import streamlit as st
import numpy as np
import pandas as pd
import streamlit as st
import unicodedata
import math
import re
from database import db, wnba_db

st.set_page_config(layout="wide")

dk_nba_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
dk_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_nba_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']

dk_wnba_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
dk_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_wnba_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']

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

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

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

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 */
    .stElementContainer [data-baseweb="button-group"] {
        gap: 2.000rem;
        padding: 4px;
    }
    .stElementContainer [kind="segmented_control"] {
        height: 2.000rem;
        white-space: pre-wrap;
        background-color: #DAA520;
        color: white;
        border-radius: 20px;
        gap: 1px;
        padding: 10px 20px;
        font-weight: bold;
        transition: all 0.3s ease;
    }
    .stElementContainer [kind="segmented_controlActive"] {
        height: 3.000rem;
        background-color: #DAA520;
        border: 3px solid #FFD700;
        border-radius: 10px;
        color: black;
    }
    .stElementContainer [kind="segmented_control"]:hover {
        background-color: #FFD700;
        cursor: pointer;
    }

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

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

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

    slate_names = []

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

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

    slate_names = []

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

@st.cache_data(ttl=60)
def init_baselines(type_var: str, league: str):

    if type_var == 'Showdown':
        if league == 'NBA':
            collection = db["Player_SD_Range_Of_Outcomes"] 
        elif league == 'WNBA':
            collection = wnba_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['Median'] = raw_display['Median'].replace('', 0).astype(float)
        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)
        dk_sd_roo_raw = sd_raw[sd_raw['site'] == 'Draftkings']
        dk_sd_id_map = dict(zip(dk_sd_roo_raw['Player'], dk_sd_roo_raw['player_ID']))
        fd_sd_roo_raw = sd_raw[sd_raw['site'] == 'Fanduel']
        fd_sd_id_map = dict(zip(fd_sd_roo_raw['Player'], fd_sd_roo_raw['player_ID']))
        fd_sd_roo_raw['player_ID'] = fd_sd_roo_raw['player_ID'].astype(str)
        fd_sd_roo_raw['player_ID'] = fd_sd_roo_raw['player_ID'].str.rsplit('-', n=1).str[0].astype(str)

        timestamp = sd_raw['timestamp'].values[0]

        roo_raw = None
        dk_roo_raw = None
        fd_roo_raw = None
        dk_id_map = None
        fd_id_map = None

    else:
        if league == 'NBA':
            collection = db["Player_Range_Of_Outcomes"] 
        elif league == 'WNBA':
            collection = wnba_db["Player_Range_Of_Outcomes"] 
        cursor = collection.find()

        raw_display = pd.DataFrame(list(cursor))
        try:
            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']]
        except:
            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['Median'] = raw_display['Median'].replace('', 0).astype(float)
        raw_display = raw_display.loc[raw_display['Median'] > 0]
        dk_roo_raw = raw_display[raw_display['site'] == 'Draftkings']
        fd_roo_raw = raw_display[raw_display['site'] == 'Fanduel']
        dk_id_map = dict(zip(dk_roo_raw['Player'], dk_roo_raw['player_ID']))
        fd_id_map = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_ID']))
        raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
        roo_raw = raw_display.sort_values(by='Median', ascending=False)

        timestamp = roo_raw['timestamp'].values[0]

        sd_raw = None
        dk_sd_roo_raw = None
        fd_sd_roo_raw = None
        dk_sd_id_map = None
        fd_sd_id_map = None
    
    return roo_raw, dk_roo_raw, fd_roo_raw, sd_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map, timestamp

@st.cache_resource(ttl = 60)
def init_DK_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lineup_num, player_var2):  

    if prio_var == 'Mix':
        prio_var = None
    
    if type_var == 'Regular':
        if slate_var == 'Main':
            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_Main Slate']
            if prio_var == None:
                if player_var2 != []:
                    player_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
                    query_conditions = []
                    
                    for player in player_var2:
                        # Create a condition for each player to check if they appear in any column
                        player_condition = {'$or': [{col: player} for col in player_columns]}
                        query_conditions.append(player_condition)
                    
                    # Combine all player conditions with $or
                    if query_conditions:
                        filter_query = {'$or': query_conditions}
                        cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find(filter_query).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    else:
                        cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
                else:
                    cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
            else:
                cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                raw_display = pd.DataFrame(list(cursor))
            
            raw_display = raw_display.drop_duplicates(subset=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX'])

            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']
            # Map names
            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
        elif slate_var == '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_seed_frame_Secondary Slate']
            if prio_var == None:
                if player_var2 != []:
                    player_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
                    query_conditions = []
                    
                    for player in player_var2:
                        # Create a condition for each player to check if they appear in any column
                        player_condition = {'$or': [{col: player} for col in player_columns]}
                        query_conditions.append(player_condition)
                    
                    # Combine all player conditions with $or
                    if query_conditions:
                        filter_query = {'$or': query_conditions}
                        cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find(filter_query).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    else:
                        cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
                else:
                    cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
            else:
                cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                raw_display = pd.DataFrame(list(cursor))

            raw_display = raw_display.drop_duplicates(subset=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX'])

            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']
            # Map names
            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
        elif slate_var == 'Auxiliary':
            collection = db['DK_NBA_Late_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_Late Slate']
            if prio_var == None:
                if player_var2 != []:
                    player_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
                    query_conditions = []
                    
                    for player in player_var2:
                        # Create a condition for each player to check if they appear in any column
                        player_condition = {'$or': [{col: player} for col in player_columns]}
                        query_conditions.append(player_condition)
                    
