Update app.py

app.py

@@ -251,7 +251,6 @@ def get_akc_breeds_link():
 
 
 def format_description(description, breed):
-    # 分別將不同的屬性分開來顯示，保持結果的可讀性
     if isinstance(description, dict):
         formatted_description = "\n".join([f"**{key}**: {value}" for key, value in description.items()])
     else:
@@ -260,10 +259,8 @@ def format_description(description, breed):
     formatted_description = f"""
 **Breed**: {breed}
 {formatted_description}
-
 **Want to learn more about dog breeds?**  
 [Visit the AKC dog breeds page]({get_akc_breeds_link()}) and search for {breed} to find detailed information.
-
 *Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. 
 You may need to search for the specific breed on that page. 
 I am not responsible for the content on external sites. 
@@ -271,12 +268,12 @@ Please refer to the AKC's terms of use and privacy policy.*
 """
     return formatted_description
 
+
 async def predict_single_dog(image):
-    # 使用 asyncio.to_thread 將同步操作轉換為異步
+    # 直接使用模型進行預測，無需通過 YOLO
     return await asyncio.to_thread(_predict_single_dog, image)
 
 def _predict_single_dog(image):
-    # 直接使用模型進行預測，無需通過 YOLO
     image_tensor = preprocess_image(image)
     with torch.no_grad():
         output = model(image_tensor)
@@ -289,11 +286,9 @@ def _predict_single_dog(image):
     return top1_prob, topk_breeds, topk_probs_percent
 
 async def detect_multiple_dogs(image):
-    # 使用 asyncio.to_thread 將同步操作轉換為異步
     return await asyncio.to_thread(_detect_multiple_dogs, image)
 
 def _detect_multiple_dogs(image):
-    # 使用 YOLO 檢測多隻狗
     results = model_yolo(image)
     dogs = []
     for result in results:
@@ -313,11 +308,9 @@ async def predict(image):
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
-        # 首先檢查圖片中是否有多隻狗
         dogs = await detect_multiple_dogs(image)
 
         if len(dogs) == 0:
-            # 沒有狗或 YOLO 未檢測到狗，使用單狗直接分類
             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), gr.update(visible=False)
@@ -327,14 +320,12 @@ async def predict(image):
             return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
         if len(dogs) == 1:
-            # 檢測到一隻狗時，直接分類不使用 YOLO 來節省時間
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
             breed = topk_breeds[0]
             description = get_dog_description(breed)
             formatted_description = format_description(description, breed)
             return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
-        # 若有多隻狗，則使用 YOLO 的檢測結果來處理
         explanations = []
         visible_buttons = []
         annotated_image = image.copy()
@@ -382,6 +373,7 @@ async def show_details(choice):
     except Exception as e:
         return f"An error occurred while showing details: {e}"
 
+
 with gr.Blocks(css="""
     .container { max-width: 900px; margin: auto; padding: 20px; }
     .gr-box { border-radius: 15px; }