src/streamlit_app.py

import streamlit as st
import numpy as np
import pandas as pd
import pymongo
import os
import unicodedata
from database import db

st.set_page_config(layout="wide")

player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '100+%': '{:.2%}', '10x%': '{:.2%}', '11x%': '{:.2%}', '12x%': '{:.2%}'}
dk_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']
fd_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']

### I should probably not have this be messy in terms of site declarations but eh

st.markdown("""
<style>
    /* Tab styling */
    .stElementContainer [data-baseweb="button-group"] {
        gap: 8px;
        padding: 4px;
    }
    .stElementContainer [kind="segmented_control"] {
        height: 45px;
        white-space: pre-wrap;
        background-color: #DAA520;
        color: white;
        border-radius: 10px;
        gap: 1px;
        padding: 10px 20px;
        font-weight: bold;
        transition: all 0.3s ease;
    }
    .stElementContainer [kind="segmented_controlActive"] {
        height: 50px;
        background-color: #DAA520;
        border: 3px solid #FFD700;
        color: white;
    }
    .stElementContainer [kind="segmented_control"]:hover {
        background-color: #FFD700;
        cursor: pointer;
    }

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

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

@st.cache_resource(ttl = 60)
def init_baselines():
    collection = db["Player_Level_ROO"] 
    cursor = collection.find()
    player_frame = pd.DataFrame(cursor)

    roo_data = player_frame.drop(columns=['_id'])
    roo_data['Salary'] = roo_data['Salary'].astype(int)
    
    return roo_data

@st.cache_data(ttl = 60)
def init_DK_lineups(type):  
        
        if type == 'Regular':
            collection = db['DK_MMA_name_map']
        elif type == 'Showdown':
            collection = db['DK_SD_MMA_name_map']
        cursor = collection.find()
        raw_data = pd.DataFrame(list(cursor))
        names_dict = dict(zip(raw_data['key'], raw_data['value']))
        
        if type == 'Regular':
            collection = db["DK_MMA_seed_frame_Main Slate"] 
        elif type == 'Showdown':
            collection = db["DK_SD_MMA_seed_frame_Main Slate"]
        cursor = collection.find().limit(10000)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']]
        dict_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
        for col in dict_columns:
            raw_display[col] = raw_display[col].map(names_dict)
        DK_seed = raw_display.to_numpy()

        return DK_seed

@st.cache_data(ttl = 60)
def init_FD_lineups(type):  
        
        if type == 'Regular':
            collection = db['FD_MMA_name_map']
        elif type == 'Showdown':
            collection = db['FD_SD_MMA_name_map']
        cursor = collection.find()
        raw_data = pd.DataFrame(list(cursor))
        names_dict = dict(zip(raw_data['key'], raw_data['value']))
        
        if type == 'Regular':
            collection = db["FD_MMA_seed_frame_Main Slate"] 
        elif type == 'Showdown':
            collection = db["FD_SD_MMA_seed_frame_Main Slate"]
        cursor = collection.find().limit(10000)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']]
        dict_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
        for col in dict_columns:
            raw_display[col] = raw_display[col].map(names_dict)
        FD_seed = raw_display.to_numpy()

        return FD_seed

def normalize_special_characters(text):
    """Convert accented characters to their ASCII equivalents"""
    if pd.isna(text):
        return text
    # Normalize unicode characters to their closest ASCII equivalents
    normalized = unicodedata.normalize('NFKD', str(text))
    # Remove diacritics (accents, umlauts, etc.)
    ascii_text = ''.join(c for c in normalized if not unicodedata.combining(c))
    return ascii_text

def convert_df_to_csv(df):
    df_clean = df.copy()
    for col in df_clean.columns:
        if df_clean[col].dtype == 'object':
            df_clean[col] = df_clean[col].apply(normalize_special_characters)
    return df_clean.to_csv(index=False).encode('utf-8')

@st.cache_data
def convert_df(array):
    array = pd.DataFrame(array, columns=column_names)
    # Normalize special characters in the dataframe before export
    for col in array.columns:
        if array[col].dtype == 'object':
            array[col] = array[col].apply(normalize_special_characters)
    return array.to_csv(index=False).encode('utf-8')

