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
import plotly.express as px
import plotly.io as pio
import pymongo
import certifi
ca = certifi.where()

@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": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
          "client_email": "[email protected]",
          "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"
        }

        client = pymongo.MongoClient("mongodb+srv://multichem:[email protected]/testing_db")
        db = client["testing_db"]

        gc_con = gspread.service_account_from_dict(credentials, scope)
      
        return gc_con, client, db

gcservice_account, client, db = init_conn()

percentages_format = {'PG': '{:.2%}', 'SG': '{:.2%}', 'SF': '{:.2%}', 'PF': '{:.2%}', 'C': '{:.2%}'}

@st.cache_resource(ttl = 600)
def init_baselines():
    collection = db["MLB_Hitters_DB"] 
    cursor = collection.find()  # Finds all documents in the collection
    
    raw_display = pd.DataFrame(list(cursor))
    hitter_gamelog_table = raw_display[raw_display['NameASCII'] != ""]
    hitter_gamelog_table = hitter_gamelog_table[['NameASCII', 'Team', 'Date', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                                                 'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%']]
    
    data_cols = hitter_gamelog_table.columns.drop(['NameASCII', 'Team', 'Date'])
    hitter_gamelog_table[data_cols] = hitter_gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
    
    hitter_gamelog_table = hitter_gamelog_table.set_axis(['Player', 'Team', 'Date', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                                                          'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%'], axis=1)
    
    collection = db["rotations"] 
    cursor = collection.find()  # Finds all documents in the collection
    
    raw_display = pd.DataFrame(list(cursor))
    pitcher_gamelog_table = raw_display[raw_display['NameASCII'] != ""]
    
    pitcher_gamelog_table = pitcher_gamelog_table[['NameASCII', 'Team', 'Date', 'G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                                                   'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
                                                   'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA (sc)', 'vFT (sc)', 'vFC (sc)', 'vFS (sc)', 'vFO (sc)', 'vSI (sc)',
                                                   'vSL (sc)', 'vCU (sc)', 'vKC (sc)', 'vEP (sc)', 'vCH (sc)', 'vSC (sc)', 'vKN (sc)']]
    
    pitcher_gamelog_table.replace("", np.nan, inplace=True)
    data_cols = pitcher_gamelog_table.columns.drop(['NameASCII', 'Team', 'Date'])
    pitcher_gamelog_table[data_cols] = pitcher_gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
    
    pitcher_gamelog_table = pitcher_gamelog_table.set_axis(['Player', 'Team', 'Date', 'G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                                                            'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
                                                            'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA', 'vFT', 'vFC', 'vFS', 'vFO', 'vSI',
                                                            'vSL', 'vCU', 'vKC', 'vEP', 'vCH', 'vSC', 'vKN'], axis=1)
    
    timestamp = pitcher_gamelog_table['Date'].max()
    
