Update app.py

app.py

@@ -251,9 +251,9 @@ 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
         formatted_description = "\n".join([f"**{key}**: {value}" for key, value in description.items()])
     else:
         formatted_description = description
@@ -271,6 +271,7 @@ 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)
 
@@ -307,6 +308,7 @@ def _detect_multiple_dogs(image, conf_threshold=0.3):
     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)
@@ -315,8 +317,10 @@ async def predict(image):
         if isinstance(image, np.ndarray):
             image = Image.fromarray(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:
@@ -326,6 +330,7 @@ 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
         if len(dogs) == 1:
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
             breed = topk_breeds[0]
@@ -333,7 +338,7 @@ 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)
 
-        # Colors and fonts for multiple dogs
+        # 處理多隻狗的情況
         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
         explanations = []
         visible_buttons = []
@@ -341,14 +346,16 @@ async def predict(image):
         draw = ImageDraw.Draw(annotated_image)
         font = ImageFont.load_default()
 
-        # Process each dog detected by YOLO
+        # 遍歷每一隻狗並返回結果
         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)]  # Cycle through colors
+            # 畫框及標籤顏色
+            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
             if top1_prob >= 0.5:
                 breed = topk_breeds[0]
                 description = get_dog_description(breed)
@@ -366,12 +373,14 @@ Dog {i+1}: Detected with moderate confidence. Here are the top 3 possible breeds
                 explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset.")
 
         final_explanation = "\n\n".join(explanations)
+
         return final_explanation, annotated_image, gr.update(visible=True, choices=visible_buttons), gr.update(visible=False), gr.update(visible=False)
 
     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."