app.py

import streamlit as st
st.set_page_config(layout="wide")

for name in dir():
    if not name.startswith('_'):
        del globals()[name]

import numpy as np
import pandas as pd
import streamlit as st
import gspread
import gc

@st.cache_resource
def init_conn():
        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']

        credentials = {
          "type": "service_account",
          "project_id": "model-sheets-connect",
          "private_key_id": st.secrets['model_sheets_connect_pk'],
          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
          "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
          "client_id": "100369174533302798535",
          "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%40model-sheets-connect.iam.gserviceaccount.com"
        }
        
        credentials2 = {
          "type": "service_account",
          "project_id": "sheets-api-connect-378620",
          "private_key_id": st.secrets['sheets_api_connect_pk'],
          "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"
        }
     
        NHL_Data = st.secrets['NHL_Data']

        gc = gspread.service_account_from_dict(credentials)
        gc2 = gspread.service_account_from_dict(credentials2)

        return gc, gc2, NHL_Data
    
gcservice_account, gcservice_account2, NHL_Data = init_conn()

@st.cache_resource(ttl = 600)
def init_baselines():
    sh = gcservice_account.open_by_url(NHL_Data)
    
    worksheet = sh.worksheet('Gamelog')
    raw_display = pd.DataFrame(worksheet.get_values())
    raw_display.columns = raw_display.iloc[0]
    raw_display = raw_display[1:]
    raw_display = raw_display.reset_index(drop=True)
    gamelog_table = raw_display[raw_display['Player'] != ""]
    gamelog_table = gamelog_table[['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'TotalAssists', 'FirstAssists', 'SecondAssists', 'TotalPoints', 'IPP',
                                   'Shots', 'SH%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'RushAttempts', 'ReboundsCreated', 'PIM', 'TotalPenalties', 'Minor',
                                   'Major', 'PenaltiesDrawn', 'Giveaways', 'Takeaways', 'Hits', 'HitsTaken', 'ShotsBlocked', 'FaceoffsWon',
                                   'FaceoffsLost', 'Faceoffs%']]
    gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
                                   'Shots', 'SH%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
                                   'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
                                   'Faceoffs Lost', 'Faceoffs %'], axis=1)
    data_cols = gamelog_table.columns.drop(['Player', 'Team', 'Position', 'Date'])
    gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
    gamelog_table['Date'] = pd.to_datetime(gamelog_table['Date']).dt.date
    gamelog_table['dk_shots_bonus'] = np.where((gamelog_table['Shots'] >= 5), 1, 0)
    gamelog_table['dk_blocks_bonus'] = np.where((gamelog_table['Shots Blocked'] >= 3), 1, 0)
    gamelog_table['dk_goals_bonus'] = np.where((gamelog_table['Goals'] >= 3), 1, 0)
    gamelog_table['dk_points_bonus'] = np.where((gamelog_table['Total Points'] >= 3), 1, 0)
    gamelog_table['dk_fantasy'] = sum([(gamelog_table['Goals'] * 8.5), (gamelog_table['Total Assists'] * 5), (gamelog_table['Shots'] * 1.5),
                                       (gamelog_table['Shots Blocked'] * 1.3), (gamelog_table['dk_shots_bonus'] * 3), (gamelog_table['dk_blocks_bonus'] * 3),
                                       (gamelog_table['dk_goals_bonus'] * 3), (gamelog_table['dk_points_bonus'] * 3)]).astype(float).round(2)
    gamelog_table['fd_fantasy'] = sum([(gamelog_table['Goals'] * 12), (gamelog_table['Total Assists'] * 8), (gamelog_table['Shots'] * 1.6),
                                       (gamelog_table['Shots Blocked'] * 1.6)]).astype(float).round(2)
    
