app.py

import streamlit as st
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import pymongo

st.set_page_config(layout="wide")

@st.cache_resource
def init_conn():

        uri = st.secrets['mongo_uri']
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
        db = client["NBA_DFS"]

        return db
    
db = init_conn()

dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', '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_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', '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']

roo_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}

@st.cache_data(ttl=60)
def load_overall_stats():
    collection = db["DK_Player_Stats"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
    raw_display = raw_display.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    dk_raw = raw_display.sort_values(by='Median', ascending=False)
    
    collection = db["FD_Player_Stats"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
    raw_display = raw_display.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    fd_raw = raw_display.sort_values(by='Median', ascending=False)
    
    collection = db["Secondary_DK_Player_Stats"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
    raw_display = raw_display.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
    
    collection = db["Secondary_FD_Player_Stats"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
    raw_display = raw_display.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)

    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.rename(columns={"player_id": "player_ID"})
    raw_display = raw_display.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    sd_raw = raw_display.sort_values(by='Median', ascending=False)

    print(sd_raw.head(10))

    collection = db["Player_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.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    roo_raw = raw_display.sort_values(by='Median', ascending=False)

    timestamp = raw_display['timestamp'].values[0]
    
    return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp

@st.cache_data(ttl = 60)
def init_DK_lineups(slate_desig: str):  
        
        if slate_desig == 'Main Slate':
            collection = db['DK_NBA_name_map']
            cursor = collection.find()
            raw_data = pd.DataFrame(list(cursor))
            names_dict = dict(zip(raw_data['key'], raw_data['value']))
        
            collection = db["DK_NBA_seed_frame"] 
            cursor = collection.find().limit(10000)
        elif slate_desig == 'Secondary':
            collection = db['DK_NBA_Secondary_name_map']
            cursor = collection.find()
            raw_data = pd.DataFrame(list(cursor))
            names_dict = dict(zip(raw_data['key'], raw_data['value']))
        
            collection = db["DK_NBA_Secondary_seed_frame"] 
            cursor = collection.find().limit(10000)
        elif slate_desig == 'Auxiliary':
            collection = db['DK_NBA_Auxiliary_name_map']
            cursor = collection.find()
            raw_data = pd.DataFrame(list(cursor))
            names_dict = dict(zip(raw_data['key'], raw_data['value']))
        
            collection = db["DK_NBA_Auxiliary_seed_frame"] 
            cursor = collection.find().limit(10000) 

        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
        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_DK_SD_lineups(slate_desig: str):

        if slate_desig == 'Main Slate':
            collection = db["DK_NBA_SD_seed_frame"] 
        elif slate_desig == 'Secondary':
            collection = db["DK_NBA_Secondary_SD_seed_frame"] 
        elif slate_desig == 'Auxiliary':
            collection = db["DK_NBA_Auxiliary_SD_seed_frame"] 

        cursor = collection.find().limit(10000)
    
        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 = 60)
def init_FD_lineups(slate_desig: str):  
        
        if slate_desig == 'Main Slate':
            collection = db['FD_NBA_name_map']
            cursor = collection.find()
            raw_data = pd.DataFrame(list(cursor))
            names_dict = dict(zip(raw_data['key'], raw_data['value']))
        
            collection = db["FD_NBA_seed_frame"] 
            cursor = collection.find().limit(10000)
        elif slate_desig == 'Secondary':
            collection = db['FD_NBA_Secondary_name_map']
            cursor = collection.find()
            raw_data = pd.DataFrame(list(cursor))
            names_dict = dict(zip(raw_data['key'], raw_data['value']))
        
            collection = db["FD_NBA_Secondary_seed_frame"] 
            cursor = collection.find().limit(10000)
        elif slate_desig == 'Auxiliary':
            collection = db['FD_NBA_Auxiliary_name_map']
            cursor = collection.find()
            raw_data = pd.DataFrame(list(cursor))
            names_dict = dict(zip(raw_data['key'], raw_data['value']))
        
            collection = db["FD_NBA_Auxiliary_seed_frame"] 
            cursor = collection.find().limit(10000) 
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
        for col in dict_columns:
            raw_display[col] = raw_display[col].map(names_dict)
        FD_seed = raw_display.to_numpy()

        return FD_seed

@st.cache_data(ttl = 60)
def init_FD_SD_lineups(slate_desig: str):

        if slate_desig == 'Main Slate':
            collection = db["FD_NBA_SD_seed_frame"] 
        elif slate_desig == 'Secondary':
            collection = db["FD_NBA_Secondary_SD_seed_frame"] 
        elif slate_desig == 'Auxiliary':
            collection = db["FD_NBA_Auxiliary_SD_seed_frame"] 

        cursor = collection.find().limit(10000)
    
