app.py

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

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

import pulp
import numpy as np
import pandas as pd
import streamlit as st
import pymongo
from itertools import combinations

@st.cache_resource
def init_conn():
        uri = st.secrets['mongo_uri']
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
        db = client["NFL_Database"]

        return db
    
db = init_conn()

game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}

player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
                   '4x%': '{:.2%}','GPP%': '{:.2%}'}

dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']

@st.cache_resource(ttl=60)
def player_stat_table():

    collection = db["Player_Baselines"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['name', 'Team', 'Opp', 'Position', 'Salary', 'team_plays', 'team_pass', 'team_rush', 'team_tds', 'team_pass_tds', 'team_rush_tds', 'dropbacks', 'pass_yards', 'pass_tds',
                               'rush_att', 'rush_yards', 'rush_tds', 'targets', 'rec', 'rec_yards', 'rec_tds', 'PPR', 'Half_PPR', 'Own']]
    player_stats = raw_display[raw_display['Position'] != 'K']

    collection = db["DK_NFL_ROO"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
    load_display = raw_display[raw_display['Position'] != 'K']
    dk_roo_raw = load_display.dropna(subset=['Median'])

    collection = db["FD_NFL_ROO"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
    load_display = raw_display[raw_display['Position'] != 'K']
    fd_roo_raw = load_display.dropna(subset=['Median'])

    collection = db["DK_DFS_Stacks"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
    dk_stacks_raw = raw_display.copy()

    collection = db["FD_DFS_Stacks"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
    fd_stacks_raw = raw_display.copy()

    return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw

@st.cache_resource(ttl = 60)
def init_DK_lineups():  
        
        collection = db['DK_NFL_name_map']
        cursor = collection.find()
        raw_data = pd.DataFrame(list(cursor))
        names_dict = dict(zip(raw_data['key'], raw_data['value']))
    
        collection = db["DK_NFL_seed_frame"] 
        cursor = collection.find().limit(10000)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
        for col in dict_columns:
            raw_display[col] = raw_display[col].map(names_dict)
        DK_seed = raw_display.to_numpy()

        return DK_seed

@st.cache_resource(ttl = 60)
def init_FD_lineups():  
        
        collection = db['FD_NFL_name_map']
        cursor = collection.find()
        raw_data = pd.DataFrame(list(cursor))
        names_dict = dict(zip(raw_data['key'], raw_data['value']))
    
        collection = db["FD_NFL_seed_frame"] 
        cursor = collection.find().limit(10000)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
        for col in dict_columns:
            raw_display[col] = raw_display[col].map(names_dict)
        FD_seed = raw_display.to_numpy()

        return FD_seed

@st.cache_data
def convert_df(array):
    array = pd.DataFrame(array, columns=column_names)
    return array.to_csv().encode('utf-8')

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

player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
try:
    dk_lineups = init_DK_lineups()
    fd_lineups = init_FD_lineups()
except:
    dk_lineups = pd.DataFrame(columns=dk_columns)
    fd_lineups = pd.DataFrame(columns=fd_columns)

t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"

tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["Team Stacks Range of Outcomes", "Overall Range of Outcomes", "QB Range of Outcomes", "RB Range of Outcomes", "WR Range of Outcomes", "TE Range of Outcomes", "Optimals"])

# ... existing code ...

@st.cache_data
def optimize_lineup(player_data, sidebar_site):
    """
    Creates optimal lineup based on median projections while respecting position and salary constraints
    """
    # Create optimization problem
    prob = pulp.LpProblem("NFL_Lineup_Optimization", pulp.LpMaximize)
    
    # Create player dictionary with binary variables
    players = {}
    for idx, row in player_data.iterrows():
        players[row['Player']] = pulp.LpVariable(f"player_{idx}", 0, 1, pulp.LpBinary)
    
    # Objective: Maximize total median points
    prob += pulp.lpSum([players[row['Player']] * row['Median'] for idx, row in player_data.iterrows()])
    
    # Constraint: Salary cap
    if sidebar_site == 'Draftkings':
        prob += pulp.lpSum([players[row['Player']] * row['Salary'] for idx, row in player_data.iterrows()]) <= 50000
    elif sidebar_site == 'Fanduel':
        prob += pulp.lpSum([players[row['Player']] * row['Salary'] for idx, row in player_data.iterrows()]) <= 60000

    # Constraint: 9 players
    prob += pulp.lpSum([players[row['Player']] for idx, row in player_data.iterrows()]) == 9
    
    # Position constraints
    qbs = player_data[player_data['Position'] == 'QB']['Player'].tolist()
    rbs = player_data[player_data['Position'] == 'RB']['Player'].tolist()
    wrs = player_data[player_data['Position'] == 'WR']['Player'].tolist()
    tes = player_data[player_data['Position'] == 'TE']['Player'].tolist()
    dsts = player_data[player_data['Position'] == 'DST']['Player'].tolist()
    
    # QB: exactly 1
    prob += pulp.lpSum([players[p] for p in qbs]) == 1
    
    # RB: 2-3
    prob += pulp.lpSum([players[p] for p in rbs]) >= 2
    prob += pulp.lpSum([players[p] for p in rbs]) <= 3
    
    # WR: 3-4
    prob += pulp.lpSum([players[p] for p in wrs]) >= 3
    prob += pulp.lpSum([players[p] for p in wrs]) <= 4
    
    # TE: 1-2
    prob += pulp.lpSum([players[p] for p in tes]) >= 1
    prob += pulp.lpSum([players[p] for p in tes]) <= 2
    
    # DST: exactly 1
    prob += pulp.lpSum([players[p] for p in dsts]) == 1
    
    # Solve the problem
    prob.solve()
    
    # Get selected players
    selected_players = []
    total_salary = 0
    total_median = 0
    
    for idx, row in player_data.iterrows():
        if players[row['Player']].value() == 1:
            selected_players.append({
                'Player': row['Player'],
                'Position': row['Position'],
                'Salary': row['Salary'],
                'Median': row['Median']
            })
            total_salary += row['Salary']
            total_median += row['Median']
    
    return selected_players, total_salary, total_median

with st.sidebar:
    st.header("Quick Builder")

    sidebar_site = st.selectbox("What site are you running?", ('Draftkings', 'Fanduel'), key='sidebar_site')
    sidebar_slate = st.selectbox("What slate are you running?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='sidebar_slate')

    if sidebar_site == 'Draftkings':
        roo_sample = dk_roo_raw[dk_roo_raw['slate'] == str(sidebar_slate)]
        roo_sample = roo_sample[roo_sample['version'] == 'overall']
        #roo_sample = roo_sample.sort_values(by='Own', ascending=False)
    elif sidebar_site == 'Fanduel':
        roo_sample = fd_roo_raw[fd_roo_raw['slate'] == str(sidebar_slate)]
        roo_sample = roo_sample[roo_sample['version'] == 'overall']
        #roo_sample = roo_sample.sort_values(by='Own', ascending=False)
    
    st.write("---")
    if st.button("Generate Optimal Lineup"):
        if sidebar_site == 'Draftkings':
            roo_data = dk_roo_raw[dk_roo_raw['slate'] == str(sidebar_slate)]
            roo_data = roo_data[roo_data['version'] == 'overall']
        else:
            roo_data = fd_roo_raw[fd_roo_raw['slate'] == str(sidebar_slate)]
            roo_data = roo_data[roo_data['version'] == 'overall']
            
        optimal_players, total_salary, total_median = optimize_lineup(roo_data, sidebar_site)
       
        st.write("Optimal Lineup:")
        
