Update app.py

app.py

@@ -249,6 +249,7 @@ def get_akc_breeds_link():
 # if __name__ == "__main__":
 #     iface.launch()
 
+# 格式化狗的品種描述函數
 def format_description(description, breed, is_multi_dog=False, dog_number=None):
     if isinstance(description, dict):
         formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items() if key != "Breed"])
@@ -271,7 +272,7 @@ Please refer to the AKC's terms of use and privacy policy.*
 """
     return formatted_description
 
-
+# 預測單隻狗的品種
 async def predict_single_dog(image):
     image_tensor = preprocess_image(image)
     with torch.no_grad():
@@ -284,6 +285,7 @@ async 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
 
+# 偵測多隻狗的函數
 async def detect_multiple_dogs(image):
     try:
         results = model_yolo(image)
@@ -300,6 +302,7 @@ async def detect_multiple_dogs(image):
         print(f"Error in detect_multiple_dogs: {e}")
         return []
 
+# 主預測函數
 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)
@@ -308,6 +311,7 @@ async def predict(image):
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
+        # YOLO 偵測多隻狗
         dogs = await detect_multiple_dogs(image)
         
         if len(dogs) == 0:
@@ -322,8 +326,9 @@ async def predict(image):
         for i, (cropped_image, _, box) in enumerate(dogs, 1):
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
 
+            # 標註框框標籤更醒目
             draw.rectangle(box, outline="red", width=3)
-            draw.text((box[0], box[1]), f"Dog {i}", fill="red", font=font)
+            draw.text((box[0], box[1]), f"Dog {i}", fill="yellow", font=font)
 
             if top1_prob >= 0.5:
                 breed = topk_breeds[0]
@@ -350,6 +355,7 @@ Dog {i}: 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)
 
+# 顯示選擇的品種詳細信息
 async def show_details(choice):
     if not choice:
         return "Please select a breed to view details."
@@ -361,7 +367,7 @@ async def show_details(choice):
     except Exception as e:
         return f"An error occurred while showing details: {e}"
 
-# 修改 Gradio 界面設置
+# Gradio 介面設置
 with gr.Blocks(css="""
     .container { max-width: 900px; margin: auto; padding: 20px; }
     .gr-box { border-radius: 15px; }
@@ -398,7 +404,7 @@ with gr.Blocks(css="""
         inputs=input_image
     )
 
-    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%20Breed_Classifier">Dog Breed Classifier</a>')
+    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()