                    # Combine all player conditions with $or
                    if query_conditions:
                        filter_query = {'$or': query_conditions}
                        cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find(filter_query).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    else:
                        cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
                else:
                    cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
            else:
                cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                raw_display = pd.DataFrame(list(cursor))

            raw_display = raw_display.drop_duplicates(subset=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX'])

            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']
            # Map names
            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
    elif type_var == 'Showdown':
        collection = db[db_translation[slate_var]]
        if prio_var == None:
            if player_var2 != []:
                player_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                query_conditions = []
                
                for player in player_var2:
                    # Create a condition for each player to check if they appear in any column
                    player_condition = {'$or': [{col: player} for col in player_columns]}
                    query_conditions.append(player_condition)
                
                # Combine all player conditions with $or
                if query_conditions:
                    filter_query = {'$or': query_conditions}
                    cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find(filter_query).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                else:
                    cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
            else:
                cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
        else:
            cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
            raw_display = pd.DataFrame(list(cursor))

        raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])

        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_resource(ttl = 60)
def init_FD_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lineup_num, player_var2):

    if prio_var == 'Mix':
        prio_var = None
    
    if type_var == 'Regular':
        if slate_var == 'Main':
            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_Main Slate']
            if prio_var == None:
                if player_var2 != []:
                    player_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
                    query_conditions = []
                    
                    for player in player_var2:
                        # Create a condition for each player to check if they appear in any column
                        player_condition = {'$or': [{col: player} for col in player_columns]}
                        query_conditions.append(player_condition)
                    
                    # Combine all player conditions with $or
                    if query_conditions:
                        filter_query = {'$or': query_conditions}
                        cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find(filter_query).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    else:
                        cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
                else:
                    cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
            else:
                cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                raw_display = pd.DataFrame(list(cursor))

            raw_display = raw_display.drop_duplicates(subset=['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C'])

            raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
            dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
            # Map names
            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
        elif slate_var == '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_Secondary Slate']
            if prio_var == None:
                if player_var2 != []:
                    player_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
                    query_conditions = []
                    
                    for player in player_var2:
                        # Create a condition for each player to check if they appear in any column
                        player_condition = {'$or': [{col: player} for col in player_columns]}
                        query_conditions.append(player_condition)
                    
                    # Combine all player conditions with $or
                    if query_conditions:
                        filter_query = {'$or': query_conditions}
                        cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find(filter_query).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    else:
                        cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
                else:
                    cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
            else:
                cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                raw_display = pd.DataFrame(list(cursor))

            raw_display = raw_display.drop_duplicates(subset=['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C'])

            raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
            dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
            # Map names
            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
        elif slate_var == 'Auxiliary':
            collection = db['FD_NBA_Late_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_Late_seed_frame_Late Slate']
            if prio_var == None:
                if player_var2 != []:
                    player_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
                    query_conditions = []

                    for player in player_var2:
                        # Create a condition for each player to check if they appear in any column
                        player_condition = {'$or': [{col: player} for col in player_columns]}
                        query_conditions.append(player_condition)

                    # Combine all player conditions with $or
                    if query_conditions:
                        filter_query = {'$or': query_conditions}
                        cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find(filter_query).sort('Own', -1).limit(math.ceil(lineup_num = ((100 - prio_mix) / 100)))
                    else:
                        cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                        cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
                else:
                    cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
            else:
                cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                raw_display = pd.DataFrame(list(cursor))

            raw_display = raw_display.drop_duplicates(subset=['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C'])

            raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
            dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
            # Map names
            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))

    elif type_var == 'Showdown':
        collection = db[db_translation[slate_var]]
        if prio_var == None:
            if player_var2 != []:
                player_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                query_conditions = []
                
                for player in player_var2:
                    # Create a condition for each player to check if they appear in any column
                    player_condition = {'$or': [{col: player} for col in player_columns]}
                    query_conditions.append(player_condition)
                
                # Combine all player conditions with $or
                if query_conditions:
                    filter_query = {'$or': query_conditions}
                    cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find(filter_query).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                else:
                    cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
            else:
                cursor1 = collection.find().limit(math.ceil(lineup_num * (prio_mix / 100)))
                cursor2 = collection.find().sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
        else:
            cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
            raw_display = pd.DataFrame(list(cursor))

        raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])

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

    FD_seed = raw_display.to_numpy()

    return FD_seed

@st.cache_data
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

@st.cache_data
def map_mask_parse(df: pd.DataFrame, map: dict, threshold: float, site_var: str):
    print(df.iloc[:, :-7].head(10))
    if site_var == 'Draftkings':
        proj_df = df.iloc[:, :-7].replace(map).astype(float)
    elif site_var == 'Fanduel':
        proj_df = df.iloc[:, :-7].replace(map).astype(float)
    print(proj_df.head(10))
    mask = (proj_df >= threshold).all(axis=1)
    df = df[mask]
    return df

@st.cache_data
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')

@st.cache_data
def convert_pm_df(array):
    array = pd.DataFrame(array)
    return array.to_csv().encode('utf-8')

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

try:
    slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
except:
    slate_names_dk = []
    slate_name_lookup_dk = {}
    
try:
    slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
except:
    slate_names_fd = []
    slate_name_lookup_fd = {}