        # Sort players into position groups
        qb = [p for p in optimal_players if p['Position'] == 'QB'][0]
        rbs = [p for p in optimal_players if p['Position'] == 'RB']
        wrs = [p for p in optimal_players if p['Position'] == 'WR']
        tes = [p for p in optimal_players if p['Position'] == 'TE']
        dst = [p for p in optimal_players if p['Position'] == 'DST'][0]
        
        # Display QB
        st.write(f"QB: {qb['Player']} (${qb['Salary']:,})")
        
        # Display RB1 and RB2
        st.write(f"RB: {rbs[0]['Player']} (${rbs[0]['Salary']:,})")
        st.write(f"RB: {rbs[1]['Player']} (${rbs[1]['Salary']:,})")
        
        # Display WR1, WR2, WR3
        st.write(f"WR: {wrs[0]['Player']} (${wrs[0]['Salary']:,})")
        st.write(f"WR: {wrs[1]['Player']} (${wrs[1]['Salary']:,})")
        st.write(f"WR: {wrs[2]['Player']} (${wrs[2]['Salary']:,})")
        
        # Display TE1
        st.write(f"TE: {tes[0]['Player']} (${tes[0]['Salary']:,})")
        
        # Display FLEX (either RB3, WR4, or TE2)
        if len(rbs) > 2:
            st.write(f"FLEX (RB): {rbs[2]['Player']} (${rbs[2]['Salary']:,})")
        elif len(wrs) > 3:
            st.write(f"FLEX (WR): {wrs[3]['Player']} (${wrs[3]['Salary']:,})")
        elif len(tes) > 1:
            st.write(f"FLEX (TE): {tes[1]['Player']} (${tes[1]['Salary']:,})")
            
        # Display DST
        st.write(f"DST: {dst['Player']} (${dst['Salary']:,})")
        
        st.write(f"Total Salary: ${total_salary:,}")
        st.write(f"Projected Points: {total_median:.2f}")

    # Create empty lists to store selected players
    selected_qbs = []
    selected_rb1 = []
    selected_rb2 = []
    selected_wr1 = []
    selected_wr2 = []
    selected_wr3 = []
    selected_te = []
    selected_flex = []
    selected_dst = []
    
    # Get unique players by position from dk_roo_raw
    qbs = roo_sample[roo_sample['Position'] == 'QB']['Player'].unique()
    rbs = roo_sample[roo_sample['Position'] == 'RB']['Player'].unique()
    wrs = roo_sample[roo_sample['Position'] == 'WR']['Player'].unique()
    tes = roo_sample[roo_sample['Position'] == 'TE']['Player'].unique()
    flex = roo_sample['Player'].unique()
    dst = roo_sample[roo_sample['Position'] == 'DST']['Player'].unique()
    # Create multiselect dropdowns for each position
    selected_qbs = st.multiselect('Select QB:', list(qbs), default=None, key='qb1')
    if selected_qbs:
        qb_team = roo_sample[roo_sample['Player'] == selected_qbs[0]]['Team'].values[0]
        qb_sample = roo_sample[roo_sample['Team'] == qb_team]
        bb_sample = roo_sample[roo_sample['Opp'] == qb_team]
        wr_suggest = qb_sample[qb_sample['Position'] == 'WR']['Player'].values[0]
        wr2_suggest = bb_sample[bb_sample['Position'] == 'WR']['Player'].values[0]
        te_suggest = qb_sample[qb_sample['Position'] == 'TE']['Player'].values[0]
    selected_rb1 = st.multiselect('Select RBs:', list(rbs), default=None, key='rb1')
    selected_rb2 = st.multiselect('Select RB2:', list(rbs), default=None, label_visibility='collapsed', key='rb2')
    if selected_qbs:
        selected_wr1 = st.multiselect('Select WRs:', list(wrs), default=None, placeholder=f'Suggestion: {wr_suggest}', key='wr1')
    else:
        selected_wr1 = st.multiselect('Select WRs:', list(wrs), default=None, key='wr1')
    if selected_qbs:
        selected_wr2 = st.multiselect('Select WR2:', list(wrs), default=None, placeholder=f'Suggestion: {wr2_suggest}', label_visibility='collapsed', key='wr2')
    else:
        selected_wr2 = st.multiselect('Select WR2:', list(wrs), default=None, label_visibility='collapsed', key='wr2')
    selected_wr3 = st.multiselect('Select WR3:', list(wrs), default=None, label_visibility='collapsed', key='wr3')
    if selected_qbs:
        selected_te = st.multiselect('Select TE:', list(tes), default=None, placeholder=f'Suggestion: {te_suggest}', key='te')
    else:
        selected_te = st.multiselect('Select TE:', list(tes), default=None, key='te')
    selected_flex = st.multiselect('Select Flex:', list(flex), default=None, key='flex')
    selected_dst = st.multiselect('Select DST:', list(dst), default=None, key='dst')

    # Combine all selected players
    all_selected = selected_qbs + selected_rb1 + selected_rb2 + selected_wr1 + selected_wr2 + selected_wr3 + selected_te + selected_flex + selected_dst

    if all_selected:
        # Get stats for selected players
        selected_stats = roo_sample[roo_sample['Player'].isin(all_selected)]
        
        # Calculate sums
        salary_sum = selected_stats['Salary'].sum()
        median_sum = selected_stats['Median'].sum()
        own_sum = selected_stats['Own'].sum()
        levx_sum = selected_stats['LevX'].sum()

