app.py

import streamlit as st
st.set_page_config(layout="wide")
import numpy as np
import pandas as pd
from rapidfuzz import process, fuzz
from collections import Counter
from pymongo.mongo_client import MongoClient
from pymongo.server_api import ServerApi
from datetime import datetime

# Just setting a note here to say that I should attempt to do some memory allocation savings and swap to numpy soon

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

        return db

def grab_contest_names(db, sport, type):
    if type == 'Classic':
        db_type = 'reg'
    elif type == 'Showdown':
        db_type = 'sd'
    collection = db[f'{sport}_{db_type}_contest_info']
    cursor = collection.find()

    curr_info = pd.DataFrame(list(cursor)).drop('_id', axis=1)
    curr_info['Date'] = pd.to_datetime(curr_info['Contest Date'].sort_values(ascending = False))
    curr_info['Date'] = curr_info['Date'].dt.strftime('%Y-%m-%d')
    contest_names = curr_info['Contest Name'] + ' - ' + curr_info['Date']
    
    return contest_names, curr_info

def grab_contest_player_info(db, sport, type, contest_date, contest_name, contest_id_map):
    if type == 'Classic':
        db_type = 'reg'
    elif type == 'Showdown':
        db_type = 'showdown'
    collection = db[f'{sport}_{db_type}_player_info']
    cursor = collection.find()

    player_info = pd.DataFrame(list(cursor)).drop('_id', axis=1)
    player_info = player_info[player_info['Contest Date'] == contest_date]
    player_info = player_info.rename(columns={'Display Name': 'Player'})
    player_info = player_info.sort_values(by='Salary', ascending=True).drop_duplicates(subset='Player', keep='first')

    info_maps = {
        'position_dict': dict(zip(player_info['Player'], player_info['Position'])),
        'salary_dict': dict(zip(player_info['Player'], player_info['Salary'])),
        'team_dict': dict(zip(player_info['Player'], player_info['Team'])),
        'opp_dict': dict(zip(player_info['Player'], player_info['Opp'])),
        'fpts_avg_dict': dict(zip(player_info['Player'], player_info['Avg FPTS']))
    }
    
    return player_info, info_maps

def grab_contest_payout_info(db, sport, type, contest_date, contest_name, contest_id_map, contest_id):
    if type == 'Classic':
        db_type = 'reg'
    elif type == 'Showdown':
        db_type = 'showdown'
    collection = db[f'{sport}_{db_type}_payout_info']
    cursor = collection.find()

    payout_info = pd.DataFrame(list(cursor)).drop('_id', axis=1)
    payout_info = payout_info[payout_info['Contest Date'] == contest_date]
    payout_info = payout_info[payout_info['Contest ID'] == contest_id_map[contest_name]]

    entry_fee = payout_info['Entry Fee'].iloc[0]
    
    return payout_info, entry_fee

def export_contest_file(db, sport, type, contest_date, contest_id, contest_data):
    if type == 'Classic':
        db_type = 'reg'
    elif type == 'Showdown':
        db_type = 'showdown'
    collection = db[f'{sport}_{db_type}_contest_data']
    try:
        cursor = collection.find()
        contest_import = pd.DataFrame(list(cursor)).drop('_id', axis=1)
        if contest_id in contest_import['Contest ID'].values:
            return_message = "Data for this contest already exists, no need to upload, but we appreciate the effort!"
            return return_message
    except:
        contest_import = pd.DataFrame(columns = ['Rank', 'EntryId', 'EntryName', 'TimeRemaining', 'Points', 'Lineup', 'Player', 'Roster Position', '%Drafted', 'FPTS', 'Contest Date', 'Contest ID'])
    
    contest_data['Contest Date'] = contest_date
    contest_data['Contest ID'] = contest_id
    contest_import = pd.concat([contest_import, contest_data], ignore_index=True)

    chunk_size = 10000
    collection.drop()
    for i in range(0, len(contest_import), chunk_size):
        for _ in range(5):
            try:
                df_chunk = contest_import.iloc[i:i + chunk_size]
                collection.insert_many(df_chunk.to_dict('records'), ordered=False)
                break
            except Exception as e:
                print(f"Retry due to error: {e}")
    return_message = "Contest data uploaded successfully! We appreciate the data!"

    return return_message

def get_payout_for_position(finish_pos, payout_df, dupes_count):
    """
    Calculate payout for a position, handling ties by splitting the combined payout.
    
    Args:
        finish_pos: The finish position (0-indexed)
        payout_df: DataFrame with payout structure
        dupes_count: Number of entries that are tied (from the 'dupes' column)
    """
    if dupes_count == 1:
        # Single position, no tie
        matching_row = payout_df[
            (payout_df['minPosition'] <= finish_pos + 1) & 
            (payout_df['maxPosition'] >= finish_pos + 1)
        ]
        if not matching_row.empty:
            return matching_row.iloc[0]['value']
        else:
            return 0
    else:
        # Handle tie - sum payouts for the range of positions and divide by number of ties
        # Convert to 1-indexed positions for payout lookup
        start_pos = finish_pos + 1  # 1-indexed start position
        end_pos = finish_pos + dupes_count  # 1-indexed end position
        
        total_payout = 0
        for pos in range(start_pos, end_pos + 1):
            matching_row = payout_df[
                (payout_df['minPosition'] <= pos) & 
                (payout_df['maxPosition'] >= pos)
            ]
            if not matching_row.empty:
                total_payout += matching_row.iloc[0]['value']
        
        # Return the split payout
        return total_payout / dupes_count

def color_roi(val):
    """Color ROI values: green if > 100%, red if < 100%"""
    if pd.isna(val):
        return ''
    if val > 1.0:  # Greater than 100%
        return 'background-color: lightgreen'
    elif val < 1.0:  # Less than 100%
        return 'background-color: lightcoral'
    else:  # Exactly 100%
        return 'background-color: lightyellow'

db = init_conn()

