Update app.py

app.py

@@ -166,6 +166,7 @@ def get_akc_breeds_link():
 #         return f"An error occurred: {e}", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
 
+
 def predict(image):
     if image is None:
         return "Please upload an image to get started.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
@@ -182,12 +183,16 @@ def predict(image):
         if len(boxes) == 0:
             return "The image is too unclear or the dog breed is not in the dataset. Please upload a clearer image of the dog.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
+        # 檢查 YOLO 偵測到的邊界框數量
+        print(f"Detected {len(boxes)} dogs in the image.")
+
         explanations = []
         visible_buttons = []
 
+        # 處理每個偵測到的狗
         for i, box in enumerate(boxes):
             x1, y1, x2, y2 = map(int, box.xyxy[0])
-            cropped_image = image.crop((x1, y1, x2, y2))
+            cropped_image = image.crop((x1, y1, x2, y2))  # 裁剪出每隻狗的區域
             image_tensor = preprocess_image(cropped_image)
 
             with torch.no_grad():
@@ -201,22 +206,26 @@ def predict(image):
                 topk_breeds = [dog_breeds[idx.item()] for idx in topk_indices[0]]
                 topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]
 
-                # 高於 50% 信心的情況，直接返回詳細資訊，不顯示信心分數
+                # 根據置信度決定返回結果
                 if top1_prob >= 0.5:
                     breed = topk_breeds[0]
                     description = get_dog_description(breed)
-                    return format_description(description, breed), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-                # 信心低於 50% 的情況，返回前三個可能的品種，並顯示按鈕
-                else:
+                    explanations.append(f"Detected a dog: **{breed}** with {topk_probs_percent[0]} confidence.\n{format_description(description, breed)}")
+                elif 0.2 <= top1_prob < 0.5:
                     explanation = (
-                        f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n"
+                        f"Detected a dog with moderate confidence. Here are the 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)\n"
-                        "Click on a button to view more information about the breed."
                     )
-                    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]}")
+                    explanations.append(explanation)
+                    visible_buttons.extend([f"More about {topk_breeds[0]}", f"More about {topk_breeds[1]}", f"More about {topk_breeds[2]}"])
+                else:
+                    explanations.append("The image is too unclear or the breed is not in the dataset. Please upload a clearer image.")
+
+        # 將結果匯總後返回
+        final_explanation = "\n\n".join(explanations)
+        return final_explanation, gr.update(visible=len(visible_buttons) >= 1), gr.update(visible=len(visible_buttons) >= 2), gr.update(visible=len(visible_buttons) >= 3)
 
     except Exception as e:
         return f"An error occurred: {e}", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)