try again.")
 else:
     st.write("Please take a picture of the car to proceed.")