Update app.py

app.py

@@ -86,7 +86,6 @@ def handle_error(message, e=None):
 def pinpoint_location(api_key, uploaded_image):
     """
     Takes an API key and an uploaded image, returns location data.
-    The `gr.Image(type="pil")` component handles the upload, replacing the need for a custom upload function.
     """
     if not api_key:
         handle_error("Google AI API Key is required.")
@@ -95,25 +94,26 @@ def pinpoint_location(api_key, uploaded_image):
 
     try:
         rp = RainboltParody(title="Location Analysis", api_key=api_key)
-        # 'uploaded_image' is already a PIL Image object, thanks to `type="pil"`.
         response, _, _ = rp.get_info(SYSTEM_PROMPT=SYSTEM_PROMPT, img=uploaded_image)
 
-        # Prepare outputs for Gradio
-        # Convert the dictionary to a list of lists for the DataFrame
         info_data = [[key, value] for key, value in response.items()]
         info_df = pd.DataFrame(info_data, columns=["Attribute", "Value"])
 
         reasoning = response.get('reasoning', 'No reasoning provided.')
         coords_str = response.get('coordinates', '0,0')
-        lat, lon = map(float, coords_str.split(','))
+        
+        # --- FIX: Generate map HTML for the single point ---
+        mgk = MapGenerator()
+        single_point_map = mgk.get_map(location1=coords_str)
+        map_html_output = single_point_map._repr_html_()
 
-        # Update visibility of subsequent sections
         ui_updates = {
             error_accordion: gr.Accordion(visible=True),
             save_accordion: gr.Accordion(visible=True)
         }
         
-        return info_df, reasoning, (lat, lon), response, ui_updates
+        # Return the map HTML instead of coordinates
+        return info_df, reasoning, map_html_output, response, ui_updates
 
     except Exception as e:
         handle_error("Failed to process the image with the AI model", e)
@@ -160,7 +160,6 @@ def calculate_error_distance(prediction_state, url, manual_coords_str):
 def save_and_download(prediction_state, error_km, direction, original_url, filename):
     """
     Saves the combined results to a CSV file and provides it for download.
-    This function replaces the Colab-specific `prepare_csv`.
     """
     if not prediction_state:
         handle_error("No prediction data to save.")
@@ -170,24 +169,20 @@ def save_and_download(prediction_state, error_km, direction, original_url, filen
         filename += '.csv'
 
     try:
-        # Prepare the new row of data
         data_to_add = prediction_state.copy()
         data_to_add['error'] = f"{error_km:.2f}" if error_km is not None else 'N/A'
         data_to_add['direction'] = direction if direction else 'N/A'
         data_to_add['actual_location_url'] = original_url if original_url else 'N/A'
         new_df = pd.DataFrame([data_to_add])
         
-        # Check if file exists to append or create new
         if os.path.exists(filename):
             existing_df = pd.read_csv(filename)
             final_df = pd.concat([existing_df, new_df], ignore_index=True)
         else:
             final_df = new_df
         
-        # Save the final dataframe to the CSV file
         final_df.to_csv(filename, index=False)
 
-        # Return the updated DataFrame for display and the file path for download
         return gr.DataFrame(value=final_df, visible=True), gr.File(value=filename, label="Download CSV", visible=True)
 
     except Exception as e:
@@ -198,7 +193,6 @@ with gr.Blocks(theme=gr.themes.Soft(), title="GeoGuessr AI") as app:
     gr.Markdown("# GeoGuessr AI 🔎")
     gr.Markdown("Pinpoint the location of any Google Street View image.")
 
-    # State variables to hold data between steps
     prediction_state = gr.State(None)
     error_state = gr.State(None)
     direction_state = gr.State(None)
@@ -217,7 +211,8 @@ with gr.Blocks(theme=gr.themes.Soft(), title="GeoGuessr AI") as app:
 
         with gr.Column(scale=2):
             gr.Markdown("### Map View")
-            map_output_single = gr.Map(label="Predicted Location", interactive=False)
+            # --- FIX: Changed gr.Map to gr.HTML ---
+            map_output_single = gr.HTML(label="Predicted Location")
             map_output_comparison = gr.HTML(visible=False)
 
     with gr.Accordion("2. Calculate Error (Optional)", open=False, visible=False) as error_accordion: