Update app.py

app.py

@@ -432,11 +432,11 @@ async def predict(image):
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
-        # 嘗試檢測多隻狗，使用較低的閾值以提高檢測率
-        dogs = await detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.5)
+        # 嘗試檢測多隻狗，進一步降低閾值以提高檢測率
+        dogs = await detect_multiple_dogs(image, conf_threshold=0.05)  # 降低閾值以檢測更多狗
         
-        # 如果只檢測到一隻或沒有檢測到狗，使用單狗處理邏輯
         if len(dogs) <= 1:
+            # 單狗情境
             return await process_single_dog(image)
 
         # 多狗情境
@@ -473,7 +473,7 @@ async def predict(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=False))
+                    gr.update(visible=True))  # 顯示 back 按鈕
         else:
             return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
@@ -503,7 +503,7 @@ async def process_single_dog(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]}"),
-                gr.update(visible=True))
+                gr.update(visible=True))  # 顯示 back 按鈕
 
 def show_details(choice, previous_output):
     if not choice:
@@ -534,12 +534,12 @@ with gr.Blocks() as iface:
     
     back_button = gr.Button("Back", visible=False)
     
-    initial_output = gr.State()
+    initial_state = gr.State()
     
     input_image.change(
         predict,
         inputs=input_image,
-        outputs=[output, output_image, btn1, btn2, btn3, back_button, initial_output]
+        outputs=[output, output_image, btn1, btn2, btn3, back_button, initial_state]
     )
 
     for btn in [btn1, btn2, btn3]:
@@ -550,9 +550,9 @@ with gr.Blocks() as iface:
         )
 
     back_button.click(
-        lambda initial: (initial, gr.update(visible=False)),
-        inputs=[initial_output],
-        outputs=[output, back_button]
+        lambda x: x,
+        inputs=[initial_state],
+        outputs=[output, btn1, btn2, btn3, back_button]
     )
 
     gr.Examples(
@@ -562,6 +562,5 @@ with gr.Blocks() as iface:
 
     gr.HTML('For more details on this project and other work, feel free to visit my GitHub <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog_Breed_Classifier">Dog Breed Classifier</a>')
 
-
 if __name__ == "__main__":
     iface.launch()