import altair

import gradio as gr
from math import sqrt
import matplotlib

matplotlib.use("Agg")

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import datetime

from sklearn.linear_model import LinearRegression
from sklearn.metrics import log_loss
from sklearn.preprocessing import StandardScaler

import requests
from bs4 import BeautifulSoup as bs
from requests_html import AsyncHTMLSession

def date_range(
    start: date, stop: date, step: timedelta = timedelta(1)
) -> Generator[date, None, None]:
    """startからendまで日付をstep日ずつループさせるジェネレータ"""
    current = start
    while current < stop:
        yield current
        current += step


def get_url(download_date: date) -> Tuple[str, str]:
    """ダウンロードするURLと日付の文字列を返す"""
    month = download_date.strftime("%Y%m")
    day = download_date.strftime("%Y%m%d")
    return (
        f"https://www.shijou-nippo.metro.tokyo.lg.jp/SN/{month}/{day}/Sui/Sui_K1.csv",
        day,
    )


def content_wrap(content):
    """1行目にヘッダ行が来るまでスキップする"""
    buffer = ""
    first = True
    for line in io.BytesIO(content):
        line_str = codecs.decode(line, "shift-jis")
        if first:
            if "品名" in line_str:
                first = False
                buffer = line_str
            else:
                continue
        else:
            buffer += line_str
    return io.StringIO(buffer)


def insert_data(data, day, low_price, center_price, high_price, quantity):
    """ "データをリストに追加する"""
    data["date"].append(day)
    data["low_price"].append(low_price)
    data["center_price"].append(center_price)
    data["high_price"].append(high_price)
    data["quantity"].append(quantity)


def to_numeric(x):
    """文字列を数値に変換する"""
    if isinstance(x, str):
        return float(x)
    else:
        return x


def get_fish_price_data(start_date: date, end_date: date) -> pd.core.frame.DataFrame:
    """
    東京卸売市場からデータを引っ張ってくる

    :param start_date: 開始日
    :param end_date: 終了日
    :return: あじの値段を結合したデータ
    """
    data = {
        "date": [],
        "low_price": [],
        "center_price": [],
        "high_price": [],
        "quantity": [],
    }
    iterator = tqdm(
        date_range(start_date, end_date), total=(end_date - start_date).days
    )

    for download_date in iterator:
        url, day = get_url(download_date)
        iterator.set_description(day)
        response = requests.get(url)

        # URLが存在しないとき
        if response.status_code == 404:
            insert_data(data, day, np.nan, np.nan, np.nan, 0)
            continue
        assert (
            response.status_code == 200
        ), f"Unexpected HTTP response. Please check the website {url}."

        df = pd.read_csv(content_wrap(response.content))

        # 欠損値補完
        price_cols = ["安値(円)", "中値(円)", "高値(円)"]
        for c in price_cols:
            df[c].mask(df[c] == "-", np.nan, inplace=True)
            df[c].mask(df[c] == "−", np.nan, inplace=True)
        df["卸売数量"].mask(df["卸売数量"] == "-", np.nan, inplace=True)
        df["卸売数量"].mask(df["卸売数量"] == "−", np.nan, inplace=True)

        # 長崎で獲れたあじの中値と卸売数量
        # 品目 == あじ の行だけ抽出
        df_aji = df.loc[df["品名"] == "あじ", ["卸売数量"] + price_cols]

        # あじの販売がなかったら欠損扱いに
        if len(df_aji) == 0:
            insert_data(data, day, np.nan, np.nan, np.nan, 0)
            continue

        isnan = lambda x: isinstance(x, float) and np.isnan(x)
        # 産地ごと(?)の鯵の販売実績を調べる
        low_prices = []
        center_prices = []
        high_prices = []
        quantities = []
        for i, row in enumerate(df_aji.iloc):
            lp, cp, hp, q = row[price_cols + ["卸売数量"]]
            lp, cp, hp, q = (
                to_numeric(lp),
                to_numeric(cp),
                to_numeric(hp),
                to_numeric(q),
            )

            # 中値だけが記録されている -> 価格帯が1個だけなので高値、安値も中値と同じにしておく
            if isnan(lp) and isnan(hp) and (not isnan(cp)):
                low_prices.append(cp)
                center_prices.append(cp)
                high_prices.append(cp)

            # 高値・安値があり中値がない -> 価格帯2個、とりあえず両者の平均を中値とする
            elif (not isnan(lp)) and (not isnan(hp)) and isnan(cp):
                low_prices.append(lp)
                center_prices.append((lp + hp) / 2)
                high_prices.append(hp)
            else:
                low_prices.append(lp)
                center_prices.append(cp)
                high_prices.append(hp)

            if isnan(row["卸売数量"]):
                quantities.append(0)
            else:
                quantities.append(q)

        low_price = int(min(low_prices))
        center_price = int(sum(center_prices) / len(center_prices))
        high_price = int(max(high_prices))
        quantity = int(float(sum(quantities)))

        # 保存
        insert_data(data, day, low_price, center_price, high_price, quantity)
        # 短期間にアクセスが集中しないようにクールタイムを設定
        time.sleep(max(0.5 + random.normalvariate(0, 0.3), 0.1))
    # DataFrameを作成
    df = pd.DataFrame(data)
    return df

# 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")

df_train = pd.read_csv('df_train.csv')
X = df_train.drop('audience', axis=1)
y = df_train['audience']
linear_regression = LinearRegression()
model = linear_regression.fit(X,y)

d_today = datetime.date.today()
d_tom = datetime.date.today() + datetime.timedelta(days = 1)

# 動作確認
d_y = datetime.date.today() + datetime.timedelta(days = -1)
if __name__ == "__main__":
    start_date = d_y
    end_date = d_today
    df_aji_pre = get_fish_price_data(start_date=start_date, end_date=end_date)
    df_aji_pre['date'] = df_aji_pre['date'].astype(int)

# 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['date'] = df_aji_pre['date'].astype(int)

url23 = 'https://www.football-lab.jp/ka-f/match/'
dfs23 = pd.read_html(url23)

#シーズン毎に分類
res23 = pd.DataFrame([['S2023']]*len(dfs23[0])).join(dfs23, lsuffix='0')

df = res23

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_pre = pd.merge(df, df_aji_pre, left_on='date_before', right_on='date', how='left')

# df_pre = df_pre.drop(['date_before', 'date_ymd'], axis=1)
# df_pre["audience"] = df_pre["audience"].str.replace(",", "").astype(int)
# df_pre["last_audience"] = df_pre["last_audience"].str.replace(",", "").astype(int)

# start_date = int(start_date)
# df_pre = df.query('date <= start_date')


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'})

def outbreak(date):
  if date:  

    # if __name__ == "__main__":
    #   import datetime
    #   d_today = datetime.date.today()
    #   d_tom = datetime.date.today() + datetime.timedelta(days = 1)
    #   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'})

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