import altair

import gradio as gr
from math import sqrt
import pandas as pd
from datetime import datetime
from IPython.display import display

matplotlib.use("Agg")

import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px



# pip install beautifulsoup4
# pip install requests_html
import requests
from bs4 import BeautifulSoup as bs
from requests_html import AsyncHTMLSession

# Webページを取得して解析する
load_url = "https://www.football-lab.jp/kyot/match/"
html = requests.get(load_url)
soup = bs(html.content, "html.parser")



url23 = 'https://www.football-lab.jp/ka-f/match/'
dfs23 = pd.read_html(url23)
url22 = 'https://www.football-lab.jp/ka-f/match/?year=2022'
dfs22 = pd.read_html(url22)
url21 = 'https://www.football-lab.jp/ka-f/match/?year=2021'
dfs21 = pd.read_html(url21)
url20 = 'https://www.football-lab.jp/ka-f/match/?year=2020'
dfs20 = pd.read_html(url20)

#シーズン毎に分類
res23 = pd.DataFrame([['S2023']]*len(dfs23[0])).join(dfs23, lsuffix='0')
res22 = pd.DataFrame([['S2022']]*len(dfs22[0])).join(dfs22, lsuffix='0')
res21 = pd.DataFrame([['S2021']]*len(dfs21[0])).join(dfs21, lsuffix='0')
res20 = pd.DataFrame([['S2020']]*len(dfs20[0])).join(dfs20, lsuffix='0')

df_tmp = pd.concat([res23, res22, res21, res20]) 
df = df_tmp

df = df.rename(columns={'会場': 'stadium', 0: 'year', '開催日': 'date', '観客数': 'audience'})
df = df.query('stadium=="等々力"').reset_index()
df = df.query('audience.notna()', engine='python').reset_index()
df = df[['audience', 'year', 'date']]
#seasonカラムから年を抽出
df["year"] = df["year"].apply(lambda x: str(x)[1:5])
#開催日から月と日を分割
df['month']  = df['date'].str.split(pat='.', expand=True)[0]
df['day'] = df['date'].str.split(pat='.', expand=True)[1]
#数値データを日付データに変換
df['date'] = pd.to_datetime({'year': df['year'], 'month': df['month'], 'day': df['day']})
#日付昇順に並び替える
df = df.sort_values('date', ascending=True)
df['date_ymd'] = pd.to_datetime(df['date']).dt.strftime('%Y%m%d')
df['date_ym'] = pd.to_datetime(df['date']).dt.strftime('%Y%m')
df["date_ymd"] = df["date_ymd"].astype(int)
df['date_before'] = df['date_ymd'] - 1
df["date_before"] = df["date_before"]
df = df[['audience', 'date_ymd', 'date_before']] 
df['last_audience'] = df['audience'].shift(1)

df_aji = pd.read_csv('fish_price.csv')

df_train = pd.merge(df, df_aji, left_on='date_before', right_on='date', how='left')

df_train = df_train.query('date > 20201202')
df_train = df_train.drop(['date_before', 'date_ymd'], axis=1)
df_train["audience"] = df_train["audience"].str.replace(",", "").astype(int)
df_train["last_audience"] = df_train["last_audience"].str.replace(",", "").astype(int)

X = df_train.drop('audience', axis=1)
y = df_train['audience']

from sklearn.linear_model import LinearRegression
from sklearn.metrics import log_loss
from sklearn.preprocessing import StandardScaler
linear_regression = LinearRegression()
linear_regression.fit(X,y)


def outbreak(date):
  if date:

    

    if __name__ == "__main__":
      start_date = d_today
      end_date = d_tom
      df_aji_pre = get_fish_price_data(start_date=start_date, end_date=end_date)
      # df_aji_pre.to_csv("fish_price_pre.csv", index=False)
    
    df_pre = df.tail(1).reset_index()
    df_pre = df_pre.drop('index', axis=1)
    df_aji_ft_pre = pd.concat([df_pre, df_aji_pre], axis=1)
    df_aji_ft_pre = df_aji_ft_pre[['audience', 'date', 'low_price', 'center_price', 'high_price', 'quantity']]
    df_aji_ft_pre = df_aji_ft_pre.rename(columns={'audience': 'last_audience', 0: 'year', '開催日': 'date', '観客数': 'audience'})

    pred = linear_regression.predict(df_aji_ft_pre)
    df_aji_ft_pre['audience_pred'] = pred
    df_aji_ft_pre['date'] = df_aji_ft_pre['date'].astype(int)

    fig = plt.figure()
    plt.plot(df_train['date'], df_train['audience'], label='original')
    plt.plot(df_aji_ft_pre['date'], df_aji_ft_pre['audience_pred'], '*', label='predict')
    plt.title("prediction of audince")
    plt.ylabel("audience")
    plt.xlabel("Days since Day 0")
    return fig

with gr.Blocks() as demo:
    gr.Markdown(
                            """
                            # 川崎フロンターレの観客動員数の予測
                             川崎フロンターレの等々力陸上競技場での試合の観客数を「あじ」の価格をもとに予測する。
                            ## 使用データ
                             * 東京卸売市場日報
                             * Football Lab 
                            ## 予測ロジック
                            観客動員数は雨天か否かで左右されると考えられる。そこで雨天の可能性をあじの価格を利用し表した。
                            一般的に雨天の場合、低気圧の影響で海面が上昇し漁に出ることが難しくなる。
                            そのため漁獲量が減少し、あじの価格が上昇すると考えられる。
                             """
                        )
    with gr.Row():
      with gr.Column():
        date_input = gr.Checkbox(label='please input date')
        prediction_btn = gr.Button(value="predict")
      with gr.Column():
        prediction = gr.Plot(label = "時系列プロット")
    prediction_btn.click(outbreak, inputs=date_input, outputs=prediction)

demo.launch()