Update app.py

app.py

@@ -45,7 +45,7 @@ def get_prediction_result():
     df.loc[df['forecast_point'].isnull(), 'forecast_point'] = df['target_date'].apply(lambda x:x-relativedelta(months=1))
     df = df.loc[~((df['target_date']<(today+relativedelta(months=1)))&(df['電気代'].isnull()))]
     df = df.rename(columns={'電気代':'電気代_予測'})
-    return df[['forecast_point', 'target_date', '電気代_予測']], df[['forecast_point', 'target_date', '電気代_予測']]
+    return df[['forecast_point', 'target_date', '電気代_予測']]
 
 def plot_prediction_result():
     update = gr.LinePlot.update(
@@ -115,6 +115,9 @@ def get_featuredrift():
     ])
     start_point = (target_drift.period['start']+relativedelta(hours=9)).strftime("%Y / %m / %d %H:%M:%S")
     end_point = (target_drift.period['end']+relativedelta(hours=9)).strftime("%Y / %m / %d %H:%M:%S")
+#         drift_df.loc[(drift_df['drift_score']>drift_threshold)&(drift_df['feature_impact']>impact_threshold), 'alert'] = '重要性の高く、大きなドリフト'
+#         drift_df.loc[(drift_df['drift_score']>drift_threshold)&(drift_df['feature_impact']<=impact_threshold), 'alert'] = '重要性低いが、大きなドリフト'
+#         drift_df.loc[(drift_df['drift_score']<=drift_threshold), 'alert'] = '正常'
 
     return drift_df, start_point, end_point
 
@@ -133,11 +136,10 @@ with gr.Blocks() as electoric_ploting:
                              """
                         )
     with gr.Tab("予測結果"):
-        with gr.Row():
-            reload_btn= gr.Button(value="再読み込み")
         with gr.Row():
             with gr.Column():
                 plot = gr.LinePlot(show_label=False)
+    #             plot = gr.Plot(label="昨日までの魚の卸売り量から予測された、来月の2人世帯の平均電気料金の推移")
             with gr.Column():
                 df = get_prediction_result()
                 gr.Textbox(df['電気代_予測'].max(),
@@ -149,7 +151,7 @@ with gr.Blocks() as electoric_ploting:
                 gr.Textbox(df['電気代_予測'].median(),
                                 label='現在までの予測値の中央値')
         with gr.Row():
-            output_dataframe = gr.DataFrame()
+            gr.DataFrame(get_prediction_result)
             
             
     with gr.Tab("モデル情報"):
@@ -219,7 +221,6 @@ with gr.Blocks() as electoric_ploting:
             gr.Textbox(f"{start_point}〜{end_point}",label=f'データドリフト確認期間')
         with gr.Row():
             if len(drift_df["drift_score"].unique())!=1:
-                print(drift_df["drift_score"].unique())
                 gr.ScatterPlot(
                                         drift_df,
                                         x="feature_impact",
@@ -243,15 +244,14 @@ with gr.Blocks() as electoric_ploting:
                 )
 
     retrain_btn.click(retrain, inputs=None, outputs = [output_model_type, output_acc, output_plot])
-    retrain_btn.click(retrain, inputs=None, outputs = [output_model_type, output_acc, output_plot])
     
     
-    electoric_ploting.load(plot_prediction_result, 
+    electoric_ploting.load(lambda: datetime.datetime.now(), 
                            None,
-                           [plot, output_dataframe],
+                           # c_time2,
                            every=3600)
-    # dep = electoric_ploting.load(plot_prediction_result, None, [plot, output_dataframe], every=3600)
+    dep = electoric_ploting.load(plot_prediction_result, None, plot, every=3600)
 
-electoric_ploting.launch()
+    electoric_ploting.queue().launch()
     
-# plt.close()
+plt.close()