src/streamlit_app.py

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


st.set_page_config(layout="wide")

from database import db
from sim_func_hold.regular_functions import *
from sim_func_hold.showdown_functions import *

percentages_format = {'Exposure': '{:.2%}'}
freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.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.markdown("""
<style>
    /* Tab styling */
    .stElementContainer [data-baseweb="button-group"] {
        gap: 8px;
        padding: 4px;
    }
    .stElementContainer [kind="segmented_control"] {
        height: 45px;
        white-space: pre-wrap;
        background-color: #DAA520;
        color: white;
        border-radius: 10px;
        gap: 1px;
        padding: 10px 20px;
        font-weight: bold;
        transition: all 0.3s ease;
    }
    .stElementContainer [kind="segmented_controlActive"] {
        height: 50px;
        background-color: #DAA520;
        border: 3px solid #FFD700;
        color: white;
    }
    .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)

if st.button("Load/Reset Data", key='reset2'):
    st.cache_data.clear()
    for key in st.session_state.keys():
        del st.session_state[key]
    DK_seed = init_DK_seed_frames('Main Slate', 10000)
    DK_sd_seed = init_DK_SD_seed_frames('Main Slate', 10000)
    FD_seed = init_FD_seed_frames('Main Slate', 10000)
    FD_sd_seed = init_FD_SD_seed_frames('Main Slate', 10000)
    dk_raw, fd_raw = init_baselines('Main Slate')
    dk_sd_raw, fd_sd_raw = init_SD_baselines('Main Slate')
    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID))
    dk_sd_id_dict = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
    fd_sd_id_dict = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))

selected_tab = st.segmented_control(
    "Select Tab",
    options=["Regular Slate Contest Sims", "Showdown Contest Sims"],
    selection_mode='single',
    default='Regular Slate Contest Sims',
    width='stretch',
    label_visibility='collapsed',
    key='tab_selector'
)
            
if selected_tab == "Regular Slate Contest Sims":
    dk_raw, fd_raw = init_baselines('Main Slate')
    raw_baselines = dk_raw
    column_names = dk_columns
    with st.expander("Info and Filters"):
        site_data_col, slate_data_col, contest_size_col, contest_sharpness_col = st.columns([1, 1, 1, 1])
        with site_data_col:
            sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')

        with slate_data_col:
            sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
            
        with contest_size_col:
            contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'))
            if contest_var1 == 'Small':
                Contest_Size = 1000
            elif contest_var1 == 'Medium':
                Contest_Size = 5000
            elif contest_var1 == 'Large':
                Contest_Size = 10000
            elif contest_var1 == 'Custom':
                Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
        with contest_sharpness_col:
            strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
            if strength_var1 == 'Not Very':
                sharp_split = 500000
            elif strength_var1 == 'Below Average':
                sharp_split = 250000
            elif strength_var1 == 'Average':
                sharp_split = 100000
            elif strength_var1 == 'Above Average':
                sharp_split = 50000
            elif strength_var1 == 'Very':
                sharp_split = 10000

    if st.button("Run Contest Sim"):
            
        if 'working_seed' not in st.session_state:
            if sim_site_var1 == 'Draftkings':
                st.session_state.working_seed = init_DK_seed_frames(sim_slate_var1, sharp_split)
                dk_raw, fd_raw = init_baselines(sim_slate_var1)
                dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
                    
                raw_baselines = dk_raw
                column_names = dk_columns
            elif sim_site_var1 == 'Fanduel':
                st.session_state.working_seed = init_FD_seed_frames(sim_slate_var1, sharp_split)
                dk_raw, fd_raw = init_baselines(sim_slate_var1)
                fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
                    
                raw_baselines = fd_raw
                column_names = fd_columns
            st.session_state.maps_dict = {
                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                    }
            
            Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
            
            #st.table(Sim_Winner_Frame)
                        
            # Initial setup
            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
            
            # Type Casting
            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
            
            # Sorting
            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
            
            # Data Copying
            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
            if sim_site_var1 == 'Draftkings':
                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
            elif sim_site_var1 == 'Fanduel':
                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(fd_id_dict)
            
            # Data Copying
            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
            st.session_state.freq_copy = st.session_state.Sim_Winner_Display
        else:
            st.session_state.maps_dict = {
                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                    }
            
            Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
            
            # Initial setup
            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
            
            # Type Casting
            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
            
            # Sorting
            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
            
            # Data Copying
            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
            
            # Data Copying
            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()    

        if sim_site_var1 == 'Draftkings':
            freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        freq_working['Freq'] = freq_working['Freq'].astype(int)
        freq_working['Position'] = freq_working['Player'].map(st.session_state.maps_dict['Pos_map'])
        freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map'])
        freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
        freq_working['Exposure'] = freq_working['Freq']/(1000)
        freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
        freq_working['Team'] = freq_working['Player'].map(st.session_state.maps_dict['Team_map'])
        st.session_state.player_freq = freq_working.copy()

        if sim_site_var1 == 'Draftkings':
            qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            qb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        qb_working['Freq'] = qb_working['Freq'].astype(int)
        qb_working['Position'] = qb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
        qb_working['Salary'] = qb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
        qb_working['Proj Own'] = qb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
        qb_working['Exposure'] = qb_working['Freq']/(1000)
        qb_working['Edge'] = qb_working['Exposure'] - qb_working['Proj Own']
        qb_working['Team'] = qb_working['Player'].map(st.session_state.maps_dict['Team_map'])
        st.session_state.qb_freq = qb_working.copy()
        
        if sim_site_var1 == 'Draftkings':
            rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            rbwrte_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:7].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        rbwrte_working['Freq'] = rbwrte_working['Freq'].astype(int)
        rbwrte_working['Position'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Pos_map'])
        rbwrte_working['Salary'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Salary_map'])
        rbwrte_working['Proj Own'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
        rbwrte_working['Exposure'] = rbwrte_working['Freq']/(1000)
        rbwrte_working['Edge'] = rbwrte_working['Exposure'] - rbwrte_working['Proj Own']
        rbwrte_working['Team'] = rbwrte_working['Player'].map(st.session_state.maps_dict['Team_map'])
        st.session_state.rbwrte_freq = rbwrte_working.copy()
        
        if sim_site_var1 == 'Draftkings':
            rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            rb_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:3].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        rb_working['Freq'] = rb_working['Freq'].astype(int)
        rb_working['Position'] = rb_working['Player'].map(st.session_state.maps_dict['Pos_map'])
        rb_working['Salary'] = rb_working['Player'].map(st.session_state.maps_dict['Salary_map'])
        rb_working['Proj Own'] = rb_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
        rb_working['Exposure'] = rb_working['Freq']/(1000)
        rb_working['Edge'] = rb_working['Exposure'] - rb_working['Proj Own']
        rb_working['Team'] = rb_working['Player'].map(st.session_state.maps_dict['Team_map'])
        st.session_state.rb_freq = rb_working.copy()
        
        if sim_site_var1 == 'Draftkings':
            wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            wr_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:6].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        wr_working['Freq'] = wr_working['Freq'].astype(int)
        wr_working['Position'] = wr_working['Player'].map(st.session_state.maps_dict['Pos_map'])
        wr_working['Salary'] = wr_working['Player'].map(st.session_state.maps_dict['Salary_map'])
        wr_working['Proj Own'] = wr_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
        wr_working['Exposure'] = wr_working['Freq']/(1000)
        wr_working['Edge'] = wr_working['Exposure'] - wr_working['Proj Own']
        wr_working['Team'] = wr_working['Player'].map(st.session_state.maps_dict['Team_map'])
        st.session_state.wr_freq = wr_working.copy()
        
        if sim_site_var1 == 'Draftkings':
            te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            te_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        te_working['Freq'] = te_working['Freq'].astype(int)
        te_working['Position'] = te_working['Player'].map(st.session_state.maps_dict['Pos_map'])
        te_working['Salary'] = te_working['Player'].map(st.session_state.maps_dict['Salary_map'])
        te_working['Proj Own'] = te_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
        te_working['Exposure'] = te_working['Freq']/(1000)
        te_working['Edge'] = te_working['Exposure'] - te_working['Proj Own']
        te_working['Team'] = te_working['Player'].map(st.session_state.maps_dict['Team_map'])
        st.session_state.te_freq = te_working.copy()
        
        if sim_site_var1 == 'Draftkings':
            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        flex_working['Freq'] = flex_working['Freq'].astype(int)
        flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map'])
        flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map'])
        flex_working['Proj Own'] = flex_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
        flex_working['Exposure'] = flex_working['Freq']/(1000)
        flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
        flex_working['Team'] = flex_working['Player'].map(st.session_state.maps_dict['Team_map'])
        st.session_state.flex_freq = flex_working.copy()
        
