Update pest/source/src/utils.py

pest/source/src/utils.py

@@ -69,13 +69,8 @@ def load_model(model_path: str):
                 break
         
         if actual_path is None:
-            # 如果找不到模型文件，返回一个模拟的模型对象
-            print(f"Model file not found, using mock model for: {model_path}")
-            return {
-                'model': 'mock_model',
-                'model_path': model_path,
-                'is_mock': True
-            }
+            # 如果找不到模型文件，抛出错误
+            raise FileNotFoundError(f"Model file not found: {model_path}")
         
         # 加载真实的TorchScript模型
         model = torch.jit.load(actual_path, map_location='cpu')
@@ -99,13 +94,8 @@ def load_model(model_path: str):
             'is_mock': False
         }
     except Exception as e:
-        # 如果加载失败，返回模拟版本
-        print(f"Model loading failed: {str(e)}, using mock model")
-        return {
-            'model': 'mock_model',
-            'model_path': model_path,
-            'is_mock': True
-        }
+        # 如果加载失败，抛出错误
+        raise RuntimeError(f"Model loading failed: {str(e)}")
 
 
 def preprocess_image(image_path: str):
@@ -134,12 +124,8 @@ def preprocess_image(image_path: str):
                 break
         
         if actual_path is None:
-            # 如果找不到图像文件，返回模拟图像信息
-            print(f"Image file not found: {image_path}, using mock image")
-            return {
-                'image_path': image_path,
-                'is_mock': True
-            }
+            # 如果找不到图像文件，抛出错误
+            raise FileNotFoundError(f"Image file not found: {image_path}")
         
         # 打开真实图像并存储为全局变量
         image = Image.open(actual_path).convert('RGB')
@@ -151,9 +137,8 @@ def preprocess_image(image_path: str):
             'is_mock': False
         }
     except Exception as e:
-        # 如果处理失败，返回简化版本
-        print(f"Image preprocessing failed: {str(e)}")
-        return f"Image preprocessed: {image_path} (Error: {str(e)})"
+        # 如果处理失败，抛出错误
+        raise RuntimeError(f"Image preprocessing failed: {str(e)}")
 
 
 def postprocess_results(detection_results):