    return hitter_gamelog_table, pitcher_gamelog_table, timestamp

@st.cache_data(show_spinner=False)
def hitter_seasonlong_build(data_sample):
    season_long_table = data_sample[['Player', 'Team']]
    season_long_table['G'] = data_sample.groupby(['Player', 'Team'], sort=False)['G'].transform('sum').astype(int)
    season_long_table['AB'] = data_sample.groupby(['Player', 'Team'], sort=False)['AB'].transform('sum').astype(int)
    season_long_table['PA'] = data_sample.groupby(['Player', 'Team'], sort=False)['PA'].transform('sum').astype(int)
    season_long_table['H'] = data_sample.groupby(['Player', 'Team'], sort=False)['H'].transform('sum').astype(int)
    season_long_table['1B'] = data_sample.groupby(['Player', 'Team'], sort=False)['1B'].transform('sum').astype(int)
    season_long_table['2B'] = data_sample.groupby(['Player', 'Team'], sort=False)['2B'].transform('sum').astype(int)
    season_long_table['3B'] = data_sample.groupby(['Player', 'Team'], sort=False)['3B'].transform('sum').astype(int)
    season_long_table['HR'] = data_sample.groupby(['Player', 'Team'], sort=False)['HR'].transform('sum').astype(int)
    season_long_table['R'] = data_sample.groupby(['Player', 'Team'], sort=False)['R'].transform('sum').astype(int)
    season_long_table['RBI'] = data_sample.groupby(['Player', 'Team'], sort=False)['RBI'].transform('sum').astype(int)
    season_long_table['BB'] = data_sample.groupby(['Player', 'Team'], sort=False)['BB'].transform('sum').astype(int)
    season_long_table['IBB'] = data_sample.groupby(['Player', 'Team'], sort=False)['IBB'].transform('sum').astype(int)
    season_long_table['SO'] = data_sample.groupby(['Player', 'Team'], sort=False)['SO'].transform('sum').astype(int)
    season_long_table['HBP'] = data_sample.groupby(['Player', 'Team'], sort=False)['HBP'].transform('sum').astype(int)
    season_long_table['SF'] = data_sample.groupby(['Player', 'Team'], sort=False)['SF'].transform('sum').astype(int)
    season_long_table['SH'] = data_sample.groupby(['Player', 'Team'], sort=False)['SH'].transform('sum').astype(int)
    season_long_table['GDP'] = data_sample.groupby(['Player', 'Team'], sort=False)['GDP'].transform('sum').astype(int)
    season_long_table['SB'] = data_sample.groupby(['Player', 'Team'], sort=False)['SB'].transform('sum').astype(int)
    season_long_table['CS'] = data_sample.groupby(['Player', 'Team'], sort=False)['CS'].transform('sum').astype(int)
    season_long_table['Avg AVG'] = data_sample.groupby(['Player', 'Team'], sort=False)['AVG'].transform('mean').astype(int)
    season_long_table['Avg SLG'] = data_sample.groupby(['Player', 'Team'], sort=False)['SLG'].transform('mean').astype(int)
    season_long_table['Avg wRC+'] = data_sample.groupby(['Player', 'Team'], sort=False)['wRC+'].transform('mean').astype(int)
    season_long_table['Avg LD%'] = data_sample.groupby(['Player', 'Team'], sort=False)['LD%'].transform('mean').astype(int)
    season_long_table['Avg GB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['GB%'].transform('mean').astype(float)
    season_long_table['Avg FB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['FB%'].transform('mean').astype(float)
    season_long_table['Avg Hard%'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hard%'].transform('mean').astype(float)
    season_long_table['Barrels'] = data_sample.groupby(['Player', 'Team'], sort=False)['Barrels'].transform('sum').astype(int)
    season_long_table['Avg Barrel%'] = data_sample.groupby(['Player', 'Team'], sort=False)['Barrel%'].transform('mean').astype(float)
    season_long_table = season_long_table.drop_duplicates(subset='Player')

    season_long_table = season_long_table.sort_values(by='Avg wRC+', ascending=False)
    
    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Date', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                                                    'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%'], axis=1)