## import global functions for usages
from global_func.load_contest_file import load_contest_file
from global_func.create_player_exposures import create_player_exposures
from global_func.create_stack_exposures import create_stack_exposures
from global_func.create_stack_size_exposures import create_stack_size_exposures
from global_func.create_general_exposures import create_general_exposures
from global_func.grab_contest_data import grab_contest_data
from global_func.create_player_comparison import create_player_comparison
from global_func.create_stack_comparison import create_stack_comparison
from global_func.create_size_comparison import create_size_comparison
from global_func.create_general_comparison import create_general_comparison

def is_valid_input(file):
    if isinstance(file, pd.DataFrame):
        return not file.empty
    else:
        return file is not None  # For Streamlit uploader objects

def highlight_row_condition(row):
    if row['BaseName'] == 'Backtesting_upload':
        return ['background-color: lightgreen'] * len(row)
    else:
        return [''] * len(row)

player_exposure_format = {'Exposure Overall': '{:.2%}', 'Exposure Top 1%': '{:.2%}', 'Exposure Top 5%': '{:.2%}', 'Exposure Top 10%': '{:.2%}', 'Exposure Top 20%': '{:.2%}'}
dupe_format = {'uniques%': '{:.2%}', 'under_5%': '{:.2%}', 'under_10%': '{:.2%}'}
roi_format = {'ROI': '{:.2%}', 'Total Fees': '{:.2f}', 'Total Payout': '{:.2f}'}

st.markdown("""
<style>
    /* Tab styling */
    .stElementContainer [data-baseweb="button-group"] {
        gap: 2.000rem;
        padding: 4px;
    }
    .stElementContainer [kind="segmented_control"] {
        height: 2.000rem;
        white-space: pre-wrap;
        background-color: #DAA520;
        color: white;
        border-radius: 20px;
        gap: 1px;
        padding: 10px 20px;
        font-weight: bold;
        transition: all 0.3s ease;
    }
    .stElementContainer [kind="segmented_controlActive"] {
        height: 3.000rem;
        background-color: #DAA520;
        border: 3px solid #FFD700;
        border-radius: 10px;
        color: black;
    }
    .stElementContainer [kind="segmented_control"]:hover {
        background-color: #FFD700;
        cursor: pointer;
    }

    div[data-baseweb="select"] > div {
        background-color: #DAA520;
        color: white;
    }

</style>""", unsafe_allow_html=True)

try:
    selected_tab = st.segmented_control(
        "Select Tab",
        options=["Data Load", "Contest Analysis"],
        selection_mode='single',
        default='Data Load',
        width='stretch',
        label_visibility='collapsed',
        key='tab_selector'
    )
except:
    selected_tab = st.segmented_control(
        "Select Tab",
        options=["Data Load", "Contest Analysis"],
        selection_mode='single',
        default='Data Load',
        label_visibility='collapsed',
        key='tab_selector'
    )

if selected_tab == 'Data Load':
    data_select, contest_upload, portfolio_upload = st.columns(3)

    with data_select:
        if st.button('Clear data', key='reset1'):
            st.session_state.clear()
        sport_options, date_options = st.columns(2)
        parse_type = 'Manual'
        with sport_options:
            sport_init = st.selectbox("Select Sport", ['NFL', 'MLB', 'MMA', 'GOLF', 'NBA', 'NHL', 'CFB', 'WNBA', 'NAS'], key='sport_init')
            type_init = st.selectbox("Select Game Type", ['Classic', 'Showdown'], key='type_init')
            try:
                contest_names, curr_info = grab_contest_names(db, sport_init, type_init)
            except:
                st.error("No contests found for this sport and/or game type")
                st.stop()
            
        with date_options:
            date_list = curr_info['Date'].sort_values(ascending=False).unique()
            # date_list = date_list[date_list != pd.Timestamp.today().strftime('%Y-%m-%d')]
            date_select = st.selectbox("Select Date", date_list, key='date_select')
            date_select2 = (pd.to_datetime(date_select) + pd.Timedelta(days=1)).strftime('%Y-%m-%d')
            
            name_parse = curr_info[curr_info['Date'] == date_select]['Contest Name'].reset_index(drop=True)
            contest_id_map = dict(zip(name_parse, curr_info[curr_info['Date'] == date_select]['Contest ID']))
            date_select = date_select.replace('-', '')
            date_select2 = date_select2.replace('-', '')

        contest_name_var = st.selectbox("Select Contest to load", name_parse)
        if parse_type == 'DB Search':
            if 'Contest_file_helper' in st.session_state:
                del st.session_state['Contest_file_helper']
            if 'Contest_file' in st.session_state:
                del st.session_state['Contest_file']
            if 'Contest_file' not in st.session_state:
                if st.button('Load Contest Data', key='load_contest_data'):
                    st.session_state['player_info'], st.session_state['info_maps'] = grab_contest_player_info(db, sport_init, type_init, date_select, contest_name_var, contest_id_map)
                    st.session_state['Contest_file'] = grab_contest_data(sport_init, contest_name_var, contest_id_map, date_select, date_select2)
                    try:
                        st.session_state['payout_info'], st.session_state['entry_fee'] = grab_contest_payout_info(db, sport_init, type_init, date_select, contest_name_var, contest_id_map, contest_id_map[contest_name_var])
                    except:
                        st.session_state['payout_info'] = None
            else:
                pass
    with contest_upload:
        st.info(f"If you are manually loading and do not have the results CSV for the contest you selected, you can find it here: https://www.draftkings.com/contest/gamecenter/{contest_id_map[contest_name_var]}#/, or you can initiate a download with this link: https://www.draftkings.com/contest/exportfullstandingscsv/{contest_id_map[contest_name_var]}")
        if parse_type == 'Manual':
            if 'Contest_file_helper' in st.session_state:
                del st.session_state['Contest_file_helper']
            if 'Contest_file' in st.session_state:
                del st.session_state['Contest_file']
            if 'Contest_file' not in st.session_state:
                st.session_state['Contest_upload'] = st.file_uploader("Upload Contest File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
                st.session_state['player_info'], st.session_state['info_maps'] = grab_contest_player_info(db, sport_init, type_init, date_select, contest_name_var, contest_id_map)
                try:
                    st.session_state['Contest_file'] = pd.read_csv(st.session_state['Contest_upload'])
                except:
                    st.warning('Please upload a Contest CSV')
                try:
                    st.session_state['payout_info'], st.session_state['entry_fee'] = grab_contest_payout_info(db, sport_init, type_init, date_select, contest_name_var, contest_id_map, contest_id_map[contest_name_var])
                except:
                    st.session_state['payout_info'] = None
            else:
                pass
    
    with portfolio_upload:
        st.info("If you have a portfolio of lineups, you can upload them here to see how they would have performed against the field")
        if st.button('Clear portfolio', key='reset2'):
            st.session_state['portfolio_df'] = None
            del st.session_state['display_contest_info']
        st.session_state['portfolio_file'] = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
        try:
            st.session_state['portfolio_df'] = pd.read_csv(st.session_state['portfolio_file'])
        
            original_columns = st.session_state['portfolio_df'].columns.tolist()
            
            for col in original_columns:
                st.session_state['portfolio_df'][col] = st.session_state['portfolio_df'][col].astype(str).str.replace(r'\s*\([^)]*\)', '', regex=True).str.strip()
            