        # Display sums
        st.write('---')
        if sidebar_site == 'Draftkings':
            if salary_sum > 50000:
                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $50,000')
            else:
                st.write(f'Total Salary: ${salary_sum:.2f}')
        elif sidebar_site == 'Fanduel':
            if salary_sum > 60000:
                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $60,000')
            else:
                st.write(f'Total Salary: ${salary_sum:.2f}')
        st.write(f'Total Median: {median_sum:.2f}')
        st.write(f'Total Ownership: {own_sum:.2f}%')
        st.write(f'Total LevX: {levx_sum:.2f}')

with tab1:
    col1, col2 = st.columns([1, 5])
    with col1:
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset1'):
            st.cache_data.clear()
            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
            for key in st.session_state.keys():
                del st.session_state[key]
        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var1')
        site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
        view_var1 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var1')
        custom_var1 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var1')
        if custom_var1 == 'No':
            if site_var1 == 'Draftkings':
                raw_baselines = dk_stacks_raw[dk_stacks_raw['slate'] == str(slate_var1)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
                raw_baselines = raw_baselines.iloc[:,:-2]
            elif site_var1 == 'Fanduel':
                raw_baselines = fd_stacks_raw[fd_stacks_raw['slate'] == str(slate_var1)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
                raw_baselines = raw_baselines.iloc[:,:-2]
            split_var1 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
            if split_var1 == 'Specific Games':
                team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
            elif split_var1 == 'Full Slate Run':
                team_var1 = raw_baselines.Team.values.tolist()
        if custom_var1 == 'Yes':
            contest_var1 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1')
            if site_var1 == 'Draftkings':
                raw_baselines = dk_stacks_raw[dk_stacks_raw['slate'] == str(slate_var1)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
            elif site_var1 == 'Fanduel':
                raw_baselines = fd_stacks_raw[fd_stacks_raw['slate'] == str(slate_var1)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
            split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
            if split_var1 == 'Specific Games':
                team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
            elif split_var1 == 'Full Slate Run':
                team_var1 = raw_baselines.Team.values.tolist()
    
    
    with col2:
        if custom_var1 == 'No':
            final_stacks = raw_baselines[raw_baselines['Team'].isin(team_var1)]
            if view_var1 == 'Simple':
                final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
            elif view_var1 == 'Advanced':
                final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
                                                             'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
            st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
            st.download_button(
                    label="Export Tables",
                    data=convert_df_to_csv(final_stacks),
                    file_name='NFL_stacks_export.csv',
                    mime='text/csv',
            )
        elif custom_var1 == 'Yes':
            hold_container = st.empty()
            if st.button('Create Range of Outcomes for Slate'):
                with hold_container:
                    if site_var1 == 'Draftkings':
                            working_roo = player_stats
                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
                            working_roo.replace('', 0, inplace=True)
                    if site_var1 == 'Fanduel':
                            working_roo = player_stats
                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
                            working_roo.replace('', 0, inplace=True)
                    working_roo = working_roo[working_roo['Team'].isin(team_var1)]
                    
                    total_sims = 1000
                    
                    salary_dict = dict(zip(working_roo.name, working_roo.Salary))
                    own_dict = dict(zip(working_roo.name, working_roo.Own))
                    fantasy_dict = dict(zip(working_roo.name, working_roo.Fantasy))

                    QB_group = working_roo.loc[working_roo['Position'] == 'QB']
                    stacks_df = pd.DataFrame(columns=['Team','QB', 'WR1', 'WR2_TE'])

                    for stack in range(0,len(QB_group)):
                        team_var = QB_group.iat[stack,1]
                        WR_group_1 = working_roo.loc[working_roo['Position'] == 'WR']
                        WR_group_2 = WR_group_1.loc[working_roo['Team'] == team_var]
                        TE_group_1 = working_roo.loc[working_roo['Position'] == 'TE']
                        TE_group_2 = TE_group_1.loc[working_roo['Team'] == team_var]
                        cur_list = []
                        qb_piece = QB_group.iat[stack,0]
                        wr_piece = WR_group_2.iat[0,0]
                        te_piece = TE_group_2.iat[0,0]
                        cur_list.append(team_var)
                        cur_list.append(qb_piece)
                        cur_list.append(wr_piece)
                        cur_list.append(te_piece)
                        stacks_df.loc[len(stacks_df)] = cur_list
                        cur_list = []
                        qb_piece = QB_group.iat[stack,0]
                        wr_piece = WR_group_2.iat[1,0]
                        te_piece = TE_group_2.iat[0,0]
                        cur_list.append(team_var)
                        cur_list.append(qb_piece)
                        cur_list.append(wr_piece)
                        cur_list.append(te_piece)
                        stacks_df.loc[len(stacks_df)] = cur_list
                        cur_list = []
                        qb_piece = QB_group.iat[stack,0]
                        wr_piece = WR_group_2.iat[0,0]
                        te_piece = WR_group_2.iat[1,0]
                        cur_list.append(team_var)
                        cur_list.append(qb_piece)
                        cur_list.append(wr_piece)
                        cur_list.append(te_piece)
                        stacks_df.loc[len(stacks_df)] = cur_list
                    
                    stacks_df['Salary'] = sum([stacks_df['QB'].map(salary_dict),
                            stacks_df['WR1'].map(salary_dict),
                            stacks_df['WR2_TE'].map(salary_dict)])

                    stacks_df['Fantasy'] = sum([stacks_df['QB'].map(fantasy_dict),
                            stacks_df['WR1'].map(fantasy_dict),
                            stacks_df['WR2_TE'].map(fantasy_dict)])

                    stacks_df['Own'] = sum([stacks_df['QB'].map(own_dict),
                            stacks_df['WR1'].map(own_dict),
                            stacks_df['WR2_TE'].map(own_dict)])

                    stacks_df['team_combo'] = stacks_df['Team'] + " " + stacks_df['QB'] + " " + stacks_df['WR1'] + " " + stacks_df['WR2_TE']

                    own_dict = dict(zip(stacks_df.team_combo, stacks_df.Own))
                    qb_dict = dict(zip(stacks_df.team_combo, stacks_df.QB))
                    wr1_dict = dict(zip(stacks_df.team_combo, stacks_df.WR1))
                    wr2_dict = dict(zip(stacks_df.team_combo, stacks_df.WR2_TE))
                    team_dict = dict(zip(stacks_df.team_combo, stacks_df.Team))

                    flex_file = stacks_df[['team_combo', 'Salary', 'Fantasy']]
                    flex_file.rename(columns={"Fantasy": "Median"}, inplace = True)
                    flex_file['Floor'] = flex_file['Median']*.25
                    flex_file['Ceiling'] = flex_file['Median'] + flex_file['Floor']
                    flex_file['STD'] = flex_file['Median']/4
                    flex_file = flex_file[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                    hold_file = flex_file
                    overall_file = flex_file
                    salary_file = flex_file

                    overall_players = overall_file[['team_combo']]

                    for x in range(0,total_sims):    
                        salary_file[x] = salary_file['Salary']

                    salary_file=salary_file.drop(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    salary_file.astype('int').dtypes

                    salary_file = salary_file.div(1000)

                    for x in range(0,total_sims):    
                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                    overall_file=overall_file.drop(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    overall_file.astype('int').dtypes

                    players_only = hold_file[['team_combo']]
                    raw_lineups_file = players_only