        if sim_site_var1 == 'Draftkings':
            dst_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            dst_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        dst_working['Freq'] = dst_working['Freq'].astype(int)
        dst_working['Position'] = dst_working['Player'].map(st.session_state.maps_dict['Pos_map'])
        dst_working['Salary'] = dst_working['Player'].map(st.session_state.maps_dict['Salary_map'])
        dst_working['Proj Own'] = dst_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
        dst_working['Exposure'] = dst_working['Freq']/(1000)
        dst_working['Edge'] = dst_working['Exposure'] - dst_working['Proj Own']
        dst_working['Team'] = dst_working['Player'].map(st.session_state.maps_dict['Team_map'])
        st.session_state.dst_freq = dst_working.copy()

        if sim_site_var1 == 'Draftkings':
            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        elif sim_site_var1 == 'Fanduel':
            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
        team_working['Freq'] = team_working['Freq'].astype(int)
        team_working['Exposure'] = team_working['Freq']/(1000)
        st.session_state.team_freq = team_working.copy()
        
    with st.container():
        if st.button("Reset Sim", key='reset_sim'):
            for key in st.session_state.keys():
                del st.session_state[key]
        if 'player_freq' in st.session_state: 
            player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
            if player_split_var2 == 'Specific Players':
                        find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
            elif player_split_var2 == 'Full Players':
                        find_var2 = st.session_state.player_freq.Player.values.tolist()

            if player_split_var2 == 'Specific Players':
                        st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
            if player_split_var2 == 'Full Players':
                        st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
        if 'Sim_Winner_Display' in st.session_state:
            st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
        if 'Sim_Winner_Export' in st.session_state:
            st.download_button(
                label="Export Full Frame",
                data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
                file_name='MLB_consim_export.csv',
                mime='text/csv',
            )  
    tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
    
    with tab1:
        if 'Sim_Winner_Display' in st.session_state:
            # Create a new dataframe with summary statistics
            summary_df = pd.DataFrame({
                'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                'Salary': [
                    st.session_state.Sim_Winner_Display['salary'].min(),
                    st.session_state.Sim_Winner_Display['salary'].mean(),
                    st.session_state.Sim_Winner_Display['salary'].max(),
                    st.session_state.Sim_Winner_Display['salary'].std()
                ],
                'Proj': [
                    st.session_state.Sim_Winner_Display['proj'].min(),
                    st.session_state.Sim_Winner_Display['proj'].mean(),
                    st.session_state.Sim_Winner_Display['proj'].max(),
                    st.session_state.Sim_Winner_Display['proj'].std()
                ],
                'Own': [
                    st.session_state.Sim_Winner_Display['Own'].min(),
                    st.session_state.Sim_Winner_Display['Own'].mean(),
                    st.session_state.Sim_Winner_Display['Own'].max(),
                    st.session_state.Sim_Winner_Display['Own'].std()
                ],
                'Fantasy': [
                    st.session_state.Sim_Winner_Display['Fantasy'].min(),
                    st.session_state.Sim_Winner_Display['Fantasy'].mean(),
                    st.session_state.Sim_Winner_Display['Fantasy'].max(),
                    st.session_state.Sim_Winner_Display['Fantasy'].std()
                ],
                'GPP_Proj': [
                    st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
                    st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
                    st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
                    st.session_state.Sim_Winner_Display['GPP_Proj'].std()
                ]
            })

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

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

    with tab2:
        if 'Sim_Winner_Display' in st.session_state:
            # Apply position mapping to FLEX column
            flex_positions = st.session_state.freq_copy['FLEX'].map(st.session_state.maps_dict['Pos_map'])
            
            # Count occurrences of each position in FLEX
            flex_counts = flex_positions.value_counts()
            
            # Calculate average statistics for each FLEX position
            flex_stats = st.session_state.freq_copy.groupby(flex_positions).agg({
                'proj': 'mean',
                'Own': 'mean',
                'Fantasy': 'mean',
                'GPP_Proj': 'mean'
            })
            
            # Combine counts and average statistics
            flex_summary = pd.concat([flex_counts, flex_stats], axis=1)
            flex_summary.columns = ['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
            flex_summary = flex_summary.reset_index()
            flex_summary.columns = ['Position', 'Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']
            
            # Display the summary dataframe
            st.subheader("FLEX Position Statistics")
            st.dataframe(flex_summary.style.format({
                'Count': '{:.0f}',
                'Avg Proj': '{:.2f}',
                'Avg Fantasy': '{:.2f}',
                'Avg GPP_Proj': '{:.2f}'
            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Count', 'Avg Proj', 'Avg Own', 'Avg Fantasy', 'Avg GPP_Proj']), use_container_width=True)
            
        else:
            st.write("Simulation data or position mapping not available.")
    with st.container():
        tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB-WR-TE Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures', 'Team Exposures'])
        with tab1:
            if 'player_freq' in st.session_state:
                