            st.session_state['portfolio_df']['BaseName'] = 'Backtesting_upload'
            st.session_state['portfolio_df']['EntryCount'] = len(st.session_state['portfolio_df'])
            
            st.session_state['portfolio_df'] = st.session_state['portfolio_df'][['BaseName', 'EntryCount'] + original_columns]

            if 'display_contest_info' in st.session_state and st.session_state['display_contest_info'][st.session_state['display_contest_info']['BaseName'] == 'Backtesting_upload'].empty:
                del st.session_state['display_contest_info']
            else:
                pass
        except:
            st.session_state['portfolio_df'] = None
            

    if 'Contest_file' in st.session_state:
        st.session_state['Contest'], st.session_state['ownership_df'], st.session_state['actual_df'], st.session_state['entry_list'], check_lineups = load_contest_file(st.session_state['Contest_file'], type_init, st.session_state['player_info'], sport_init, st.session_state['portfolio_df'])
        st.session_state['Contest'] = st.session_state['Contest'].dropna(how='all')
        st.session_state['Contest'] = st.session_state['Contest'].reset_index(drop=True)
        if st.session_state['Contest'] is not None:
            success_col, info_col, upload_col, message_col = st.columns([2, 3, 1, 2])
            with success_col:
                st.success('Contest file loaded, please wait for tables to load below before you switch tabs!')
            with info_col:
                st.warning("If you have confirmed that the data is correct, you can send the CSV to the database to enrich Paydirt's sources and help us create actionable tools and algorithms >>")
            with upload_col:
                if st.button('Send file to Database?', key='export_contest_file'):
                    return_message = export_contest_file(db, sport_init, type_init, date_select, contest_id_map[contest_name_var], st.session_state['Contest_file'])
            with message_col:
                try:
                    st.info(return_message)
                except:
                    pass
        
    if 'Contest_file' in st.session_state:
        st.session_state['ownership_dict'] = dict(zip(st.session_state['ownership_df']['Player'], st.session_state['ownership_df']['Own']))
        st.session_state['actual_dict'] = dict(zip(st.session_state['actual_df']['Player'], st.session_state['actual_df']['FPTS']))
        st.session_state['salary_dict'] = st.session_state['info_maps']['salary_dict']
        st.session_state['team_dict'] = st.session_state['info_maps']['team_dict']
        st.session_state['pos_dict'] = st.session_state['info_maps']['position_dict']
    
    excluded_cols = ['BaseName', 'EntryCount']
    exclude_stacks = ['BaseName', 'EntryCount', 'SP', 'SP1', 'SP2', 'P1', 'P2', 'RB1', 'RB2', 'DST', 'G']
    if 'Contest' in st.session_state and 'display_contest_info' not in st.session_state:
        st.session_state['player_columns'] = [col for col in st.session_state['Contest'].columns if col not in excluded_cols]
        st.session_state['stack_columns'] = [col for col in st.session_state['Contest'].columns if col not in exclude_stacks]
        print(st.session_state['player_columns'])
        
        # Vectorized string operations
        for col in st.session_state['player_columns']:
            st.session_state['Contest'][col] = st.session_state['Contest'][col].astype(str).str.strip()
    
        # Create mapping dictionaries
        st.session_state['map_dict'] = {
            'pos_map': st.session_state['pos_dict'],
            'team_map': st.session_state['team_dict'],
            'salary_map': st.session_state['salary_dict'],
            'own_map': st.session_state['ownership_dict'],
            'own_percent_rank': dict(zip(st.session_state['ownership_df']['Player'], st.session_state['ownership_df']['Own'].rank(pct=True)))
        }
        
        working_df = st.session_state['Contest'].copy()
        
        # Pre-compute lookup arrays for vectorized operations
        team_map = st.session_state['map_dict']['team_map']
        salary_map = st.session_state['salary_dict']
        actual_map = st.session_state['actual_dict']
        ownership_map = st.session_state['ownership_dict']
        
        if st.session_state['type_init'] == 'Classic':
            # Vectorized stack calculation
            player_teams = working_df[st.session_state['stack_columns']].apply(
                lambda x: x.map(team_map).fillna('')
            )
            
            # Vectorized stack and stack_size calculation
            def get_most_common_team(teams):
                if teams.empty or teams.isna().all():
                    return '', 0
                non_empty_teams = teams[teams != '']
                if len(non_empty_teams) == 0:
                    return '', 0
                team_counts = non_empty_teams.value_counts()
                return team_counts.index[0], team_counts.iloc[0]
            
            stack_results = player_teams.apply(get_most_common_team, axis=1)
            working_df['stack'] = [result[0] for result in stack_results]
            working_df['stack_size'] = [result[1] for result in stack_results]
            
            # Vectorized salary calculation
            player_salaries = working_df[st.session_state['player_columns']].apply(
                lambda x: x.map(salary_map).fillna(0)
            )
            working_df['salary'] = player_salaries.sum(axis=1)
            
            # Vectorized actual_fpts calculation
            player_fpts = working_df[st.session_state['player_columns']].apply(
                lambda x: x.map(actual_map).fillna(0)
            )
            working_df['actual_fpts'] = player_fpts.sum(axis=1)
            
            # Vectorized actual_own calculation
            player_ownership = working_df[st.session_state['player_columns']].apply(
                lambda x: x.map(ownership_map).fillna(0)
            )
            working_df['actual_own'] = player_ownership.sum(axis=1)
            
            # Vectorized duplication calculation
            working_df['sorted'] = working_df[st.session_state['player_columns']].apply(
                lambda row: ','.join(sorted(row.values)), axis=1
            )
            working_df['dupes'] = working_df.groupby(['actual_fpts', 'actual_own', 'salary']).transform('size')
            
            # Vectorized unique calculations
            working_df['uniques'] = working_df.groupby('BaseName')['dupes'].transform(
                lambda x: (x == 1).sum()
            )
            working_df['under_5'] = working_df.groupby('BaseName')['dupes'].transform(
                lambda x: (x <= 5).sum()
            )
            working_df['under_10'] = working_df.groupby('BaseName')['dupes'].transform(
                lambda x: (x <= 10).sum()
            )

            working_df = working_df.sort_values(by='actual_fpts', ascending=False)
            working_df = working_df.reset_index(drop=True)
            working_df = working_df.reset_index()
            working_df['percentile_finish'] = working_df['index'].rank(pct=True)
            working_df['finish'] = working_df['index']
            working_df = working_df.drop(['sorted', 'index'], axis=1)
            try:
                # Calculate payouts efficiently by processing each unique fantasy points group
                working_df['payout'] = 0  # Initialize payout column
                