                    for x in range(0,total_sims):
                        maps_dict = {'proj_map':dict(zip(hold_file.team_combo,hold_file[x]))}
                        raw_lineups_file[x] = sum([raw_lineups_file['team_combo'].map(maps_dict['proj_map'])])
                        players_only[x] = raw_lineups_file[x].rank(ascending=False)

                    players_only=players_only.drop(['team_combo'], axis=1)
                    players_only.astype('int').dtypes

                    salary_2x_check = (overall_file - (salary_file*2))
                    salary_3x_check = (overall_file - (salary_file*3))
                    salary_4x_check = (overall_file - (salary_file*4))

                    players_only['Average_Rank'] = players_only.mean(axis=1)
                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                    players_only['60+%'] = overall_file[overall_file >= 60].count(axis=1)/float(total_sims)
                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)

                    players_only['team_combo'] = hold_file[['team_combo']]

                    final_outcomes = players_only[['team_combo', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']]

                    final_stacks = pd.merge(hold_file, final_outcomes, on="team_combo")
                    final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']]
                    final_stacks['Own'] = final_stacks['team_combo'].map(own_dict)
                    final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own']]
                    final_stacks['Projection Rank'] = final_stacks.Median.rank(pct = True)
                    final_stacks['Own Rank'] = final_stacks.Own.rank(pct = True)
                    final_stacks['LevX'] = final_stacks['Projection Rank'] - final_stacks['Own Rank']
                    final_stacks['Team'] = final_stacks['team_combo'].map(team_dict)
                    final_stacks['QB'] = final_stacks['team_combo'].map(qb_dict)
                    final_stacks['WR1_TE'] = final_stacks['team_combo'].map(wr1_dict)
                    final_stacks['WR2_TE'] = final_stacks['team_combo'].map(wr2_dict)

                    final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
                                                        'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]

                    final_stacks = final_stacks.sort_values(by='Median', ascending=False)

                with hold_container:
                    hold_container = st.empty()
                    final_stacks = final_stacks
                    if view_var1 == 'Simple':
                        final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
                    elif view_var1 == 'Advanced':
                        final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
                                                                    'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
                    st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)

                st.download_button(
                    label="Export Tables",
                    data=convert_df_to_csv(final_stacks),
                    file_name='Custom_NFL_stacks_export.csv',
                    mime='text/csv',
                )

with tab2:
    col1, col2 = st.columns([1, 5])
    with col1:
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset2'):
            st.cache_data.clear()
            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
            for key in st.session_state.keys():
                    del st.session_state[key]   
        slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var2')
        site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
        view_var2 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var2')
        custom_var2 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var2')
        if custom_var2 == 'No':
            if site_var2 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var2)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
                raw_baselines = raw_baselines.iloc[:,:-2]
            elif site_var2 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var2)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
                raw_baselines = raw_baselines.iloc[:,:-2]
            split_var2 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
            if split_var2 == 'Specific Games':
                team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
            elif split_var2 == 'Full Slate Run':
                team_var2 = raw_baselines.Team.values.tolist()
            pos_split2 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split2')
            if pos_split2 == 'Specific Positions':
                pos_var2 = st.multiselect('What Positions would you like to view?', options = ['QB', 'RB', 'WR', 'TE'])
            elif pos_split2 == 'All Positions':
                pos_var2 = 'All'
            sal_var2 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var2')
        if custom_var2 == 'Yes':
            contest_var2 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var2')
            if site_var2 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var2)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
            elif site_var2 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var2)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
            split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
            if split_var2 == 'Specific Games':
                team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
            elif split_var2 == 'Full Slate Run':
                team_var2 = raw_baselines.Team.values.tolist()
            pos_split2 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split2')
            if pos_split2 == 'Specific Positions':
                pos_var2 = st.multiselect('What Positions would you like to view?', options = ['QB', 'RB', 'WR', 'TE'])
            elif pos_split2 == 'All Positions':
                pos_var2 = 'All'
            sal_var2 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var2')
    
    
    with col2:
        if custom_var2 == 'No':
            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var2[0]]
            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var2[1]]
            if pos_var2 != 'All':
                    final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2))]
                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
            
            if view_var2 == 'Simple':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                disp_proj = final_Proj.set_index('Player')
            elif view_var2 == 'Advanced':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                disp_proj = final_Proj.set_index('Player')
            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
            st.download_button(
                label="Export Tables",
                data=convert_df_to_csv(final_Proj),
                file_name='NFL_overall_export.csv',
                mime='text/csv',
            )
        elif custom_var2 == 'Yes':
            hold_container = st.empty()
            if st.button('Create Range of Outcomes for Slate'):
                with hold_container:
                    if site_var2 == 'Draftkings':
                        working_roo = player_stats
                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
                        working_roo.replace('', 0, inplace=True)
                    if site_var2 == 'Fanduel':
                        working_roo = player_stats
                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
                        working_roo.replace('', 0, inplace=True)
                    working_roo = working_roo[working_roo['Team'].isin(team_var2)]
                    working_roo = working_roo[working_roo['Salary'] >= sal_var2[0]]
                    working_roo = working_roo[working_roo['Salary'] <= sal_var2[1]]
                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
                    total_sims = 1000

                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                    hold_file = flex_file
                    overall_file = flex_file
                    salary_file = flex_file

                    overall_players = overall_file[['Player']]

                    for x in range(0,total_sims):    
                        salary_file[x] = salary_file['Salary']

                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    salary_file.astype('int').dtypes

                    salary_file = salary_file.div(1000)

                    for x in range(0,total_sims):    
                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    overall_file.astype('int').dtypes

                    players_only = hold_file[['Player']]
                    raw_lineups_file = players_only

                    for x in range(0,total_sims):
                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                        players_only[x] = raw_lineups_file[x].rank(ascending=False)

                    players_only=players_only.drop(['Player'], axis=1)
                    players_only.astype('int').dtypes

                    salary_2x_check = (overall_file - (salary_file*2))
                    salary_3x_check = (overall_file - (salary_file*3))
                    salary_4x_check = (overall_file - (salary_file*4))

                    players_only['Average_Rank'] = players_only.mean(axis=1)
                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)

                    players_only['Player'] = hold_file[['Player']]

                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]

                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
                    final_Proj['LevX'] = 0
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4

