Update app.py

app.py

@@ -251,6 +251,7 @@ def get_akc_breeds_link():
 #     iface.launch()
 
 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:
@@ -258,10 +259,9 @@ def format_description(description, breed):
 
     formatted_description = f"""
 **Breed**: {breed}
-
 {formatted_description}
 
-**Want to learn more about dog breeds?**
+**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. 
@@ -271,7 +271,9 @@ Please refer to the AKC's terms of use and privacy policy.*
 """
     return formatted_description
 
+
 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)
@@ -283,18 +285,21 @@ def predict_single_dog(image):
         topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]
     return top1_prob, topk_breeds, topk_probs_percent
 
+
 def detect_multiple_dogs(image):
+    # 使用 YOLO 檢測多隻狗
     results = model_yolo(image)
     dogs = []
     for result in results:
         for box in result.boxes:
-            if box.cls == 16:  # COCO dataset class for dog is 16
+            if box.cls == 16:  # COCO 資料集中狗的類別是 16
                 xyxy = box.xyxy[0].tolist()
                 confidence = box.conf.item()
                 cropped_image = image.crop((xyxy[0], xyxy[1], xyxy[2], xyxy[3]))
                 dogs.append((cropped_image, confidence, xyxy))
     return dogs
 
+
 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)
@@ -303,22 +308,28 @@ def predict(image):
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
-        # First, check if there are multiple dogs using YOLO
+        # 首先檢查圖片中是否有多隻狗
         dogs = detect_multiple_dogs(image)
 
-        if len(dogs) <= 1:
-            # Single dog or no dog detected, use direct classification
+        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 = 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)
             breed = topk_breeds[0]
             description = get_dog_description(breed)
             formatted_description = format_description(description, breed)
-            
-            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)
-            
             return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-        
-        # Multiple dogs detected, process each dog
+
+        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 = 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()
@@ -337,7 +348,6 @@ def predict(image):
             elif 0.2 <= top1_prob < 0.5:
                 explanation = f"""
 Dog {i+1}: Detected with moderate confidence. Here are the top 3 possible breeds:
-
 1. **{topk_breeds[0]}** ({topk_probs_percent[0]})
 2. **{topk_breeds[1]}** ({topk_probs_percent[1]})
 3. **{topk_breeds[2]}** ({topk_probs_percent[2]})
@@ -353,6 +363,7 @@ Dog {i+1}: Detected with moderate confidence. Here are the top 3 possible breeds
     except Exception as e:
         return f"An error occurred: {e}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
+
 def show_details(breed):
     breed_name = breed.split("More about ")[-1]
     description = get_dog_description(breed_name)