                # Group by actual_fpts since tied entries will have the same fantasy points
                for fpts, group in working_df.groupby('actual_fpts'):
                    # Get the first row's finish position and dupes count
                    first_row = group.iloc[0]
                    finish_pos = first_row['finish']
                    dupes_count = first_row['dupes']
                    
                    if dupes_count == 1:
                        # Single entry - no tie
                        payout = get_payout_for_position(finish_pos, st.session_state['payout_info'], 1)
                    else:
                        # Multiple entries tied - calculate split payout once
                        split_payout = get_payout_for_position(finish_pos, st.session_state['payout_info'], dupes_count)
                        payout = split_payout
                    
                    # Apply the same payout to all rows in this group
                    working_df.loc[group.index, 'payout'] = payout
            except:
                pass
        
        elif st.session_state['type_init'] == 'Showdown':
            # Vectorized stack calculation for Showdown
            player_teams = working_df.iloc[:, 2:].apply(
                lambda x: x.map(team_map).fillna('')
            )

            # Vectorized stack and stack_size calculation
            def get_most_common_team(teams):
                if teams.empty or teams.isna().all():
                    return '', 0
                non_empty_teams = teams[teams != '']
                if len(non_empty_teams) == 0:
                    return '', 0
                team_counts = non_empty_teams.value_counts()
                return team_counts.index[0], team_counts.iloc[0]
            
            stack_results = player_teams.apply(get_most_common_team, axis=1)
            working_df['stack'] = [result[0] for result in stack_results]
            working_df['stack_size'] = [result[1] for result in stack_results]
            
            if st.session_state['sport_init'] == 'GOLF':
                # Vectorized calculations for GOLF
                player_salaries = working_df.apply(
                    lambda x: x.map(salary_map).fillna(0)
                )
                working_df['salary'] = player_salaries.sum(axis=1)
                
                player_fpts = working_df.apply(
                    lambda x: x.map(actual_map).fillna(0)
                )
                working_df['actual_fpts'] = player_fpts.sum(axis=1)
            else:
                # Vectorized calculations with 1.5x multiplier for first player
                first_player_salary = working_df.iloc[:, 2].map(salary_map).fillna(0) * 1.5
                other_players_salary = working_df.iloc[:, 3:].apply(
                    lambda x: x.map(salary_map).fillna(0)
                ).sum(axis=1)
                working_df['salary'] = first_player_salary + other_players_salary
                
                first_player_fpts = working_df.iloc[:, 2].map(actual_map).fillna(0) * 1.5
                other_players_fpts = working_df.iloc[:, 3:].apply(
                    lambda x: x.map(actual_map).fillna(0)
                ).sum(axis=1)
                working_df['actual_fpts'] = first_player_fpts + other_players_fpts
            
            # Vectorized actual_own calculation
            player_ownership = working_df.apply(
                lambda x: x.map(ownership_map).fillna(0)
            )
            working_df['actual_own'] = player_ownership.sum(axis=1)
            
            # Vectorized duplication calculation
            working_df['sorted'] = working_df[st.session_state['player_columns']].apply(
                lambda row: ','.join(sorted(row.values)), axis=1
            )
            working_df['dupes'] = working_df.groupby(['actual_fpts', 'actual_own', 'salary']).transform('size')
            
            # Vectorized unique calculations
            working_df['uniques'] = working_df.groupby('BaseName')['dupes'].transform(
                lambda x: (x == 1).sum()
            )
            working_df['under_5'] = working_df.groupby('BaseName')['dupes'].transform(
                lambda x: (x <= 5).sum()
            )
            working_df['under_10'] = working_df.groupby('BaseName')['dupes'].transform(
                lambda x: (x <= 10).sum()
            )

            working_df = working_df.sort_values(by='actual_fpts', ascending=False)
            working_df = working_df.reset_index(drop=True)
            working_df = working_df.reset_index()
            working_df['percentile_finish'] = working_df['index'].rank(pct=True)
            working_df['finish'] = working_df['index']
            working_df = working_df.drop(['sorted', 'index'], axis=1)
            try:
                # Calculate payouts efficiently by processing each unique fantasy points group
                working_df['payout'] = 0  # Initialize payout column
                
                # Group by actual_fpts since tied entries will have the same fantasy points
                for fpts, group in working_df.groupby('actual_fpts'):
                    # Get the first row's finish position and dupes count
                    first_row = group.iloc[0]
                    finish_pos = first_row['finish']
                    dupes_count = first_row['dupes']
                    
                    if dupes_count == 1:
                        # Single entry - no tie
                        payout = get_payout_for_position(finish_pos, st.session_state['payout_info'], 1)
                    else:
                        # Multiple entries tied - calculate split payout once
                        split_payout = get_payout_for_position(finish_pos, st.session_state['payout_info'], dupes_count)
                        payout = split_payout
                    
                    # Apply the same payout to all rows in this group
                    working_df.loc[group.index, 'payout'] = payout
            except:
                pass
        
        # Store results
        st.session_state['field_player_frame'] = create_player_exposures(working_df, st.session_state['player_columns'])
        st.session_state['field_stack_frame'] = create_stack_exposures(working_df)
        st.session_state['display_contest_info'] = working_df.copy()
        st.session_state['contest_info_reset'] = working_df.copy()
        st.session_state['unique_players'] = pd.unique(st.session_state['display_contest_info'][st.session_state['player_columns']].values.ravel('K'))
        st.session_state['unique_players'] = [p for p in st.session_state['unique_players'] if p != 'nan']

        st.write('Contest data:')
        st.dataframe(st.session_state['Contest'].head(25))
        if st.session_state['portfolio_df'] is not None:
            st.write('Portfolio data:')
            st.dataframe(st.session_state['portfolio_df'].head(25))
        else:
            pass

if selected_tab == 'Contest Analysis':
    if 'sport_select' not in st.session_state:
        st.session_state['sport_select'] = st.session_state['sport_init']
    