                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                    final_Proj = final_Proj.set_index('Player')
                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)

                with hold_container:
                    hold_container = st.empty()
                    final_Proj = final_Proj
                    if view_var2 == 'Simple':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                        disp_proj = final_Proj.set_index('Player')
                    elif view_var2 == 'Advanced':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                        disp_proj = final_Proj.set_index('Player')
                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)

                st.download_button(
                        label="Export Tables",
                        data=convert_df_to_csv(final_Proj),
                        file_name='Custom_NFL_overall_export.csv',
                        mime='text/csv',
                )

with tab3:
    col1, col2 = st.columns([1, 5])
    with col1:
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset3'):
            st.cache_data.clear()
            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
            for key in st.session_state.keys():
                del st.session_state[key]   
        slate_var3 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var3')
        site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3')
        view_var3 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var3')
        custom_var3 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var3')
        if custom_var3 == 'No':
            if site_var3 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var3)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_qbs']
                raw_baselines = raw_baselines.iloc[:,:-3]
            elif site_var3 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var3)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_qbs']
                raw_baselines = raw_baselines.iloc[:,:-3]
            split_var3 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var3')
            if split_var3 == 'Specific Games':
                team_var3 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var3')
            elif split_var3 == 'Full Slate Run':
                team_var3 = raw_baselines.Team.values.tolist()
            pos_split3 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split3')
            if pos_split3 == 'Specific Positions':
                pos_var3 = st.multiselect('What Positions would you like to view?', options = ['QB'], key='pos_var3')
            elif pos_split3 == 'All Positions':
                pos_var3 = 'All'
            sal_var3 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var3')
        if custom_var3 == 'Yes':
            contest_var3 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var3')
            if site_var3 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var3)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_qbs']
                raw_baselines = raw_baselines.iloc[:,:-3]
            elif site_var3 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var3)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_qbs']
                raw_baselines = raw_baselines.iloc[:,:-3]
            split_var3 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var3')
            if split_var3 == 'Specific Games':
                team_var3 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var3')
            elif split_var3 == 'Full Slate Run':
                team_var3 = raw_baselines.Team.values.tolist()
            pos_split3 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split3')
            if pos_split3 == 'Specific Positions':
                pos_var3 = st.multiselect('What Positions would you like to view?', options = ['QB'])
            elif pos_split3 == 'All Positions':
                pos_var3 = 'All'
            sal_var3 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var3')
    
    
    with col2:
        if custom_var3 == 'No':
            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var3)]
            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var3[0]]
            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var3[1]]
            if pos_var3 != 'All':
                final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var3))]
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
            
            if view_var3 == 'Simple':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                disp_proj = final_Proj.set_index('Player')
            elif view_var3 == 'Advanced':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                disp_proj = final_Proj.set_index('Player')
            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
            st.download_button(
                label="Export Tables",
                data=convert_df_to_csv(final_Proj),
                file_name='NFL_qb_export.csv',
                mime='text/csv',
            )
        elif custom_var3 == 'Yes':
            hold_container = st.empty()
            if st.button('Create Range of Outcomes for Slate'):
                with hold_container:
                    if site_var3 == 'Draftkings':
                        working_roo = player_stats
                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
                        working_roo.replace('', 0, inplace=True)
                        working_roo = working_roo[working_roo['Position'] == 'QB']
                    if site_var3 == 'Fanduel':
                        working_roo = player_stats
                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
                        working_roo.replace('', 0, inplace=True)
                        working_roo = working_roo[working_roo['Position'] == 'QB']
                    working_roo = working_roo[working_roo['Team'].isin(team_var3)]
                    working_roo = working_roo[working_roo['Salary'] >= sal_var2[0]]
                    working_roo = working_roo[working_roo['Salary'] <= sal_var2[1]]
                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
                    total_sims = 1000

                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                    hold_file = flex_file
                    overall_file = flex_file
                    salary_file = flex_file

                    overall_players = overall_file[['Player']]

                    for x in range(0,total_sims):    
                        salary_file[x] = salary_file['Salary']

                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    salary_file.astype('int').dtypes

                    salary_file = salary_file.div(1000)

                    for x in range(0,total_sims):    
                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    overall_file.astype('int').dtypes

                    players_only = hold_file[['Player']]
                    raw_lineups_file = players_only

                    for x in range(0,total_sims):
                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                        players_only[x] = raw_lineups_file[x].rank(ascending=False)

                    players_only=players_only.drop(['Player'], axis=1)
                    players_only.astype('int').dtypes

                    salary_2x_check = (overall_file - (salary_file*2))
                    salary_3x_check = (overall_file - (salary_file*3))
                    salary_4x_check = (overall_file - (salary_file*4))

                    players_only['Average_Rank'] = players_only.mean(axis=1)
                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)

                    players_only['Player'] = hold_file[['Player']]

                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]

                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
                    final_Proj['LevX'] = 0
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4

                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                    final_Proj = final_Proj.set_index('Player')
                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)

                with hold_container:
                    hold_container = st.empty()
                    final_Proj = final_Proj
                    if view_var3 == 'Simple':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                        disp_proj = final_Proj.set_index('Player')
                    elif view_var3 == 'Advanced':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                        disp_proj = final_Proj.set_index('Player')
                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)

                st.download_button(
                        label="Export Tables",
                        data=convert_df_to_csv(final_Proj),
                        file_name='Custom_NFL_qb_export.csv',
                        mime='text/csv',
                )
                            
with tab4:
    col1, col2 = st.columns([1, 5])
    with col1:
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset4'):
            st.cache_data.clear()
            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
            for key in st.session_state.keys():
                del st.session_state[key]
        slate_var4 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var4')
        site_var4 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var4')
        view_var4 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var4')
        custom_var4 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var4')
        if custom_var4 == 'No':
            if site_var4 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var4)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_rbs']
                raw_baselines = raw_baselines.iloc[:,:-3]
            elif site_var4 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var4)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_rbs']
                raw_baselines = raw_baselines.iloc[:,:-3]
            split_var4 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var4')
            if split_var4 == 'Specific Games':
                team_var4 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var4')
            elif split_var4 == 'Full Slate Run':
                team_var4 = raw_baselines.Team.values.tolist()
            pos_split4 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split4')
            if pos_split4 == 'Specific Positions':
                pos_var4 = st.multiselect('What Positions would you like to view?', options = ['RB'], key='pos_var4')
            elif pos_split4 == 'All Positions':
                pos_var4 = 'All'
            sal_var4 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var4')
        if custom_var4 == 'Yes':
            contest_var4 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var4')
            if site_var4 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var4)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_rbs']
            elif site_var4 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var4)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_rbs']
            split_var4 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var4')
            if split_var4 == 'Specific Games':
                team_var4 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var4')
            elif split_var4 == 'Full Slate Run':
                team_var4 = raw_baselines.Team.values.tolist()
            pos_split4 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split4')
            if pos_split4 == 'Specific Positions':
                pos_var4 = st.multiselect('What Positions would you like to view?', options = ['RB'])
            elif pos_split4 == 'All Positions':
                pos_var4 = 'All'
            sal_var4 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var4')
    
    
    with col2:
        if custom_var4 == 'No':
            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var4)]
            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var4[0]]
            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var4[1]]
            if pos_var4 != 'All':
                final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var4))]
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                final_Proj = final_Proj.set_index('Player')
                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
                     
