Update app.py

app.py

@@ -325,117 +325,117 @@ async def detect_multiple_dogs(image, conf_threshold=0.25, iou_threshold=0.4, me
     # 如果沒有檢測到狗狗，返回整張圖片
     return [(image, 1.0, [0, 0, image.width, image.height])]
 
-async def predict(image):
-    if image is None:
-        return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), None
+# async def predict(image):
+#     if image is None:
+#         return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), None
 
-    try:
-        if isinstance(image, np.ndarray):
-            image = Image.fromarray(image)
+#     try:
+#         if isinstance(image, np.ndarray):
+#             image = Image.fromarray(image)
 
-        dogs = await detect_multiple_dogs(image)
+#         dogs = await detect_multiple_dogs(image)
         
-        # 如果沒有檢測到狗狗或只檢測到一隻，使用整張圖像進行分類
-        if len(dogs) <= 1:
-            top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
-            if top1_prob >= 0.5:
-                return await process_single_dog(image)
-            else:
-                dogs = [(image, 1.0, [0, 0, image.width, image.height])]
+#         # 如果沒有檢測到狗狗或只檢測到一隻，使用整張圖像進行分類
+#         if len(dogs) <= 1:
+#             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
+#             if top1_prob >= 0.5:
+#                 return await process_single_dog(image)
+#             else:
+#                 dogs = [(image, 1.0, [0, 0, image.width, image.height])]
 
-        # 多狗情境處理保持不變
-        color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
-        explanations = []
-        buttons = []
-        annotated_image = image.copy()
-        draw = ImageDraw.Draw(annotated_image)
-        font = ImageFont.load_default()
+#         # 多狗情境處理保持不變
+#         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
+#         explanations = []
+#         buttons = []
+#         annotated_image = image.copy()
+#         draw = ImageDraw.Draw(annotated_image)
+#         font = ImageFont.load_default()
 
-        for i, (cropped_image, _, box) in enumerate(dogs):
-            top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
-            color = color_list[i % len(color_list)]
-            draw.rectangle(box, outline=color, width=3)
-            draw.text((box[0], box[1]), f"Dog {i+1}", fill=color, font=font)
+#         for i, (cropped_image, _, box) in enumerate(dogs):
+#             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
+#             color = color_list[i % len(color_list)]
+#             draw.rectangle(box, outline=color, width=3)
+#             draw.text((box[0], box[1]), f"Dog {i+1}", fill=color, font=font)
 
-            breed = topk_breeds[0]
-            if top1_prob >= 0.5:
-                description = get_dog_description(breed)
-                formatted_description = format_description(description, breed)
-                explanations.append(f"Dog {i+1}: {formatted_description}")
-            else:
-                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.update(visible=True, value=f"Dog {i+1}: More about {breed}") for breed in topk_breeds[:3]])
+#             breed = topk_breeds[0]
+#             if top1_prob >= 0.5:
+#                 description = get_dog_description(breed)
+#                 formatted_description = format_description(description, breed)
+#                 explanations.append(f"Dog {i+1}: {formatted_description}")
+#             else:
+#                 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.update(visible=True, value=f"Dog {i+1}: More about {breed}") for breed in topk_breeds[:3]])
 
-        final_explanation = "\n\n".join(explanations)
-        if buttons:
-            final_explanation += "\n\nClick on a button to view more information about the breed."
-            initial_state = {
-                "explanation": final_explanation,
-                "buttons": buttons,
-                "show_back": True
-            }
-            return (final_explanation, annotated_image, 
-                    buttons[0] if len(buttons) > 0 else gr.update(visible=False),
-                    buttons[1] if len(buttons) > 1 else gr.update(visible=False),
-                    buttons[2] if len(buttons) > 2 else gr.update(visible=False),
-                    gr.update(visible=True),
-                    initial_state)
-        else:
-            initial_state = {
-                "explanation": final_explanation,
-                "buttons": [],
-                "show_back": False
-            }
-            return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
+#         final_explanation = "\n\n".join(explanations)
+#         if buttons:
+#             final_explanation += "\n\nClick on a button to view more information about the breed."
+#             initial_state = {
+#                 "explanation": final_explanation,
+#                 "buttons": buttons,
+#                 "show_back": True
+#             }
+#             return (final_explanation, annotated_image, 
+#                     buttons[0] if len(buttons) > 0 else gr.update(visible=False),
+#                     buttons[1] if len(buttons) > 1 else gr.update(visible=False),
+#                     buttons[2] if len(buttons) > 2 else gr.update(visible=False),
+#                     gr.update(visible=True),
+#                     initial_state)
+#         else:
+#             initial_state = {
+#                 "explanation": final_explanation,
+#                 "buttons": [],
+#                 "show_back": False
+#             }
+#             return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
 
