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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, 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(retrain = False):
today = datetime.datetime.now()
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))
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=400,
)
return update
# def plot_prediction_result():
# df = get_prediction_result()
# 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
with gr.Blocks() as electoric_ploting:
gr.Markdown("# 📈 Real-Time Line Plot")
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.Row():
gr.DataFrame(get_prediction_result)
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=3600)
dep = electoric_ploting.load(plot_prediction_result, None, plot, every=3600)
electoric_ploting.queue().launch()
plt.close() |