                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.player_freq.to_csv().encode('utf-8'),
                    file_name='player_freq_export.csv',
                    mime='text/csv',
                    key='overall'
                )
        with tab2:
            if 'qb_freq' in st.session_state:
                
                st.dataframe(st.session_state.qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.qb_freq.to_csv().encode('utf-8'),
                    file_name='qb_freq.csv',
                    mime='text/csv',
                    key='qb'
                )
        with tab3:
            if 'rbwrte_freq' in st.session_state:
                
                st.dataframe(st.session_state.rbwrte_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.rbwrte_freq.to_csv().encode('utf-8'),
                    file_name='rbwrte_freq.csv',
                    mime='text/csv',
                    key='rbwrte'
                )
        with tab4:
            if 'rb_freq' in st.session_state:
                
                st.dataframe(st.session_state.rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.rb_freq.to_csv().encode('utf-8'),
                    file_name='rb_freq.csv',
                    mime='text/csv',
                    key='rb'
                )
        with tab5:
            if 'wr_freq' in st.session_state:
                
                st.dataframe(st.session_state.wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.wr_freq.to_csv().encode('utf-8'),
                    file_name='wr_freq.csv',
                    mime='text/csv',
                    key='wr'
                )
        with tab6:
            if 'te_freq' in st.session_state:
                
                st.dataframe(st.session_state.te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.te_freq.to_csv().encode('utf-8'),
                    file_name='te_freq.csv',
                    mime='text/csv',
                    key='te'
                )
        with tab7:
            if 'flex_freq' in st.session_state:
                
                st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.flex_freq.to_csv().encode('utf-8'),
                    file_name='flex_freq.csv',
                    mime='text/csv',
                    key='flex'
                )
        with tab8:
            if 'dst_freq' in st.session_state:
                
                st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.dst_freq.to_csv().encode('utf-8'),
                    file_name='dst_freq.csv',
                    mime='text/csv',
                    key='dst'
                )
        with tab9:
            if 'team_freq' in st.session_state:
                
                st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
                st.download_button(
                    label="Export Exposures",
                    data=st.session_state.team_freq.to_csv().encode('utf-8'),
                    file_name='team_freq.csv',
                    mime='text/csv',
                    key='team'
                )

if selected_tab == "Showdown Contest Sims":
    dk_raw, fd_raw = init_SD_baselines('Main Slate')
    raw_baselines = dk_raw
    column_names = dk_columns
    with st.expander("Info and Filters"):
        site_data_col, slate_data_col, contest_size_col, contest_sharpness_col = st.columns([1, 1, 1, 1])
        with site_data_col:
            sim_site_var2 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var2')

        with slate_data_col:
            sim_slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var2')
            
        with contest_size_col:
            contest_var2 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'), key='contest_var2')
            if contest_var2 == 'Small':
                Contest_Size = 1000
            elif contest_var2 == 'Medium':
                Contest_Size = 5000
            elif contest_var2 == 'Large':
                Contest_Size = 10000
            elif contest_var2 == 'Custom':
                Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
        with contest_sharpness_col:
            strength_var2 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'), key='strength_var2')
            if strength_var2 == 'Not Very':
                sharp_split = 500000
            elif strength_var2 == 'Below Average':
                sharp_split = 250000
            elif strength_var2 == 'Average':
                sharp_split = 100000
            elif strength_var2 == 'Above Average':
                sharp_split = 50000
            elif strength_var2 == 'Very':
                sharp_split = 10000

    if st.button("Run Contest Sim"):
            
        if 'sd_working_seed' not in st.session_state:
            if sim_site_var2 == 'Draftkings':
                st.session_state.sd_working_seed = init_DK_SD_seed_frames(sim_slate_var2, sharp_split)
                export_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
                raw_baselines = dk_raw
                column_names = dk_columns
            elif sim_site_var2 == 'Fanduel':
                st.session_state.sd_working_seed = init_FD_SD_seed_frames(sim_slate_var2, sharp_split)
                export_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
                raw_baselines = fd_raw
                column_names = fd_columns
            maps_dict = {
                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                    'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
                    'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
                    'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
                    }
            Sim_Winners = sim_SD_contest(1000, st.session_state.sd_working_seed, maps_dict, Contest_Size)
            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                
            #st.table(Sim_Winner_Frame)
                        
            # Initial setup
            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
            # Add percent rank columns for ownership at each roster position
            # Calculate Dupes column for Fanduel
            dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
            own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']
            calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc']
            Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True)
            Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True)
            Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True)
            Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True)
            Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True)
            Sim_Winner_Frame['FLEX5_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']).rank(pct=True)
            Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100
            Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100
            Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100
            Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100
            Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100
            Sim_Winner_Frame['FLEX5_Own'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']) / 100