            if view_var4 == 'Simple':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                disp_proj = final_Proj.set_index('Player')
            elif view_var4 == 'Advanced':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                disp_proj = final_Proj.set_index('Player')
            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
            st.download_button(
                label="Export Tables",
                data=convert_df_to_csv(final_Proj),
                file_name='NFL_rb_export.csv',
                mime='text/csv',
            )
        elif custom_var4 == 'Yes':
            hold_container = st.empty()
            if st.button('Create Range of Outcomes for Slate'):
                with hold_container:
                    if site_var4 == 'Draftkings':
                        working_roo = player_stats
                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
                        working_roo.replace('', 0, inplace=True)
                        working_roo = working_roo[working_roo['Position'] == 'RB']
                    if site_var4 == 'Fanduel':
                        working_roo = player_stats
                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
                        working_roo.replace('', 0, inplace=True)
                        working_roo = working_roo[working_roo['Position'] == 'RB']
                    working_roo = working_roo[working_roo['Team'].isin(team_var4)]
                    working_roo = working_roo[working_roo['Salary'] >= sal_var4[0]]
                    working_roo = working_roo[working_roo['Salary'] <= sal_var4[1]]
                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
                    total_sims = 1000

                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                    hold_file = flex_file
                    overall_file = flex_file
                    salary_file = flex_file

                    overall_players = overall_file[['Player']]

                    for x in range(0,total_sims):    
                        salary_file[x] = salary_file['Salary']

                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    salary_file.astype('int').dtypes

                    salary_file = salary_file.div(1000)

                    for x in range(0,total_sims):    
                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    overall_file.astype('int').dtypes

                    players_only = hold_file[['Player']]
                    raw_lineups_file = players_only

                    for x in range(0,total_sims):
                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                        players_only[x] = raw_lineups_file[x].rank(ascending=False)

                    players_only=players_only.drop(['Player'], axis=1)
                    players_only.astype('int').dtypes

                    salary_2x_check = (overall_file - (salary_file*2))
                    salary_3x_check = (overall_file - (salary_file*3))
                    salary_4x_check = (overall_file - (salary_file*4))

                    players_only['Average_Rank'] = players_only.mean(axis=1)
                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)

                    players_only['Player'] = hold_file[['Player']]

                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]

                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
                    final_Proj['LevX'] = 0
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4

                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                    final_Proj = final_Proj.set_index('Player')
                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)

                with hold_container:
                    hold_container = st.empty()
                    final_Proj = final_Proj
                    if view_var4 == 'Simple':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                        disp_proj = final_Proj.set_index('Player')
                    elif view_var4 == 'Advanced':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                        disp_proj = final_Proj.set_index('Player')
                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)

                st.download_button(
                    label="Export Tables",
                    data=convert_df_to_csv(final_Proj),
                    file_name='Custom_NFL_rb_export.csv',
                    mime='text/csv',
                )
                            
with tab5:
    col1, col2 = st.columns([1, 5])
    with col1:
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset5'):
            st.cache_data.clear()
            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
            for key in st.session_state.keys():
                del st.session_state[key]
        slate_var5 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var5')
        site_var5 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var5')
        view_var5 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var5')
        custom_var5 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var5')
        if custom_var5 == 'No':
            if site_var5 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var5)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_wrs']
                raw_baselines = raw_baselines.iloc[:,:-3]
            elif site_var5 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var5)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_wrs']
                raw_baselines = raw_baselines.iloc[:,:-3]
            split_var5 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var5')
            if split_var5 == 'Specific Games':
                team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5')
            elif split_var5 == 'Full Slate Run':
                team_var5 = raw_baselines.Team.values.tolist()
            pos_split5 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split5')
            if pos_split5 == 'Specific Positions':
                pos_var5 = st.multiselect('What Positions would you like to view?', options = ['WR'], key='pos_var5')
            elif pos_split5 == 'All Positions':
                pos_var5 = 'All'
            sal_var5 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var5')
        if custom_var5 == 'Yes':
            contest_var5 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var5')
            if site_var5 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var5)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_wrs']
            elif site_var5 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var5)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_wrs']
            split_var5 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var5')
            if split_var5 == 'Specific Games':
                team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5')
            elif split_var5 == 'Full Slate Run':
                team_var5 = raw_baselines.Team.values.tolist()
            pos_split5 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split5')
            if pos_split5 == 'Specific Positions':
                pos_var5 = st.multiselect('What Positions would you like to view?', options = ['WR'])
            elif pos_split5 == 'All Positions':
                pos_var5 = 'All'
            sal_var5 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var5')
    
    
    with col2:
        if custom_var5 == 'No':
            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var5)]
            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var5[0]]
            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var5[1]]
            if pos_var5 != 'All':
                final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var5))]
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                final_Proj = final_Proj.set_index('Player')
                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
                     
            if view_var5 == 'Simple':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                disp_proj = final_Proj.set_index('Player')
            elif view_var5 == 'Advanced':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                disp_proj = final_Proj.set_index('Player')
            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
            st.download_button(
                label="Export Tables",
                data=convert_df_to_csv(final_Proj),
                file_name='NFL_wr_export.csv',
                mime='text/csv',
            )
        elif custom_var5 == 'Yes':
            hold_container = st.empty()
            if st.button('Create Range of Outcomes for Slate'):
                with hold_container:
                    if site_var5 == 'Draftkings':
                            working_roo = player_stats
                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
                            working_roo.replace('', 0, inplace=True)
                            working_roo = working_roo[working_roo['Position'] == 'WR']
                    if site_var5 == 'Fanduel':
                            working_roo = player_stats
                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
                            working_roo.replace('', 0, inplace=True)
                            working_roo = working_roo[working_roo['Position'] == 'WR']
                    working_roo = working_roo[working_roo['Team'].isin(team_var5)]
                    working_roo = working_roo[working_roo['Salary'] >= sal_var5[0]]
                    working_roo = working_roo[working_roo['Salary'] <= sal_var5[1]]
                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
                    total_sims = 1000

                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                    hold_file = flex_file
                    overall_file = flex_file
                    salary_file = flex_file

                    overall_players = overall_file[['Player']]

                    for x in range(0,total_sims):    
                        salary_file[x] = salary_file['Salary']

                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    salary_file.astype('int').dtypes

                    salary_file = salary_file.div(1000)

                    for x in range(0,total_sims):    
                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    overall_file.astype('int').dtypes

                    players_only = hold_file[['Player']]
                    raw_lineups_file = players_only

                    for x in range(0,total_sims):
                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                        players_only[x] = raw_lineups_file[x].rank(ascending=False)

                    players_only=players_only.drop(['Player'], axis=1)
                    players_only.astype('int').dtypes

                    salary_2x_check = (overall_file - (salary_file*2))
                    salary_3x_check = (overall_file - (salary_file*3))
                    salary_4x_check = (overall_file - (salary_file*4))

                    players_only['Average_Rank'] = players_only.mean(axis=1)
                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)

                    players_only['Player'] = hold_file[['Player']]