    if 'display_contest_info' in st.session_state:
        with st.expander("Info and filters"):
            st.info("Note that any filtering here needs to be reset manually, i.e. if you parse down the specific users and want to reset the table, just backtrack your filtering by setting it back to 'All'")
            clear_col, reset_col, blank_col = st.columns([1, 1, 7])
            with clear_col:
                if st.button('Clear data', key='reset3'):
                    st.session_state.clear()
            with reset_col:
                if st.button('Reset filters', key='reset4'):
                    st.session_state['entry_parse_var'] = 'All'
                    st.session_state['entry_names'] = []
                    st.session_state['low_entries_var'] = 1
                    st.session_state['high_entries_var'] = 150
                    st.session_state['stack_parse_var'] = 'All'
                    st.session_state['stack_names'] = []
                    st.session_state['stack_size_parse_var'] = 'All'
                    st.session_state['stack_size_names'] = []
                    st.session_state['player_parse_var'] = 'All'
                    st.session_state['player_names'] = []
                    st.session_state['remove_var'] = 'No'
                    st.session_state['remove_names'] = []
                    st.session_state['display_contest_info'] = st.session_state['contest_info_reset'].copy()
                    st.session_state['unique_players'] = pd.unique(st.session_state['display_contest_info'][st.session_state['player_columns']].values.ravel('K'))
                    st.session_state['unique_players'] = [p for p in st.session_state['unique_players'] if p != 'nan']
                    for keys in ['player_frame', 'stack_frame', 'stack_size_frame', 'general_frame', 'duplication_frame', 'player_exp_comp_download', 'stack_exp_comp_download', 'size_exp_comp_download', 'general_exp_comp_download', 'dupe_exp_comp_download']:
                        if keys in st.session_state:
                            del st.session_state[keys]

            with st.form(key='filter_form'):
                users_var, entries_var, stack_var, stack_size_var, player_var, remove_var = st.columns(6)
                with users_var:
                    st.session_state['entry_parse_var'] = st.selectbox("Do you want to view a specific user(s)?", ['All', 'Specific'])
                    st.session_state['entry_names'] = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
                with entries_var:
                    st.session_state['low_entries_var'] = st.number_input("Low end of entries range", min_value=0, max_value=150, value=1)
                    st.session_state['high_entries_var'] = st.number_input("High end of entries range", min_value=0, max_value=150, value=150)
                with stack_var:
                    st.session_state['stack_parse_var'] = st.selectbox("Do you want to view lineups with specific team(s)?", ['All', 'Specific'])
                    st.session_state['stack_names'] = st.multiselect("Select teams", options=st.session_state['display_contest_info']['stack'].unique(), default=[])
                with stack_size_var:
                    st.session_state['stack_size_parse_var'] = st.selectbox("Do you want to view a specific stack size(s)?", ['All', 'Specific'])
                    st.session_state['stack_size_names'] = st.multiselect("Select stack sizes", options=st.session_state['display_contest_info']['stack_size'].unique(), default=[])
                with player_var:
                    st.session_state['player_parse_var'] = st.selectbox("Do you want to view lineups with specific player(s)?", ['All', 'Specific'])
                    st.session_state['player_names'] = st.multiselect("Select players to lock", options=st.session_state['unique_players'], default=[])
                with remove_var:
                    st.session_state['remove_var'] = st.selectbox("Do you want to remove a specific player(s)?", ['No', 'Yes'])
                    st.session_state['remove_names'] = st.multiselect("Select players to remove", options=st.session_state['unique_players'], default=[])
                submitted = st.form_submit_button("Submit")
                if submitted:
                    if 'player_frame' in st.session_state:
                        del st.session_state['player_frame']
                    if 'stack_frame' in st.session_state:
                        del st.session_state['stack_frame']
                    if 'stack_size_frame' in st.session_state:
                        del st.session_state['stack_size_frame']
                    if 'general_frame' in st.session_state:
                        del st.session_state['general_frame']
                    if 'duplication_frame' in st.session_state:
                        del st.session_state['duplication_frame']
                    if 'ROI_frame' in st.session_state:
                        del st.session_state['ROI_frame']

                    if st.session_state['entry_parse_var'] == 'Specific' and st.session_state['entry_names']:
                        st.session_state['display_contest_info'] = st.session_state['display_contest_info'][st.session_state['display_contest_info']['BaseName'].isin(st.session_state['entry_names'])]
                    if st.session_state['stack_parse_var'] == 'Specific' and st.session_state['stack_names']:
                        st.session_state['display_contest_info'] = st.session_state['display_contest_info'][st.session_state['display_contest_info']['stack'].isin(st.session_state['stack_names'])]
                    if st.session_state['stack_size_parse_var'] == 'Specific' and st.session_state['stack_size_names']:
                        st.session_state['display_contest_info'] = st.session_state['display_contest_info'][st.session_state['display_contest_info']['stack_size'].isin(st.session_state['stack_size_names'])]
                    if st.session_state['player_parse_var'] == 'Specific' and st.session_state['player_names']:
                        mask = st.session_state['display_contest_info'][st.session_state['player_columns']].apply(lambda row: all(player in row.values for player in st.session_state['player_names']), axis=1)
                        st.session_state['display_contest_info'] = st.session_state['display_contest_info'][mask]
                    if st.session_state['remove_var'] == 'Yes' and st.session_state['remove_names']:
                        mask = st.session_state['display_contest_info'][st.session_state['player_columns']].apply(lambda row: any(player in row.values for player in st.session_state['remove_names']), axis=1)
                        st.session_state['display_contest_info'] = st.session_state['display_contest_info'][~mask]
                    if st.session_state['low_entries_var'] and st.session_state['high_entries_var']:
                        st.session_state['display_contest_info'] = st.session_state['display_contest_info'][st.session_state['display_contest_info']['EntryCount'].between(st.session_state['low_entries_var'], st.session_state['high_entries_var'])]

    if 'display_contest_info' in st.session_state:
        # Initialize pagination in session state if not exists
        if 'current_page' not in st.session_state:
            st.session_state.current_page = 1

        # Calculate total pages
        rows_per_page = 500
        total_rows = len(st.session_state['display_contest_info'])
        total_pages = (total_rows + rows_per_page - 1) // rows_per_page

        # Create pagination controls in a single row
        pagination_cols = st.columns([4, 1, 1, 1, 4])
        with pagination_cols[1]:
            if st.button(f"Previous Page"):
                if st.session_state['current_page'] > 1:
                    st.session_state.current_page -= 1
                else:
                    st.session_state.current_page = 1
                    if 'player_frame' in st.session_state:
                        del st.session_state['player_frame']
                    if 'stack_frame' in st.session_state:
                        del st.session_state['stack_frame']

        with pagination_cols[3]:
            if st.button(f"Next Page"):
                st.session_state.current_page += 1
                if 'player_frame' in st.session_state:
                    del st.session_state['player_frame']
                if 'stack_frame' in st.session_state:
                    del st.session_state['stack_frame']

