src/streamlit_app.py

import streamlit as st
import numpy as np
from numpy import where as np_where
import pandas as pd
import plotly.express as px
import scipy.stats as stats
st.set_page_config(layout="wide")

from database import props_db, dfs_db

game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'}
bet_format = {'Edge': '{:.2%}'}
american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.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)

def calculate_poisson(row):
    mean_val = row['Mean_Outcome']
    threshold = row['Prop']
    cdf_value = stats.poisson.cdf(threshold, mean_val)
    probability = 1 - cdf_value
    return probability

@st.cache_resource(ttl=600)
def init_baselines():
    collection = dfs_db["Game_Betting_Model"] 
    cursor = collection.find()
    raw_display = pd.DataFrame(list(cursor))
    game_model = raw_display[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
    
    collection = dfs_db["Player_Stats"] 
    cursor = collection.find()
    raw_display = pd.DataFrame(list(cursor))
    overall_stats = raw_display[['Player', 'Position', 'Team', 'Opp', 'rush_att', 'rec', 'dropbacks', 'rush_yards', 'rush_tds', 'rec_yards', 'rec_tds', 'pass_att', 'pass_yards', 'pass_tds', 'PPR', 'Half_PPR']]
    
    collection = dfs_db["Prop_Trends"] 
    cursor = collection.find()
    raw_display = pd.DataFrame(list(cursor))
    prop_trends = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
    
    collection = dfs_db["DK_SD_NFL_ROO"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
    load_display = raw_display[raw_display['Position'] != 'K']
    timestamp = load_display['timestamp'][0]

    collection = dfs_db["Bet_Sheet"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display.replace('', np.nan, inplace=True)
    raw_display = raw_display[['Name', 'PropType', 'No Vig', 'Projection', 'Edge']]
    raw_display = raw_display.rename(columns={"Name": "Player", "PropType": "prop_type"})
    raw_display = raw_display.dropna(subset='Player')
    raw_display = raw_display.drop_duplicates(subset=['Player', 'prop_type'], keep='first')
    raw_display = raw_display.sort_values(by='Edge', ascending=False)
    raw_display = raw_display.reset_index(drop=True)
    raw_display['O/U'] = np.where(raw_display['No Vig'] > raw_display['Projection'], 'Under', 'Over')
    bet_sheet = raw_display[['Player', 'prop_type', 'O/U', 'No Vig', 'Projection', 'Edge']]
    
    collection = dfs_db["Prop_Trends"] 
    cursor = collection.find()
    raw_display = pd.DataFrame(list(cursor))
    prop_frame = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
    
    collection = dfs_db['Pick6_Trends']
    cursor = collection.find()
    raw_display = pd.DataFrame(list(cursor))
    pick_frame = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge', 'last_name', 'P6_name', 'Full_name']]

    collection = props_db["NFL_Props"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    market_props = raw_display[['Name', 'Position', 'Projection', 'PropType', 'OddsType', 'over_pay', 'under_pay']]
    market_props['over_prop'] = market_props['Projection']
    market_props['over_line'] = market_props['over_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
    market_props['under_prop'] = market_props['Projection']
    market_props['under_line'] = market_props['under_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))

    return game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet

def calculate_no_vig(row):
    def implied_probability(american_odds):
        if american_odds < 0:
            return (-american_odds) / ((-american_odds) + 100)
        else:
            return 100 / (american_odds + 100)

    over_line = row['over_line']
    under_line = row['under_line']
    over_prop = row['over_prop']
    
    over_prob = implied_probability(over_line)
    under_prob = implied_probability(under_line)
    
    total_prob = over_prob + under_prob
    no_vig_prob = (over_prob / total_prob + 0.5) * over_prop
    
    return no_vig_prob

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

game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
qb_stats = overall_stats[overall_stats['Position'] == 'QB']
qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
t_stamp = f"Last Update: " + str(timestamp) + f" CST"