                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]

                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
                    final_Proj['LevX'] = 0
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4

                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                    final_Proj = final_Proj.set_index('Player')
                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)

                with hold_container:
                    hold_container = st.empty()
                    final_Proj = final_Proj
                    if view_var5 == 'Simple':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                        disp_proj = final_Proj.set_index('Player')
                    elif view_var5 == 'Advanced':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                        disp_proj = final_Proj.set_index('Player')
                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)

                st.download_button(
                        label="Export Tables",
                        data=convert_df_to_csv(final_Proj),
                        file_name='Custom_NFL_wr_export.csv',
                        mime='text/csv',
                )
                            
with tab6:
    col1, col2 = st.columns([1, 5])
    with col1:
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset6'):
            st.cache_data.clear()
            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
            for key in st.session_state.keys():
                del st.session_state[key]
        slate_var6 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var6')
        site_var6 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var6')
        view_var6 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var6')
        custom_var6 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var6')
        if custom_var6 == 'No':
            if site_var6 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var6)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_tes']
                raw_baselines = raw_baselines.iloc[:,:-3]
            elif site_var6 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var6)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_tes']
                raw_baselines = raw_baselines.iloc[:,:-3]
            split_var6 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var6')
            if split_var6 == 'Specific Games':
                team_var6 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var6')
            elif split_var6 == 'Full Slate Run':
                team_var6 = raw_baselines.Team.values.tolist()
            pos_split6 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split6')
            if pos_split6 == 'Specific Positions':
                pos_var6 = st.multiselect('What Positions would you like to view?', options = ['TE'], key='pos_var6')
            elif pos_split5 == 'All Positions':
                pos_var6 = 'All'
            sal_var6 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var6')
        if custom_var6 == 'Yes':
            contest_var6 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var6')
            if site_var6 == 'Draftkings':
                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var6)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_tes']
            elif site_var6 == 'Fanduel':
                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var6)]
                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_tes']
            split_var6 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var6')
            if split_var6 == 'Specific Games':
                team_var6 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var6')
            elif split_var6 == 'Full Slate Run':
                team_var6 = raw_baselines.Team.values.tolist()
            pos_split6 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split6')
            if pos_split6 == 'Specific Positions':
                pos_var6 = st.multiselect('What Positions would you like to view?', options = ['TE'])
            elif pos_split6 == 'All Positions':
                pos_var6 = 'All'
            sal_var6 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var6')
    
    
    with col2:
        if custom_var6 == 'No':
            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var6)]
            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var6[0]]
            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var6[1]]
            if pos_var6 != 'All':
                final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var6))]
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                final_Proj = final_Proj.set_index('Player')
                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
                     
            if view_var6 == 'Simple':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                disp_proj = final_Proj.set_index('Player')
            elif view_var6 == 'Advanced':
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
                disp_proj = final_Proj.set_index('Player')
            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
            st.download_button(
                label="Export Tables",
                data=convert_df_to_csv(final_Proj),
                file_name='NFL_te_export.csv',
                mime='text/csv',
            )
        elif custom_var6 == 'Yes':
            hold_container = st.empty()
            if st.button('Create Range of Outcomes for Slate'):
                with hold_container:
                    if site_var6 == 'Draftkings':
                        working_roo = player_stats
                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
                        working_roo.replace('', 0, inplace=True)
                        working_roo = working_roo[working_roo['Position'] == 'TE']
                    if site_var6 == 'Fanduel':
                        working_roo = player_stats
                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
                        working_roo.replace('', 0, inplace=True)
                        working_roo = working_roo[working_roo['Position'] == 'TE']
                    working_roo = working_roo[working_roo['Team'].isin(team_var6)]
                    working_roo = working_roo[working_roo['Salary'] >= sal_var6[0]]
                    working_roo = working_roo[working_roo['Salary'] <= sal_var6[1]]
                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
                    total_sims = 1000

                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                    hold_file = flex_file
                    overall_file = flex_file
                    salary_file = flex_file

                    overall_players = overall_file[['Player']]

                    for x in range(0,total_sims):    
                        salary_file[x] = salary_file['Salary']

                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    salary_file.astype('int').dtypes

                    salary_file = salary_file.div(1000)

                    for x in range(0,total_sims):    
                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    overall_file.astype('int').dtypes

                    players_only = hold_file[['Player']]
                    raw_lineups_file = players_only

                    for x in range(0,total_sims):
                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                        players_only[x] = raw_lineups_file[x].rank(ascending=False)

                    players_only=players_only.drop(['Player'], axis=1)
                    players_only.astype('int').dtypes

                    salary_2x_check = (overall_file - (salary_file*2))
                    salary_3x_check = (overall_file - (salary_file*3))
                    salary_4x_check = (overall_file - (salary_file*4))

                    players_only['Average_Rank'] = players_only.mean(axis=1)
                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)

                    players_only['Player'] = hold_file[['Player']]

                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]

                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
                    final_Proj['LevX'] = 0
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4

                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                    final_Proj = final_Proj.set_index('Player')
                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)

                with hold_container:
                    hold_container = st.empty()
                    final_Proj = final_Proj
                    if view_var6 == 'Simple':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
                        disp_proj = final_Proj.set_index('Player')
                    elif view_var6 == 'Advanced':
                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
                        disp_proj = final_Proj.set_index('Player')
                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)

                st.download_button(
                    label="Export Tables",
                    data=convert_df_to_csv(final_Proj),
                    file_name='Custom_NFL_te_export.csv',
                    mime='text/csv',
                )
with tab7:
    col1, col2 = st.columns([1, 7])
    with col1:
        if st.button("Load/Reset Data", key='reset7'):
            st.cache_data.clear()
            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
            for key in st.session_state.keys():
                del st.session_state[key]
              
        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate'))
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)

        if site_var1 == 'Draftkings':
            ROO_slice = dk_roo_raw[dk_roo_raw['site'] == 'Draftkings']
            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_id))
            column_names = dk_columns
            
            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
            if player_var1 == 'Specific Players':
                    player_var2 = st.multiselect('Which players do you want?', options = dk_roo_raw['Player'].unique())
            elif player_var1 == 'Full Slate':
                    player_var2 = dk_roo_raw.Player.values.tolist()
                    
        elif site_var1 == 'Fanduel':
            ROO_slice = fd_roo_raw[fd_roo_raw['site'] == 'Fanduel']
            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_id))
            column_names = fd_columns
            