        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

def convert_df_to_csv(df):
    return df.to_csv().encode('utf-8')

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

dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
dk_lineups = pd.DataFrame(columns=dk_columns)
dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
fd_lineups = pd.DataFrame(columns=fd_columns)
fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
t_stamp = f"Last Update: " + str(timestamp) + f" CST"

tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals'])

with tab1:
    
    with st.expander("Info and Filters"):
        with st.container():
            st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
        with st.container():
            # First row - timestamp and reset button
            col1, col2 = st.columns([3, 1])
            with col1:
                st.info(t_stamp)
            with col2:
                if st.button("Load/Reset Data", key='reset1'):
                    st.cache_data.clear()
                    dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
                    salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
                    id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
                    salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
                    id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
                    dk_lineups = pd.DataFrame(columns=dk_columns)
                    dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
                    fd_lineups = pd.DataFrame(columns=fd_columns)
                    fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
                    t_stamp = f"Last Update: " + str(timestamp) + f" CST"
                    for key in st.session_state.keys():
                        del st.session_state[key]
        col1, col2, col3, col4, col5 = st.columns(5)
        with col1:
            view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
        with col2:
            slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
        with col3:
            site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')

            # Process site selection
            if site_var2 == 'Draftkings':
                if slate_type_var2 == 'Regular':
                    site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
                elif slate_type_var2 == 'Showdown':
                    site_baselines = sd_raw[sd_raw['site'] == 'Draftkings']
            elif site_var2 == 'Fanduel':
                if slate_type_var2 == 'Regular':
                    site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
                elif slate_type_var2 == 'Showdown':
                    site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
        with col4:
            slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split')

            if slate_split == 'Main Slate':
                if slate_type_var2 == 'Regular':
                    raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
                elif slate_type_var2 == 'Showdown':
                    raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1']
            elif slate_split == 'Secondary':
                if slate_type_var2 == 'Regular':
                    raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
                elif slate_type_var2 == 'Showdown':
                    raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
        
        with col5:
            split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2')
            if split_var2 == 'Specific Games':
                team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2')
            else:
                team_var2 = raw_baselines.Team.values.tolist()

        pos_var2 = st.selectbox('Position Filter', options=['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2')
        col1, col2 = st.columns(2)
        with col1:
            low_salary = st.number_input('Enter Lowest Salary', min_value=300, max_value=15000, value=300, step=100, key='low_salary')
        with col2:
            high_salary = st.number_input('Enter Highest Salary', min_value=300, max_value=25000, value=25000, step=100, key='high_salary')
    
    display_container_1 = st.empty()
    display_dl_container_1 = st.empty()
    display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
    display_proj = display_proj[display_proj['Salary'].between(low_salary, high_salary)]
    if view_var2 == 'Advanced':
        display_proj = display_proj[['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']]
    elif view_var2 == 'Simple':
        display_proj = display_proj[['Player', 'Position', 'Salary', 'Median', 'GPP%', 'Own']]
    export_data = display_proj.copy()

    # display_proj = display_proj.set_index('Player')
    st.session_state.display_proj = display_proj.set_index('Player', drop=True)
        
    with display_container_1:
        display_container = st.empty()
        if 'display_proj' in st.session_state:
            if pos_var2 == 'All':
                st.session_state.display_proj = st.session_state.display_proj
            elif pos_var2 != 'All':
                st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
            st.dataframe(st.session_state.display_proj.style.set_properties(**{'font-size': '6pt'}).background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2),
            height=1000, use_container_width = True)
    
    with display_dl_container_1:
            display_dl_container = st.empty()
            if 'display_proj' in st.session_state:
                st.download_button(
                            label="Export Tables",
                            data=convert_df_to_csv(export_data),
                            file_name='NBA_ROO_export.csv',
                            mime='text/csv',
                )