        # Calculate start and end indices for current page
        start_idx = (st.session_state.current_page - 1) * rows_per_page
        end_idx = min((st.session_state.current_page) * rows_per_page, total_rows)
        st.dataframe(
            st.session_state['display_contest_info'].iloc[start_idx:end_idx].style
            .apply(highlight_row_condition, axis=1)
            .background_gradient(axis=0)
            .background_gradient(cmap='RdYlGn')
            .format(precision=2), 
            height=500,
            use_container_width=True,
            hide_index=True
        )
    else:
        st.stop()
    st.download_button(label="Download Contest Info", data=st.session_state['display_contest_info'].to_csv(index=False), file_name="contest_info.csv", mime="text/csv", key='download_contest')
    if 'Contest' in st.session_state:
        with st.container():
            tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(['Player Used Info', 'Stack Used Info', 'Stack Size Info', 'General Info', 'Duplication Info', 'ROI Info'])
            with tab1:
                player_pos_form_col, player_comp_form_col = st.columns(2)
                with player_pos_form_col:
                    with st.form(key='player_info_pos_form'):
                        col1, col2 = st.columns(2)
                        with col1:
                            pos_var = st.selectbox("Which position(s) would you like to view?",  ['All', 'Specific'], key='pos_var')
                        with col2:
                            if st.session_state['sport_select'] == 'NFL':
                                pos_select = st.multiselect("Select your position(s)", ['QB', 'RB', 'WR', 'TE', 'DST'], key='pos_select')
                            elif st.session_state['sport_select'] == 'MLB':
                                pos_select = st.multiselect("Select your position(s)", ['P', 'C', '1B', '2B', '3B', 'SS', 'OF'], key='pos_select')
                            elif st.session_state['sport_select'] == 'NBA':
                                pos_select = st.multiselect("Select your position(s)", ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_select')
                            elif st.session_state['sport_select'] == 'WNBA':
                                pos_select = st.multiselect("Select your position(s)", ['PG', 'SG', 'SF', 'PF'], key='pos_select')
                            elif st.session_state['sport_select'] == 'NHL':
                                pos_select = st.multiselect("Select your position(s)", ['W', 'C', 'D', 'G'], key='pos_select')
                            elif st.session_state['sport_select'] == 'MMA':
                                pos_select = st.multiselect("Select your position(s)", ['All the same position', 'So', 'Yeah', 'Idk'], key='pos_select')
                            elif st.session_state['sport_select'] == 'GOLF':
                                pos_select = st.multiselect("Select your position(s)", ['All the same position', 'So', 'Yeah', 'Idk'], key='pos_select')
                            elif st.session_state['sport_select'] == 'NAS':
                                pos_select = st.multiselect("Select your position(s)", ['All the same position', 'So', 'Yeah', 'Idk'], key='pos_select')
                            elif st.session_state['sport_select'] == 'CFB':
                                pos_select = st.multiselect("Select your position(s)", ['QB', 'RB', 'WR'], key='pos_select')
                        submitted = st.form_submit_button("Submit")
                        if submitted:
                            if pos_var == 'Specific':
                                pos_select = pos_select
                            else:
                                pos_select = None
                with player_comp_form_col:
                    with st.form(key='player_exp_comp_form'):
                        col1, col2 = st.columns(2)
                        with col1:
                            comp_player_var = st.selectbox("Would you like to compare with anyone?",  ['No', 'Yes'], key='comp_player_var')
                        with col2:
                            comp_player_select = st.multiselect("Select players to compare with:", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='comp_player_select')
                        submitted = st.form_submit_button("Submit")
                        if submitted:
                            if comp_player_var == 'No':
                                comp_player_select = None
                            else:
                                comp_player_select = comp_player_select
                if comp_player_var == 'Yes':
                    player_exp_comp = create_player_comparison(st.session_state['display_contest_info'], st.session_state['player_columns'], comp_player_select)
                    hold_frame = player_exp_comp.copy()
                    if st.session_state['sport_select'] == 'GOLF':
                        hold_frame['Pos'] = 'G'
                    elif st.session_state['sport_select'] == 'NAS':
                        hold_frame['Pos'] = 'C'
                    else:
                        hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
                    player_exp_comp.insert(1, 'Pos', hold_frame['Pos'])
                    player_exp_comp = player_exp_comp.dropna(subset=['Pos'])
                    if pos_select:
                        position_mask = player_exp_comp['Pos'].apply(lambda x: any(pos in x for pos in pos_select))
                        player_exp_comp = player_exp_comp[position_mask]
                    st.dataframe(player_exp_comp.style.background_gradient(cmap='RdYlGn', axis=0).format(formatter='{:.2%}', subset=player_exp_comp.select_dtypes(include=['number']).columns), hide_index=True)
                    st.download_button(label="Download Player Info", data=player_exp_comp.to_csv(index=False), file_name="player_info.csv", mime="text/csv", key='player_exp_comp_download')
                else:
                    if st.session_state['entry_parse_var'] == 'All':