            # Calculate ownership product and convert to probability
            Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1))
            
            # Calculate average of ownership percent rank columns
            Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1)
            
            # Calculate dupes formula
            Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 49800) / 100)
            
            # Round and handle negative values
            Sim_Winner_Frame['Dupes'] = np.where(
                np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
                0, 
                np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1
            )
            Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2
                
            Sim_Winner_Frame['Dupes'] = np.round(Sim_Winner_Frame['Dupes'], 0)
            Sim_Winner_Frame['Dupes'] = np.where(
                    np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0,
                    0, 
                    np.round(Sim_Winner_Frame['dupes_calc'], 0)
                )
            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns)
            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=own_columns)
            Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=calc_columns)

            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
            
            # Type Casting
            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32, 'Dupes': int}
            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
            
            # Sorting
            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
            
            # Data Copying
            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
            st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict))
            
            # Data Copying
            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
            freq_copy = st.session_state.Sim_Winner_Display
        
        else:
            maps_dict = {
                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                    'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
                    'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])),
                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
                    'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
                    }
            Sim_Winners = sim_SD_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size)
            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
            
            #st.table(Sim_Winner_Frame)
                        
            # Initial setup
            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
            
            # Type Casting
            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
            
            # Sorting
            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
            
            # Data Copying
            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
            
            # Data Copying
            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
            freq_copy = st.session_state.Sim_Winner_Display
            
            if sim_site_var2 == 'Draftkings':
                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var2 == 'Fanduel':
                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            freq_working['Freq'] = freq_working['Freq'].astype(int)
            freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
            freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
            freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
            freq_working['Exposure'] = freq_working['Freq']/(1000)
            freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
            freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map'])
            st.session_state.player_freq = freq_working.copy()

            cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            cpt_working['Freq'] = cpt_working['Freq'].astype(int)
            cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
            cpt_working['Exposure'] = cpt_working['Freq']/(1000)
            cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
            cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
            st.session_state.sp_freq = cpt_working.copy()
            
            flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            cpt_own_div = 600
            flex_working['Freq'] = flex_working['Freq'].astype(int)
            flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
            flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']) / 100)
            flex_working['Exposure'] = flex_working['Freq']/(1000)
            flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
            flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map'])
            st.session_state.flex_freq = flex_working.copy()

            team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            team_working['Freq'] = team_working['Freq'].astype(int)
            team_working['Exposure'] = team_working['Freq']/(1000)
            st.session_state.team_freq = team_working.copy()
            
        with st.container():
            if st.button("Reset Sim", key='reset_sim'):
                for key in st.session_state.keys():
                    del st.session_state[key]
            if 'player_freq' in st.session_state: 
                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
                if player_split_var2 == 'Specific Players':
                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
                elif player_split_var2 == 'Full Players':
                          find_var2 = st.session_state.player_freq.Player.values.tolist()
    
                if player_split_var2 == 'Specific Players':
                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
                if player_split_var2 == 'Full Players':
                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
            if 'Sim_Winner_Display' in st.session_state:
                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
            if 'Sim_Winner_Export' in st.session_state:
                st.download_button(
                    label="Export Full Frame",
                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
                    file_name='NFL_SD_consim_export.csv',
                    mime='text/csv',
                )  
                
        with st.container():
            tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures', 'Team Exposures'])
            with tab1:
                if 'player_freq' in st.session_state:
                    
                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                    st.download_button(
                        label="Export Exposures",
                        data=st.session_state.player_freq.to_csv().encode('utf-8'),
                        file_name='player_freq_export.csv',
                        mime='text/csv',
                        key='overall'
                    )
            with tab2:
                if 'sp_freq' in st.session_state:
                    
                    st.dataframe(st.session_state.sp_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                    st.download_button(
                        label="Export Exposures",
                        data=st.session_state.sp_freq.to_csv().encode('utf-8'),
                        file_name='cpt_freq.csv',
                        mime='text/csv',
                        key='sp'
                    )
            with tab3:
                if 'flex_freq' in st.session_state:
                    
                    st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
                    st.download_button(
                        label="Export Exposures",
                        data=st.session_state.flex_freq.to_csv().encode('utf-8'),
                        file_name='flex_freq.csv',
                        mime='text/csv',
                        key='flex'
                    )
            with tab4:
                if 'team_freq' in st.session_state:
                    
                    st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
                    st.download_button(
                        label="Export Exposures",
                        data=st.session_state.team_freq.to_csv().encode('utf-8'),
                        file_name='team_freq.csv',
                        mime='text/csv',
                        key='team'
                    )