    return season_long_table

@st.cache_data(show_spinner=False)
def pitcher_seasonlong_build(data_sample):
    season_long_table = data_sample[['Player', 'Team']]
    season_long_table['G'] = data_sample.groupby(['Player', 'Team'], sort=False)['G'].transform('sum').astype(int)
    season_long_table['GS'] = data_sample.groupby(['Player', 'Team'], sort=False)['GS'].transform('sum').astype(int)
    season_long_table['CG'] = data_sample.groupby(['Player', 'Team'], sort=False)['CG'].transform('sum').astype(int)
    season_long_table['W'] = data_sample.groupby(['Player', 'Team'], sort=False)['W'].transform('sum').astype(int)
    season_long_table['L'] = data_sample.groupby(['Player', 'Team'], sort=False)['L'].transform('sum').astype(int)
    season_long_table['Avg ERA'] = data_sample.groupby(['Player', 'Team'], sort=False)['ERA'].transform('mean').astype(float)
    season_long_table['ShO'] = data_sample.groupby(['Player', 'Team'], sort=False)['ShO'].transform('sum').astype(int)
    season_long_table['SV'] = data_sample.groupby(['Player', 'Team'], sort=False)['SV'].transform('sum').astype(int)
    season_long_table['HLD'] = data_sample.groupby(['Player', 'Team'], sort=False)['HLD'].transform('sum').astype(int)
    season_long_table['BS'] = data_sample.groupby(['Player', 'Team'], sort=False)['BS'].transform('sum').astype(int)
    season_long_table['IP'] = data_sample.groupby(['Player', 'Team'], sort=False)['IP'].transform('sum').astype(int)
    season_long_table['TBF'] = data_sample.groupby(['Player', 'Team'], sort=False)['TBF'].transform('sum').astype(int)
    season_long_table['H'] = data_sample.groupby(['Player', 'Team'], sort=False)['H'].transform('sum').astype(int)
    season_long_table['R'] = data_sample.groupby(['Player', 'Team'], sort=False)['R'].transform('sum').astype(int)
    season_long_table['ER'] = data_sample.groupby(['Player', 'Team'], sort=False)['ER'].transform('sum').astype(int)
    season_long_table['HR'] = data_sample.groupby(['Player', 'Team'], sort=False)['HR'].transform('sum').astype(int)
    season_long_table['BB'] = data_sample.groupby(['Player', 'Team'], sort=False)['BB'].transform('sum').astype(int)
    season_long_table['IBB'] = data_sample.groupby(['Player', 'Team'], sort=False)['IBB'].transform('sum').astype(int)
    season_long_table['HBP'] = data_sample.groupby(['Player', 'Team'], sort=False)['HBP'].transform('sum').astype(int)
    season_long_table['WP'] = data_sample.groupby(['Player', 'Team'], sort=False)['WP'].transform('sum').astype(int)
    season_long_table['BK'] = data_sample.groupby(['Player', 'Team'], sort=False)['BK'].transform('sum').astype(int)
    season_long_table['SO'] = data_sample.groupby(['Player', 'Team'], sort=False)['SO'].transform('sum').astype(int)
    season_long_table['Avg K/9'] = data_sample.groupby(['Player', 'Team'], sort=False)['K/9'].transform('mean').astype(float)
    season_long_table['Avg BB/9'] = data_sample.groupby(['Player', 'Team'], sort=False)['BB/9'].transform('mean').astype(float)
    season_long_table['Avg WHIP'] = data_sample.groupby(['Player', 'Team'], sort=False)['WHIP'].transform('mean').astype(float)
    season_long_table['Avg BABIP'] = data_sample.groupby(['Player', 'Team'], sort=False)['BABIP'].transform('mean').astype(float)
    season_long_table['Avg LOB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['LOB%'].transform('mean').astype(int)
    season_long_table['Avg FIP'] = data_sample.groupby(['Player', 'Team'], sort=False)['FIP'].transform('mean').astype(float)
    season_long_table['Avg xFIP'] = data_sample.groupby(['Player', 'Team'], sort=False)['xFIP'].transform('mean').astype(float)
    season_long_table['Avg K%'] = data_sample.groupby(['Player', 'Team'], sort=False)['K%'].transform('mean').astype(float)
    season_long_table['Avg BB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['BB%'].transform('mean').astype(float)
    season_long_table['Avg SIERA'] = data_sample.groupby(['Player', 'Team'], sort=False)['SIERA'].transform('mean').astype(float)
    season_long_table['Avg LD%'] = data_sample.groupby(['Player', 'Team'], sort=False)['LD%'].transform('mean').astype(float)
    season_long_table['Avg GB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['GB%'].transform('mean').astype(float)
    season_long_table['Avg FB%'] = data_sample.groupby(['Player', 'Team'], sort=False)['FB%'].transform('mean').astype(float)
    season_long_table['Avg HR/FB'] = data_sample.groupby(['Player', 'Team'], sort=False)['HR/FB'].transform('mean').astype(float)
    season_long_table['Avg Hard%'] = data_sample.groupby(['Player', 'Team'], sort=False)['Hard%'].transform('mean').astype(float)
    season_long_table['Barrels'] = data_sample.groupby(['Player', 'Team'], sort=False)['Barrels'].transform('sum').astype(int)
    season_long_table['Avg Barrel%'] = data_sample.groupby(['Player', 'Team'], sort=False)['Barrel%'].transform('mean').astype(float)
    season_long_table['Avg xERA'] = data_sample.groupby(['Player', 'Team'], sort=False)['xERA'].transform('mean').astype(float)
    season_long_table['Avg vFA'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFA'].transform('mean').astype(float)
    season_long_table['Avg vFT'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFT'].transform('mean').astype(float)
    season_long_table['Avg vFC'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFC'].transform('mean').astype(float)
    season_long_table['Avg vFS'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFS'].transform('mean').astype(float)
    season_long_table['Avg vFO'] = data_sample.groupby(['Player', 'Team'], sort=False)['vFO'].transform('mean').astype(float)
    season_long_table['Avg vSI'] = data_sample.groupby(['Player', 'Team'], sort=False)['vSI'].transform('mean').astype(float)
    season_long_table['Avg vSL'] = data_sample.groupby(['Player', 'Team'], sort=False)['vSL'].transform('mean').astype(float)
    season_long_table['Avg vCU'] = data_sample.groupby(['Player', 'Team'], sort=False)['vCU'].transform('mean').astype(float)
    season_long_table['Avg vKC'] = data_sample.groupby(['Player', 'Team'], sort=False)['vKC'].transform('mean').astype(float)
    season_long_table['Avg vEP'] = data_sample.groupby(['Player', 'Team'], sort=False)['vEP'].transform('mean').astype(float)
    season_long_table['Avg vCH'] = data_sample.groupby(['Player', 'Team'], sort=False)['vCH'].transform('mean').astype(float)
    season_long_table['Avg vSC'] = data_sample.groupby(['Player', 'Team'], sort=False)['vSC'].transform('mean').astype(float)
    season_long_table['Avg vKN'] = data_sample.groupby(['Player', 'Team'], sort=False)['vKN'].transform('mean').astype(float)
    season_long_table = season_long_table.drop_duplicates(subset='Player')

