app.py

import streamlit as st
st.set_page_config(layout="wide")
import numpy as np
import pandas as pd
import gspread
import pymongo
import time

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

        return client
    
client = init_conn()

percentages_format = {'Exposure': '{:.2%}'}
freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']

@st.cache_data(ttl = 599)
def init_DK_seed_frames(sport, split): 
        if sport == 'NFL':
            db = client["NFL_Database"]
        elif sport == 'NBA':
            db = client["NBA_DFS"]
        
        collection = db[f"DK_{sport}_SD_seed_frame"] 
        cursor = collection.find().limit(split)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        DK_seed = raw_display.to_numpy()

        return DK_seed
    
@st.cache_data(ttl = 599)
def init_DK_secondary_seed_frames(sport, split):  
        
        if sport == 'NFL':
            db = client["NFL_Database"]
        elif sport == 'NBA':
            db = client["NBA_DFS"]
    
        collection = db[f"DK_{sport}_Secondary_SD_seed_frame"] 
        cursor = collection.find().limit(split)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        DK_second_seed = raw_display.to_numpy()

        return DK_second_seed

@st.cache_data(ttl = 599)
def init_DK_auxiliary_seed_frames(sport, split):  
        
        if sport == 'NFL':
            db = client["NFL_Database"]
        elif sport == 'NBA':
            db = client["NBA_DFS"]
    
        collection = db[f"DK_{sport}_Auxiliary_SD_seed_frame"] 
        cursor = collection.find().limit(split)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        DK_auxiliary_seed = raw_display.to_numpy()

        return DK_auxiliary_seed
    
@st.cache_data(ttl = 599)
def init_FD_seed_frames(sport, split):  
        
        if sport == 'NFL':
            db = client["NFL_Database"]
        elif sport == 'NBA':
            db = client["NBA_DFS"]
    
        collection = db[f"FD_{sport}_SD_seed_frame"] 
        cursor = collection.find().limit(split)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        FD_seed = raw_display.to_numpy()

        return FD_seed
    
@st.cache_data(ttl = 599)
def init_FD_secondary_seed_frames(sport, split):  
        
        if sport == 'NFL':
            db = client["NFL_Database"]
        elif sport == 'NBA':
            db = client["NBA_DFS"]
    
        collection = db[f"FD_{sport}_Secondary_SD_seed_frame"] 
        cursor = collection.find().limit(split)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        FD_second_seed = raw_display.to_numpy()

        return FD_second_seed

@st.cache_data(ttl = 599)
def init_FD_auxiliary_seed_frames(sport, split):  
        
        if sport == 'NFL':
            db = client["NFL_Database"]
        elif sport == 'NBA':
            db = client["NBA_DFS"]
    
        collection = db[f"FD_{sport}_Auxiliary_SD_seed_frame"] 
        cursor = collection.find().limit(split)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        FD_auxiliary_seed = raw_display.to_numpy()

        return FD_auxiliary_seed

@st.cache_data(ttl = 599)
def init_baselines(sport):
    if sport == 'NFL':
        db = client["NFL_Database"] 
        collection = db['DK_SD_NFL_ROO'] 
        cursor = collection.find()
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                   'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
        raw_display['Small_Field_Own'] = raw_display['Large_Field_Own']
        raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100)
        small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum()
        raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var
        raw_display['cpt_Median'] = raw_display['Median'] * 1.25
        raw_display['STDev'] = raw_display['Median'] / 4
        raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
        
        dk_raw = raw_display.dropna(subset=['Median'])
        
        collection = db['FD_SD_NFL_ROO'] 
        cursor = collection.find()
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                   'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
        raw_display['Small_Field_Own'] = raw_display['Large_Field_Own']
        raw_display['small_CPT_Own'] = raw_display['CPT_Own']
        raw_display['cpt_Median'] = raw_display['Median']
        raw_display['STDev'] = raw_display['Median'] / 4
        raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
        
        fd_raw = raw_display.dropna(subset=['Median'])
    
    elif sport == 'NBA':
        db = client["NBA_DFS"] 
        collection = db['Player_SD_Range_Of_Outcomes'] 
        cursor = collection.find()
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
                                   'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
        raw_display = raw_display[raw_display['site'] == 'Draftkings']
        raw_display['Small_Field_Own'] = raw_display['Small_Own']
        raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100)
        small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum()
        raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var
        raw_display['cpt_Median'] = raw_display['Median'] * 1.25
        raw_display['STDev'] = raw_display['Median'] / 4
        raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
        
        dk_raw = raw_display.dropna(subset=['Median'])
        
        collection = db['Player_SD_Range_Of_Outcomes'] 
        cursor = collection.find()
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
                                   'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
        raw_display = raw_display[raw_display['site'] == 'Fanduel']
        raw_display['Small_Field_Own'] = raw_display['Large_Own']
        raw_display['small_CPT_Own'] = raw_display['CPT_Own']
        raw_display['cpt_Median'] = raw_display['Median']
        raw_display['STDev'] = raw_display['Median'] / 4
        raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4
        
        fd_raw = raw_display.dropna(subset=['Median'])

    return dk_raw, fd_raw

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

@st.cache_data
def calculate_DK_value_frequencies(np_array):
    unique, counts = np.unique(np_array[:, :6], return_counts=True)
    frequencies = counts / len(np_array)  # Normalize by the number of rows 
    combined_array = np.column_stack((unique, frequencies))  
    return combined_array 

