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():
        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']

        credentials = {
          "type": "service_account",
          "project_id": "model-sheets-connect",
          "private_key_id": st.secrets['model_sheets_connect_pk'],
          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
          "client_email": "[email protected]",
          "client_id": "100369174533302798535",
          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
          "token_uri": "https://oauth2.googleapis.com/token",
          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
        }
        
        credentials2 = {
          "type": "service_account",
          "project_id": "sheets-api-connect-378620",
          "private_key_id": st.secrets['sheets_api_connect_pk'],
          "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
          "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
          "client_id": "106625872877651920064",
          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
          "token_uri": "https://oauth2.googleapis.com/token",
          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
        }

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

        gc = gspread.service_account_from_dict(credentials)
        gc2 = gspread.service_account_from_dict(credentials2)

        return gc, gc2, db, NBA_Data
    
gcservice_account, gcservice_account2, db, NBA_Data = init_conn()

dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', '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']

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

st.markdown("""
<style>
    /* Tab styling */
    .stTabs [data-baseweb="tab-list"] {
        gap: 8px;
        padding: 4px;
    }

    .stTabs [data-baseweb="tab"] {
        height: 50px;
        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;
    }

    .stTabs [aria-selected="true"] {
        background-color: #DAA520;
        border: 3px solid #FFD700;
        color: white;
    }

    .stTabs [data-baseweb="tab"]:hover {
        background-color: #FFD700;
        cursor: pointer;
    }
</style>""", unsafe_allow_html=True)

@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_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]

    collection = db["Range_Of_Outcomes_Backlog"] 
    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', 'Date']]
    roo_backlog = raw_display.sort_values(by='Date', ascending=False)
    roo_backlog = roo_backlog[roo_backlog['slate'] == 'Main Slate']
    
    return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog

@st.cache_data(ttl = 60)
def init_DK_lineups():  
        
        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)
    
        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_FD_lineups():  
        
        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)
    
        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

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, timestamp, roo_backlog = load_overall_stats()
salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))

try:
    dk_lineups = init_DK_lineups()
    fd_lineups = init_FD_lineups()
except:
    dk_lineups = pd.DataFrame(columns=dk_columns)
    fd_lineups = pd.DataFrame(columns=fd_columns)
t_stamp = f"Last Update: " + str(timestamp) + f" CST"

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

with st.sidebar:
    st.header("Quick Builder")
    st.info("This is a quick hand building helper to give you some basic info about player combos and lineup feasibility")
    sidebar_site = st.selectbox("What site are you running?", ('Draftkings', 'Fanduel'), key='sidebar_site')
    sidebar_slate = st.selectbox("What slate are you running?", ('Main Slate', 'Secondary Slate'), key='sidebar_slate')

    if sidebar_site == 'Draftkings':
        roo_sample = roo_raw[roo_raw['slate'] == str(sidebar_slate)]
        roo_sample = roo_sample[roo_sample['site'] == 'Draftkings']
        roo_sample = roo_sample.sort_values(by='Own', ascending=False)
        selected_pg = []
        selected_sg = []
        selected_sf = []
        selected_pf = []
        selected_c = []
        selected_g = []
        selected_f = []
        selected_flex = []
    elif sidebar_site == 'Fanduel':
        roo_sample = roo_raw[roo_raw['slate'] == str(sidebar_slate)]
        roo_sample = roo_sample[roo_sample['site'] == 'Fanduel']
        roo_sample = roo_sample.sort_values(by='Own', ascending=False)
        selected_pg1 = []
        selected_pg2 = []
        selected_sg1 = []
        selected_sg2 = []
        selected_sf1 = []
        selected_sf2 = []
        selected_pf1 = []
        selected_pf2 = []
        selected_c1 = []

    # Get unique players by position from dk_roo_raw
    pgs = roo_sample[roo_sample['Position'].str.contains('PG')]['Player'].unique()
    sgs = roo_sample[roo_sample['Position'].str.contains('SG')]['Player'].unique()
    sfs = roo_sample[roo_sample['Position'].str.contains('SF')]['Player'].unique()
    pfs = roo_sample[roo_sample['Position'].str.contains('PF')]['Player'].unique()
    centers = roo_sample[roo_sample['Position'].str.contains('C')]['Player'].unique()
    guards = roo_sample[roo_sample['Position'].str.contains('G')]['Player'].unique()
    forwards = roo_sample[roo_sample['Position'].str.contains('F')]['Player'].unique()
    flex = roo_sample['Player'].unique()

    if sidebar_site == 'Draftkings':
        selected_pgs = st.multiselect('Select PG:', list(pgs), default=None, placeholder='Select PG', label_visibility='collapsed', key='pg1')
        selected_sgs = st.multiselect('Select SG:', list(sgs), default=None, placeholder='Select SG', label_visibility='collapsed', key='sg1')
        selected_sfs = st.multiselect('Select SF:', list(sfs), default=None, placeholder='Select SF', label_visibility='collapsed', key='sf1')
        selected_pfs = st.multiselect('Select PF:', list(pfs), default=None, placeholder='Select PF', label_visibility='collapsed', key='pf1')
        selected_cs = st.multiselect('Select C:', list(centers), default=None, placeholder='Select C', label_visibility='collapsed', key='c1')
        selected_g = st.multiselect('Select G:', list(guards), default=None, placeholder='Select G', label_visibility='collapsed', key='g')
        selected_f = st.multiselect('Select F:', list(forwards), default=None, placeholder='Select F', label_visibility='collapsed', key='f')
        selected_flex = st.multiselect('Select Flex:', list(flex), default=None, placeholder='Select Flex', label_visibility='collapsed', key='flex')