@st.cache_data
def convert_pm_df(array):
    array = pd.DataFrame(array)
    # Normalize special characters in the dataframe before export
    for col in array.columns:
        if array[col].dtype == 'object':
            array[col] = array[col].apply(normalize_special_characters)
    return array.to_csv(index=False).encode('utf-8')

roo_data = init_baselines()
dk_id_dict = dict(zip(roo_data['Player'], roo_data['player_id']))
dk_id_dict_sd = {}
fd_id_dict = dict(zip(roo_data['Player'], roo_data['player_id']))
fd_id_dict_sd = {}
hold_display = roo_data

app_load_reset_column, app_view_site_column = st.columns([1, 9])
with app_load_reset_column:
    if st.button("Load/Reset Data", key='reset_data_button'):
        st.cache_data.clear()
        roo_data = init_baselines()
        dk_id_dict = dict(zip(roo_data['Player'], roo_data['player_id']))
        dk_id_dict_sd = {}
        fd_id_dict = dict(zip(roo_data['Player'], roo_data['player_id']))
        fd_id_dict_sd = {}
        dk_lineups = init_DK_lineups('Regular')
        fd_lineups = init_FD_lineups('Regular')
        hold_display = roo_data
        for key in st.session_state.keys():
            del st.session_state[key]
with app_view_site_column:
    with st.container():
        app_view_column, app_site_column = st.columns([3, 3])
        with app_view_column:
            view_var = st.selectbox("Select view", ["Simple", "Advanced"], key='view_selectbox')
        with app_site_column:
            site_var = st.selectbox("What site do you want to view?", ('Draftkings'), key='site_selectbox')

selected_tab = st.segmented_control(
    "Select Tab",
    options=["Player ROO", "Optimals"],
    selection_mode='single',
    default='Player ROO',
    width='stretch',
    label_visibility='collapsed',
    key='tab_selector'
)

if selected_tab == "Player ROO":
    with st.expander("Info and Filters"):
        if st.button("Reset Data", key='reset1'):
                st.cache_data.clear()
                roo_data = init_baselines()
                dk_id_dict = dict(zip(roo_data['Player'], roo_data['player_id']))
                dk_id_dict_sd = {}
                fd_id_dict = dict(zip(roo_data['Player'], roo_data['player_id']))
                fd_id_dict_sd = {}
                dk_lineups = init_DK_lineups('Regular')
                fd_lineups = init_FD_lineups('Regular')
                hold_display = roo_data
                for key in st.session_state.keys():
                    del st.session_state[key]
        
        type_var = st.radio("Select a Type", ["Full Slate"])
        if type_var == "Full Slate":
            display = hold_display[hold_display['Site'] == site_var]
            display = display.drop_duplicates(subset=['Player'])
        elif type_var == "Showdown":
            display = pd.DataFrame()
            display = display.drop_duplicates(subset=['Player'])
    
    export_data = display.copy()
    export_data_pm = display[['Player', 'Salary', 'Median', 'Own']]
    export_data_pm['Position'] = 'FLEX'
    export_data_pm['Team'] = 'MMA'
    export_data_pm['captain ownership'] = ''
    export_data_pm = export_data_pm.rename(columns={'Own': 'ownership', 'Median': 'median', 'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary'})
    
    reg_dl_col, pm_dl_col, blank_col = st.columns([2, 2, 6])
    with reg_dl_col:
        st.download_button(
                    label="Export ROO (Regular)",
                    data=convert_df_to_csv(export_data),
                    file_name='MMA_ROO_export.csv',
                    mime='text/csv',
        )
    with pm_dl_col:
        st.download_button(
                    label="Export ROO (Portfolio Manager)",
                    data=convert_df_to_csv(export_data_pm),
                    file_name='MMA_ROO_export.csv',
                    mime='text/csv',
        )

    with st.container():
        
        if view_var == "Simple":
            if type_var == "Full Slate":
                display = display[['Player', 'Salary', 'Median', '10x%', 'Own']]
                display = display.set_index('Player')
            elif type_var == "Showdown":
                display = display[['Player', 'Salary', 'Median', '5x%', 'Own']]
                display = display.set_index('Player')
            st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True)
        elif view_var == "Advanced":
            display = display
            display = display.set_index('Player')
            st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True)

if selected_tab == "Optimals":
    with st.expander("Info and Filters"):
        if st.button("Load/Reset Data", key='reset2'):
            st.cache_data.clear()
            roo_data = init_baselines()
            dk_id_dict = dict(zip(roo_data['Player'], roo_data['player_id']))
            dk_id_dict_sd = {}
            fd_id_dict = dict(zip(roo_data['Player'], roo_data['player_id']))
            fd_id_dict_sd = {}
            hold_display = roo_data
            dk_lineups = init_DK_lineups('Regular')
            fd_lineups = init_FD_lineups('Regular')
            for key in st.session_state.keys():
                del st.session_state[key]
        