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_raw, dk_roo_raw, fd_roo_raw, sd_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map, timestamp = init_baselines('Regular', 'NBA')
        try:
            slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
        except:
            slate_names_dk = []
            slate_name_lookup_dk = {}
            
        try:
            slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
        except:
            slate_names_fd = []
            slate_name_lookup_fd = {}
        dk_lineups = init_DK_lineups('Regular', 'Main', 'proj', 50, dk_showdown_db_translation, 25000, [])
        fd_lineups = init_FD_lineups('Regular', 'Main', 'proj', 50, fd_showdown_db_translation, 25000, [])
        for key in st.session_state.keys():
            del st.session_state[key]
with app_view_site_column:
    with st.container():
        app_view_column, sport_select_column, app_site_column, app_type_column = st.columns([2, 2, 2, 2])
        with app_view_column:
            view_var = st.selectbox("Select view", ["Simple", "Advanced"], key='view_selectbox')
        with sport_select_column:
            sport_var = st.selectbox("What league do you want to view?", ('NBA', 'WNBA'), key='sport_selectbox')
        with app_site_column:
            site_var = st.selectbox("What site do you want to view?", ('Draftkings', 'Fanduel'), key='site_selectbox')
        with app_type_column:
            type_var = st.selectbox("What type of data do you want to view?", ('Regular', 'Showdown'), key='type_selectbox')

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

if selected_tab == 'Handbuilder':

    if 'handbuilder_data' not in st.session_state:
        roo_raw, dk_roo_raw, fd_roo_raw, sd_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map, timestamp = init_baselines('Regular', 'NBA')
        st.session_state['handbuilder_data'] = roo_raw
        t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    else:
        pass
    
    # Use the lightweight handbuilder data
    if site_var == 'Draftkings':
        handbuild_roo = st.session_state['handbuilder_data'][st.session_state['handbuilder_data']['site'] == 'Draftkings']
    else:
        handbuild_roo = st.session_state['handbuilder_data'][st.session_state['handbuilder_data']['site'] == 'Fanduel']
    
    handbuilder_header_column, handbuilder_slate_column = st.columns(2)
    with handbuilder_header_column:
        st.header("Handbuilder")
    with handbuilder_slate_column:
        slate_var3 = st.selectbox("Slate Selection", options=['Main', 'Secondary', 'Auxiliary'], key='handbuilder_slate_selectbox')
        if site_var == 'Draftkings':
            if slate_var3 == 'Main':
                handbuild_roo = handbuild_roo[handbuild_roo['slate'] == 'Main Slate']
            elif slate_var3 == 'Secondary':
                handbuild_roo = handbuild_roo[handbuild_roo['slate'] == 'Secondary Slate']
            elif slate_var3 == 'Auxiliary':
                handbuild_roo = handbuild_roo[handbuild_roo['slate'] == 'Late Slate']
        elif site_var == 'Fanduel':
            if slate_var3 == 'Main':
                handbuild_roo = handbuild_roo[handbuild_roo['slate'] == 'Main Slate']
            elif slate_var3 == 'Secondary':
                handbuild_roo = handbuild_roo[handbuild_roo['slate'] == 'Secondary Slate']
            elif slate_var3 == 'Auxiliary':
                handbuild_roo = handbuild_roo[handbuild_roo['slate'] == 'Late Slate']

    # --- POSITION LIMITS ---
    if site_var == 'Draftkings':
        position_limits = {
            'PG': 1,
            'SG': 1,
            'SF': 1,
            'PF': 1,
            'C': 1,
            'G': 1,
            'F': 1,
            'FLEX': 1,
        }
        max_salary = 50000
        max_players = 8
    else:
        position_limits = {
            'PG': 2,
            'SG': 2,
            'SF': 2,
            'PF': 2,
            'C': 1,
        }
        max_salary = 60000
        max_players = 9

    # --- LINEUP STATE ---
    if 'handbuilder_lineup' not in st.session_state:
        st.session_state['handbuilder_lineup'] = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own'])

    # Count positions in the current lineup
    lineup = st.session_state['handbuilder_lineup']
    slot_counts = lineup['Slot'].value_counts() if not lineup.empty else {}

    # --- PLAYER FILTERS ---
    with st.expander("Player Filters"):
        handbuilder_player_filters_column, handbuilder_player_filters_salary_column = st.columns(2)
        with handbuilder_player_filters_column:
            pos_select3 = st.multiselect("Select your position(s)", options=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX'] if site_var == 'Draftkings' else ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_select3_multiselect')
        with handbuilder_player_filters_salary_column:
            salary_var = st.number_input("Salary Max", min_value = 0, max_value = 15000, value = 15000, step = 100, key='handbuilder_salary_max_input')

    # --- TEAM FILTER UI ---
    with st.expander("Team Filters"):
        all_teams = sorted(handbuild_roo['Team'].unique())
        st.markdown("**Toggle teams to include:**")
        team_cols = st.columns(len(all_teams) // 2 + 1)
    
    selected_teams = []
    for idx, team in enumerate(all_teams):
        col = team_cols[idx % len(team_cols)]
        if f"handbuilder_team_{team}" not in st.session_state:
            st.session_state[f"handbuilder_team_{team}"] = False
        checked = col.toggle(team, value=st.session_state[f"handbuilder_team_{team}"], key=f"handbuilder_team_{team}_toggle")
        if checked:
            selected_teams.append(team)

    # If no teams selected, show all teams
    if selected_teams:
        st.session_state['player_select_df'] = handbuild_roo[
            handbuild_roo['Team'].isin(selected_teams)
        ][['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
    else:
        st.session_state['player_select_df'] = handbuild_roo[['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
    
    # If no teams selected, show all teams
    if pos_select3:
        position_mask_2 = handbuild_roo['Position'].apply(lambda x: any(pos in x for pos in pos_select3))
        st.session_state['player_select_df'] = st.session_state['player_select_df'][position_mask_2][['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
    else:
        st.session_state['player_select_df'] = st.session_state['player_select_df'][['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
    
    st.session_state['player_select_df'] = st.session_state['player_select_df'][st.session_state['player_select_df']['Salary'] <= salary_var]