@st.cache_data
def calculate_FD_value_frequencies(np_array):
    unique, counts = np.unique(np_array[:, :5], return_counts=True)
    frequencies = counts / len(np_array)  # Normalize by the number of rows 
    combined_array = np.column_stack((unique, frequencies))  
    return combined_array

@st.cache_data
def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
    SimVar = 1
    Sim_Winners = []
    
    # Pre-vectorize functions
    vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
    vec_cpt_projection_map = np.vectorize(maps_dict['cpt_projection_map'].__getitem__)
    vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
    vec_cpt_stdev_map = np.vectorize(maps_dict['cpt_STDev_map'].__getitem__)
    
    st.write('Simulating contest on frames')
    
    while SimVar <= Sim_size:
        fp_random = seed_frame[np.random.choice(seed_frame.shape[0], Contest_Size)]
            
        sample_arrays1 = np.c_[
            fp_random,
            np.sum(np.random.normal(
                loc=np.concatenate([
                    vec_cpt_projection_map(fp_random[:, 0:1]),  # Apply cpt_projection_map to first column
                    vec_projection_map(fp_random[:, 1:-7])  # Apply original projection to remaining columns
                ], axis=1),
                scale=np.concatenate([
                    vec_cpt_stdev_map(fp_random[:, 0:1]),  # Apply cpt_projection_map to first column
                    vec_stdev_map(fp_random[:, 1:-7])  # Apply original projection to remaining columns
                ], axis=1)),
            axis=1)
        ]

        sample_arrays = sample_arrays1

        final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]]
        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
        Sim_Winners.append(best_lineup)
        SimVar += 1
        
    return Sim_Winners

dk_raw, fd_raw = init_baselines('NFL')

tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
with tab2:
    col1, col2 = st.columns([1, 7])
    with col1:
        if st.button("Load/Reset Data", key='reset1'):
              st.cache_data.clear()
              for key in st.session_state.keys():
                  del st.session_state[key]
              dk_raw, fd_raw = init_baselines('NFL')
        
        sport_var1 = st.radio("What sport are you working with?", ('NFL', 'NBA'), key='sport_var1')
        slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='slate_var1')
        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
            
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
        if site_var1 == 'Draftkings':
            
            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
            if team_var1 == 'Specific Teams':
                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
            elif team_var1 == 'Full Slate':
                    team_var2 = dk_raw.Team.values.tolist()
            
            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
            if stack_var1 == 'Specific Stack Sizes':
                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
            elif stack_var1 == 'Full Slate':
                    stack_var2 = [5, 4, 3, 2, 1, 0]
                    
        elif site_var1 == 'Fanduel':
            
            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
            if team_var1 == 'Specific Teams':
                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique())
            elif team_var1 == 'Full Slate':
                    team_var2 = fd_raw.Team.values.tolist()
            
            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
            if stack_var1 == 'Specific Stack Sizes':
                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0])
            elif stack_var1 == 'Full Slate':
                    stack_var2 = [4, 3, 2, 1, 0]
        

        if st.button("Prepare data export", key='data_export'):
            if 'working_seed' in st.session_state:
                data_export = st.session_state.working_seed.copy()
            elif 'working_seed' not in st.session_state:
                if site_var1 == 'Draftkings':
                    if slate_var1 == 'Showdown':
                        st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    elif slate_var1 == 'Secondary Showdown':
                        st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    elif slate_var1 == 'Auxiliary Showdown':
                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    raw_baselines = dk_raw
                    column_names = dk_columns
                elif site_var1 == 'Fanduel':
                    if slate_var1 == 'Showdown':
                        st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                    elif slate_var1 == 'Secondary Showdown':
                        st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                    elif slate_var1 == 'Auxiliary Showdown':
                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                    raw_baselines = fd_raw
                    column_names = fd_columns
            data_export = st.session_state.working_seed.copy()
            for col in range(6):
                data_export[:, col] = np.array([export_id_dict.get(x, x) for x in data_export[:, col]])
            st.download_button(
                label="Export optimals set",
                data=convert_df(data_export),
                file_name='NFL_SD_optimals_export.csv',
                    mime='text/csv',
                )
            