prop_table_options = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NFL_GAME_PLAYER_PASSING_COMPLETIONS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS',
                      'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
prop_format = {'L3 Success': '{:.2%}', 'L6_Success': '{:.2%}', 'L10_success': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
               'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
all_sim_vars = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NFL_GAME_PLAYER_PASSING_COMPLETIONS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS',
                      'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
pick6_sim_vars = ['Rush + Rec Yards', 'Rush + Rec TDs', 'Passing Yards', 'Passing Attempts', 'Passing TDs', 'Completions', 'Rushing Yards', 'Receptions', 'Receiving Yards']
sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge'])

selected_tab = st.segmented_control(
    "Select Tab",
    options=["Bet Sheet", "Game Betting Model", 'Prop Market', "QB Projections", "RB/WR/TE Projections", "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"],
    selection_mode='single',
    default='Bet Sheet',
    width='stretch',
    label_visibility='collapsed',
    key='tab_selector'
)

if selected_tab == 'Bet Sheet':
    with st.expander("Info and Filters"):
        st.info(t_stamp)
        if st.button("Reset Data", key='reset_bet_sheet'):
                st.cache_data.clear()
                game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
                t_stamp = f"Last Update: " + str(timestamp) + f" CST"
        prop_selection = st.multiselect('Select prop type to view', default = bet_sheet['prop_type'].unique(), options = bet_sheet['prop_type'].unique(), key='prop_selection')
    bet_sheet = bet_sheet[bet_sheet['prop_type'].isin(prop_selection)]
    st.dataframe(bet_sheet.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(bet_format, precision=2), use_container_width = True)
    st.download_button(
        label="Export Bet Sheet",
        data=convert_df_to_csv(bet_sheet),
        file_name='NFL_bet_sheet_export.csv',
        mime='text/csv',
        key='bet_sheet_export',
    )

if selected_tab == 'Game Betting Model':
    st.info(t_stamp)
    if st.button("Reset Data", key='reset1'):
              st.cache_data.clear()
              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
    team_frame = game_model
    if line_var1 == 'Percentage':
        team_frame = team_frame[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
        team_frame = team_frame.set_index('Team')
        try:
            st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Spread Diff']).format(game_format, precision=2), use_container_width = True)
        except:
            st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Spread Diff']).format(precision=2), use_container_width = True)
    if line_var1 == 'American':
        team_frame = team_frame[['Team', 'Opp', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
        team_frame = team_frame.set_index('Team')
        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Spread Diff']).format(precision=2), height = 1000, use_container_width = True)
    
    st.download_button(
        label="Export Team Model",
        data=convert_df_to_csv(team_frame),
        file_name='NFL_team_betting_export.csv',
        mime='text/csv',
        key='team_export',
    )

if selected_tab == 'Prop Market':
    st.info(t_stamp)
    if st.button("Reset Data", key='reset4'):
              st.cache_data.clear()
              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    market_type = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options, key = 'market_type_key')
    disp_market = market_props.copy()
    disp_market = disp_market[disp_market['PropType'] == market_type]
    disp_market['No_Vig_Prop'] = disp_market.apply(calculate_no_vig, axis=1)
    fanduel_frame = disp_market[disp_market['OddsType'] == 'FANDUEL']
    fanduel_dict = dict(zip(fanduel_frame['Name'], fanduel_frame['No_Vig_Prop']))
    draftkings_frame = disp_market[disp_market['OddsType'] == 'DRAFTKINGS']
    draftkings_dict = dict(zip(draftkings_frame['Name'], draftkings_frame['No_Vig_Prop']))
    mgm_frame = disp_market[disp_market['OddsType'] == 'MGM']
    mgm_dict = dict(zip(mgm_frame['Name'], mgm_frame['No_Vig_Prop']))
    bet365_frame = disp_market[disp_market['OddsType'] == 'BET_365']
    bet365_dict = dict(zip(bet365_frame['Name'], bet365_frame['No_Vig_Prop']))

    disp_market['FANDUEL'] = disp_market['Name'].map(fanduel_dict)
    disp_market['DRAFTKINGS'] = disp_market['Name'].map(draftkings_dict)
    disp_market['MGM'] = disp_market['Name'].map(mgm_dict)
    disp_market['BET365'] = disp_market['Name'].map(bet365_dict)

