Update app.py

app.py

@@ -418,7 +418,7 @@ async def process_single_dog(image):
 
 async def predict(image):
     if image is None:
-        return "Please upload an image to start.", None, [], gr.update(visible=False), None
+        return "Please upload an image to start.", None, gr.update(visible=False), None
 
     try:
         if isinstance(image, np.ndarray):
@@ -448,7 +448,7 @@ async def predict(image):
                 dog_explanation = f"Dog {i+1}: Top 3 possible breeds:\n"
                 dog_explanation += "\n".join([f"{j+1}. **{breed}** ({prob} confidence)" for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3]))])
                 explanations.append(dog_explanation)
-                buttons.extend([gr.Button(f"Dog {i+1}: More about {breed}", visible=True) for breed in topk_breeds[:3]])
+                buttons.extend([f"Dog {i+1}: More about {breed}" for breed in topk_breeds[:3]])
             else:
                 explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset.")
 
@@ -463,7 +463,7 @@ async def predict(image):
                 "is_multi_dog": len(dogs) > 1,
                 "dogs_info": explanations
             }
-            return final_explanation, annotated_image, buttons, gr.update(visible=True), initial_state
+            return final_explanation, annotated_image, gr.update(visible=True, choices=buttons), initial_state
         else:
             initial_state = {
                 "explanation": final_explanation,
@@ -473,30 +473,30 @@ async def predict(image):
                 "is_multi_dog": len(dogs) > 1,
                 "dogs_info": explanations
             }
-            return final_explanation, annotated_image, [], gr.update(visible=False), initial_state
+            return final_explanation, annotated_image, gr.update(visible=False), initial_state
 
     except Exception as e:
         error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
         logger.error(error_msg)
-        return error_msg, None, [], gr.update(visible=False), None
+        return error_msg, None, gr.update(visible=False), None
+        
 
 def show_details(choice, previous_output, initial_state):
     if not choice:
         return previous_output, gr.update(visible=True), initial_state
 
     try:
-        dog_num, breed = choice.split(": More about ")
+        breed = choice.split("More about ")[-1]
         description = get_dog_description(breed)
         formatted_description = format_description(description, breed)
         
-        # Save current description and original button state
         initial_state["current_description"] = formatted_description
         initial_state["original_buttons"] = initial_state.get("buttons", [])
         
         return formatted_description, gr.update(visible=True), initial_state
     except Exception as e:
         error_msg = f"An error occurred while showing details: {e}"
-        print(error_msg)
+        logger.error(error_msg)
         return error_msg, gr.update(visible=True), initial_state
 
 def go_back(state):
@@ -504,11 +504,12 @@ def go_back(state):
     return (
         state["explanation"],
         state["image"],
-        buttons,
-        gr.update(visible=False),  # Hide back button
+        gr.update(visible=True, choices=buttons),
+        gr.update(visible=False),
         state
     )
 
+# 修改 Gradio 界面結構
 with gr.Blocks() as iface:
     gr.HTML("<h1 style='text-align: center;'>🐶 Dog Breed Classifier 🔍</h1>")
     gr.HTML("<p style='text-align: center;'>Upload a picture of a dog, and the model will predict its breed, provide detailed information, and include an extra information link!</p>")
@@ -519,9 +520,7 @@ with gr.Blocks() as iface:
     
     output = gr.Markdown(label="Prediction Results")
     
-    button_group = gr.Group()
-    with button_group:
-        buttons = []
+    breed_buttons = gr.Radio(choices=[], label="More Information", visible=False)
     
     back_button = gr.Button("Back", visible=False)
     
@@ -530,25 +529,19 @@ with gr.Blocks() as iface:
     input_image.change(
         predict,
         inputs=input_image,
-        outputs=[output, output_image, button_group, back_button, initial_state]
+        outputs=[output, output_image, breed_buttons, initial_state]
     )
 
-    def update_buttons(buttons):
-        button_group.clear()
-        for btn in buttons:
-            button = gr.Button(btn.value)
-            button.click(
-                show_details,
-                inputs=[button, output, initial_state],
-                outputs=[output, back_button, initial_state]
-            )
-            buttons.append(button)
-        return button_group
+    breed_buttons.change(
+        show_details,
+        inputs=[breed_buttons, output, initial_state],
+        outputs=[output, back_button, initial_state]
+    )
 
     back_button.click(
         go_back,
         inputs=[initial_state],
-        outputs=[output, output_image, button_group, back_button, initial_state]
+        outputs=[output, output_image, breed_buttons, back_button, initial_state]
     )
 
     gr.Examples(