            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
            if player_var1 == 'Specific Players':
                    player_var2 = st.multiselect('Which players do you want?', options = fd_roo_raw['Player'].unique())
            elif player_var1 == 'Full Slate':
                    player_var2 = fd_roo_raw.Player.values.tolist()

        if st.button("Prepare data export", key='data_export'):
            data_export = st.session_state.working_seed.copy()
            if site_var1 == 'Draftkings':
                for col_idx in range(9):
                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
            elif site_var1 == 'Fanduel':
                for col_idx in range(9):
                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
            st.download_button(
                label="Export optimals set",
                data=convert_df(data_export),
                file_name='NFL_optimals_export.csv',
                mime='text/csv',
            )
    with col2:
        
        if site_var1 == 'Draftkings':
            if 'working_seed' in st.session_state:
                st.session_state.working_seed = st.session_state.working_seed
                if player_var1 == 'Specific Players':
                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                elif player_var1 == 'Full Slate':
                    st.session_state.working_seed = dk_lineups.copy()
                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
            elif 'working_seed' not in st.session_state:
                st.session_state.working_seed = dk_lineups.copy()
                st.session_state.working_seed = st.session_state.working_seed
                if player_var1 == 'Specific Players':
                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                elif player_var1 == 'Full Slate':
                    st.session_state.working_seed = dk_lineups.copy()
                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
            
        elif site_var1 == 'Fanduel':
            if 'working_seed' in st.session_state:
                st.session_state.working_seed = st.session_state.working_seed
                if player_var1 == 'Specific Players':
                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                elif player_var1 == 'Full Slate':
                    st.session_state.working_seed = fd_lineups.copy()
                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
            elif 'working_seed' not in st.session_state:
                st.session_state.working_seed = fd_lineups.copy()
                st.session_state.working_seed = st.session_state.working_seed
                if player_var1 == 'Specific Players':
                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                elif player_var1 == 'Full Slate':
                    st.session_state.working_seed = fd_lineups.copy()
                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)

        export_file = st.session_state.data_export_display.copy()
        if site_var1 == 'Draftkings':
            for col_idx in range(9):
                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
        elif site_var1 == 'Fanduel':
            for col_idx in range(9):
                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
                
        with st.container():
            if st.button("Reset Optimals", key='reset9'):
                for key in st.session_state.keys():
                    del st.session_state[key]
                if site_var1 == 'Draftkings':
                    st.session_state.working_seed = dk_lineups.copy()
                elif site_var1 == 'Fanduel':
                    st.session_state.working_seed = fd_lineups.copy()
            if 'data_export_display' in st.session_state:
                st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
            st.download_button(
                label="Export display optimals",
                data=convert_df(export_file),
                file_name='NFL_display_optimals.csv',
                mime='text/csv',
            )
        
        with st.container():
            if 'working_seed' in st.session_state:
                # Create a new dataframe with summary statistics
                if site_var1 == 'Draftkings':
                    summary_df = pd.DataFrame({
                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                        'Salary': [
                            np.min(st.session_state.working_seed[:,9]),
                            np.mean(st.session_state.working_seed[:,9]),
                            np.max(st.session_state.working_seed[:,9]),
                            np.std(st.session_state.working_seed[:,9])
                        ],
                        'Proj': [
                            np.min(st.session_state.working_seed[:,10]),
                            np.mean(st.session_state.working_seed[:,10]),
                            np.max(st.session_state.working_seed[:,10]),
                            np.std(st.session_state.working_seed[:,10])
                        ],
                        'Own': [
                            np.min(st.session_state.working_seed[:,15]),
                            np.mean(st.session_state.working_seed[:,15]),
                            np.max(st.session_state.working_seed[:,15]),
                            np.std(st.session_state.working_seed[:,15])
                        ]
                    })
                elif site_var1 == 'Fanduel':
                    summary_df = pd.DataFrame({
                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                        'Salary': [
                            np.min(st.session_state.working_seed[:,9]),
                            np.mean(st.session_state.working_seed[:,9]),
                            np.max(st.session_state.working_seed[:,9]),
                            np.std(st.session_state.working_seed[:,9])
                        ],
                        'Proj': [
                            np.min(st.session_state.working_seed[:,10]),
                            np.mean(st.session_state.working_seed[:,10]),
                            np.max(st.session_state.working_seed[:,10]),
                            np.std(st.session_state.working_seed[:,10])
                        ],
                        'Own': [
                            np.min(st.session_state.working_seed[:,15]),
                            np.mean(st.session_state.working_seed[:,15]),
                            np.max(st.session_state.working_seed[:,15]),
                            np.std(st.session_state.working_seed[:,15])
                        ]
                    })

                # Set the index of the summary dataframe as the "Metric" column
                summary_df = summary_df.set_index('Metric')

                # Display the summary dataframe
                st.subheader("Optimal Statistics")
                st.dataframe(summary_df.style.format({
                    'Salary': '{:.2f}',
                    'Proj': '{:.2f}',
                    'Own': '{:.2f}'
                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)

        with st.container():
            tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
            with tab1:
                if 'data_export_display' in st.session_state:
                    if site_var1 == 'Draftkings':
                        player_columns = st.session_state.data_export_display.iloc[:, :9]
                    elif site_var1 == 'Fanduel':
                        player_columns = st.session_state.data_export_display.iloc[:, :9]
                    
                    # Flatten the DataFrame and count unique values
                    value_counts = player_columns.values.flatten().tolist()
                    value_counts = pd.Series(value_counts).value_counts()
                    
                    percentages = (value_counts / lineup_num_var * 100).round(2)
                    
                    # Create a DataFrame with the results
                    summary_df = pd.DataFrame({
                        'Player': value_counts.index,
                        'Frequency': value_counts.values,
                        'Percentage': percentages.values
                    })
                    
                    # Sort by frequency in descending order
                    summary_df = summary_df.sort_values('Frequency', ascending=False)
                    
                    # Display the table
                    st.write("Player Frequency Table:")
                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
                
                    st.download_button(
                        label="Export player frequency",
                        data=convert_df_to_csv(summary_df),
                        file_name='NFL_player_frequency.csv',
                        mime='text/csv',
                    )
            with tab2:
                if 'working_seed' in st.session_state:
                    if site_var1 == 'Draftkings':
                        player_columns = st.session_state.working_seed[:, :9]
                    elif site_var1 == 'Fanduel':
                        player_columns = st.session_state.working_seed[:, :9]
                    
                    # Flatten the DataFrame and count unique values
                    value_counts = player_columns.flatten().tolist()
                    value_counts = pd.Series(value_counts).value_counts()
                    
                    percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
                    # Create a DataFrame with the results
                    summary_df = pd.DataFrame({
                        'Player': value_counts.index,
                        'Frequency': value_counts.values,
                        'Percentage': percentages.values
                    })
                    
                    # Sort by frequency in descending order
                    summary_df = summary_df.sort_values('Frequency', ascending=False)
                    
                    # Display the table
                    st.write("Seed Frame Frequency Table:")
                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
                
                    st.download_button(
                        label="Export seed frame frequency",
                        data=convert_df_to_csv(summary_df),
                        file_name='NFL_seed_frame_frequency.csv',
                        mime='text/csv',
                    )