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            slate_var1 = st.radio("Which data are you loading?", ('Regular'))
            if slate_var1 == 'Regular':
                if site_var == 'Draftkings':
                    dk_lineups = init_DK_lineups('Regular')
                    id_dict = dk_id_dict.copy()
                elif site_var == 'Fanduel':
                    fd_lineups = init_FD_lineups('Regular')
                    id_dict = fd_id_dict.copy()
            elif slate_var1 == 'Showdown':
                if site_var == 'Draftkings':
                    dk_lineups = init_DK_lineups('Showdown')
                    id_dict_sd = dk_id_dict_sd.copy()
                elif site_var == 'Fanduel':
                    fd_lineups = init_FD_lineups('Showdown')
                    id_dict_sd = fd_id_dict_sd.copy()

            if slate_var1 == 'Regular':
                raw_baselines = roo_data
            elif slate_var1 == 'Showdown':
                raw_baselines = pd.DataFrame()
            
            if site_var == 'Draftkings':
                if slate_var1 == 'Regular':
                    ROO_slice = raw_baselines
                    player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
                elif slate_var1 == 'Showdown':
                    player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
                # Get the minimum and maximum ownership values from dk_lineups
                min_own = np.min(dk_lineups[:,8])
                max_own = np.max(dk_lineups[:,8])
                column_names = dk_columns

            elif site_var == 'Fanduel':
                raw_baselines = hold_display
                if slate_var1 == 'Regular':
                    ROO_slice = raw_baselines
                    player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
                elif slate_var1 == 'Showdown':
                    player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
                min_own = np.min(fd_lineups[:,8])
                max_own = np.max(fd_lineups[:,8])
                column_names = fd_columns
        with col2:
            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
        
        with col3:
            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
            if player_var1 == 'Specific Players':
                    player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique())
            elif player_var1 == 'Full Slate':
                    player_var2 = raw_baselines.Player.values.tolist()
        
        with col4:
            if site_var == 'Draftkings':
                salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 50000, value = 49000, step = 100, key = 'salary_min_var')
                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 50000, value = 50000, step = 100, key = 'salary_max_var')
            elif site_var == 'Fanduel':
                salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 60000, value = 59000, step = 100, key = 'salary_min_var')
                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 60000, value = 60000, step = 100, key = 'salary_max_var')

        reg_dl_col, filtered_dl_col, blank_dl_col = st.columns([2, 2, 6])
        with reg_dl_col:
            if st.button("Prepare full data export", key='data_export'):
                name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                if site_var == 'Draftkings':
                    if slate_var1 == 'Regular':
                        map_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
                    elif slate_var1 == 'Showdown':
                        map_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
                elif site_var == 'Fanduel':
                    if slate_var1 == 'Regular':
                        map_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
                    elif slate_var1 == 'Showdown':
                        map_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
                for col_idx in map_columns:
                    if slate_var1 == 'Regular':
                        data_export[col_idx] = data_export[col_idx].map(id_dict)
                    elif slate_var1 == 'Showdown':
                        data_export[col_idx] = data_export[col_idx].map(id_dict_sd)

                pm_name_export = name_export.drop(columns=['salary', 'proj', 'Own'], axis=1)
                pm_data_export = data_export.drop(columns=['salary', 'proj', 'Own'], axis=1)
                reg_opt_col, pm_opt_col = st.columns(2)