with tab2:
    with st.expander("Info and Filters"):
        if st.button("Load/Reset Data", key='reset2'):
            st.cache_data.clear()
            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
            salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
            id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
            salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
            id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
            dk_lineups = pd.DataFrame(columns=dk_columns)
            dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
            fd_lineups = pd.DataFrame(columns=fd_columns)
            fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
            t_stamp = f"Last Update: " + str(timestamp) + f" CST"
            for key in st.session_state.keys():
                del st.session_state[key]
    
        col1, col2, col3, col4, col5 = st.columns(5)
        with col1:
            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
        with col2:
            site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
            if 'working_seed' in st.session_state:
                del st.session_state['working_seed']
        with col3:
            slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
        with col4:
            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 col5:
            if site_var1 == 'Draftkings':
                if slate_type_var1 == 'Regular':
                    column_names = dk_columns
                elif slate_type_var1 == 'Showdown':
                    column_names = dk_sd_columns
                
                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 = dk_raw['Player'].unique())
                elif player_var1 == 'Full Slate':
                        player_var2 = dk_raw.Player.values.tolist()
                        
            elif site_var1 == 'Fanduel':
                if slate_type_var1 == 'Regular':
                    column_names = fd_columns
                elif slate_type_var1 == 'Showdown':
                    column_names = fd_sd_columns
                
                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 = fd_raw['Player'].unique())
                elif player_var1 == 'Full Slate':
                        player_var2 = fd_raw.Player.values.tolist()
        if st.button("Prepare data export", key='data_export'):
            data_export = st.session_state.working_seed.copy()
            if site_var1 == 'Draftkings':
                for col_idx in range(8):
                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
            elif site_var1 == 'Fanduel':
                for col_idx in range(9):
                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
            st.download_button(
                label="Export optimals set",
                data=convert_df(data_export),
                file_name='NBA_optimals_export.csv',
                mime='text/csv',
        )
            

    if site_var1 == '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 = st.session_state.working_seed
                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:
            if slate_type_var1 == 'Regular':
                st.session_state.working_seed = init_DK_lineups(slate_var1)
            elif slate_type_var1 == 'Showdown':
                st.session_state.working_seed = init_DK_SD_lineups(slate_var1)
            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':
                if slate_type_var1 == 'Regular':
                    st.session_state.working_seed = init_DK_lineups(slate_var1)
                elif slate_type_var1 == 'Showdown':
                    st.session_state.working_seed = init_DK_SD_lineups(slate_var1)
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
        
    elif site_var1 == '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 = st.session_state.working_seed
            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:
            if slate_type_var1 == 'Regular':
                st.session_state.working_seed = init_FD_lineups(slate_var1)
            elif slate_type_var1 == 'Showdown':
                st.session_state.working_seed = init_FD_SD_lineups(slate_var1)
            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':
                if slate_type_var1 == 'Regular':
                    st.session_state.working_seed = init_FD_lineups(slate_var1)
                elif slate_type_var1 == 'Showdown':
                    st.session_state.working_seed = init_FD_SD_lineups(slate_var1)
            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':
        if slate_type_var1 == 'Regular':
            for col_idx in range(8):
                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
        elif slate_type_var1 == 'Showdown':
            for col_idx in range(5):
                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict_sd)
    elif site_var1 == 'Fanduel':
        if slate_type_var1 == 'Regular':
            for col_idx in range(9):
                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
        elif slate_type_var1 == 'Showdown':
            for col_idx in range(5):
                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict_sd)
            
    with st.container():
        if st.button("Reset Optimals", key='reset3'):
            for key in st.session_state.keys():
                del st.session_state[key]
            if site_var1 == 'Draftkings':
                if slate_type_var1 == 'Regular':
                    st.session_state.working_seed = dk_lineups.copy()
                elif slate_type_var1 == 'Showdown':
                    st.session_state.working_seed = dk_sd_lineups.copy()
            elif site_var1 == 'Fanduel':
                if slate_type_var1 == 'Regular':
                    st.session_state.working_seed = fd_lineups.copy()
                elif slate_type_var1 == 'Showdown':
                    st.session_state.working_seed = fd_sd_lineups.copy()
        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='NBA_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_var1 == 'Draftkings':
                if slate_type_var1 == 'Regular':
                    summary_df = pd.DataFrame({
                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                        'Salary': [
                            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])
                        ],
                        'Proj': [
                            np.min(st.session_state.working_seed[:,9]),
                            np.mean(st.session_state.working_seed[:,9]),
                            np.max(st.session_state.working_seed[:,9]),
                            np.std(st.session_state.working_seed[:,9])
                        ],
                        'Own': [
                            np.min(st.session_state.working_seed[:,14]),
                            np.mean(st.session_state.working_seed[:,14]),
                            np.max(st.session_state.working_seed[:,14]),
                            np.std(st.session_state.working_seed[:,14])
                        ]
                    })
                elif slate_type_var1 == 'Showdown':
                    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[:,12]),
                            np.mean(st.session_state.working_seed[:,12]),
                            np.max(st.session_state.working_seed[:,12]),
                            np.std(st.session_state.working_seed[:,12])
                        ]
                    })
                
