Update app.py

app.py

@@ -251,9 +251,10 @@ def get_akc_breeds_link():
 
 
 
+# Update the format_description to handle descriptions more cleanly
 def format_description(description, breed):
     if isinstance(description, dict):
-        # 確保每一個描述項目換行顯示
+        # 確保每一個描述項目換行顯示，並避免重複顯示 Breed
         formatted_description = "\n".join([f"**{key}**: {value}" for key, value in description.items()])
     else:
         formatted_description = description
@@ -270,12 +271,9 @@ Please refer to the AKC's terms of use and privacy policy.*
 """
     return formatted_description
 
-
-
 async def predict_single_dog(image):
     return await asyncio.to_thread(_predict_single_dog, image)
 
-
 def _predict_single_dog(image):
     image_tensor = preprocess_image(image)
     with torch.no_grad():
@@ -288,13 +286,11 @@ 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
 
-
-async def detect_multiple_dogs(image):
-    return await asyncio.to_thread(_detect_multiple_dogs, image)
-
-
-def _detect_multiple_dogs(image, conf_threshold=0.3):
+async def detect_multiple_dogs(image, conf_threshold=0.3):
     # 調整 YOLO 模型的置信度閾值
+    return await asyncio.to_thread(_detect_multiple_dogs, image, conf_threshold)
+
+def _detect_multiple_dogs(image, conf_threshold):
     results = model_yolo(image, conf=conf_threshold)
     dogs = []
     for result in results:
@@ -307,8 +303,6 @@ def _detect_multiple_dogs(image, conf_threshold=0.3):
                     dogs.append((cropped_image, confidence, xyxy))
     return dogs
 
-
-
 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)
@@ -320,7 +314,6 @@ async def predict(image):
         # 偵測圖片中的多個狗
         dogs = await detect_multiple_dogs(image)
 
-        # 單一狗的情況
         if len(dogs) == 0:
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
             if top1_prob < 0.2:
@@ -330,7 +323,7 @@ async def predict(image):
             formatted_description = format_description(description, breed)
             return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
-        # 單隻狗時，直接返回結果
+        # 處理單一狗情況
         if len(dogs) == 1:
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
             breed = topk_breeds[0]
@@ -338,7 +331,7 @@ async def predict(image):
             formatted_description = format_description(description, breed)
             return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
-        # 處理多隻狗的情況
+        # 多狗處理
         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
         explanations = []
         visible_buttons = []
@@ -346,16 +339,16 @@ async def predict(image):
         draw = ImageDraw.Draw(annotated_image)
         font = ImageFont.load_default()
 
-        # 遍歷每一隻狗並返回結果
+        # 遍歷每一隻狗
         for i, (cropped_image, _, box) in enumerate(dogs):
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
 
-            # 畫框及標籤顏色
-            color = color_list[i % len(color_list)] 
+            # 繪製方框
+            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)
 
-            # 高置信度情況
+            # 高置信度返回
             if top1_prob >= 0.5:
                 breed = topk_breeds[0]
                 description = get_dog_description(breed)
@@ -379,8 +372,6 @@ 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)
 
-
-
 async def show_details(choice):
     if not choice:
         return "Please select a breed to view details."
@@ -397,6 +388,7 @@ async def show_details(choice):
 
 
 
+
 with gr.Blocks(css="""
     .container { max-width: 900px; margin: auto; padding: 20px; }
     .gr-box { border-radius: 15px; }