    # --- FILTER OUT PLAYERS WHOSE ALL ELIGIBLE POSITIONS ARE FILLED ---
    def is_player_eligible(row):
        eligible_positions = re.split(r'[/, ]+', row['Position'])
        # Player is eligible if at least one of their positions is not at max
        for pos in eligible_positions:
            if slot_counts.get(pos, 0) < position_limits.get(pos, 0):
                return True
        return False

    # st.session_state['player_select_df'] = st.session_state['player_select_df'][st.session_state['player_select_df'].apply(is_player_eligible, axis=1)]
    print(st.session_state['player_select_df'].head(10))

    handbuilder_lineup_build_column, handbuilder_player_select_column = st.columns([1, 2])
    with handbuilder_player_select_column:
        st.subheader("Player Select")
        
        # Display player selection dataframe with single row selection
        event = st.dataframe(
            st.session_state['player_select_df'].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Salary', 'Own']).format(precision=2),
            on_select="rerun",
            selection_mode="single-row",
            key="player_select_dataframe",
            height=500,
            hide_index=True
        )
        
        # Add Player button
        if st.button("Add Selected Player to Lineup", key="add_player_button"):
            if event and "rows" in event.selection and len(event.selection["rows"]) > 0:
                idx = event.selection["rows"][0]
                player_row = st.session_state['player_select_df'].iloc[[idx]]
                eligible_positions = re.split(r'[/, ]+', player_row['Position'].iloc[0])
                
                # Find the first eligible slot that is not full
                slot_to_fill = None
                player_position_string = player_row['Position'].iloc[0]
                
                if site_var == 'Draftkings':
                    # DraftKings NBA slots
                    for slot in ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']:
                        if slot_counts.get(slot, 0) < position_limits.get(slot, 0):
                            if slot == 'FLEX':
                                # FLEX can be any position
                                slot_to_fill = slot
                                break
                            elif slot == 'G':
                                # G can be PG or SG
                                if 'PG' in player_position_string or 'SG' in player_position_string:
                                    slot_to_fill = slot
                                    break
                            elif slot == 'F':
                                # F can be SF or PF
                                if 'SF' in player_position_string or 'PF' in player_position_string:
                                    slot_to_fill = slot
                                    break
                            elif slot in player_position_string:
                                # Explicit positions (PG, SG, SF, PF, C) - check if slot appears in position string
                                slot_to_fill = slot
                                break
                else:
                    # FanDuel NBA slots
                    for slot in ['PG', 'SG', 'SF', 'PF', 'C']:
                        if slot_counts.get(slot, 0) < position_limits.get(slot, 0):
                            if slot in player_position_string:
                                slot_to_fill = slot
                                break

                if slot_to_fill is not None:
                    # Avoid duplicates
                    if not player_row['Player'].iloc[0] in st.session_state['handbuilder_lineup']['Player'].values:
                        # Add the slot info
                        player_row = player_row.assign(Slot=slot_to_fill)
                        st.session_state['handbuilder_lineup'] = pd.concat(
                            [st.session_state['handbuilder_lineup'], player_row[['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own', 'Slot']]],
                            ignore_index=True
                        )
                        st.success(f"Added {player_row['Player'].iloc[0]} to {slot_to_fill} slot")
                    else:
                        st.warning(f"{player_row['Player'].iloc[0]} is already in the lineup")
                else:
                    st.error("No available slots for this player")
            else:
                st.warning("Please select a player first")

    with handbuilder_lineup_build_column:
        st.subheader("Lineup Build")

        # --- EXPLICIT LINEUP ORDER ---
        if site_var == 'Draftkings':
            lineup_slots = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
        else:
            lineup_slots = ['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C']
        display_columns = ['Slot', 'Player', 'Position', 'Team', 'Salary', 'Median', 'Own']

        filled_lineup = st.session_state['handbuilder_lineup']
        display_rows = []
        used_indices = set()
        if not filled_lineup.empty:
            for slot in lineup_slots:
                match = filled_lineup[(filled_lineup['Slot'] == slot) & (~filled_lineup.index.isin(used_indices))]
                if not match.empty:
                    row = match.iloc[0]
                    used_indices.add(match.index[0])
                    display_rows.append({
                        'Slot': slot,
                        'Player': row['Player'],
                        'Position': row['Position'],
                        'Team': row['Team'],
                        'Salary': row['Salary'],
                        'Median': row['Median'],
                        '4x%': row['4x%'],
                        'Own': row['Own']
                    })
                else:
                    display_rows.append({
                        'Slot': slot,
                        'Player': '',
                        'Position': '',
                        'Team': '',
                        'Salary': np.nan,
                        'Median': np.nan,
                        '4x%': np.nan,
                        'Own': np.nan
                    })

        st.session_state['lineup_display_df'] = pd.DataFrame(display_rows, columns=display_columns)

        # Show the lineup table as a static display
        st.dataframe(
            st.session_state['lineup_display_df'].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn', subset=['Median']).background_gradient(cmap='RdYlGn_r', subset=['Salary', 'Own']).format(precision=2),
            height=445,
            hide_index=True
        )