    
    
    gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
                                            'Shots', 'Shots%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
                                            'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
                                            'Faceoffs Lost', 'Faceoffs%', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
                                            'dk_fantasy', 'fd_fantasy'], axis=1)
    
    return gamelog_table

@st.cache_data(show_spinner=False)
def seasonlong_build(data_sample):
    season_long_table = data_sample[['Player', 'Team', 'Position']]
    season_long_table['TOI'] = data_sample.groupby(['Player', 'Team'], sort=False)['TOI'].transform('mean').astype(float)
    season_long_table['Goals'] = data_sample.groupby(['Player', 'Team'], sort=False)['Goals'].transform('mean').astype(float)
    season_long_table['Total Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Assists'].transform('mean').astype(float)
    season_long_table['First Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['First Assists'].transform('mean').astype(float)
    season_long_table['Second Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['Second Assists'].transform('mean').astype(float)
    season_long_table['Total Points'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Points'].transform('mean').astype(float)
    season_long_table['IPP'] = data_sample.groupby(['Player', 'Team'], sort=False)['IPP'].transform('mean').astype(float)
    season_long_table['Shots'] = data_sample.groupby(['Player', 'Team'], sort=False)['Shots'].transform('mean').astype(float)
    season_long_table['ixG'] = data_sample.groupby(['Player', 'Team'], sort=False)['ixG'].transform('mean').astype(float)
    season_long_table['iCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iCF'].transform('mean').astype(float)
    season_long_table['iFF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iFF'].transform('mean').astype(float)
    season_long_table['iSCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iSCF'].transform('mean').astype(float)
    season_long_table['iHDCF'] = data_sample.groupby(['Player', 'Team'], sort=False)['iHDCF'].transform('mean').astype(float)
    season_long_table['Rush Attempts'] = data_sample.groupby(['Player', 'Team'], sort=False)['Rush Attempts'].transform('mean').astype(float)
    season_long_table['Rebounds Created'] = data_sample.groupby(['Player', 'Team'], sort=False)['Rebounds Created'].transform('mean').astype(float)
    season_long_table['PIM'] = data_sample.groupby(['Player', 'Team'], sort=False)['PIM'].transform('mean').astype(float)
    season_long_table['Total Penalties'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Penalties'].transform('mean').astype(float)
    season_long_table['Minor'] = data_sample.groupby(['Player', 'Team'], sort=False)['Minor'].transform('mean').astype(float)
    season_long_table['Major'] = data_sample.groupby(['Player', 'Team'], sort=False)['Major'].transform('mean').astype(float)
    season_long_table['Penalties Drawn'] = data_sample.groupby(['Player', 'Team'], sort=False)['Penalties Drawn'].transform('mean').astype(float)
    season_long_table['Giveaways'] = data_sample.groupby(['Player', 'Team'], sort=False)['Giveaways'].transform('mean').astype(float)
    season_long_table['Takeaways'] = data_sample.groupby(['Player', 'Team'], sort=False)['Takeaways'].transform('mean').astype(float)
    season_long_table['Hits'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hits'].transform('mean').astype(float)
    season_long_table['Hits Taken'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hits Taken'].transform('mean').astype(float)
    season_long_table['Shots Blocked'] = data_sample.groupby(['Player', 'Team'], sort=False)['Shots Blocked'].transform('mean').astype(float)
    season_long_table['Faceoffs Won'] = data_sample.groupby(['Player', 'Team'], sort=False)['Faceoffs Won'].transform('mean').astype(float)
    season_long_table['Faceoffs Lost'] = data_sample.groupby(['Player', 'Team'], sort=False)['Faceoffs Lost'].transform('mean').astype(float)
    season_long_table['dk_shots_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_shots_bonus'].transform('mean').astype(float)
    season_long_table['dk_blocks_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_blocks_bonus'].transform('mean').astype(float)
    season_long_table['dk_goals_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_goals_bonus'].transform('mean').astype(float)
    season_long_table['dk_points_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_points_bonus'].transform('mean').astype(float)
    season_long_table['dk_fantasy'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_fantasy'].transform('mean').astype(float)
    season_long_table['fd_fantasy'] = data_sample.groupby(['Player', 'Team'], sort=False)['fd_fantasy'].transform('mean').astype(float)
    season_long_table = season_long_table.drop_duplicates(subset='Player')
    
    season_long_table = season_long_table.sort_values(by='dk_fantasy', ascending=False)
    
