Update app.py

app.py

@@ -41,31 +41,6 @@ from ultralytics import YOLO
 import asyncio
 import traceback
 
-# def setup_environment():
-#     """配置適合 ZeroGPU 環境的設置"""
-#     # 啟用 CUDA 錯誤的同步報告，幫助診斷問題
-#     os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
-    
-#     # 檢查 CUDA 是否可用
-#     if torch.cuda.is_available():
-#         # 顯示 GPU 信息
-#         device_name = torch.cuda.get_device_name(0)
-#         print(f"使用 GPU: {device_name}")
-        
-#         # 針對 A100 的優化設置
-#         if "A100" in device_name:
-#             print("檢測到 A100 GPU，應用特殊優化...")
-#             torch.backends.cudnn.benchmark = True
-            
-#         # 清理 GPU 內存
-#         torch.cuda.empty_cache()
-#         return True
-#     else:
-#         print("CUDA 不可用，使用 CPU 模式")
-#         return False
-
-# cuda_available = setup_environment()
-
 history_manager = UserHistoryManager()
 
 class ModelManager:
@@ -101,36 +76,6 @@ class ModelManager:
             self._yolo_model = YOLO('yolov8x.pt')
         return self._yolo_model
 
-    # @property
-    # def yolo_model(self):
-    #     if self._yolo_model is None:
-    #         try:
-    #             print("正在加載 YOLO 模型...")
-    #             # 不指定設備，讓 YOLO 自動選擇
-    #             self._yolo_model = YOLO('yolov8x.pt')
-                
-    #             # 禁用模型融合來避免 CUDA 錯誤
-    #             if hasattr(self._yolo_model, 'model') and hasattr(self._yolo_model.model, 'fuse'):
-    #                 print("在 ZeroGPU 環境下禁用模型融合以避免 CUDA 錯誤")
-                    
-    #                 # 備份原始融合方法
-    #                 original_fuse = self._yolo_model.model.fuse
-                    
-    #                 # 創建一個空的融合方法
-    #                 def no_fuse(*args, **kwargs):
-    #                     print("已跳過融合操作")
-    #                     return self._yolo_model.model
-                    
-    #                 # 替換融合方法
-    #                 self._yolo_model.model.fuse = no_fuse
-    #         except Exception as e:
-    #             print(f"加載 YOLO 模型時出錯: {str(e)}")
-    #             print("嘗試降級到較小的模型和 CPU 模式...")
-    #             # 降級到較小的模型並明確使用 CPU
-    #             self._yolo_model = YOLO('yolov8n.pt', device='cpu')
-        
-    #     return self._yolo_model
-
     @property
     def breed_model(self):
         if self._breed_model is None:
@@ -252,7 +197,7 @@ def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.3):
             })
 
     if not detected_boxes:
-        return [(image, 1.0, [0, 0, img_width, img_height], False)]
+        return [(image, 1, [0, 0, img_width, img_height], False)]
 
     # Phase 2: Analysis of detection relationships
     avg_height = sum(box['height'] for box in detected_boxes) / len(detected_boxes)
@@ -266,7 +211,7 @@ def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.3):
         y2 = min(box1['coords'][3], box2['coords'][3])
 
         if x2 <= x1 or y2 <= y1:
-            return 0.0
+            return 0
 
         intersection = (x2 - x1) * (y2 - y1)
         area1 = box1['area']
@@ -604,4 +549,4 @@ def main():
 
 if __name__ == "__main__":
     iface = main()
-    iface.launch()
+    iface.launch()