        # Combine all selected players
        all_selected = selected_pgs + selected_sgs + selected_sfs + selected_pfs + selected_cs + selected_g + selected_f + selected_flex
    
    elif sidebar_site == 'Fanduel':
        selected_pg1 = st.multiselect('Select PG1:', list(pgs), default=None, placeholder='Select PG1', label_visibility='collapsed', key='pg1')
        selected_pg2 = st.multiselect('Select PG2:', list(pgs), default=None, placeholder='Select PG2', label_visibility='collapsed', key='pg2')
        selected_sg1 = st.multiselect('Select SG1:', list(sgs), default=None, placeholder='Select SG1', label_visibility='collapsed', key='sg1')
        selected_sg2 = st.multiselect('Select SG2:', list(sgs), default=None, placeholder='Select SG2', label_visibility='collapsed', key='sg2')
        selected_sf1 = st.multiselect('Select SF1:', list(sfs), default=None, placeholder='Select SF1', label_visibility='collapsed', key='sf1')
        selected_sf2 = st.multiselect('Select SF2:', list(sfs), default=None, placeholder='Select SF2', label_visibility='collapsed', key='sf2')
        selected_pf1 = st.multiselect('Select PF1:', list(pfs), default=None, placeholder='Select PF1', label_visibility='collapsed', key='pf1')
        selected_pf2 = st.multiselect('Select PF2:', list(pfs), default=None, placeholder='Select PF2', label_visibility='collapsed', key='pf2')
        selected_c1 = st.multiselect('Select C1:', list(centers), default=None, placeholder='Select C1', label_visibility='collapsed', key='c1')

        # Combine all selected players
        all_selected = selected_pg1 + selected_pg2 + selected_sg1 + selected_sg2 + selected_sf1 + selected_sf2 + selected_pf1 + selected_pf2 + selected_c1

    if all_selected:
        # Get stats for selected players
        selected_stats = roo_sample[roo_sample['Player'].isin(all_selected)]
        
        # Calculate sums
        salary_sum = selected_stats['Salary'].sum()
        median_sum = selected_stats['Median'].sum()
        own_sum = selected_stats['Own'].sum()
        levx_sum = selected_stats['LevX'].sum()

        # Display sums
        st.write('---')
        if sidebar_site == 'Draftkings':
            if salary_sum > 50000:
                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $50,000')
            else:
                st.write(f'Total Salary: ${salary_sum:.2f}')
        elif sidebar_site == 'Fanduel':
            if salary_sum > 60000:
                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $60,000')
            else:
                st.write(f'Total Salary: ${salary_sum:.2f}')
        st.write(f'Total Median: {median_sum:.2f}')
        st.write(f'Total Ownership: {own_sum:.2f}%')
        st.write(f'Total LevX: {levx_sum:.2f}')

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, timestamp, roo_backlog = load_overall_stats()
                    id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
                    dk_lineups = init_DK_lineups()
                    fd_lineups = init_FD_lineups()
                    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 = st.columns(4)
        with col1:
            view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
        with col2:
            site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')

            # Process site selection
            if site_var2 == 'Draftkings':
                site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
                site_backlog = roo_backlog[roo_backlog['site'] == 'Draftkings']
            elif site_var2 == 'Fanduel':
                site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
                site_backlog = roo_backlog[roo_backlog['site'] == 'Fanduel']
        with col3:
            slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary', 'Backlog'), key='slate_split')

            if slate_split == 'Main Slate':
                raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
            elif slate_split == 'Secondary':
                raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
            elif slate_split == 'Backlog':
                raw_baselines = site_backlog
                # Third row - backlog options
                col1, col2 = st.columns(2)
                with col1:
                    view_all = st.checkbox("View all dates?", key='view_all')
                with col2:
                    if not view_all:
                        date_var2 = st.date_input("Select date", key='date_var2')
                
                if view_all:
                    raw_baselines = raw_baselines.sort_values(by=['Median', 'Date'], ascending=[False, False])
                else:
                    raw_baselines = raw_baselines[raw_baselines['Date'] == date_var2.strftime('%m-%d-%Y')]
                    raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
        
        with col4:
            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=3000, max_value=15000, value=3000, step=100, key='low_salary')
        with col2:
            high_salary = st.number_input('Enter Highest Salary', min_value=3000, max_value=15000, value=15000, 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
        
    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, hide_index=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, timestamp, roo_backlog = load_overall_stats()
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            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', 'Just the Main Slate'))
        with col2:
            site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
        with col3:
            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 col4:
            if site_var1 == 'Draftkings':
                raw_baselines = dk_raw
                ROO_slice = roo_raw[roo_raw['site'] == 'Draftkings']
                id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
                # Get the minimum and maximum ownership values from dk_lineups
                min_own = np.min(dk_lineups[:,14])
                max_own = np.max(dk_lineups[:,14])
                column_names = dk_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':
                raw_baselines = fd_raw
                ROO_slice = roo_raw[roo_raw['site'] == 'Fanduel']
                id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
                min_own = np.min(fd_lineups[:,15])
                max_own = np.max(fd_lineups[:,15])
                column_names = fd_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()
        with col5:
            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 = 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_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 = 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(8):
            export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
    elif site_var1 == 'Fanduel':
        for col_idx in range(9):
            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_var1 == 'Draftkings':
                st.session_state.working_seed = dk_lineups.copy()
            elif site_var1 == 'Fanduel':
                st.session_state.working_seed = fd_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':
                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 site_var1 == 'Fanduel':
                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])
                    ]
                })

            # 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_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]
                
                # 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 site_var1 == 'Draftkings':
                    player_columns = st.session_state.working_seed[:, :8]
                elif site_var1 == 'Fanduel':
                    player_columns = st.session_state.working_seed[:, :9]
                
                # 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',
                )