Update app.py

app.py

@@ -155,8 +155,8 @@ def advanced_xgboost_predict(data, predict_days):
         input_df['NEWS'] = news_score if news_score is not None else 0
         
         # 【修改】: 獲取最新月份的單一數值
-        input_df['business_climate'] = get_business_climate_data()
-        input_df['PMI'] = get_pmi_data()
+        input_df['business_climate'] = get_business_climate_data1()
+        input_df['PMI'] = get_pmi_data1()
         
         # 5. 數據清洗與格式化
         # 向前填充所有缺失值 (例如假日)
@@ -299,7 +299,7 @@ def calculate_volume_profile(df, num_bins=50):
         print(f"Volume profile 計算錯誤: {e}")
         return None, None, None
 
-def get_business_climate_data(get_latest_value=False):
+def get_business_climate_data1(get_latest_value=False):
     try:
         if not os.path.exists('business_climate.csv'): return None
         df = pd.read_csv('business_climate.csv', index_col='Date', parse_dates=True)
@@ -310,7 +310,7 @@ def get_business_climate_data(get_latest_value=False):
         print(f"無法獲取景氣燈號資料: {str(e)}")
         return None if get_latest_value else pd.DataFrame()
 
-def get_pmi_data(get_latest_value=False):
+def get_pmi_data1(get_latest_value=False):
     try:
         if not os.path.exists('taiwan_pmi.csv'): return None
         df = pd.read_csv('taiwan_pmi.csv', index_col='Date', parse_dates=True)