Update app.py

app.py

@@ -303,7 +303,7 @@ def _detect_multiple_dogs(image, conf_threshold):
 
 async def predict(image):
     if image is None:
-        return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False)
+        return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
     try:
         if isinstance(image, np.ndarray):
@@ -315,14 +315,14 @@ async def predict(image):
             # 單狗情境
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
             if top1_prob < 0.2:
-                return "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.", None, gr.update(visible=False), gr.update(visible=False)
+                return "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
             breed = topk_breeds[0]
             description = get_dog_description(breed)
 
             if top1_prob >= 0.5:
                 formatted_description = format_description(description, breed)
-                return formatted_description, image, gr.update(visible=False), gr.update(visible=False)
+                return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
             else:
                 explanation = (
                     f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
@@ -331,13 +331,11 @@ async def predict(image):
                     f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n"
                     "Click on a button to view more information about the breed."
                 )
-                choices = [f"More about {breed}" for breed in topk_breeds[:3]]
-                return explanation, image, gr.update(visible=True, choices=choices), gr.update(visible=False)
+                return explanation, image, gr.update(visible=True, value=f"More about {topk_breeds[0]}"), gr.update(visible=True, value=f"More about {topk_breeds[1]}"), gr.update(visible=True, value=f"More about {topk_breeds[2]}")
 
         # 多狗情境
         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
         explanations = []
-        choices = []
         annotated_image = image.copy()
         draw = ImageDraw.Draw(annotated_image)
         font = ImageFont.load_default()
@@ -358,25 +356,21 @@ async def predict(image):
                                     f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
                                     f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n"
                                     f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)")
-                choices.extend([f"Dog {i+1}: {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.")
 
         final_explanation = "\n\n".join(explanations)
-        return final_explanation, annotated_image, gr.update(visible=len(choices) > 0, choices=choices), gr.update(visible=False)
+        return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
     except Exception as e:
-        return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False)
+        return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
-async def show_details(choice):
+def show_details(choice):
     if not choice:
         return "Please select a breed to view details."
 
     try:
-        if "Dog" in choice:
-            _, breed = choice.split(": ", 1)
-        else:
-            _, breed = choice.split("More about ", 1)
+        breed = choice.split("More about ")[-1]
         description = get_dog_description(breed)
         return format_description(description, breed)
     except Exception as e:
@@ -391,20 +385,21 @@ with gr.Blocks() as iface:
         output_image = gr.Image(label="Annotated Image")
     
     output = gr.Markdown(label="Prediction Results")
-    breed_buttons = gr.Radio([], label="Select breed for more details", visible=False)
-    breed_details = gr.Markdown(label="Breed Details")
+    
+    with gr.Row():
+        btn1 = gr.Button("View More 1", visible=False)
+        btn2 = gr.Button("View More 2", visible=False)
+        btn3 = gr.Button("View More 3", visible=False)
 
     input_image.change(
         predict,
         inputs=input_image,
-        outputs=[output, output_image, breed_buttons, breed_details]
+        outputs=[output, output_image, btn1, btn2, btn3]
     )
 
-    breed_buttons.select(
-        show_details,
-        inputs=breed_buttons,
-        outputs=breed_details
-    )
+    btn1.click(show_details, inputs=btn1, outputs=output)
+    btn2.click(show_details, inputs=btn2, outputs=output)
+    btn3.click(show_details, inputs=btn3, outputs=output)
 
     gr.Examples(
         examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'],
@@ -415,4 +410,3 @@ with gr.Blocks() as iface:
 
 if __name__ == "__main__":
     iface.launch()
-