            elif site_var1 == 'Fanduel':
                if slate_type_var1 == 'Regular':
                    summary_df = pd.DataFrame({
                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                        'Salary': [
                            np.min(st.session_state.working_seed[:,9]),
                            np.mean(st.session_state.working_seed[:,9]),
                            np.max(st.session_state.working_seed[:,9]),
                            np.std(st.session_state.working_seed[:,9])
                        ],
                        'Proj': [
                            np.min(st.session_state.working_seed[:,10]),
                            np.mean(st.session_state.working_seed[:,10]),
                            np.max(st.session_state.working_seed[:,10]),
                            np.std(st.session_state.working_seed[:,10])
                        ],
                        'Own': [
                            np.min(st.session_state.working_seed[:,15]),
                            np.mean(st.session_state.working_seed[:,15]),
                            np.max(st.session_state.working_seed[:,15]),
                            np.std(st.session_state.working_seed[:,15])
                        ]
                    })
                elif slate_type_var1 == 'Showdown':
                    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[:,12]),
                            np.mean(st.session_state.working_seed[:,12]),
                            np.max(st.session_state.working_seed[:,12]),
                            np.std(st.session_state.working_seed[:,12])
                        ]
                    })

            # 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 slate_type_var1 == 'Regular':
                    if site_var1 == 'Draftkings':
                        player_columns = st.session_state.data_export_display.iloc[:, :8]
                    elif site_var1 == 'Fanduel':
                        player_columns = st.session_state.data_export_display.iloc[:, :9]
                elif slate_type_var1 == 'Showdown':
                    if site_var1 == 'Draftkings':
                        player_columns = st.session_state.data_export_display.iloc[:, :5]
                    elif site_var1 == 'Fanduel':
                        player_columns = st.session_state.data_export_display.iloc[:, :5]
                
                # 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,
                    'Salary': [salary_dict.get(player, player) for player in value_counts.index],
                    'Frequency': value_counts.values,
                    'Percentage': percentages.values                        
                })
                
                # Sort by frequency in descending order
                summary_df = summary_df.sort_values('Frequency', ascending=False)
                
                # Display the table
                st.write("Player Frequency Table:")
                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
            
                st.download_button(
                    label="Export player frequency",
                    data=convert_df_to_csv(summary_df),
                    file_name='NBA_player_frequency.csv',
                    mime='text/csv',
                )
        with tab2:
            if 'working_seed' in st.session_state:
                if slate_type_var1 == 'Regular':
                    if site_var1 == 'Draftkings':
                        player_columns = st.session_state.working_seed[:, :8]
                    elif site_var1 == 'Fanduel':
                        player_columns = st.session_state.working_seed[:, :9]
                elif slate_type_var1 == 'Showdown':
                    if site_var1 == 'Draftkings':
                        player_columns = st.session_state.working_seed[:, :5]
                    elif site_var1 == 'Fanduel':
                        player_columns = st.session_state.working_seed[:, :5]
                
                # 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,
                    'Salary': [salary_dict.get(player, player) for player in value_counts.index],
                    'Frequency': value_counts.values,
                    'Percentage': percentages.values                        
                })
                
                # Sort by frequency in descending order
                summary_df = summary_df.sort_values('Frequency', ascending=False)
                
                # Display the table
                st.write("Seed Frame Frequency Table:")
                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
            
                st.download_button(
                    label="Export seed frame frequency",
                    data=convert_df_to_csv(summary_df),
                    file_name='NBA_seed_frame_frequency.csv',
                    mime='text/csv',
                )