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-----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\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")

roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}',
              '40+%': '{:.2%}','3x%': '{:.2%}','4x%': '{:.2%}','5x%': '{:.2%}','Own': '{:.2%}','LevX': '{:.2%}'}
stat_format = {'Odds%': '{:.2%}', 'Boosts': '{:.2%}'}

overall_table = 'LOL_Overall_Proj'
wins_table = 'LOL_Win_Proj'
losses_table = 'LOL_Loss_Proj'
stacks_table = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0'
bo1_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0'
bo3_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0'
bo5_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0'

@st.cache_data
def load_roo_model(outcome):
    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0')
    worksheet = sh.worksheet('ROO')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    if outcome == 'Overall':
        raw_display = raw_display.loc[raw_display['type'] == 'Overall']
    elif outcome == 'Wins':
        raw_display = raw_display.loc[raw_display['type'] == 'Wins']
    elif outcome == 'Losses':
        raw_display = raw_display.loc[raw_display['type'] == 'Losses']
    raw_display["Salary"] = raw_display["Salary"].replace("$", "", regex=True).astype(float)
    raw_display['Top_finish'] = raw_display['Top_finish'].astype(float)/100
    raw_display['Top_5_finish'] = raw_display['Top_5_finish'].astype(float)/100
    raw_display['Top_10_finish'] = raw_display['Top_10_finish'].astype(float)/100
    raw_display['40+%'] = raw_display['40+%'].astype(float)/100
    raw_display['3x%'] = raw_display['3x%'].astype(float)/100
    raw_display['4x%'] = raw_display['4x%'].astype(float)/100
    raw_display['5x%'] = raw_display['5x%'].astype(float)/100
    raw_display['Own'] = raw_display['Own'].astype(float)/100
    raw_display['LevX'] = raw_display['LevX'].astype(float)/100

    return raw_display

@st.cache_data
def load_bo1_proj_model(URL):
    sh = gc.open_by_url(URL)
    worksheet = sh.worksheet('Overall_BO1_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player"}, inplace = True)
    raw_display['Odds%'] = raw_display['Odds%'].astype(float)/100
    raw_display['Boosts'] = raw_display['Kill Boost'].astype(float)/100
    raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    raw_display = raw_display.sort_values(by='Kills', ascending=False)

    return raw_display

@st.cache_data
def load_bo3_proj_model(URL):
    sh = gc.open_by_url(URL)
    worksheet = sh.worksheet('Overall_BO3_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player"}, inplace = True)
    raw_display['Odds%'] = raw_display['Odds%'].astype(float)/100
    raw_display['Boosts'] = raw_display['Kill Boost'].astype(float)/100
    raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    raw_display = raw_display.sort_values(by='Kills', ascending=False)

    return raw_display

@st.cache_data
def load_bo5_proj_model(URL):
    sh = gc.open_by_url(URL)
    worksheet = sh.worksheet('Overall_BO5_Stats')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player"}, inplace = True)
    raw_display['Odds%'] = raw_display['Odds%'].astype(float)/100
    raw_display['Boosts'] = raw_display['Kill Boost'].astype(float)/100
    raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!']
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    raw_display = raw_display.sort_values(by='Kills', ascending=False)
          
    return raw_display  

@st.cache_data
def load_stacks_table(URL):
    sh = gc.open_by_url(URL)
    worksheet = sh.worksheet('Overall_Stacks')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.sort_values(by='Stack+', ascending=False)

    return raw_display    
  
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()
    hold_display = load_stacks_table(stacks_table)
    display = hold_display.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()
    model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table')
    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')
    hold_display = load_roo_model(model_choice)
    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(roo_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()
    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 gametype_choice == 'Best of 1':
      hold_display = load_bo1_proj_model(bo1_player_stats)
    elif gametype_choice == 'Best of 3':
      hold_display = load_bo3_proj_model(bo3_player_stats)
    elif gametype_choice == 'Best of 5':
      hold_display = load_bo5_proj_model(bo5_player_stats)
    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(stat_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',
    )