        # --- SUMMARY ROW ---
        if not filled_lineup.empty:
            total_salary = filled_lineup['Salary'].sum()
            total_median = filled_lineup['Median'].sum()
            avg_4x = filled_lineup['4x%'].mean()
            total_own = filled_lineup['Own'].sum()
            most_common_team = filled_lineup['Team'].mode()[0] if not filled_lineup['Team'].mode().empty else ""

            summary_row = pd.DataFrame({
                'Slot': [''],
                'Player': ['TOTAL'],
                'Position': [''],
                'Team': [most_common_team],
                'Salary': [total_salary],
                'Median': [total_median],
                '4x%': [avg_4x],
                'Own': [total_own]
            })
            summary_row = summary_row[['Salary', 'Median', 'Own']].head(max_players)
            
            handbuilder_lineup_build_salary_column, handbuilder_lineup_build_median_column = st.columns([2, 3])

            with handbuilder_lineup_build_salary_column:
                if (max_players - len(filled_lineup)) > 0:
                    st.markdown(f"""
                            <div style='text-align: left; vertical-align: top; margin-top: 0; padding-top: 0;''>
                                <b>💰 Per Player:</b> ${round((max_salary - total_salary) / (max_players - len(filled_lineup)), 0)} &nbsp;
                            </div>
                            """,
                            unsafe_allow_html=True)
                else:
                    st.markdown(f"""
                            <div style='text-align: left; vertical-align: top; margin-top: 0; padding-top: 0;''>
                                <b>💰 Leftover:</b> ${round(max_salary - total_salary, 0)} &nbsp;
                            </div>
                            """,
                            unsafe_allow_html=True)

            with handbuilder_lineup_build_median_column:
                if total_salary <= max_salary:
                    st.markdown(
                        f"""
                        <div style='text-align: right; vertical-align: top; margin-top: 0; padding-top: 0;''>
                            <b>💰 Salary:</b> ${round(total_salary, 0)} &nbsp;
                            <b>🔥 Median:</b> {round(total_median, 2)} &nbsp;
                        </div>
                        """,
                        unsafe_allow_html=True
                    )
                else:
                    st.markdown(
                        f"""
                        <div style='text-align: right; vertical-align: top; margin-top: 0; padding-top: 0;''>
                            <b>❌ Salary:</b> ${round(total_salary, 0)} &nbsp;
                            <b>🔥 Median:</b> {round(total_median, 2)} &nbsp;
                        </div>
                        """,
                        unsafe_allow_html=True
                    )
        # Remove Player button
        if not filled_lineup.empty:
            players_to_remove = st.multiselect(
                "Select players to remove:",
                options=filled_lineup['Player'].tolist(),
                key="remove_player_multiselect"
            )
            if st.button("Confirm Remove", key="confirm_remove_button"):
                st.session_state['handbuilder_lineup'] = st.session_state['handbuilder_lineup'][
                    ~st.session_state['handbuilder_lineup']['Player'].isin(players_to_remove)
                ]
                if players_to_remove:
                    st.success(f"Removed {', '.join(players_to_remove)} from lineup")
                else:
                    st.warning("No players selected for removal")
                st.rerun()

    # Optionally, add a button to clear the lineup
    clear_col, save_col, export_col, clear_saved_col, blank_col = st.columns([2, 2, 2, 2, 12])
    with clear_col:
        if st.button("Clear Lineup", key='clear_lineup_button'):
            st.session_state['handbuilder_lineup'] = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own', 'Slot'])
            # Clear the dataframe selections by resetting the previous selection state
            st.session_state['previous_player_selection'] = []
            # Force dataframe to re-render with new key to clear selections
            st.session_state['dataframe_key_counter'] = st.session_state.get('dataframe_key_counter', 0) + 1
            st.rerun()
    with save_col:
        if st.button("Save Lineup", key='save_lineup_button'):
            if 'saved_lineups' in st.session_state:
                st.session_state['saved_lineups'].append(st.session_state['lineup_display_df']['Player'].tolist())
                print(st.session_state['saved_lineups'])
            else:
                st.session_state['saved_lineups'] = [st.session_state['lineup_display_df']['Player'].tolist()]
                print(st.session_state['saved_lineups'])
    with export_col:
        if 'saved_lineups' in st.session_state and st.session_state['saved_lineups']:
            # Convert list of lists to numpy array
            saved_lineups_array = np.array(st.session_state['saved_lineups'])
            st.download_button(
                label="Export Handbuilds",
                data=convert_hb_df(saved_lineups_array, dk_hb_columns if site_var == 'Draftkings' else fd_hb_columns),
                file_name='handbuilds_export.csv',
                mime='text/csv',
                key='export_handbuilds_button'
            )
        else:
            st.write("No saved lineups to export")
    
    if 'saved_lineups' in st.session_state:
        st.table(pd.DataFrame(st.session_state['saved_lineups'], columns=dk_hb_columns if site_var == 'Draftkings' else fd_hb_columns))
    else:
        st.write("No saved lineups")
    
    with clear_saved_col:
        if st.button("Clear Saved", key='clear_saved_button'):
            if 'saved_lineups' in st.session_state:
                del st.session_state['saved_lineups']

if selected_tab == 'Player ROO':
    roo_raw, dk_roo_raw, fd_roo_raw, sd_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map, timestamp = init_baselines(type_var, sport_var)
    
    t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    
    with st.expander("Info and Filters"):
        col1, col2, col3 = st.columns(3)
        
        with col1:
            if site_var == 'Draftkings':
                slate_var2 = st.radio("Which slate data are you loading?", (slate_names_dk if type_var == 'Showdown' else ['Main Slate', 'Secondary Slate', 'Late Slate']), key='slate_var3_radio')
            elif site_var == 'Fanduel':
                slate_var2 = st.radio("Which slate data are you loading?", (slate_names_fd if type_var == 'Showdown' else ['Main Slate', 'Secondary Slate', 'Late Slate']), key='slate_var3_radio')

            if type_var == 'Regular':
                if slate_var2 == 'Main Slate':
                    if site_var == 'Draftkings':
                        site_baselines = dk_roo_raw
                        raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
                    elif site_var == 'Fanduel':
                        site_baselines = fd_roo_raw
                        raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
                    
                elif slate_var2 == 'Secondary Slate':
                    if site_var == 'Draftkings':
                        site_baselines = dk_roo_raw
                        raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
                    elif site_var == 'Fanduel':
                        site_baselines = fd_roo_raw
                        raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']

