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']
dk_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']

showdown_selections = ['Showdown #1', 'Showdown #2', 'Showdown #3', 'Showdown #4', 'Showdown #5', 'Showdown #6', 'Showdown #7', 'Showdown #8', 'Showdown #9', 'Showdown #10', 'Showdown #11', 'Showdown #12', 'Showdown #13', 'Showdown #14', 'Showdown #15']
dk_db_showdown_selections = ['DK_NFL_SD_seed_frame_Showdown #1', 'DK_NFL_SD_seed_frame_Showdown #2', 'DK_NFL_SD_seed_frame_Showdown #3', 'DK_NFL_SD_seed_frame_Showdown #4', 'DK_NFL_SD_seed_frame_Showdown #5', 'DK_NFL_SD_seed_frame_Showdown #6',
'DK_NFL_SD_seed_frame_Showdown #7', 'DK_NFL_SD_seed_frame_Showdown #8', 'DK_NFL_SD_seed_frame_Showdown #9', 'DK_NFL_SD_seed_frame_Showdown #10', 'DK_NFL_SD_seed_frame_Showdown #11', 'DK_NFL_SD_seed_frame_Showdown #12', 'DK_NFL_SD_seed_frame_Showdown #13',
'DK_NFL_SD_seed_frame_Showdown #14', 'DK_NFL_SD_seed_frame_Showdown #15']
fd_db_showdown_selections = ['FD_NFL_SD_seed_frame_Showdown #1', 'FD_NFL_SD_seed_frame_Showdown #2', 'FD_NFL_SD_seed_frame_Showdown #3', 'FD_NFL_SD_seed_frame_Showdown #4', 'FD_NFL_SD_seed_frame_Showdown #5', 'FD_NFL_SD_seed_frame_Showdown #6',
'FD_NFL_SD_seed_frame_Showdown #7', 'FD_NFL_SD_seed_frame_Showdown #8', 'FD_NFL_SD_seed_frame_Showdown #9', 'FD_NFL_SD_seed_frame_Showdown #10', 'FD_NFL_SD_seed_frame_Showdown #11', 'FD_NFL_SD_seed_frame_Showdown #12', 'FD_NFL_SD_seed_frame_Showdown #13',
'FD_NFL_SD_seed_frame_Showdown #14', 'FD_NFL_SD_seed_frame_Showdown #15']

dk_showdown_db_translation = dict(zip(showdown_selections, dk_db_showdown_selections))
fd_showdown_db_translation = dict(zip(showdown_selections, fd_db_showdown_selections))

# Probably should have done this in a dictionary to start with
wrong_team_names = ['Denver Broncos', 'Washington Commanders', 'Cincinnati Bengals', 'Arizona Cardinals', 'Los Angeles Rams', 'Pittsburgh Steelers',
'Jacksonville Jaguars', 'New England Patriots', 'Tampa Bay Buccaneers', 'San Francisco 49ers', 'Green Bay Packers', 'New York Jets',
'Indianapolis Colts', 'Miami Dolphins', 'Detroit Lions', 'Las Vegas Raiders', 'Atlanta Falcons', 'Seattle Seahawks', 'Houston Texans',
'New Orleans Saints', 'Carolina Panthers', 'New York Giants', 'Cleveland Browns', 'Tennessee Titans', 'Philadelphia Eagles', 'Dallas Cowboys',
'Kansas City Chiefs', 'Los Angeles Chargers', 'Baltimore Ravens', 'Buffalo Bills', 'Minnesota Vikings', 'Chicago Bears']
right_name_teams = ['Broncos', 'Commanders', 'Bengals', 'Cardinals', 'Rams', 'Steelers', 'Jaguars', 'Patriots', 'Buccaneers', '49ers', 'Packers', 
'Jets', 'Colts', 'Dolphins', 'Lions', 'Raiders', 'Falcons', 'Seahawks', 'Texans', 'Saints', 'Panthers', 'Giants', 'Browns', 'Titans', 'Eagles', 'Cowboys',
'Chiefs', 'Chargers', 'Ravens', 'Bills', 'Vikings', 'Bears']

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)

@st.cache_resource(ttl=60)
def define_dk_showdown_slates():
    collection = db["DK_SD_NFL_ROO"]
    cursor = collection.find()
    raw_display = pd.DataFrame(list(cursor))
    unique_slates = raw_display['slate'].unique()

    slate_names = []

    for slate in unique_slates:
        slate_data = raw_display[raw_display['slate'] == slate]
        slate_name = slate_data.iloc[0]['Team'] + ' vs. ' + slate_data.iloc[0]['Opp']
        slate_names.append(slate_name)
    
    slate_name_lookup = dict(zip(slate_names, unique_slates))
    return slate_names, slate_name_lookup

@st.cache_resource(ttl=60)
def define_fd_showdown_slates():
    collection = db["FD_SD_NFL_ROO"]
    cursor = collection.find()
    raw_display = pd.DataFrame(list(cursor))
    unique_slates = raw_display['slate'].unique()

    slate_names = []

    for slate in unique_slates:
        slate_data = raw_display[raw_display['slate'] == slate]
        slate_name = slate_data.iloc[0]['Team'] + ' vs. ' + slate_data.iloc[0]['Opp']
        slate_names.append(slate_name)
    
    slate_name_lookup = dict(zip(slate_names, unique_slates))
    return slate_names, slate_name_lookup

slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()

if st.button("Load/Reset Data", key='reset2'):
    st.cache_data.clear()
    for key in st.session_state.keys():
        del st.session_state[key]
    slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
    slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
    DK_seed = init_DK_seed_frames('Main Slate', 10000)
    DK_sd_seed = init_DK_SD_seed_frames("Showdown #1", 10000, dk_showdown_db_translation)
    FD_seed = init_FD_seed_frames('Main Slate', 10000)
    FD_sd_seed = init_FD_SD_seed_frames("Showdown #1", 10000, fd_showdown_db_translation)
    dk_raw, fd_raw = init_baselines('Main Slate')
    dk_sd_raw, fd_sd_raw = init_SD_baselines('Showdown #1')
    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', 'Late 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_sd_raw, fd_sd_raw = init_SD_baselines('Showdown #1')
    raw_baselines = dk_sd_raw
    column_names = dk_sd_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?", slate_names_dk if sim_site_var2 == 'Draftkings' else slate_names_fd, 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(slate_name_lookup_dk[sim_slate_var2], sharp_split, dk_showdown_db_translation)
                export_id_dict = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID)) 
                raw_baselines = dk_sd_raw
                column_names = dk_sd_columns
            elif sim_site_var2 == 'Fanduel':
                st.session_state.sd_working_seed = init_FD_SD_seed_frames(slate_name_lookup_fd[sim_slate_var2], sharp_split, fd_showdown_db_translation)
                export_id_dict = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
                raw_baselines = fd_sd_raw
                column_names = fd_sd_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)

            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 = st.session_state.Sim_Winner_Frame.drop(columns=['unique_id', 'win_count'])
            
            # 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.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)
            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 = st.session_state.Sim_Winner_Frame.drop(columns=['unique_id', 'win_count'])
            
            # 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
            
        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[:,0:6].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'
                    )