app.py

import pandas as pd
import streamlit as st
import gspread

scope = ['https://www.googleapis.com/auth/spreadsheets',
          "https://www.googleapis.com/auth/drive"]

credentials = {
  "type": "service_account",
  "project_id": "sheets-api-connect-378620",
  "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
  "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQCtKa01beXwc88R\nnPZVQTNPVQuBnbwoOfc66gW3547ja/UEyIGAF112dt/VqHprRafkKGmlg55jqJNt\na4zceLKV+wTm7vBu7lDISTJfGzCf2TrxQYNqwMKE2LOjI69dBM8u4Dcb4k0wcp9v\ntW1ZzLVVuwTvmrg7JBHjiSaB+x5wxm/r3FOiJDXdlAgFlytzqgcyeZMJVKKBQHyJ\njEGg/1720A0numuOCt71w/2G0bDmijuj1e6tH32MwRWcvRNZ19K9ssyDz2S9p68s\nYDhIxX69OWxwScTIHLY6J2t8txf/XMivL/636fPlDADvBEVTdlT606n8CcKUVQeq\npUVdG+lfAgMBAAECggEAP38SUA7B69eTfRpo658ycOs3Amr0JW4H/bb1rNeAul0K\nZhwd/HnU4E07y81xQmey5kN5ZeNrD5EvqkZvSyMJHV0EEahZStwhjCfnDB/cxyix\nZ+kFhv4y9eK+kFpUAhBy5nX6T0O+2T6WvzAwbmbVsZ+X8kJyPuF9m8ldcPlD0sce\ntj8NwVq1ys52eosqs7zi2vjt+eMcaY393l4ls+vNq8Yf27cfyFw45W45CH/97/Nu\n5AmuzlCOAfFF+z4OC5g4rei4E/Qgpxa7/uom+BVfv9G0DIGW/tU6Sne0+37uoGKt\nW6DzhgtebUtoYkG7ZJ05BTXGp2lwgVcNRoPwnKJDxQKBgQDT5wYPUBDW+FHbvZSp\nd1m1UQuXyerqOTA9smFaM8sr/UraeH85DJPEIEk8qsntMBVMhvD3Pw8uIUeFNMYj\naLmZFObsL+WctepXrVo5NB6RtLB/jZYxiKMatMLUJIYtcKIp+2z/YtKiWcLnwotB\nWdCjVnPTxpkurmF2fWP/eewZ+wKBgQDRMtJg7etjvKyjYNQ5fARnCc+XsI3gkBe1\nX9oeXfhyfZFeBXWnZzN1ITgFHplDznmBdxAyYGiQdbbkdKQSghviUQ0igBvoDMYy\n1rWcy+a17Mj98uyNEfmb3X2cC6WpvOZaGHwg9+GY67BThwI3FqHIbyk6Ko09WlTX\nQpRQjMzU7QKBgAfi1iflu+q0LR+3a3vvFCiaToskmZiD7latd9AKk2ocsBd3Woy9\n+hXXecJHPOKV4oUJlJgvAZqe5HGBqEoTEK0wyPNLSQlO/9ypd+0fEnArwFHO7CMF\nycQprAKHJXM1eOOFFuZeQCaInqdPZy1UcV5Szla4UmUZWkk1m24blHzXAoGBAMcA\nyH4qdbxX9AYrC1dvsSRvgcnzytMvX05LU0uF6tzGtG0zVlub4ahvpEHCfNuy44UT\nxRWW/oFFaWjjyFxO5sWggpUqNuHEnRopg3QXx22SRRTGbN45li/+QAocTkgsiRh1\nqEcYZsO4mPCsQqAy6E2p6RcK+Xa+omxvSnVhq0x1AoGAKr8GdkCl4CF6rieLMAQ7\nLNBuuoYGaHoh8l5E2uOQpzwxVy/nMBcAv+2+KqHEzHryUv1owOi6pMLv7A9mTFoS\n18B0QRLuz5fSOsVnmldfC9fpUc6H8cH1SINZpzajqQA74bPwELJjnzrCnH79TnHG\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
  "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
  "client_id": "106625872877651920064",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
}

gc = gspread.service_account_from_dict(credentials)

st.set_page_config(layout="wide")

@st.cache_data
def init_baselines():
    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit#gid=1858245367')
    worksheet = sh.worksheet('ROO')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.loc[raw_display['Salary'] > 0]
    raw_display = raw_display.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    roo_table = raw_display.sort_values(by='Median', ascending=False)
    
    # worksheet = sh.worksheet('Positional_Boosts')
    # raw_display = pd.DataFrame(worksheet.get_all_records())
    # raw_display.replace("", 'Welp', inplace=True)
    # raw_display = raw_display.loc[raw_display['teamname'] != 'Welp']
    # raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    # positional_boosts = raw_display.sort_values(by='Avg_Allowed', ascending=False)
    
    worksheet = sh.worksheet('Overall_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lck_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Win_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lck_win_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Loss_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lck_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Overall_BO1_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lck_bo1 = raw_display
    
    worksheet = sh.worksheet('Overall_BO3_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lck_bo3 = raw_display
    
    worksheet = sh.worksheet('Overall_BO5_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lck_bo5 = raw_display
    
    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit#gid=1858245367')
    worksheet = sh.worksheet('Overall_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lcs_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Win_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lcs_win_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Loss_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lcs_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Overall_BO1_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lcs_bo1 = raw_display
    
    worksheet = sh.worksheet('Overall_BO3_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lcs_bo3 = raw_display
    
    worksheet = sh.worksheet('Overall_BO5_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lcs_bo5 = raw_display
    
