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 = {'Win%': '{:.2%}', 'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}',
              '60+%': '{:.2%}','5x%': '{:.2%}','6x%': '{:.2%}','7x%': '{:.2%}','Own': '{:.2%}','LevX': '{:.2%}'}
stat_format = {'Odds%': '{:.2%}'}
table_format = {'Odds': '{:.2%}'}

csgo_overall = 'CSGO_Overall_Proj'
csgo_rpl = 'CSGO_RPL_Proj'
csgo_neutral = 'CSGO_Neutral_Proj'
csgo_wins = 'CSGO_Win_Proj'
csgo_losses = 'CSGO_Loss_Proj'
overall_odds = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
RPL_odds = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
csgo_bo1 = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
csgo_bo3 = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
csgo_bo5 = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'
player_baselines = 'https://docs.google.com/spreadsheets/d/1aLVN4izjSuqZGRyz73Kip6U1q3rubh6v9GrckgEqbfs/edit?pli=1#gid=1545712013'

@st.cache_data
def load_roo_model(URL):
    sh = gc.open(URL)
    worksheet = sh.get_worksheet(0)
    raw_display = pd.DataFrame(worksheet.get_all_records())
    try:
          raw_display["Salary"] = raw_display["Salary"].replace("$", "", regex=True).astype(float)
    except:
          pass
    try:
          raw_display['Win%'] = raw_display['Win%'].str.replace('%', '').astype(float)/100
    except:
          pass
    try:
          raw_display['Top_finish'] = raw_display['Top_finish'].str.replace('%', '').astype(float)/100
    except:
          pass
    try:
          raw_display['Top_5_finish'] = raw_display['Top_5_finish'].str.replace('%', '').astype(float)/100
    except:
          pass
    try:
          raw_display['Top_10_finish'] = raw_display['Top_10_finish'].str.replace('%', '').astype(float)/100
    except:
          pass
    try: 
          raw_display['60+%'] = raw_display['60+%'].str.replace('%', '').astype(float)/100
    except:
          pass
    try:
          raw_display['5x%'] = raw_display['5x%'].str.replace('%', '').astype(float)/100
    except:
          pass
    try:
          raw_display['6x%'] = raw_display['6x%'].str.replace('%', '').astype(float)/100
    except:
          pass
    try:
          raw_display['7x%'] = raw_display['7x%'].str.replace('%', '').astype(float)/100
    except:
          pass
    try:
          raw_display['Own'] = raw_display['Own'].str.replace('%', '').astype(float)/100
    except:
          pass
    try:
          raw_display['LevX'] = raw_display['LevX'].str.replace('%', '').astype(float)/100
    except:
          pass

    return raw_display

@st.cache_data
def load_overall_odds(URL):
    sh = gc.open_by_url(URL)
    worksheet = sh.get_worksheet(12)
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display['Odds'] = raw_display['Odds'].str.replace('%', '').astype(float)/100

    return raw_display

@st.cache_data
def load_rpl_odds(URL):
    sh = gc.open_by_url(URL)
    worksheet = sh.get_worksheet(13)
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display['Odds'] = raw_display['Odds'].str.replace('%', '').astype(float)/100
    raw_display['Vegas'] = raw_display['Vegas'].str.replace('%', '').astype(float)/100
    raw_display = raw_display[['Team', 'Opponent', 'RPL', 'Opp_RPL', 'RPL_Diff', 'Vegas', 'Odds', 'P Rounds']]

    return raw_display

@st.cache_data
def load_bo1_proj_model(URL):
    sh = gc.open_by_url(URL)
    worksheet = sh.get_worksheet(3)
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player"}, inplace = True)
    raw_display['Odds%'] = raw_display['Odds%'].str.replace('%', '').astype(float)/100
    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.get_worksheet(4)
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player"}, inplace = True)
    raw_display['Odds%'] = raw_display['Odds%'].str.replace('%', '').astype(float)/100
    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.get_worksheet(5)
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player"}, inplace = True)
    raw_display['Odds%'] = raw_display['Odds%'].str.replace('%', '').astype(float)/100
    raw_display = raw_display.sort_values(by='Kills', ascending=False)
          
    return raw_display  

@st.cache_data
def load_slate_baselines(URL):
    sh = gc.open_by_url(URL)
    worksheet = sh.get_worksheet(6)
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player"}, inplace = True)
    raw_display = raw_display.sort_values(by='Kills/Round', ascending=False)

    return raw_display    

hold_display = load_roo_model(csgo_overall)

tab1, tab2, tab3, tab4 = st.tabs(["CSGO Odds Tables", "CSGO Range of Outcomes", "CSGO Player Stat Projections", "CSGO Slate Baselines"])

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

with tab1:
    if st.button("Reset Data", key='reset4'):
              # 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()
    odds_choice = st.radio("What table would you like to display?", ('Overall', 'RPL'), key='odds_table')
    if odds_choice == 'Overall':
        hold_display = load_overall_odds(overall_odds)
    elif odds_choice == 'RPL':
        hold_display = load_rpl_odds(RPL_odds)
    display = hold_display.set_index('Team')
    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(table_format, precision=2), use_container_width = True)
    st.download_button(
        label="Export Tables",
        data=convert_df_to_csv(display),
        file_name='CSGO_Odds_Tables_export.csv',
        mime='text/csv',
    )

with tab2:
    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()
    model_choice = st.radio("What table would you like to display?", ('Overall', 'RPL', 'Neutral', 'Wins', 'Losses'), key='roo_table')
    team_var1 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'roo_teamvar')
    if model_choice == 'Overall':
      hold_display = load_roo_model(csgo_overall)
    elif model_choice == 'RPL':
      hold_display = load_roo_model(csgo_rpl)
    elif model_choice == 'Neutral':
      hold_display = load_roo_model(csgo_neutral)
    elif model_choice == 'Wins':
      hold_display = load_roo_model(csgo_wins)
    elif model_choice == 'Losses':
      hold_display = load_roo_model(csgo_losses)
    display = hold_display.set_index('Player')
    export_display = display
    export_display['Own'] =  export_display['Own'] *100
    export_display['Position'] = "FLEX"
    if team_var1:
              display = display[display['Team'].isin(team_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(export_display),
        file_name='CSGO_ROO_export.csv',
        mime='text/csv',
    )

with tab3:
    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()
    gametype_choice = st.radio("What format are the games being played?", ('Best of 1', 'Best of 3', 'Best of 5'), key='player_stats')
    team_var2 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'stat_teamvar')
    if gametype_choice == 'Best of 1':
      hold_display = load_bo1_proj_model(csgo_bo1)
    elif gametype_choice == 'Best of 3':
      hold_display = load_bo3_proj_model(csgo_bo3)
    elif gametype_choice == 'Best of 5':
      hold_display = load_bo5_proj_model(csgo_bo5)
    display = hold_display.set_index('Player')
    if team_var2:
          display = display[display['Team'].isin(team_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 Projections",
        data=convert_df_to_csv(display),
        file_name='CSGO_Projections_export.csv',
        mime='text/csv',
    )

with tab4:
    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()
    hold_display = load_slate_baselines(player_baselines)
    display = hold_display.set_index('Player')
    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='CSGO_Baselines_export.csv',
        mime='text/csv',
    )