config_path

psy_api.py

@@ -106,6 +106,7 @@ def start_scheduler():
 # 定义训练接口
 @app.post("/train/")
 async def train_model(request: Request, features_list: List[Features]):
+    global config_path
     # 遍历每个特征对象，并将其添加到 all_features 中
     all_features = create_feature_df(features_list)
 
@@ -171,9 +172,6 @@ async def train_model(request: Request, features_list: List[Features]):
     end_time = time.time()  # 记录结束时间
     log_print("预测耗时: " + str(end_time - start_time) + " 秒")  # 打印执行时间
 
-    static_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "train_api"))  # 设置模型文件和配置文件的存放目录，和本py同级
-    config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "config/config.yaml"))
-
     # 替换现有检测模型
     if(config["replace_model"] == True):
         # 如果模型路径不是绝对路径，则转换为绝对路径
@@ -213,6 +211,7 @@ async def train_model(request: Request, features_list: List[Features]):
 # 定义验证接口
 @app.post("/evaluate/")
 async def evaluate_model(request: Request, features_list: List[Features]):
+    global config_path
     # 遍历每个特征对象，并将其添加到 all_features 中
     all_features = create_feature_df(features_list)
 
@@ -291,6 +290,7 @@ async def evaluate_model(request: Request, features_list: List[Features]):
 # 定义推理接口
 @app.post("/inference/")
 async def inference_model(request: Request, features_list: List[Features]):
+    global config_path
     # 遍历每个特征对象，并将其添加到 all_features 中
     all_features = create_feature_df(features_list)
 
@@ -349,6 +349,7 @@ async def inference_model(request: Request, features_list: List[Features]):
 # 定义模型上传接口
 @app.post("/upload_model/")
 async def upload_model(file: UploadFile = File(...)):
+    global config_path
     # 创建模型存放文件夹
     models_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "models"))
     os.makedirs(models_dir, exist_ok=True)