                elif slate_var2 == 'Late Slate':
                    if site_var == 'Draftkings':
                        site_baselines = dk_roo_raw
                        raw_baselines = site_baselines[site_baselines['slate'] == 'Late Slate']
                    elif site_var == 'Fanduel':
                        site_baselines = fd_roo_raw
                        raw_baselines = site_baselines[site_baselines['slate'] == 'Late Slate']
                        
            elif type_var == 'Showdown':
                if site_var == 'Draftkings':
                    site_baselines = dk_sd_roo_raw
                    raw_baselines = site_baselines[site_baselines['slate'] == slate_name_lookup_dk[slate_var2]]
                elif site_var == 'Fanduel':
                    site_baselines = fd_sd_roo_raw
                    raw_baselines = site_baselines[site_baselines['slate'] == slate_name_lookup_fd[slate_var2]]
        
        with col3:
            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_var == '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_var == 'Simple':
        display_proj = display_proj[['Player', 'Position', 'Salary', 'Median', 'GPP%', 'Own']]
    export_data = raw_baselines.copy()
    export_data_pm = raw_baselines[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own']]
    export_data_pm = export_data_pm.rename(columns={'Own': 'ownership', 'Median': 'median', 'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'CPT_Own': 'captain ownership'})

    # display_proj = display_proj.set_index('Player')
    st.session_state.display_proj = display_proj.set_index('Player', drop=True)

    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='NBA_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='NBA_ROO_export.csv',
                    mime='text/csv',
        )
        
    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)
    
    

if selected_tab == 'Optimals':
    roo_raw, dk_roo_raw, fd_roo_raw, sd_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map, timestamp = init_baselines(type_var, sport_var)
    
    t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    
    st.header("Optimals")
    with st.expander("Info and Filters"):
        st.info("These filters will display various optimals in the table below. If you want to export the entire set of 10,000 optimals, hit the 'Prepare full data export' button. If you would like to apply the filters here to the 10,000 optimals before you export, use the 'Prepare full data export (Filter)' button.")
        prio_col, optimals_site_col, optimals_macro_col, optimals_salary_col, optimals_stacks_col = st.columns(5)

        with prio_col:
            prio_var = st.radio("Which priority variable do you want to use?", ('proj', 'Own', 'Mix'), key='prio_var_radio')
            prio_mix = st.number_input("If Mix, what split of Projection/Ownership to dedicate to Projection?", min_value=0, max_value=100, value=50, step=1)
            lineup_num = st.number_input("How many lineups do you want to work with?", min_value=1000, max_value=50000, value=25000, step=100, key='lineup_download_var_input')

        with optimals_site_col:
            if type_var == 'Regular':
                if site_var == 'Draftkings':
                    raw_baselines = dk_roo_raw
                elif site_var == 'Fanduel':
                    raw_baselines = fd_roo_raw
            elif type_var == 'Showdown':
                if site_var == 'Draftkings':
                    raw_baselines = dk_sd_roo_raw
                elif site_var == 'Fanduel':
                    raw_baselines = fd_sd_roo_raw
            if site_var == 'Draftkings':
                slate_var3 = st.radio("Which slate data are you loading?", (slate_names_dk if type_var == 'Showdown' else ['Main', 'Secondary', 'Auxiliary']), key='slate_var3_radio')
            elif site_var == 'Fanduel':
                slate_var3 = st.radio("Which slate data are you loading?", (slate_names_fd if type_var == 'Showdown' else ['Main', 'Secondary', 'Auxiliary']), key='slate_var3_radio')
            
        with optimals_macro_col:
            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1, key='lineup_num_var_input')
            player_var2 = st.multiselect('Query for lineups including:', options = raw_baselines['Player'].unique(), key='player_var2_multiselect', default=[])
        
        if type_var == 'Regular':
            if site_var == 'Draftkings':
                dk_lineups = init_DK_lineups(type_var, slate_var3, prio_var, prio_mix, dk_showdown_db_translation, lineup_num, player_var2)
            elif site_var == 'Fanduel':
                fd_lineups = init_FD_lineups(type_var, slate_var3, prio_var, prio_mix, fd_showdown_db_translation, lineup_num, player_var2)
        elif type_var == 'Showdown':
            if site_var == 'Draftkings':
                dk_lineups = init_DK_lineups(type_var, slate_name_lookup_dk[slate_var3], prio_var, prio_mix, dk_showdown_db_translation, lineup_num, player_var2)
            elif site_var == 'Fanduel':
                fd_lineups = init_FD_lineups(type_var, slate_name_lookup_fd[slate_var3], prio_var, prio_mix, fd_showdown_db_translation, lineup_num, player_var2)

        with optimals_salary_col:
            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_dk')
                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 50000, value = 50000, step = 100, key = 'salary_max_var_dk')
            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_fd')
                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 60000, value = 60000, step = 100, key = 'salary_max_var_fd')
            projection_var = st.number_input("Minimum projection used", min_value=0.0, max_value=100.0, value=0.0, step=1.0, key='projection_var_input')
        with optimals_stacks_col:
            if site_var == 'Draftkings':
                min_stacks_var = st.number_input("Minimum stacks used", min_value = 0, max_value = 5, value = 1, step = 1, key = 'min_stacks_var_dk')
                max_stacks_var = st.number_input("Maximum stacks used", min_value = 0, max_value = 5, value = 5, step = 1, key = 'max_stacks_var_dk')
            elif site_var == 'Fanduel':
                min_stacks_var = st.number_input("Minimum stacks used", min_value = 0, max_value = 4, value = 1, step = 1, key = 'min_stacks_var_fd')
                max_stacks_var = st.number_input("Maximum stacks used", min_value = 0, max_value = 4, value = 4, step = 1, key = 'max_stacks_var_fd')
            ownership_var = st.number_input("Minimum ownership used", min_value=0.0, max_value=100.0, value=0.0, step=1.0, key='ownership_var_input')
        
        
        if site_var == 'Draftkings':
            if type_var == 'Regular':
                ROO_slice = raw_baselines
                player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
                if sport_var == 'NBA':
                    column_names = dk_nba_columns
                elif sport_var == 'WNBA':
                    column_names = dk_wnba_columns
            elif type_var == 'Showdown':
                player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
                if sport_var == 'NBA':
                    column_names = dk_nba_sd_columns
                elif sport_var == 'WNBA':
                    column_names = dk_wnba_sd_columns
            