    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1oOJD_QcBeDJ1f7e9FfgUHOQEPT6kvU0Sa9hQ_4B8gqc/edit?gid=1288836099#gid=1288836099')
    worksheet = sh.worksheet('Overall_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lec_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Win_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lec_win_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Loss_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Team'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lec_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False)
    
    worksheet = sh.worksheet('Overall_BO1_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lec_bo1 = raw_display
    
    worksheet = sh.worksheet('Overall_BO3_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lec_bo3 = raw_display
    
    worksheet = sh.worksheet('Overall_BO5_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    lec_bo5 = raw_display

    return roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5

roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines()

tab1, tab2, tab3 = st.tabs(["LOL Stacks Table", "LOL Range of Outcomes", "LOL Player Base Stats"])

def convert_df_to_csv(df):
    return df.to_csv().encode('utf-8')

with tab1:
    if st.button("Reset Data", key='reset1'):
              # Clear values from *all* all in-memory and on-disk data caches:
              # i.e. clear values from both square and cube
              st.cache_data.clear()
              roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines()
    league_choice1 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var1')
    if league_choice1 == 'LCK/LPL':
      league_hold = lck_overall_stacks
    elif league_choice1 == 'LCS':
      league_hold = lcs_overall_stacks
    elif league_choice1 == 'LEC':
      league_hold = lec_overall_stacks
    display = league_hold.set_index('Team')
    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
    st.download_button(
        label="Export Stacks",
        data=convert_df_to_csv(display),
        file_name='LOL_Stacks_export.csv',
        mime='text/csv',
    )
  
with tab2:
    if st.button("Reset Data", key='reset2'):
              # Clear values from *all* all in-memory and on-disk data caches:
              # i.e. clear values from both square and cube
              st.cache_data.clear()
              roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines()
    with st.container():
        col1, col2, col3, col4 = st.columns([4, 2, 2, 2])

        with col1:
            league_choice2 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var2')
            if league_choice2 == 'LCK/LPL':
              league_hold = roo_table[roo_table['league'] == 'LCK']
            elif league_choice2 == 'LCS':
              league_hold = roo_table[roo_table['league'] == 'LCS']
            elif league_choice2 == 'LEC':
              league_hold = roo_table[roo_table['league'] == 'LEC']
        with col2:
            model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table')
            if model_choice == 'Overall':
              hold_display = league_hold[league_hold['type'] == 'Overall']
            elif model_choice == 'Wins':
              hold_display = league_hold[league_hold['type'] == 'Wins']
            elif model_choice == 'Losses':
              hold_display = league_hold[league_hold['type'] == 'Losses']
        with col3:
            pos_var1 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'roo_posvar')
        with col4:
            team_var1 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'roo_teamvar')
    display = hold_display.set_index('Player')
    if team_var1:
          display = display[display['Team'].isin(team_var1)]
    if pos_var1 == 'All':
          display = display
    elif pos_var1 != 'All':
          display = display[display['Position'].str.contains(pos_var1)]
    display = display.drop(columns=['type', 'league', 'Timestamp'])
    display['Cpt_Own'] = ((display['Own']) * ((100 - (100-display['Own'])))) / 100
    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=750, use_container_width = True)
    st.download_button(
        label="Export Range of Outcomes",
        data=convert_df_to_csv(display),
        file_name='LOL_ROO_export.csv',
        mime='text/csv',
    )

with tab3:
    if st.button("Reset Data", key='reset3'):
              # Clear values from *all* all in-memory and on-disk data caches:
              # i.e. clear values from both square and cube
              st.cache_data.clear()
              roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines()
    with st.container():
        col1, col2, col3, col4 = st.columns([4, 2, 2, 2])

        with col1:
            league_choice3 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var3')
        with col2:
            gametype_choice = st.radio("What format are the games being played?", ('Best of 1', 'Best of 3', 'Best of 5'), key='player_stats')
        with col3:
            pos_var2 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'proj_posvar')
        with col4:
            team_var2 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'proj_teamvar')
            
    if league_choice3 == 'LCK/LPL':
        if gametype_choice == 'Best of 1':
          hold_display = lck_bo1
        elif gametype_choice == 'Best of 3':
          hold_display = lck_bo3
        elif gametype_choice == 'Best of 5':
          hold_display = lck_bo5
        display = hold_display.set_index('Player')
    elif league_choice3 == 'LCS':
        if gametype_choice == 'Best of 1':
          hold_display = lcs_bo1
        elif gametype_choice == 'Best of 3':
          hold_display = lcs_bo3
        elif gametype_choice == 'Best of 5':
          hold_display = lcs_bo5
        display = hold_display.set_index('Player')
    elif league_choice3 == 'LEC':
        if gametype_choice == 'Best of 1':
          hold_display = lec_bo1
        elif gametype_choice == 'Best of 3':
          hold_display = lec_bo3
        elif gametype_choice == 'Best of 5':
          hold_display = lec_bo5
        display = hold_display.set_index('Player')
    if team_var2:
          display = display[display['Team'].isin(team_var2)]
    if pos_var2 == 'All':
          display = display
    elif pos_var2 != 'All':
          display = display[display['Position'].str.contains(pos_var2)]
    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=750, use_container_width = True)
    st.download_button(
        label="Export Baselines",
        data=convert_df_to_csv(display),
        file_name='LOL_Baselines_export.csv',
        mime='text/csv',
    )