                        st.session_state['player_frame'] = create_player_exposures(st.session_state['display_contest_info'], st.session_state['player_columns'])
                        hold_frame = st.session_state['player_frame'].copy()
                        if st.session_state['sport_select'] == 'GOLF':
                            hold_frame['Pos'] = 'G'
                        elif st.session_state['sport_select'] == 'NAS':
                            hold_frame['Pos'] = 'NAS'
                        else:
                            hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
                        st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
                        st.session_state['player_frame'] = st.session_state['player_frame'].dropna(subset=['Pos'])
                        if pos_select:
                            position_mask = st.session_state['player_frame']['Pos'].apply(lambda x: any(pos in x for pos in pos_select))
                            st.session_state['player_frame'] = st.session_state['player_frame'][position_mask]
                        st.dataframe(st.session_state['player_frame'].
                            sort_values(by='Exposure Overall', ascending=False).
                            style.background_gradient(cmap='RdYlGn').
                            format(formatter='{:.2%}', subset=st.session_state['player_frame'].iloc[:, 2:].select_dtypes(include=['number']).columns),
                            hide_index=True)
                        st.download_button(label="Download Player Info", data=st.session_state['player_frame'].to_csv(index=False), file_name="player_info.csv", mime="text/csv", key='player_exp_comp_download')
                    else:
                        st.session_state['player_frame'] = create_player_exposures(st.session_state['display_contest_info'], st.session_state['player_columns'], st.session_state['entry_names'])
                        hold_frame = st.session_state['player_frame'].copy()
                        if st.session_state['sport_select'] == 'GOLF':
                            hold_frame['Pos'] = 'G'
                        elif st.session_state['sport_select'] == 'NAS':
                            hold_frame['Pos'] = 'NAS'
                        else:
                            hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
                        st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
                        st.session_state['player_frame'] = st.session_state['player_frame'].dropna(subset=['Pos'])
                        if pos_select:
                            position_mask = st.session_state['player_frame']['Pos'].apply(lambda x: any(pos in x for pos in pos_select))
                            st.session_state['player_frame'] = st.session_state['player_frame'][position_mask]
                        st.dataframe(st.session_state['player_frame'].
                            sort_values(by='Exposure Overall', ascending=False).
                            style.background_gradient(cmap='RdYlGn').
                            format(formatter='{:.2%}', subset=st.session_state['player_frame'].iloc[:, 2:].select_dtypes(include=['number']).columns),
                            hide_index=True)
                        st.download_button(label="Download Player Info", data=st.session_state['player_frame'].to_csv(index=False), file_name="player_info.csv", mime="text/csv", key='player_exp_comp_download')
            with tab2:
                with st.form(key='stack_exp_comp_form'):
                    col1, col2 = st.columns(2)
                    with col1:
                        comp_stack_var = st.selectbox("Would you like to compare with anyone?",  ['No', 'Yes'], key='comp_stack_var')
                    with col2:
                        comp_stack_select = st.multiselect("Select stacks to compare with:", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='comp_stack_select')
                    submitted = st.form_submit_button("Submit")
                    if submitted:
                        if comp_stack_var == 'No':
                            comp_stack_select = None
                        else:
                            comp_stack_select = comp_stack_select
                if comp_stack_var == 'Yes':
                    stack_exp_comp = create_stack_comparison(st.session_state['display_contest_info'], comp_stack_select)
                    st.dataframe(stack_exp_comp.style.background_gradient(cmap='RdYlGn', axis=0).format(formatter='{:.2%}', subset=stack_exp_comp.select_dtypes(include=['number']).columns), hide_index=True)
                    st.download_button(label="Download Stack Info", data=stack_exp_comp.to_csv(index=False), file_name="stack_info.csv", mime="text/csv", key='stack_exp_comp_download')
                else:
                    if st.session_state['entry_parse_var'] == 'All':
                        st.session_state['stack_frame'] = create_stack_exposures(st.session_state['display_contest_info'])
                        st.dataframe(st.session_state['stack_frame'].
                            sort_values(by='Exposure Overall', ascending=False).
                            style.background_gradient(cmap='RdYlGn').
                            format(formatter='{:.2%}', subset=st.session_state['stack_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                            hide_index=True)
                        st.download_button(label="Download Stack Info", data=st.session_state['stack_frame'].to_csv(index=False), file_name="stack_info.csv", mime="text/csv", key='stack_exp_comp_download')
                    else:
                        st.session_state['stack_frame'] = create_stack_exposures(st.session_state['display_contest_info'], st.session_state['entry_names'])
                        st.dataframe(st.session_state['stack_frame'].
                            sort_values(by='Exposure Overall', ascending=False).
                            style.background_gradient(cmap='RdYlGn').
                            format(formatter='{:.2%}', subset=st.session_state['stack_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                            hide_index=True)
                        st.download_button(label="Download Stack Info", data=st.session_state['stack_frame'].to_csv(index=False), file_name="stack_info.csv", mime="text/csv", key='stack_exp_comp_download')
            with tab3:
                with st.form(key='size_exp_comp_form'):
                    col1, col2 = st.columns(2)
                    with col1:
                        comp_size_var = st.selectbox("Would you like to compare with anyone?",  ['No', 'Yes'], key='comp_size_var')
                    with col2:
                        comp_size_select = st.multiselect("Select sizes to compare with:", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='comp_size_select')
                    submitted = st.form_submit_button("Submit")
                    if submitted:
                        if comp_size_var == 'No':
                            comp_size_select = None
                        else:
                            comp_size_select = comp_size_select
                if comp_size_var == 'Yes':
                    size_exp_comp = create_size_comparison(st.session_state['display_contest_info'], comp_size_select)
                    st.dataframe(size_exp_comp.style.background_gradient(cmap='RdYlGn', axis=0).format(formatter='{:.2%}', subset=size_exp_comp.select_dtypes(include=['number']).columns), hide_index=True)
                    st.download_button(label="Download Stack Size Info", data=size_exp_comp.to_csv(index=False), file_name="stack_size_info.csv", mime="text/csv", key='size_exp_comp_download')
                else:
                    if st.session_state['entry_parse_var'] == 'All':
                        st.session_state['stack_size_frame'] = create_stack_size_exposures(st.session_state['display_contest_info'])
                        st.dataframe(st.session_state['stack_size_frame'].
                            sort_values(by='Exposure Overall', ascending=False).
                            style.background_gradient(cmap='RdYlGn').
                            format(formatter='{:.2%}', subset=st.session_state['stack_size_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                            hide_index=True)
                        st.download_button(label="Download Stack Size Info", data=st.session_state['stack_size_frame'].to_csv(index=False), file_name="stack_size_info.csv", mime="text/csv", key='size_exp_comp_download')
                    else:
                        st.session_state['stack_size_frame'] = create_stack_size_exposures(st.session_state['display_contest_info'], st.session_state['entry_names'])
                        # st.session_state['stack_size_frame']['Player'] = st.session_state['stack_size_frame']['Player'].astype(str)
                        st.dataframe(st.session_state['stack_size_frame'].
                            sort_values(by='Exposure Overall', ascending=False).
                            style.background_gradient(cmap='RdYlGn').
                            format(formatter='{:.2%}', subset=st.session_state['stack_size_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                            hide_index=True)
                        st.download_button(label="Download Stack Size Info", data=st.session_state['stack_size_frame'].to_csv(index=False), file_name="stack_size_info.csv", mime="text/csv", key='size_exp_comp_download')
            with tab4:
                with st.form(key='general_comp_form'):
                    col1, col2 = st.columns(2)
                    with col1:
                        comp_general_var = st.selectbox("Would you like to compare with anyone?",  ['No', 'Yes'], key='comp_general_var')
                    with col2:
                        comp_general_select = st.multiselect("Select generals to compare with:", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='comp_general_select')
                    submitted = st.form_submit_button("Submit")
                    if submitted:
                        if comp_general_var == 'No':
                            comp_general_select = None
                        else:
                            comp_general_select = comp_general_select
                if comp_general_var == 'Yes':
                    general_comp = create_general_comparison(st.session_state['display_contest_info'], comp_general_select)
                    st.dataframe(general_comp.style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2))
                    st.download_button(label="Download General Info", data=general_comp.to_csv(index=False), file_name="general_info.csv", mime="text/csv", key='general_exp_comp_download')
                else:
                    if st.session_state['entry_parse_var'] == 'All':
                        st.session_state['general_frame'] = create_general_exposures(st.session_state['display_contest_info'])
                        st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
                        st.download_button(label="Download General Info", data=st.session_state['general_frame'].to_csv(index=False), file_name="general_info.csv", mime="text/csv", key='general_exp_comp_download')
                    else:
                        st.session_state['general_frame'] = create_general_exposures(st.session_state['display_contest_info'], st.session_state['entry_names'])
                        st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
                        st.download_button(label="Download General Info", data=st.session_state['general_frame'].to_csv(index=False), file_name="general_info.csv", mime="text/csv", key='general_exp_comp_download')