                    
        elif site_var == 'Fanduel':
            if type_var == 'Regular':
                ROO_slice = raw_baselines
                player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
                if sport_var == 'NBA':
                    column_names = fd_nba_columns
                elif sport_var == 'WNBA':
                    column_names = fd_wnba_columns
            elif type_var == 'Showdown':
                player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
                if sport_var == 'NBA':
                    column_names = fd_nba_sd_columns
                elif sport_var == 'WNBA':
                    column_names = fd_wnba_sd_columns

        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_button'):
                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 type_var == 'Regular':
                        if sport_var == 'NBA':
                            map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
                        elif sport_var == 'WNBA':
                            map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
                        for col_idx in map_columns:
                            data_export[col_idx] = data_export[col_idx].map(dk_id_map)
                    elif type_var == 'Showdown':
                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                        for col_idx in map_columns:
                            data_export[col_idx] = data_export[col_idx].map(dk_sd_id_map)
                elif site_var == 'Fanduel':
                    if type_var == 'Regular':
                        if sport_var == 'NBA':
                            map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
                        elif sport_var == 'WNBA':
                            map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
                        for col_idx in map_columns:
                            data_export[col_idx] = data_export[col_idx].map(fd_id_map)
                    elif type_var == 'Showdown':
                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                        for col_idx in map_columns:
                            data_export[col_idx] = data_export[col_idx].map(fd_sd_id_map)
                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='NBA_optimals_export.csv',
                        mime='text/csv',
                        key='export_optimals_ids_button'
                    )
                    st.download_button(
                        label="Export optimals set (Names)",
                        data=convert_df(name_export),
                        file_name='NBA_optimals_export.csv',
                        mime='text/csv',
                        key='export_optimals_names_button'
                    )
                with pm_opt_col:
                    if site_var == 'Draftkings':
                        if type_var == 'Regular':
                            data_export = data_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                        elif type_var == 'Showdown':
                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                    elif site_var == 'Fanduel':
                        if type_var == 'Regular':
                            data_export = data_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                        elif type_var == 'Showdown':
                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                    st.download_button(
                        label="Portfolio Manager Export (IDs)",
                        data=convert_pm_df(data_export),
                        file_name='NBA_optimals_export.csv',
                        mime='text/csv',
                        key='export_pm_ids_button'
                    )
                    
                    if site_var == 'Draftkings':
                        if type_var == 'Regular':
                            name_export = name_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                        elif type_var == 'Showdown':
                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                    elif site_var == 'Fanduel':
                        if type_var == 'Regular':
                            name_export = name_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                        elif type_var == 'Showdown':
                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                    st.download_button(
                        label="Portfolio Manager Export (Names)",
                        data=convert_pm_df(name_export),
                        file_name='NBA_optimals_export.csv',
                        mime='text/csv',
                        key='export_pm_names_button'
                    )
        with filtered_dl_col:
            if st.button("Prepare full data export (Filtered)", key='data_export_filtered_button'):
                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)
                
                # Apply filters BEFORE converting to IDs
                data_export = data_export[data_export['salary'] >= salary_min_var]
                data_export = data_export[data_export['salary'] <= salary_max_var]
                data_export = data_export[data_export['Team_count'] >= min_stacks_var]
                data_export = data_export[data_export['Team_count'] <= max_stacks_var]
                if site_var == 'Draftkings':
                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
                elif site_var == 'Fanduel':
                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)

                name_export = name_export[name_export['salary'] >= salary_min_var]
                name_export = name_export[name_export['salary'] <= salary_max_var]
                name_export = name_export[name_export['Team_count'] >= min_stacks_var]
                name_export = name_export[name_export['Team_count'] <= max_stacks_var]
                if site_var == 'Draftkings':
                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
                elif site_var == 'Fanduel':
                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
                
                # NOW convert player names to IDs after filtering
                if site_var == 'Draftkings':
                    if type_var == 'Regular':
                        if sport_var == 'NBA':
                            map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
                        elif sport_var == 'WNBA':
                            map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
                        for col_idx in map_columns:
                            data_export[col_idx] = data_export[col_idx].map(dk_id_map)
                    elif type_var == 'Showdown':
                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                        for col_idx in map_columns:
                            data_export[col_idx] = data_export[col_idx].map(dk_sd_id_map)
                    
                elif site_var == 'Fanduel':
                    if type_var == 'Regular':
                        if sport_var == 'NBA':
                            map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
                        elif sport_var == 'WNBA':
                            map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
                        for col_idx in map_columns:
                            data_export[col_idx] = data_export[col_idx].map(fd_id_map)
                    elif type_var == 'Showdown':
                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                        for col_idx in map_columns:
                            data_export[col_idx] = data_export[col_idx].map(fd_sd_id_map)
                