    
    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Position', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
                                                    'IPP', 'Shots', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties',
                                                    'Minor', 'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
                                                    'Faceoffs Lost', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
                                                    'dk_fantasy', 'fd_fantasy'], axis=1)

    return season_long_table

@st.cache_data(show_spinner=False)
def run_fantasy_corr(data_sample):
    cor_testing = data_sample
    date_list = cor_testing['Date'].unique().tolist()
    player_list = cor_testing['Player'].unique().tolist()
    corr_frame = pd.DataFrame()
    corr_frame['DATE'] = date_list
    for player in player_list:
        player_testing = cor_testing[cor_testing['Player'] == player]
        fantasy_map = dict(zip(player_testing['Date'], player_testing['dk_fantasy']))
        corr_frame[player] = corr_frame['DATE'].map(fantasy_map)
    players_fantasy = corr_frame.drop('DATE', axis=1)
    corrM = players_fantasy.corr()
    
    return corrM

@st.cache_data(show_spinner=False)
def run_min_corr(data_sample):
    cor_testing = data_sample
    date_list = cor_testing['Date'].unique().tolist()
    player_list = cor_testing['Player'].unique().tolist()
    corr_frame = pd.DataFrame()
    corr_frame['DATE'] = date_list
    for player in player_list:
        player_testing = cor_testing[cor_testing['Player'] == player]
        fantasy_map = dict(zip(player_testing['Date'], player_testing['TOI']))
        corr_frame[player] = corr_frame['DATE'].map(fantasy_map)
    players_fantasy = corr_frame.drop('DATE', axis=1)
    corrM = players_fantasy.corr()
    
    return corrM

@st.cache_data(show_spinner=False)
def split_frame(input_df, rows):
    df = [input_df.loc[i : i + rows - 1, :] for i in range(0, len(input_df), rows)]
    return df

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

gamelog_table = init_baselines()
basic_cols = ['Player', 'Team', 'Position', 'Date', 'TOI']
basic_season_cols = ['Player', 'Team', 'Position', 'TOI']
data_cols = ['Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
             'Shots', 'Shots%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
             'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
             'Faceoffs Lost', 'Faceoffs%', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
             'dk_fantasy', 'fd_fantasy']
season_data_cols = ['Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
                    'IPP', 'Shots', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties',
                    'Minor', 'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
                    'Faceoffs Lost', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
                    'dk_fantasy', 'fd_fantasy']
indv_teams = gamelog_table.drop_duplicates(subset='Team')
total_teams = indv_teams.Team.values.tolist()
indv_players = gamelog_table.drop_duplicates(subset='Player')
total_players = indv_players.Player.values.tolist()
total_dates = gamelog_table.Date.values.tolist()

tab1, tab2 = st.tabs(['Gamelogs', 'Correlation Matrix'])

with tab1:
    col1, col2 = st.columns([1, 9])
    with col1:
        if st.button("Reset Data", key='reset1'):
                  st.cache_data.clear()
                  gamelog_table = init_baselines()
                  basic_cols = ['Player', 'Team', 'Position', 'Date', 'TOI']
                  basic_season_cols = ['Player', 'Team', 'Position', 'TOI']
                  data_cols = ['Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
                               'Shots', 'Shots%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
                               'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
                               'Faceoffs Lost', 'Faceoffs%', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
                               'dk_fantasy', 'fd_fantasy']
                  season_data_cols = ['Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
                                      'IPP', 'Shots', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties',
                                      'Minor', 'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
                                      'Faceoffs Lost', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
                                      'dk_fantasy', 'fd_fantasy']
                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
                  total_teams = indv_teams.Team.values.tolist()
                  indv_players = gamelog_table.drop_duplicates(subset='Player')
                  total_players = indv_players.Player.values.tolist()
                  total_dates = gamelog_table.Date.values.tolist()
        