-    except Exception as e:
-        error_msg = f"An error occurred: {str(e)}"
-        print(error_msg)  # 添加日誌輸出
-        return error_msg, None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), None
+#     except Exception as e:
+#         error_msg = f"An error occurred: {str(e)}"
+#         print(error_msg)  # 添加日誌輸出
+#         return error_msg, None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), None
         
 
-async def process_single_dog(image):
-    top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
-    if top1_prob < 0.2:
-        initial_state = {
-            "explanation": "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.",
-            "buttons": [],
-            "show_back": False
-        }
-        return initial_state["explanation"], None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
+# async def process_single_dog(image):
+#     top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
+#     if top1_prob < 0.2:
+#         initial_state = {
+#             "explanation": "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.",
+#             "buttons": [],
+#             "show_back": False
+#         }
+#         return initial_state["explanation"], None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
 
-    breed = topk_breeds[0]
-    description = get_dog_description(breed)
+#     breed = topk_breeds[0]
+#     description = get_dog_description(breed)
 
-    if top1_prob >= 0.5:
-        formatted_description = format_description(description, breed)
-        initial_state = {
-            "explanation": formatted_description,
-            "buttons": [],
-            "show_back": False
-        }
-        return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
-    else:
-        explanation = (
-            f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
-            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)\n\n"
-            "Click on a button to view more information about the breed."
-        )
-        buttons = [
-            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]}")
-        ]
-        initial_state = {
-            "explanation": explanation,
-            "buttons": buttons,
-            "show_back": True
-        }
-        return explanation, image, buttons[0], buttons[1], buttons[2], gr.update(visible=True), initial_state
+#     if top1_prob >= 0.5:
+#         formatted_description = format_description(description, breed)
+#         initial_state = {
+#             "explanation": formatted_description,
+#             "buttons": [],
+#             "show_back": False
+#         }
+#         return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
+#     else:
+#         explanation = (
+#             f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
+#             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)\n\n"
+#             "Click on a button to view more information about the breed."
+#         )
+#         buttons = [
+#             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]}")
+#         ]
+#         initial_state = {
+#             "explanation": explanation,
+#             "buttons": buttons,
+#             "show_back": True
+#         }
+#         return explanation, image, buttons[0], buttons[1], buttons[2], gr.update(visible=True), initial_state
 
 # def show_details(choice, previous_output, initial_state):
 #     if not choice:
@@ -488,7 +488,6 @@ async def process_single_dog(image):
 #         lambda state: (state["explanation"], 
 #                        state["buttons"][0] if len(state["buttons"]) > 0 else gr.update(visible=False),
 #                        state["buttons"][1] if len(state["buttons"]) > 1 else gr.update(visible=False),
-#                        state["buttons"][2] if len(state["buttons"]) > 2 else gr.update(visible=False),
 #                        gr.update(visible=state["show_back"])),
 #         inputs=[initial_state],
 #         outputs=[output, btn1, btn2, btn3, back_button]
@@ -504,13 +503,110 @@ async def process_single_dog(image):
 #     if __name__ == "__main__":
 #         iface.launch()
 