    with col2:
        if st.button("Load Data", key='load_data'):
            if site_var1 == 'Draftkings':
                if 'working_seed' in st.session_state:
                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                elif 'working_seed' not in st.session_state:
                    if slate_var1 == 'Showdown':
                        st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    elif slate_var1 == 'Secondary Showdown':
                        st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    elif slate_var1 == 'Auxiliary Showdown':
                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                
            elif site_var1 == 'Fanduel':
                if 'working_seed' in st.session_state:
                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                elif 'working_seed' not in st.session_state:
                    if slate_var1 == 'Showdown':
                        st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                    elif slate_var1 == 'Secondary Showdown':
                        st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                    elif slate_var1 == 'Auxiliary Showdown':
                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                    raw_baselines = fd_raw
                    column_names = fd_columns
                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                
        with st.container():
            if 'data_export_display' in st.session_state:
                st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
            
with tab1:
    col1, col2 = st.columns([1, 7])
    with col1:
        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_raw, fd_raw = init_baselines('NFL')
        sim_sport_var1 = st.radio("What sport are you working with?", ('NFL', 'NBA'), key='sim_sport_var1')
        dk_raw, fd_raw = init_baselines(sim_sport_var1)
        sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='sim_slate_var1')
        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
        if sim_site_var1 == 'Draftkings':
            raw_baselines = dk_raw
            column_names = dk_columns
        elif sim_site_var1 == 'Fanduel':
            raw_baselines = fd_raw
            column_names = fd_columns
        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
        if contest_var1 == 'Small':
            Contest_Size = 1000
            st.write("Small field size is 1,000 entrants.")
            raw_baselines['Own'] = raw_baselines['Small_Field_Own']
            raw_baselines['CPT_Own'] = raw_baselines['small_CPT_Own']
        elif contest_var1 == 'Medium':
            Contest_Size = 5000
            st.write("Medium field size is 5,000 entrants.")
        elif contest_var1 == 'Large':
            Contest_Size = 10000
            st.write("Large field size is 10,000 entrants.")
        elif contest_var1 == 'Custom':
            Contest_Size = st.number_input("Insert contest size", value=100, min_value=1, max_value=100000)
        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 = 400000
        elif strength_var1 == 'Average':
            sharp_split = 300000
        elif strength_var1 == 'Above Average':
            sharp_split = 200000
        elif strength_var1 == 'Very':
            sharp_split = 100000

    
    with col2:
        if st.button("Run Contest Sim"):
            if 'working_seed' in st.session_state:
                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_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()
                
            else:
                if sim_site_var1 == 'Draftkings':
                    if sim_slate_var1 == 'Showdown':
                        st.session_state.working_seed = init_DK_seed_frames(sim_sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    elif sim_slate_var1 == 'Secondary Showdown':
                        st.session_state.working_seed = init_DK_secondary_seed_frames(sim_sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    elif sim_slate_var1 == 'Auxiliary Showdown':
                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                    raw_baselines = dk_raw
                    column_names = dk_columns
                elif sim_site_var1 == 'Fanduel':
                    if sim_slate_var1 == 'Showdown':
                        st.session_state.working_seed = init_FD_seed_frames(sim_sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                    elif sim_slate_var1 == 'Secondary Showdown':
                        st.session_state.working_seed = init_FD_secondary_seed_frames(sim_sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                    elif sim_slate_var1 == 'Auxiliary Showdown':
                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
                        if sport_var1 == 'NFL':
                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
                        elif sport_var1 == 'NBA':
                            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_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)
                # Add percent rank columns for ownership at each roster position
                # Calculate Dupes column for Fanduel
                if sim_site_var1 == 'Fanduel':
                    dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank']
                    own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_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['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
                    
                    # Calculate ownership product and convert to probability
                    Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) + 0.0001
                    
                    # 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'] - 59800) / 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
                elif sim_site_var1 == 'Draftkings':
                    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
            
            if sim_site_var1 == '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_var1 == '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'])
            if sim_site_var1 == 'Draftkings':
                if sim_sport_var1 == 'NFL':
                    freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
                elif sim_sport_var1 == 'NBA':
                    freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
            elif sim_site_var1 == 'Fanduel':
                freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
            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()

            if sim_site_var1 == 'Draftkings':
                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)
            elif sim_site_var1 == 'Fanduel':
                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'])
            if sim_sport_var1 == 'NFL':
                cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
            elif sim_sport_var1 == 'NBA':
                cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) * 1.5
            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()
            
            if sim_site_var1 == 'Draftkings':
                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
            elif sim_site_var1 == 'Fanduel':
                flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                cpt_own_div = 500
            flex_working['Freq'] = flex_working['Freq'].astype(int)
            flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
            if sim_site_var1 == 'Draftkings':
                if sim_sport_var1 == 'NFL':
                    flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
                elif sim_sport_var1 == 'NBA':
                    flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
            elif sim_site_var1 == 'Fanduel':
                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
            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()

            if sim_site_var1 == 'Draftkings':
                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)
            elif sim_site_var1 == 'Fanduel':
                team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,7:8].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'
                    )