                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='NBA_optimals_export.csv',
                        mime='text/csv',
                        key='export_filtered_optimals_ids_button'
                    )
                    st.download_button(
                        label="Export optimals set (Names)",
                        data=convert_df(name_export),
                        file_name='NBA_optimals_export.csv',
                        mime='text/csv',
                        key='export_filtered_optimals_names_button'
                    )
                with pm_opt_col:
                    if site_var == 'Draftkings':
                        if type_var == 'Regular':
                            data_export = data_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                        elif type_var == 'Showdown':
                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                    elif site_var == 'Fanduel':
                        if type_var == 'Regular':
                            data_export = data_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                        elif type_var == 'Showdown':
                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                    st.download_button(
                        label="Portfolio Manager Export (IDs)",
                        data=convert_pm_df(data_export),
                        file_name='NBA_optimals_export.csv',
                        mime='text/csv',
                        key='export_filtered_pm_ids_button'
                    )
                    
                    if site_var == 'Draftkings':
                        if type_var == 'Regular':
                            name_export = name_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                        elif type_var == 'Showdown':
                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                    elif site_var == 'Fanduel':
                        if type_var == 'Regular':
                            name_export = name_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                        elif type_var == 'Showdown':
                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                    st.download_button(
                        label="Portfolio Manager Export (Names)",
                        data=convert_pm_df(name_export),
                        file_name='NBA_optimals_export.csv',
                        mime='text/csv',
                        key='export_filtered_pm_names_button'
                    )
        
    if site_var == 'Draftkings':
        st.session_state.working_seed = dk_lineups.copy()
        st.session_state.working_seed = st.session_state.working_seed
        if player_var2 != []:
            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_var2 == []:
            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':
        st.session_state.working_seed = fd_lineups.copy()
        st.session_state.working_seed = st.session_state.working_seed
        if player_var2 != []:
            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_var2 == []:
            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)
            
    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'] >= salary_min_var]
    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'] <= salary_max_var]
    if site_var == 'Draftkings':
        st.session_state.data_export_display = map_mask_parse(st.session_state.data_export_display, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
        st.session_state.data_export_display = map_mask_parse(st.session_state.data_export_display, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
    elif site_var == 'Fanduel':
        st.session_state.data_export_display = map_mask_parse(st.session_state.data_export_display, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
        st.session_state.data_export_display = map_mask_parse(st.session_state.data_export_display, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['Team_count'] >= min_stacks_var]
    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['Team_count'] <= max_stacks_var]
    st.session_state.data_export_display = st.session_state.data_export_display.reset_index(drop=True)
    export_file = st.session_state.data_export_display.copy()
    name_export = st.session_state.data_export_display.copy()
    if site_var == 'Draftkings':
        if type_var == 'Regular':
            if sport_var == 'NBA':
                map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
            elif sport_var == 'WNBA':
                map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
            for col_idx in map_columns:
                export_file[col_idx] = export_file[col_idx].map(dk_id_map)
        elif type_var == 'Showdown':
            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
            for col_idx in map_columns:
                export_file[col_idx] = export_file[col_idx].map(dk_sd_id_map)
    elif site_var == 'Fanduel':
        if type_var == 'Regular':
            if sport_var == 'NBA':
                map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
            elif sport_var == 'WNBA':
                map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
            for col_idx in map_columns:
                export_file[col_idx] = export_file[col_idx].map(fd_id_map)
        elif type_var == 'Showdown':
            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
            for col_idx in map_columns:
                export_file[col_idx] = export_file[col_idx].map(fd_sd_id_map)
            
    with st.container():
        if st.button("Reset Optimals", key='reset_optimals_button'):
            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()
        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, key='optimals_dataframe')
        st.download_button(
            label="Export display optimals (IDs)",
            data=convert_df(export_file),
            file_name='NBA_display_optimals.csv',
            mime='text/csv',
            key='export_display_optimals_ids_button'
        )
        st.download_button(
            label="Export display optimals (Names)",
            data=convert_df(name_export),
            file_name='NBA_display_optimals.csv',
            mime='text/csv',
            key='export_display_optimals_names_button'
        )
    
    with st.container():
        if type_var == 'Regular':
            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[:,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_var == '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])
                        ]
                    })
        elif type_var == 'Showdown':
            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[:,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_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[:,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, key='optimal_stats_dataframe')

    with st.container():
        display_freq_tab, seed_frame_freq_tab = st.tabs(["Display Frequency", "Seed Frame Frequency"])
        with display_freq_tab:
            if 'data_export_display' in st.session_state:
                if site_var == 'Draftkings':
                    if type_var == 'Regular':
                        player_columns = st.session_state.data_export_display.iloc[:, :8]
                    elif type_var == 'Showdown':
                        player_columns = st.session_state.data_export_display.iloc[:, :6]
                elif site_var == 'Fanduel':
                    if type_var == 'Regular':
                        player_columns = st.session_state.data_export_display.iloc[:, :9]
                    elif type_var == 'Showdown':
                        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, key='player_frequency_dataframe')
            
                st.download_button(
                    label="Export player frequency",
                    data=convert_df_to_csv(summary_df),
                    file_name='NBA_player_frequency.csv',
                    mime='text/csv',
                    key='export_player_frequency_button'
                )
        with seed_frame_freq_tab:
            if 'working_seed' in st.session_state:
                if site_var == 'Draftkings':
                    if type_var == 'Regular':
                        player_columns = st.session_state.working_seed[:, :8]
                    elif type_var == 'Showdown':
                        player_columns = st.session_state.working_seed[:, :6]
                elif site_var == 'Fanduel':
                    if type_var == 'Regular':
                        player_columns = st.session_state.working_seed[:, :9]
                    elif type_var == 'Showdown':
                        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, key='seed_frame_frequency_dataframe')
            
                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',
                    key='export_seed_frame_frequency_button'
                )