Update app.py

app.py

@@ -1,49 +1,65 @@
 import pandas as pd
 import numpy as np
 import gradio as gr
+import datetime
+import calendar
 import matplotlib.pyplot as plt
 import japanize_matplotlib
 import matplotlib.dates as mdates
 from dateutil.relativedelta import relativedelta
 import datetime
-from func import get_fish_qty, get_estat, dr_prediction_deployment, prediction_func
+from func import get_fish_qty, get_estat, dr_prediction_deployment, prediction_func, train_modeling
 
 import yaml
 with open('config.yaml') as file:
     config = yaml.safe_load(file.read())
+    
+def retrain():
+    get_prediction_result(retrain = True)
 
-def get_prediction_result():
+def get_prediction_result(retrain = False):
     today = datetime.datetime.now()
-    prediction_month = (today + relativedelta(months=1)).strftime('%Y%m')
+    if retrain:
+        train_modeling.modeling()
+    prediction_month = (today+relativedelta(months=1)).strftime('%Y%m')
+    month_days = month_days = [pd.to_datetime(prediction_month + str(i+1).zfill(2)) for i in range(calendar.monthrange((today+relativedelta(months=1)).year, (today+relativedelta(months=1)).month)[1])]
+    dfc = pd.DataFrame({'target_date':month_days})
     df = prediction_func.prediction_to_dr(config['oil_price_url'], config['fuel_procurement_cost_url'])
     df = df.loc[df['target_date'].astype(str).str[:6]==prediction_month]
     df['target_date'] = pd.to_datetime(df['target_date'].astype(str))
-    return df[['target_date', '電気代']]
+    df['forecast_point'] = pd.to_datetime(df['forecast_point'].astype(str))
+    df = pd.merge(dfc,
+                  df,
+                  on='target_date',
+                  how='left')
+    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()))]
+    return df[['forecast_point', 'target_date', '電気代']]
 
-# def plot_prediction_result():
-#     update = gr.LinePlot.update(
-#         value=get_prediction_result(),
-#         x="target_date",
-#         y="電気代",
-#         title="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",
-#         width=500,
-#         height=500,
-#     )
-#     return update
 def plot_prediction_result():
-    df = get_prediction_result()
+    update = gr.LinePlot.update(
+        value=get_prediction_result(),
+        x="target_date",
+        y="電気代",
+        title="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",
+        width=500,
+        height=400,
+    )
+    return update
+# def plot_prediction_result():
+#     df = get_prediction_result()
 
-    fig, ax1 = plt.subplots(1, 1, figsize=(20, 5))
-    fig.autofmt_xdate()
-    ax1.xaxis.set_major_formatter(mdates.DateFormatter("%Y/%m/%d"))
-    ax1.xaxis.set_major_locator(mdates.DayLocator(interval=1))
-    plt.rcParams["font.size"] = 14
-    ax1.grid(True)
-    ax1.set_xlim(df['target_date'].min(), df['target_date'].max())
-    ax1.set_xlabel('target_date')
-    ax1.set_ylabel('電気代（円）')
-    ax1.plot(df['target_date'], df['電気代'])
-    ax1.legend(loc="upper left", bbox_to_anchor=(1.0, 1.0))
+#     fig, ax1 = plt.subplots(1, 1, figsize=(30, 10))
+#     fig.autofmt_xdate()
+#     ax1.xaxis.set_major_formatter(mdates.DateFormatter("%Y/%m/%d"))
+#     ax1.xaxis.set_major_locator(mdates.DayLocator(interval=1))
+#     plt.rcParams["font.size"] = 14
+#     ax1.grid(True)
+#     ax1.set_xlim(df['target_date'].min(), df['target_date'].max())
+#     ax1.set_xlabel('target_date')
+#     ax1.set_ylabel('電気代（円）')
+#     ax1.plot(df['target_date'], df['電気代'])
+#     ax1.legend(loc="upper left", bbox_to_anchor=(1.0, 1.0))
     
     return fig
 
@@ -51,20 +67,24 @@ def plot_prediction_result():
 with gr.Blocks() as electoric_ploting:
     gr.Markdown("# 📈 Real-Time Line Plot")
     with gr.Row():
-        # plot = gr.LinePlot(show_label=False)
-        plot = gr.Plot(label="Plot")
+        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.Row():
         gr.DataFrame(get_prediction_result)
-        # with gr.Column():
 
-        # with gr.Column():
+        with gr.Column():
+            btn= gr.Button(value="再学習")
+            btn.click(retrain, inputs=None, outputs=None)
+        
 
+    # demo.load(make_plot, inputs=[button], outputs=[plot])
     
     electoric_ploting.load(lambda: datetime.datetime.now(), 
                            None,
                            # c_time2,
-                           every=360)
-    dep = electoric_ploting.load(plot_prediction_result, None, plot, every=360)
+                           every=3600)
+    dep = electoric_ploting.load(plot_prediction_result, None, plot, every=3600)
 
     electoric_ploting.queue().launch()