        split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1')
        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
        
        if split_var2 == 'Specific Teams':
            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='team_var1')
        elif split_var2 == 'All':
            team_var1 = total_teams
            
        split_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3')
        
        if split_var3 == 'Specific Dates':
            low_date = st.date_input('Min Date:', value=None, format="MM/DD/YYYY", key='low_date')
            if low_date is not None:
                low_date = pd.to_datetime(low_date).date()
            high_date = st.date_input('Max Date:', value=None, format="MM/DD/YYYY", key='high_date')
            if high_date is not None:
                high_date = pd.to_datetime(high_date).date()
        elif split_var3 == 'All':
            low_date = gamelog_table['Date'].min()
            high_date = gamelog_table['Date'].max()
        
        split_var4 = st.radio("Would you like to view all players or specific ones?", ('All', 'Specific Players'), key='split_var4')
        
        if split_var4 == 'Specific Players':
            player_var1 = st.multiselect('Which players would you like to include in the tables?', options = total_players, key='player_var1')
        elif split_var4 == 'All':
            player_var1 = total_players
        
        min_var1 = st.slider("Is there a certain TOI range you want to view?", 0, 50, (0, 50), key='min_var1')
    
    with col2:
        working_data = gamelog_table
        if split_var1 == 'Season Logs':
            choose_cols = st.container()
            with choose_cols:
                choose_disp = st.multiselect('Which stats would you like to view?', options = season_data_cols, default = season_data_cols, key='col_display')
            disp_stats = basic_season_cols + choose_disp
            display = st.container()
            working_data = working_data[working_data['Date'] >= low_date]
            working_data = working_data[working_data['Date'] <= high_date]
            working_data = working_data[working_data['TOI'] >= min_var1[0]]
            working_data = working_data[working_data['TOI'] <= min_var1[1]]
            working_data = working_data[working_data['Team'].isin(team_var1)]
            working_data = working_data[working_data['Player'].isin(player_var1)]
            season_long_table = seasonlong_build(working_data)
            season_long_table = season_long_table.set_index('Player')
            season_long_table_disp = season_long_table.reindex(disp_stats,axis="columns")
            display.dataframe(season_long_table_disp.style.format(precision=2), height=750, use_container_width = True)  
            st.download_button(
                    label="Export seasonlogs Model",
                    data=convert_df_to_csv(season_long_table),
                    file_name='Seasonlogs_NHL_View.csv',
                    mime='text/csv',
            )
            
        elif split_var1 == 'Gamelogs':
            choose_cols = st.container()
            with choose_cols:
                choose_disp = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='col_display')
            gamelog_disp_stats = basic_cols + choose_disp
            working_data = working_data[working_data['Date'] >= low_date]
            working_data = working_data[working_data['Date'] <= high_date]
            working_data = working_data[working_data['TOI'] >= min_var1[0]]
            working_data = working_data[working_data['TOI'] <= min_var1[1]]
            working_data = working_data[working_data['Team'].isin(team_var1)]
            working_data = working_data[working_data['Player'].isin(player_var1)]
            working_data = working_data.sort_values(by='Date', ascending=False)
            working_data = working_data.reset_index(drop=True)
            gamelog_data = working_data.reindex(gamelog_disp_stats,axis="columns")
            display = st.container()
        
            bottom_menu = st.columns((4, 1, 1))
            with bottom_menu[2]:
                batch_size = st.selectbox("Page Size", options=[25, 50, 100])
            with bottom_menu[1]:
                total_pages = (
                    int(len(gamelog_data) / batch_size) if int(len(gamelog_data) / batch_size) > 0 else 1
                )
                current_page = st.number_input(
                    "Page", min_value=1, max_value=total_pages, step=1
                )
            with bottom_menu[0]:
                st.markdown(f"Page **{current_page}** of **{total_pages}** ")
            
