added the backtest and limit forecasts

app.py

@@ -202,6 +202,8 @@ def get_forecast(period_: str, pred_model: str):
     df_110.rename(columns={'common_unit_price': 'Upper_Limit'},inplace=True)
     merged_df = pd.merge(df_90,df_, on=['Date']).merge(df_110, on=['Date'])
     merged_df = merged_df.reset_index()
+    
+    merged_df.to_csv('data/afghan_wheatfcasts.csv',index=False)
 
 
     start=pd.Timestamp("20180131")
@@ -216,9 +218,15 @@ def get_forecast(period_: str, pred_model: str):
 )
     series_time = series_transformed[-len(backtest_series_):].time_index
     series_vals = (transformer.inverse_transform(series_transformed[-len(backtest_series_):])).values()
-    df_series = pd.DataFrame(data={'date': series_time, 'actual_prices': series_vals.ravel() })
+    df_series = pd.DataFrame(data={'Date': series_time, 'actual_prices': series_vals.ravel() })
     vals = (transformer.inverse_transform(backtest_series_)).values()
-    df_backtest = pd.DataFrame(data={'date': backtest_series_.time_index, 'historical_forecasts': vals.ravel() })
+    df_backtest = pd.DataFrame(data={'Date': backtest_series_.time_index, 'historical_forecasts': vals.ravel() })
+
+
+    # df_backtest_wheat = pd.DataFrame(data={'Date': backtest_series_.time_index, 'historical_wheat_forecasts': vals.ravel() })
+    df_wheat_output = pd.merge(df_series,df_backtest[['Date',"historical_forecasts"]],on=['Date'],how='left')
+    df_wheat_output.to_csv('data/aghanwheat_allhistorical.csv',index=False)
+
 
 
     # Create figure
@@ -303,7 +311,7 @@ def main():
         """
         **Timeseries Forecasting model Temporal Fusion Transformer(TFT) built on Darts library**.
         """)
-        commodity = gr.Radio(["Wheat Price Forecasting","Maize Price Forecasting"],label="Commodity to Forecast")
+        commodity = gr.Radio(["Wheat Price Forecasting"],label="Commodity to Forecast")
         period = gr.Radio(['3 months',"6 months"],label="Forecast horizon")
 
         # with gr.Row():