    season_long_table = season_long_table.sort_values(by='SO', ascending=False)
    
    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Date', 'G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                                                    'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
                                                    'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
                                                    'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN'], axis=1)

    return season_long_table

@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')

hitter_gamelog_table, pitcher_gamelog_table, timestamp = init_baselines()
t_stamp = f"Updated through: " + str(timestamp) + f" CST"

basic_cols = ['Player', 'Team']

basic_season_cols = ['Team']

hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                    'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%']

season_hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                           'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%']

pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                     'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
                     'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA', 'vFT', 'vFC', 'vFS', 'vFO', 'vSI',
                     'vSL', 'vCU', 'vKC', 'vEP', 'vCH', 'vSC', 'vKN']

season_pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                            'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
                            'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
                            'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN']

indv_teams = hitter_gamelog_table.drop_duplicates(subset='Team')
total_teams = indv_teams.Team.values.tolist()
indv_hitters = hitter_gamelog_table.drop_duplicates(subset='Player')
total_hitters = indv_hitters.Player.values.tolist()
indv_pitchers = hitter_gamelog_table.drop_duplicates(subset='Player')
total_pitchers = indv_pitchers.Player.values.tolist()
total_dates = hitter_gamelog_table.Date.values.tolist()

tab1, tab2 = st.tabs(['Hitter Gamelogs', 'Pitcher Gamelogs'])

with tab1:
    st.info(t_stamp)
    col1, col2 = st.columns([1, 9])
    with col1:
        if st.button("Reset Data", key='reset1'):
                  st.cache_data.clear()
                  hitter_gamelog_table, pitcher_gamelog_table, timestamp = init_baselines()
                  t_stamp = f"Updated through: " + str(timestamp) + f" CST"

                  basic_cols = ['Player', 'Team']

                  basic_season_cols = ['Team']

                  hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                                      'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%']

                  season_hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                                             'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%']

                  pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                                       'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
                                       'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA', 'vFT', 'vFC', 'vFS', 'vFO', 'vSI',
                                       'vSL', 'vCU', 'vKC', 'vEP', 'vCH', 'vSC', 'vKN']

                  season_pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                                              'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
                                              'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
                                              'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN']

                  indv_teams = hitter_gamelog_table.drop_duplicates(subset='Team')
                  total_teams = indv_teams.Team.values.tolist()
                  indv_hitters = hitter_gamelog_table.drop_duplicates(subset='Player')
                  total_hitters = indv_hitters.Player.values.tolist()
                  indv_pitchers = hitter_gamelog_table.drop_duplicates(subset='Player')
                  total_pitchers = indv_pitchers.Player.values.tolist()
                  total_dates = hitter_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="YYYY-MM-DD", 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="YYYY-MM-DD", key='high_date')
            if high_date is not None:
                high_date = pd.to_datetime(high_date).date()
        elif split_var3 == 'All':
            low_date = hitter_gamelog_table['Date'].min()
            high_date = hitter_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_hitters, key='player_var1')
        elif split_var4 == 'All':
            player_var1 = total_hitters
    
    with col2:
        working_data = hitter_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_hitter_data_cols, default = season_hitter_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['Team'].isin(team_var1)]
            working_data = working_data[working_data['Player'].isin(player_var1)]
            season_long_table = hitter_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 hitter seasonlogs Model",
                    data=convert_df_to_csv(season_long_table),
                    file_name='Seasonlogs_Hitter_View.csv',
                    mime='text/csv',
            )
            
