Update app.py

app.py

@@ -303,29 +303,27 @@ def _detect_multiple_dogs(image, conf_threshold):
 
 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)
+        return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False)
 
     try:
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
         # 嘗試檢測多隻狗
-        dogs = await detect_multiple_dogs(image)  # 使用 YOLO 檢測多狗
+        dogs = await detect_multiple_dogs(image)
         if len(dogs) == 0:
-            # 單狗情境 - 不使用 YOLO，直接進行單狗預測
+            # 單狗情境
             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)
+                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)
 
             breed = topk_breeds[0]
             description = get_dog_description(breed)
-            formatted_description = format_description(description, breed)
 
-            # 如果置信度高於 0.5，返回結果
             if top1_prob >= 0.5:
-                return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+                formatted_description = format_description(description, breed)
+                return formatted_description, image, gr.update(visible=False), gr.update(visible=False)
             else:
-                # 如果置信度不足，顯示前三個可能的品種
                 explanation = (
                     f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
                     f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
@@ -333,13 +331,13 @@ async def predict(image):
                     f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n"
                     "Click on a button to view more information about the breed."
                 )
-                # 返回可選擇的按鈕以顯示詳細資訊
-                return explanation, gr.update(visible=True, value=f"More about {topk_breeds[0]}"), gr.update(visible=True, value=f"More about {topk_breeds[1]}"), gr.update(visible=True, value=f"More about {topk_breeds[2]}")
+                choices = [f"More about {breed}" for breed in topk_breeds[:3]]
+                return explanation, image, gr.update(visible=True, choices=choices), gr.update(visible=False)
 
-        # 多狗情境 - 使用 YOLO 偵測並處理多狗
+        # 多狗情境
         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
         explanations = []
-        visible_buttons = []
+        choices = []
         annotated_image = image.copy()
         draw = ImageDraw.Draw(annotated_image)
         font = ImageFont.load_default()
@@ -352,24 +350,28 @@ async def predict(image):
 
             if top1_prob >= 0.5:
                 breed = topk_breeds[0]
-                description = get_dog_description(breed)
-                explanations.append(f"Dog {i+1}:\n{format_description(description, breed)}")
+                choices.append(f"Dog {i+1}: {breed}")
+                explanations.append(f"Dog {i+1}: **{breed}** ({topk_probs_percent[0]} confidence)")
+            elif top1_prob >= 0.2:
+                explanations.append(f"Dog {i+1}: Top 3 possible breeds:\n"
+                                    f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
+                                    f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n"
+                                    f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)")
+                choices.extend([f"Dog {i+1}: {breed}" for breed in topk_breeds[:3]])
             else:
                 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)
+        return final_explanation, annotated_image, gr.update(visible=True, choices=choices), gr.update(visible=False)
 
     except Exception as e:
-        return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible(False))
-
+        return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False)
 
 async def show_details(choice):
     if not choice:
         return "Please select a breed to view details."
 
     try:
-        # 解析出用戶選擇��品種
         if "Dog" in choice:
             _, breed = choice.split(": ", 1)
         else:
@@ -379,17 +381,7 @@ 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; }
-    .output-markdown { margin-top: 20px; padding: 15px; background-color: #f5f5f5; border-radius: 10px; }
-    .examples { display: flex; justify-content: center; flex-wrap: wrap; gap: 10px; margin-top: 20px; }
-    .examples img { width: 100px; height: 100px; object-fit: cover; }
-    """) as iface:
-
+with gr.Blocks() as iface:
     gr.HTML("<h1 style='text-align: center;'>🐶 Dog Breed Classifier 🔍</h1>")
     gr.HTML("<p style='text-align: center;'>Upload a picture of a dog, and the model will predict its breed, provide detailed information, and include an extra information link!</p>")
     
@@ -401,14 +393,8 @@ with gr.Blocks(css="""
     breed_buttons = gr.Radio([], label="Select breed for more details", visible=False)
     breed_details = gr.Markdown(label="Breed Details")
 
-    async def safe_predict(image):
-        try:
-            return await predict(image)
-        except Exception as e:
-            return str(e), None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
     input_image.change(
-        safe_predict,
+        predict,
         inputs=input_image,
         outputs=[output, output_image, breed_buttons, breed_details]
     )
@@ -424,7 +410,6 @@ 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_Breed_Classifier">Dog Breed Classifier</a>')
 
 if __name__ == "__main__":