                with reg_opt_col:
                    st.download_button(
                        label="Export optimals set (IDs)",
                        data=convert_df(data_export),
                        file_name='MMA_optimals_export.csv',
                        mime='text/csv',
                    )
                    st.download_button(
                        label="Export optimals set (Names)",
                        data=convert_df(name_export),
                        file_name='MMA_optimals_export.csv',
                        mime='text/csv',
                    )
                with pm_opt_col:
                    st.download_button(
                        label="Portfolio Manager Export (IDs)",
                        data=convert_pm_df(pm_data_export),
                        file_name='MMA_optimals_export.csv',
                        mime='text/csv',
                    )
                    st.download_button(
                        label="Portfolio Manager Export (Names)",
                        data=convert_pm_df(pm_name_export),
                        file_name='MMA_optimals_export.csv',
                        mime='text/csv',
                    )
        with filtered_dl_col:
            if st.button("Prepare full data export (Filtered)", key='data_export_filtered'):
                name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                if site_var == 'Draftkings':
                    if slate_var1 == 'Regular':
                        map_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
                    elif slate_var1 == 'Showdown':
                        map_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
                elif site_var == 'Fanduel':
                    if slate_var1 == 'Regular':
                        map_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
                    elif slate_var1 == 'Showdown':
                        map_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']
                for col_idx in map_columns:
                    if slate_var1 == 'Regular':
                        data_export[col_idx] = data_export[col_idx].map(id_dict)
                    elif slate_var1 == 'Showdown':
                        data_export[col_idx] = data_export[col_idx].map(id_dict_sd)
                data_export = data_export[data_export['salary'] >= salary_min_var]
                data_export = data_export[data_export['salary'] <= salary_max_var]

                name_export = name_export[name_export['salary'] >= salary_min_var]
                name_export = name_export[name_export['salary'] <= salary_max_var]
                
                pm_name_export = name_export.drop(columns=['salary', 'proj', 'Own'], axis=1)
                pm_data_export = data_export.drop(columns=['salary', 'proj', 'Own'], axis=1)

                reg_opt_col, pm_opt_col = st.columns(2)
                with reg_opt_col:
                    st.download_button(
                        label="Export optimals set (IDs)",
                        data=convert_df(data_export),
                        file_name='MMA_optimals_export.csv',
                        mime='text/csv',
                    )
                    st.download_button(
                        label="Export optimals set (Names)",
                        data=convert_df(name_export),
                        file_name='MMA_optimals_export.csv',
                        mime='text/csv',
                    )
                with pm_opt_col:
                    st.download_button(
                        label="Portfolio Manager Export (IDs)",
                        data=convert_pm_df(pm_data_export),
                        file_name='MMA_optimals_export.csv',
                        mime='text/csv',
                    )
                    st.download_button(
                        label="Portfolio Manager Export (Names)",
                        data=convert_pm_df(pm_name_export),
                        file_name='MMA_optimals_export.csv',
                        mime='text/csv',
                    )
        
    if site_var == 'Draftkings':
        if 'working_seed' in st.session_state:
            st.session_state.working_seed = st.session_state.working_seed
            if player_var1 == 'Specific Players':
                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
            elif player_var1 == 'Full Slate':
                st.session_state.working_seed = dk_lineups.copy()
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
        elif 'working_seed' not in st.session_state:
            st.session_state.working_seed = dk_lineups.copy()
            st.session_state.working_seed = st.session_state.working_seed
            if player_var1 == 'Specific Players':
                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
            elif player_var1 == 'Full Slate':
                st.session_state.working_seed = dk_lineups.copy()
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
        
    elif site_var == 'Fanduel':
        if 'working_seed' in st.session_state:
            st.session_state.working_seed = st.session_state.working_seed
            if player_var1 == 'Specific Players':
                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
            elif player_var1 == 'Full Slate':
                st.session_state.working_seed = fd_lineups.copy()
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
        elif 'working_seed' not in st.session_state:
            st.session_state.working_seed = fd_lineups.copy()
            st.session_state.working_seed = st.session_state.working_seed
            if player_var1 == 'Specific Players':
                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
            elif player_var1 == 'Full Slate':
                st.session_state.working_seed = fd_lineups.copy()
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)

    export_file = st.session_state.data_export_display.copy()
    # if site_var1 == 'Draftkings':
    #     for col_idx in range(6):
    #         export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
    # elif site_var1 == 'Fanduel':
    #     for col_idx in range(6):
    #         export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
            
    with st.container():
        if st.button("Reset Optimals", key='reset3'):
            for key in st.session_state.keys():
                del st.session_state[key]
            if site_var == 'Draftkings':
                st.session_state.working_seed = dk_lineups.copy()
            elif site_var == 'Fanduel':
                st.session_state.working_seed = fd_lineups.copy()
        
        st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'].between(salary_min_var, salary_max_var)]
        if 'data_export_display' in st.session_state:
            st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
        st.download_button(
            label="Export display optimals",
            data=convert_df(export_file),
            file_name='MMA_display_optimals.csv',
            mime='text/csv',
        )
    