            
            pages = split_frame(gamelog_data, batch_size)
            # pages = pages.set_index('Player')
            display.dataframe(data=pages[current_page - 1].style.format(precision=2), height=500, use_container_width=True)
            st.download_button(
                    label="Export gamelogs Model",
                    data=convert_df_to_csv(gamelog_data),
                    file_name='Gamelogs_NBA_View.csv',
                    mime='text/csv',
            )
            
with tab2:
    col1, col2 = st.columns([1, 9])
    with col1:
        if st.button("Reset Data", key='reset2'):
                  st.cache_data.clear()
                  gamelog_table = init_baselines()
                  indv_teams = gamelog_table.drop_duplicates(subset='Team')
                  total_teams = indv_teams.Team.values.tolist()
                  indv_players = gamelog_table.drop_duplicates(subset='Player')
                  total_players = indv_players.Player.values.tolist()
                  total_dates = gamelog_table.Date.values.tolist()
        
        corr_var = st.radio("Are you correlating fantasy or TOI?", ('Fantasy', 'TOI'), key='corr_var')
        
        split_var1_t2 = st.radio("Would you like to view specific teams or specific players?", ('Specific Teams', 'Specific Players'), key='split_var1_t2')
        
        if split_var1_t2 == 'Specific Teams':
            corr_var1_t2 = st.multiselect('Which teams would you like to include in the correlation?', options = total_teams, key='corr_var1_t2')
        elif split_var1_t2 == 'Specific Players':
            corr_var1_t2 = st.multiselect('Which players would you like to include in the correlation?', options = total_players, key='corr_var1_t2')
            
        split_var2_t2 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3_t2')
        
        if split_var2_t2 == 'Specific Dates':
            low_date_t2 = st.date_input('Min Date:', value=None, format="MM/DD/YYYY", key='low_date_t2')
            if low_date_t2 is not None:
                low_date_t2 = pd.to_datetime(low_date_t2).date()
            high_date_t2 = st.date_input('Max Date:', value=None, format="MM/DD/YYYY", key='high_date_t2')
            if high_date_t2 is not None:
                high_date_t2 = pd.to_datetime(high_date_t2).date()
        elif split_var2_t2 == 'All':
            low_date_t2 = gamelog_table['Date'].min()
            high_date_t2 = gamelog_table['Date'].max()
        
        min_var1_t2 = st.slider("Is there a certain minutes range you want to view?", 0, 50, (0, 50), key='min_var1_t2')
    
    with col2:
        if split_var1_t2 == 'Specific Teams':
            display = st.container()
            gamelog_table = gamelog_table.sort_values(by='dk_fantasy', ascending=False)
            gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date_t2]
            gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date_t2]
            gamelog_table = gamelog_table[gamelog_table['TOI'] >= min_var1_t2[0]]
            gamelog_table = gamelog_table[gamelog_table['TOI'] <= min_var1_t2[1]]
            gamelog_table = gamelog_table[gamelog_table['Team'].isin(corr_var1_t2)]
            if corr_var == 'Fantasy':
                corr_display = run_fantasy_corr(gamelog_table)
            elif corr_var == 'TOI':
                corr_display = run_min_corr(gamelog_table)
            display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)
            
        elif split_var1_t2 == 'Specific Players':
            display = st.container()
            gamelog_table = gamelog_table.sort_values(by='dk_fantasy', ascending=False)
            gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date_t2]
            gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date_t2]
            gamelog_table = gamelog_table[gamelog_table['TOI'] >= min_var1_t2[0]]
            gamelog_table = gamelog_table[gamelog_table['TOI'] <= min_var1_t2[1]]
            gamelog_table = gamelog_table[gamelog_table['Player'].isin(corr_var1_t2)]
            if corr_var == 'Fantasy':
                corr_display = run_fantasy_corr(gamelog_table)
            elif corr_var == 'TOI':
                corr_display = run_min_corr(gamelog_table)
            display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)