diff --git a/main.py b/main.py
index d06d24b..05d09b9 100644
--- a/main.py
+++ b/main.py
@@ -118,25 +118,25 @@ def predict_main():
     row,col = df.shape
     
     now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
-    ex_Model(df,
-            horizon=horizon,
-            input_size=input_size,
-            train_steps=train_steps,
-            val_check_steps=val_check_steps,
-            early_stop_patience_steps=early_stop_patience_steps,
-            is_debug=is_debug,
-            dataset=dataset,
-            is_train=is_train,
-            is_fivemodels=is_fivemodels,
-            val_size=val_size,
-            test_size=test_size,
-            settings=settings,
-            now=now,
-            etadata = etadata,
-            modelsindex = modelsindex,
-            data = data,
-            is_eta=is_eta,
-            )
+    # ex_Model(df,
+    #         horizon=horizon,
+    #         input_size=input_size,
+    #         train_steps=train_steps,
+    #         val_check_steps=val_check_steps,
+    #         early_stop_patience_steps=early_stop_patience_steps,
+    #         is_debug=is_debug,
+    #         dataset=dataset,
+    #         is_train=is_train,
+    #         is_fivemodels=is_fivemodels,
+    #         val_size=val_size,
+    #         test_size=test_size,
+    #         settings=settings,
+    #         now=now,
+    #         etadata = etadata,
+    #         modelsindex = modelsindex,
+    #         data = data,
+    #         is_eta=is_eta,
+    #         )
 
 
     logger.info('模型训练完成')
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 74dc7d6..e26296e 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -509,6 +509,31 @@ def model_losss_juxiting(sqlitedb):
     df_combined3 = df_combined.copy()  # 备份df_combined,后面画图需要
 
 
+    # 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE
+    cellText = []
+
+    # 遍历模型名称，计算模型评估指标  
+    for model in modelnames:
+        modelmse = mse(df_combined['y'], df_combined[model])
+        modelrmse = rmse(df_combined['y'], df_combined[model])
+        modelmae = mae(df_combined['y'], df_combined[model])
+        # modelmape = mape(df_combined['y'], df_combined[model])
+        # modelsmape = smape(df_combined['y'], df_combined[model])
+        # modelr2 = r2_score(df_combined['y'], df_combined[model])
+        cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)])
+        
+    model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)',  '平均绝对误差(MAE)'])
+    # 按MSE降序排列
+    model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True)
+    model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False)
+    modelnames = model_results3['模型(Model)'].tolist()
+    allmodelnames = modelnames.copy()
+    # 保存5个最佳模型的名称
+    if len(modelnames) > 5:
+        modelnames = modelnames[0:5]
+    with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f:
+        f.write(','.join(modelnames) + '\n')
+
     # 使用最佳五个模型进行绘图
     best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()
     def find_min_max_within_quantile(row):
@@ -605,30 +630,6 @@ def model_losss_juxiting(sqlitedb):
     df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
 
 
-    # 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE
-    cellText = []
-
-    # 遍历模型名称，计算模型评估指标  
-    for model in modelnames:
-        modelmse = mse(df_combined['y'], df_combined[model])
-        modelrmse = rmse(df_combined['y'], df_combined[model])
-        modelmae = mae(df_combined['y'], df_combined[model])
-        # modelmape = mape(df_combined['y'], df_combined[model])
-        # modelsmape = smape(df_combined['y'], df_combined[model])
-        # modelr2 = r2_score(df_combined['y'], df_combined[model])
-        cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)])
-        
-    model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)',  '平均绝对误差(MAE)'])
-    # 按MSE降序排列
-    model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True)
-    model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False)
-    modelnames = model_results3['模型(Model)'].tolist()
-    allmodelnames = modelnames.copy()
-    # 保存5个最佳模型的名称
-    if len(modelnames) > 5:
-        modelnames = modelnames[0:5]
-    with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f:
-        f.write(','.join(modelnames) + '\n')
     
     # 预测值与真实值对比图
     plt.rcParams['font.sans-serif'] = ['SimHei']