        elif split_var1 == 'Gamelogs':
            choose_cols = st.container()
            with choose_cols:
                choose_disp_gamelog = st.multiselect('Which stats would you like to view?', options = hitter_data_cols, default = hitter_data_cols, key='choose_disp_gamelog')
            gamelog_disp_stats = basic_cols + choose_disp_gamelog
            working_data = working_data[working_data['Date'] >= low_date]
            working_data = working_data[working_data['Date'] <= high_date]
            working_data = working_data[working_data['Team'].isin(team_var1)]
            working_data = working_data[working_data['Player'].isin(player_var1)]
            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 hitter gamelogs model",
                    data=convert_df_to_csv(gamelog_data),
                    file_name='Gamelogs_Hitter_View.csv',
                    mime='text/csv',
            )

with tab2:
    st.info(t_stamp)
    col1, col2 = st.columns([1, 9])
    with col1:
        if st.button("Reset Data", key='reset2'):
                  st.cache_data.clear()
                  hitter_gamelog_table, pitcher_gamelog_table, timestamp = init_baselines()
                  t_stamp = f"Updated through: " + str(timestamp) + f" CST"

                  basic_cols = ['Player', 'Team']

                  basic_season_cols = ['Team']

                  hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                                      'GDP', 'SB', 'CS', 'AVG', 'SLG', 'wRC+', 'LD%', 'GB%', 'FB%', 'Hard%', 'Barrels', 'Barrel%']

                  season_hitter_data_cols = ['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
                                             'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%']

                  pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                                       'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9', 'BB/9', 'WHIP', 'BABIP', 'LOB%', 'FIP', 'xFIP', 'K%', 'BB%', 'SIERA', 'LD%', 'GB%',
                                       'FB%', 'HR/FB', 'Hard%', 'Barrels', 'Barrel%', 'xERA', 'vFA', 'vFT', 'vFC', 'vFS', 'vFO', 'vSI',
                                       'vSL', 'vCU', 'vKC', 'vEP', 'vCH', 'vSC', 'vKN']

                  season_pitcher_data_cols = ['G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
                                              'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
                                              'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
                                              'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN']

                  indv_teams = hitter_gamelog_table.drop_duplicates(subset='Team')
                  total_teams = indv_teams.Team.values.tolist()
                  indv_hitters = hitter_gamelog_table.drop_duplicates(subset='Player')
                  total_hitters = indv_hitters.Player.values.tolist()
                  indv_pitchers = hitter_gamelog_table.drop_duplicates(subset='Player')
                  total_pitchers = indv_pitchers.Player.values.tolist()
                  total_dates = hitter_gamelog_table.Date.values.tolist()
        
        split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='sp_split_var1')
        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='sp_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='sp_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='sp_split_var3')
        
        if split_var3 == 'Specific Dates':
            low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='sp_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="YYYY-MM-DD", key='sp_high_date')
            if high_date is not None:
                high_date = pd.to_datetime(high_date).date()
        elif split_var3 == 'All':
            low_date = hitter_gamelog_table['Date'].min()
            high_date = hitter_gamelog_table['Date'].max()
        
        split_var4 = st.radio("Would you like to view all players or specific ones?", ('All', 'Specific Players'), key='sp_split_var4')
        
        if split_var4 == 'Specific Players':
            player_var1 = st.multiselect('Which players would you like to include in the tables?', options = total_hitters, key='sp_player_var1')
        elif split_var4 == 'All':
            player_var1 = total_hitters
    
    with col2:
        working_data = hitter_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_hitter_data_cols, default = season_hitter_data_cols, key='sp_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['Team'].isin(team_var1)]
            working_data = working_data[working_data['Player'].isin(player_var1)]
            season_long_table = hitter_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 pitcher seasonlogs Model",
                    data=convert_df_to_csv(season_long_table),
                    file_name='Seasonlogs_Pitcher_View.csv',
                    mime='text/csv',
            )
            
        elif split_var1 == 'Gamelogs':
            choose_cols = st.container()
            with choose_cols:
                choose_disp_gamelog = st.multiselect('Which stats would you like to view?', options = hitter_data_cols, default = hitter_data_cols, key='sp_choose_disp_gamelog')
            gamelog_disp_stats = basic_cols + choose_disp_gamelog
            working_data = working_data[working_data['Date'] >= low_date]
            working_data = working_data[working_data['Date'] <= high_date]
            working_data = working_data[working_data['Team'].isin(team_var1)]
            working_data = working_data[working_data['Player'].isin(player_var1)]
            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 pitcher gamelogs model",
                    data=convert_df_to_csv(gamelog_data),
                    file_name='Gamelogs_Hitter_View.csv',
                    mime='text/csv',
            )