app.py

import pandas as pd
import numpy as np
import gradio as gr
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

import yaml
with open('config.yaml') as file:
    config = yaml.safe_load(file.read())

def get_prediction_result():
    today = datetime.datetime.now()
    prediction_month = (today + relativedelta(months=1)).strftime('%Y%m')
    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']]

# 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()

    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))
    
    return fig


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.Row():
        gr.DataFrame(get_prediction_result)
        # with gr.Column():

        # with gr.Column():

    
    electoric_ploting.load(lambda: datetime.datetime.now(), 
                           None,
                           # c_time2,
                           every=360)
    dep = electoric_ploting.load(plot_prediction_result, None, plot, every=360)

    electoric_ploting.queue().launch()
    
plt.close()