Update app.py

app.py

@@ -1,97 +1,104 @@
-# import altair
+import altair
 
 import gradio as gr
 from math import sqrt
-import pandas as pd
-import numpy as np
-from datetime import datetime
 import matplotlib
+
 matplotlib.use("Agg")
+
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
 
+import datetime
 
-# # 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)
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import log_loss
+from sklearn.preprocessing import StandardScaler
 
-df_train = pd.read_csv('df_train.csv')
+import requests
+from bs4 import BeautifulSoup as bs
+from requests_html import AsyncHTMLSession
 
+df_train = pd.read_csv('df_train.csv')
 X = df_train.drop('audience', axis=1)
 y = df_train['audience']
-
-import sklearn
-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)
-
+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__":
-      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)
+  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)
@@ -99,6 +106,9 @@ def outbreak(date):
     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)