            with tab5:
                with st.form(key='dupe_form'):
                    col1, col2 = st.columns(2)
                    with col1:
                        user_dupe_var = st.selectbox("Which usage(s) would you like to view?",  ['All', 'Specific'], key='user_dupe_var')
                    with col2:
                        user_dupe_select = st.multiselect("Select your user(s)", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='user_dupe_select')
                    submitted = st.form_submit_button("Submit")
                    if submitted:
                        if user_dupe_var == 'Specific':
                            user_dupe_select = user_dupe_select
                        else:
                            user_dupe_select = None
                if 'duplication_frame' not in st.session_state:
                    dupe_frame = st.session_state['display_contest_info'][['BaseName', 'EntryCount', 'dupes', 'uniques', 'under_5', 'under_10']]
                    dupe_frame['average_dupes'] = dupe_frame['dupes'].mean()
                    dupe_frame['uniques%'] = dupe_frame['uniques'] / dupe_frame['EntryCount']
                    dupe_frame['under_5%'] = dupe_frame['under_5'] / dupe_frame['EntryCount']
                    dupe_frame['under_10%'] = dupe_frame['under_10'] / dupe_frame['EntryCount']
                    dupe_frame = dupe_frame[['BaseName', 'EntryCount', 'average_dupes', 'uniques', 'uniques%', 'under_5', 'under_5%', 'under_10', 'under_10%']].drop_duplicates(subset='BaseName', keep='first')
                    st.session_state['duplication_frame'] = dupe_frame.sort_values(by='uniques%', ascending=False)
                if user_dupe_var == 'Specific':
                    st.session_state['duplication_frame'] = st.session_state['duplication_frame'][st.session_state['duplication_frame']['BaseName'].isin(user_dupe_select)]
                
                # Initialize pagination in session state if not exists
                if 'dupe_page' not in st.session_state:
                    st.session_state.dupe_page = 1

                # Calculate total pages
                rows_per_page = 50
                total_rows = len(st.session_state['duplication_frame'])
                total_pages = (total_rows + rows_per_page - 1) // rows_per_page

                # Create pagination controls in a single row
                pagination_cols = st.columns([4, 1, 1, 1, 4])
                with pagination_cols[1]:
                    if st.button(f"Previous Dupes Page"):
                        if st.session_state['dupe_page'] > 1:
                            st.session_state.dupe_page -= 1

                with pagination_cols[3]:
                    if st.button(f"Next Dupes Page"):
                        st.session_state.dupe_page += 1

                # Calculate start and end indices for current page
                start_dupe_idx = (st.session_state.dupe_page - 1) * rows_per_page
                end_dupe_idx = min((st.session_state.dupe_page) * rows_per_page, total_rows)

                st.dataframe(st.session_state['duplication_frame'].iloc[start_dupe_idx:end_dupe_idx].style.
                             background_gradient(cmap='RdYlGn', subset=['uniques%', 'under_5%', 'under_10%'], axis=0).
                             background_gradient(cmap='RdYlGn', subset=['uniques', 'under_5', 'under_10'], axis=0).
                             format(dupe_format, precision=2), hide_index=True)
                st.download_button(label="Download Duplication Info", data=st.session_state['duplication_frame'].to_csv(index=False), file_name="duplication_info.csv", mime="text/csv", key='dupe_exp_comp_download')
            
            with tab6:
                if st.session_state['payout_info'] is not None:
                    with st.form(key='ROI_form'):
                        col1, col2 = st.columns(2)
                        with col1:
                            user_ROI_var = st.selectbox("Which user(s) would you like to view?",  ['All', 'Specific'], key='user_ROI_var')
                        with col2:
                            user_ROI_select = st.multiselect("Select your user(s)", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='user_ROI_select')
                        submitted = st.form_submit_button("Submit")
                        if submitted:
                            if user_ROI_var == 'Specific':
                                user_ROI_select = user_ROI_select
                            else:
                                user_ROI_select = None
                    if 'ROI_frame' not in st.session_state:
                        roi_frame = st.session_state['display_contest_info'][['BaseName', 'EntryCount', 'finish', 'payout']]
                        roi_frame['Total Fees'] = roi_frame['EntryCount'] * st.session_state['entry_fee']
                        roi_frame['Total Payout'] = roi_frame.groupby('BaseName')['payout'].transform('sum')
                        roi_frame['ROI'] = (roi_frame['Total Payout'] / roi_frame['Total Fees'])
                        roi_frame = roi_frame[['BaseName', 'EntryCount', 'Total Fees', 'Total Payout', 'ROI']].drop_duplicates(subset='BaseName', keep='first')
                        st.session_state['ROI_frame'] = roi_frame.sort_values(by='Total Payout', ascending=False)
                    if user_ROI_var == 'Specific':
                        st.session_state['ROI_frame'] = st.session_state['ROI_frame'][st.session_state['ROI_frame']['BaseName'].isin(user_ROI_select)]
                    
                    # Initialize pagination in session state if not exists
                    if 'ROI_page' not in st.session_state:
                        st.session_state.ROI_page = 1

                    # Calculate total pages
                    rows_per_page = 50
                    total_rows = len(st.session_state['ROI_frame'])
                    total_pages = (total_rows + rows_per_page - 1) // rows_per_page

                    # Create pagination controls in a single row
                    pagination_cols = st.columns([4, 1, 1, 1, 4])
                    with pagination_cols[1]:
                        if st.button(f"Previous ROI Page"):
                            if st.session_state['ROI_page'] > 1:
                                st.session_state.ROI_page -= 1

                    with pagination_cols[3]:
                        if st.button(f"Next ROI Page"):
                            st.session_state.ROI_page += 1

                    # Calculate start and end indices for current page
                    start_ROI_idx = (st.session_state.ROI_page - 1) * rows_per_page
                    end_ROI_idx = min((st.session_state.ROI_page) * rows_per_page, total_rows)

                    st.dataframe(st.session_state['ROI_frame'].iloc[start_ROI_idx:end_ROI_idx].style.
                            applymap(color_roi, subset=['ROI']).
                            background_gradient(cmap='RdYlGn', subset=['Total Fees', 'Total Payout'], axis=0).
                            background_gradient(cmap='RdYlGn', subset=['EntryCount'], axis=0).
                            format(roi_format, precision=2), hide_index=True)
                    st.download_button(label="Download ROI Info", data=st.session_state['ROI_frame'].to_csv(index=False), file_name="ROI_info.csv", mime="text/csv", key='ROI_exp_comp_download')
                else:
                    st.write('No ROI info available')