-def safe_predict(image):
+
+async def predict(image):
+    if image is None:
+        return "Please upload an image to start.", None, [], gr.update(visible=False), None
+
+    try:
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+
+        dogs = await detect_multiple_dogs(image)
+        
+        if len(dogs) <= 1:
+            return await process_single_dog(image)
+
+        color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
+        explanations = []
+        buttons = []
+        annotated_image = image.copy()
+        draw = ImageDraw.Draw(annotated_image)
+        font = ImageFont.load_default()
+
+        for i, (cropped_image, _, box) in enumerate(dogs):
+            top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
+            color = color_list[i % len(color_list)]
+            draw.rectangle(box, outline=color, width=3)
+            draw.text((box[0], box[1]), f"Dog {i+1}", fill=color, font=font)
+
+            breed = topk_breeds[0]
+            if top1_prob >= 0.5:
+                description = get_dog_description(breed)
+                formatted_description = format_description(description, breed)
+                explanations.append(f"Dog {i+1}: {formatted_description}")
+            else:
+                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([f"Dog {i+1}: More about {breed}" for breed in topk_breeds[:3]])
+
+        final_explanation = "\n\n".join(explanations)
+        if buttons:
+            final_explanation += "\n\nClick on a button to view more information about the breed."
+        
+        initial_state = {
+            "explanation": final_explanation,
+            "buttons": buttons,
+            "show_back": bool(buttons)
+        }
+        return final_explanation, annotated_image, buttons, gr.update(visible=bool(buttons)), initial_state
+
+    except Exception as e:
+        error_msg = f"An error occurred: {str(e)}"
+        print(error_msg)  # 添加日誌輸出
+        return error_msg, None, [], gr.update(visible=False), None
+
+async def process_single_dog(image):
+    top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
+    if top1_prob < 0.2:
+        initial_state = {
+            "explanation": "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.",
+            "buttons": [],
+            "show_back": False
+        }
+        return initial_state["explanation"], None, [], gr.update(visible=False), initial_state
+
+    breed = topk_breeds[0]
+    description = get_dog_description(breed)
+
+    if top1_prob >= 0.5:
+        formatted_description = format_description(description, breed)
+        initial_state = {
+            "explanation": formatted_description,
+            "buttons": [],
+            "show_back": False
+        }
+        return formatted_description, image, [], gr.update(visible=False), initial_state
+    else:
+        explanation = (
+            f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
+            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)\n\n"
+            "Click on a button to view more information about the breed."
+        )
+        buttons = [f"More about {breed}" for breed in topk_breeds[:3]]
+        initial_state = {
+            "explanation": explanation,
+            "buttons": buttons,
+            "show_back": True
+        }
+        return explanation, image, buttons, gr.update(visible=True), initial_state
+
+def show_details(choice, previous_output, initial_state):
+    if not choice:
+        return previous_output, gr.update(visible=True), initial_state
+
     try:
-        return predict(image)
+        breed = choice.split("More about ")[-1]
+        description = get_dog_description(breed)
+        formatted_description = format_description(description, breed)
+        return formatted_description, gr.update(visible=True), initial_state
     except Exception as e:
-        error_msg = f"An error occurred: {str(e)}\n\n{traceback.format_exc()}"
-        print(error_msg)  # 打印詳細錯誤信息到控制台
-        return error_msg, None, *[gr.update(visible=False) for _ in range(9)], gr.update(visible=False), None
+        error_msg = f"An error occurred while showing details: {e}"
+        print(error_msg)  # 添加日誌輸出
+        return error_msg, gr.update(visible=True), initial_state
 
 with gr.Blocks() as iface:
     gr.HTML("<h1 style='text-align: center;'>🐶 Dog Breed Classifier 🔍</h1>")
@@ -522,43 +618,41 @@ with gr.Blocks() as iface:
     
     output = gr.Markdown(label="Prediction Results")
     
-    with gr.Row():
-        buttons = [gr.Button(f"View More {i+1}", visible=False) for i in range(9)]
+    button_container = gr.Column()
     
     back_button = gr.Button("Back", visible=False)
     
     initial_state = gr.State()
     
-    def show_details(button_text, current_output, state):
-        breed = button_text.split("More about ")[-1]
-        description = get_dog_description(breed)
-        formatted_description = format_description(description, breed)
-        state["current_breed"] = breed
-        return formatted_description, gr.update(visible=True), state
-
-    def go_back(state):
-        return (state["explanation"], 
-                *[btn if i < len(state["buttons"]) else gr.update(visible=False) 
-                  for i, btn in enumerate(state["buttons"])],
-                gr.update(visible=state["show_back"]))
+    def create_buttons(button_texts):
+        with button_container:
+            button_container.clear()
+            buttons = [gr.Button(text) for text in button_texts]
+        return buttons
 
     input_image.change(
-        safe_predict,
+        predict,
         inputs=input_image,
-        outputs=[output, output_image] + buttons + [back_button, initial_state]
+        outputs=[output, output_image, button_container, back_button, initial_state]
     )
     
-    for btn in buttons:
-        btn.click(
-            show_details,
-            inputs=[btn, output, initial_state],
-            outputs=[output, back_button, initial_state]
-        )
+    def on_button_click(button_text, output, state):
+        return show_details(button_text, output, state)
+    
+    button_container.select(
+        on_button_click,
+        inputs=["selected", output, initial_state],
+        outputs=[output, back_button, initial_state]
+    )
     
+    def go_back(state):
+        buttons = create_buttons(state["buttons"])
+        return state["explanation"], gr.update(visible=state["show_back"]), state
+
     back_button.click(
         go_back,
         inputs=[initial_state],
-        outputs=[output] + buttons + [back_button]
+        outputs=[output, back_button, initial_state]
     )
     
     gr.Examples(