app.py

import pulp
import numpy as np
import pandas as pd
import random
import sys
import openpyxl
import re
import time
import streamlit as st
import matplotlib
from  matplotlib.colors import LinearSegmentedColormap
from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode
import json
import requests
import gspread
import plotly.figure_factory as ff

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/1W5gH-ef2K6gX3Dw86n-YbXxQhbpUiUosDOiH4-mzX8g/edit#gid=582025223')
    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=0')
    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()
    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']
    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']
    pos_var1 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'roo_posvar')
    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)]
    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), 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()
    league_choice3 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var3')
    gametype_choice = st.radio("What format are the games being played?", ('Best of 1', 'Best of 3', 'Best of 5'), key='player_stats')
    pos_var2 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'proj_posvar')
    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), 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',
    )