    with st.container():
        if 'working_seed' in st.session_state:
            # Create a new dataframe with summary statistics
            if site_var == 'Draftkings':
                summary_df = pd.DataFrame({
                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                    'Salary': [
                        np.min(st.session_state.working_seed[:,6]),
                        np.mean(st.session_state.working_seed[:,6]),
                        np.max(st.session_state.working_seed[:,6]),
                        np.std(st.session_state.working_seed[:,6])
                    ],
                    'Proj': [
                        np.min(st.session_state.working_seed[:,7]),
                        np.mean(st.session_state.working_seed[:,7]),
                        np.max(st.session_state.working_seed[:,7]),
                        np.std(st.session_state.working_seed[:,7])
                    ],
                    'Own': [
                        np.min(st.session_state.working_seed[:,8]),
                        np.mean(st.session_state.working_seed[:,8]),
                        np.max(st.session_state.working_seed[:,8]),
                        np.std(st.session_state.working_seed[:,8])
                    ]
                })
            elif site_var == 'Fanduel':
                summary_df = pd.DataFrame({
                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                    'Salary': [
                        np.min(st.session_state.working_seed[:,6]),
                        np.mean(st.session_state.working_seed[:,6]),
                        np.max(st.session_state.working_seed[:,6]),
                        np.std(st.session_state.working_seed[:,6])
                    ],
                    'Proj': [
                        np.min(st.session_state.working_seed[:,7]),
                        np.mean(st.session_state.working_seed[:,7]),
                        np.max(st.session_state.working_seed[:,7]),
                        np.std(st.session_state.working_seed[:,7])
                    ],
                    'Own': [
                        np.min(st.session_state.working_seed[:,8]),
                        np.mean(st.session_state.working_seed[:,8]),
                        np.max(st.session_state.working_seed[:,8]),
                        np.std(st.session_state.working_seed[:,8])
                    ]
                })

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

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

    with st.container():
        tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
        with tab1:
            if 'data_export_display' in st.session_state:
                if site_var == 'Draftkings':
                    player_columns = st.session_state.data_export_display.iloc[:, :6]
                elif site_var == 'Fanduel':
                    player_columns = st.session_state.data_export_display.iloc[:, :6]
                
                # Flatten the DataFrame and count unique values
                value_counts = player_columns.values.flatten().tolist()
                value_counts = pd.Series(value_counts).value_counts()
                
                percentages = (value_counts / lineup_num_var * 100).round(2)
                
                # Create a DataFrame with the results
                summary_df = pd.DataFrame({
                    'Player': value_counts.index,
                    'Frequency': value_counts.values,
                    'Percentage': percentages.values
                })
                
                # Sort by frequency in descending order
                summary_df['Salary'] = summary_df['Player'].map(player_salaries)
                summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']]
                summary_df = summary_df.sort_values('Frequency', ascending=False)
                summary_df = summary_df.set_index('Player')
                
                # Display the table
                st.write("Player Frequency Table:")
                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
            
                st.download_button(
                    label="Export player frequency",
                    data=convert_df_to_csv(summary_df),
                    file_name='MMA_player_frequency.csv',
                    mime='text/csv',
                )
        with tab2:
            if 'working_seed' in st.session_state:
                if site_var == 'Draftkings':
                    player_columns = st.session_state.working_seed[:, :6]
                elif site_var == 'Fanduel':
                    player_columns = st.session_state.working_seed[:, :6]
                
                # Flatten the DataFrame and count unique values
                value_counts = player_columns.flatten().tolist()
                value_counts = pd.Series(value_counts).value_counts()
                
                percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
                # Create a DataFrame with the results
                summary_df = pd.DataFrame({
                    'Player': value_counts.index,
                    'Frequency': value_counts.values,
                    'Percentage': percentages.values
                })
                
                # Sort by frequency in descending order
                summary_df['Salary'] = summary_df['Player'].map(player_salaries)
                summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']]
                summary_df = summary_df.sort_values('Frequency', ascending=False)
                summary_df = summary_df.set_index('Player')
                
                # Display the table
                st.write("Seed Frame Frequency Table:")
                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
            
                st.download_button(
                    label="Export seed frame frequency",
                    data=convert_df_to_csv(summary_df),
                    file_name='MMA_seed_frame_frequency.csv',
                    mime='text/csv',
                )