    disp_market = disp_market[['Name', 'Position','FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365']]
    disp_market = disp_market.drop_duplicates(subset=['Name'], keep='first', ignore_index=True)

    st.dataframe(disp_market.style.background_gradient(axis=1, subset=['FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365'], cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True)
    st.download_button(
        label="Export Market Props",
        data=convert_df_to_csv(disp_market),
        file_name='NFL_market_props_export.csv',
        mime='text/csv',
    )

if selected_tab == 'QB Projections':
    st.info(t_stamp)
    if st.button("Reset Data", key='reset2'):
              st.cache_data.clear()
              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
    if split_var1 == 'Specific Teams':
        team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = qb_stats['Team'].unique(), key='team_var1')
    elif split_var1 == 'All':
        team_var1 = qb_stats.Team.values.tolist()
    qb_stats = qb_stats[qb_stats['Team'].isin(team_var1)]
    qb_stats_disp = qb_stats.set_index('Player')
    qb_stats_disp = qb_stats_disp.sort_values(by='PPR', ascending=False)
    st.dataframe(qb_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
    st.download_button(
        label="Export Prop Model",
        data=convert_df_to_csv(qb_stats_disp),
        file_name='NFL_qb_stats_export.csv',
        mime='text/csv',
        key='NFL_qb_stats_export',
    )

if selected_tab == 'RB/WR/TE Projections':
    st.info(t_stamp)
    if st.button("Reset Data", key='reset3'):
              st.cache_data.clear()
              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
    if split_var2 == 'Specific Teams':
        team_var2 = st.multiselect('Which teams would you like to include in the tables?', options = non_qb_stats['Team'].unique(), key='team_var2')
    elif split_var2 == 'All':
        team_var2 = non_qb_stats.Team.values.tolist()
    non_qb_stats = non_qb_stats[non_qb_stats['Team'].isin(team_var2)]
    non_qb_stats_disp = non_qb_stats.set_index('Player')
    non_qb_stats_disp = non_qb_stats_disp.sort_values(by='PPR', ascending=False)
    st.dataframe(non_qb_stats_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height = 1000, use_container_width = True)
    st.download_button(
        label="Export Prop Model",
        data=convert_df_to_csv(non_qb_stats_disp),
        file_name='NFL_nonqb_stats_export.csv',
        mime='text/csv',
        key='NFL_nonqb_stats_export',
    )

if selected_tab == 'Player Prop Trends':
    st.info(t_stamp)
    if st.button("Reset Data", key='reset5'):
              st.cache_data.clear()
              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
    if split_var5 == 'Specific Teams':
        team_var5 = st.multiselect('Which teams would you like to include in the tables?', options = prop_trends['Team'].unique(), key='team_var5')
    elif split_var5 == 'All':
        team_var5 = prop_trends.Team.values.tolist()
    prop_type_var2 = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options)
    book_var2 = st.selectbox('Select type of book do you want to view?', options = ['FANDUEL', 'BET365', 'DRAFTKINGS', 'CONSENSUS'])
    prop_frame_disp = prop_trends[prop_trends['Team'].isin(team_var5)]
    prop_frame_disp = prop_frame_disp[prop_frame_disp['book'] == book_var2]
    prop_frame_disp = prop_frame_disp[prop_frame_disp['prop_type'] == prop_type_var2]
    #prop_frame_disp = prop_frame_disp.set_index('Player')
    prop_frame_disp = prop_frame_disp.sort_values(by='Trending Over', ascending=False)
    st.dataframe(prop_frame_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True)
    st.download_button(
        label="Export Prop Trends Model",
        data=convert_df_to_csv(prop_frame_disp),
        file_name='NFL_prop_trends_export.csv',
        mime='text/csv',
    )

if selected_tab == 'Player Prop Simulations':
    st.info(t_stamp)
    if st.button("Reset Data", key='reset6'):
              st.cache_data.clear()
              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    col1, col2 = st.columns([1, 5])
    
    with col2:
        df_hold_container = st.empty()
        info_hold_container = st.empty()
        plot_hold_container = st.empty()
    
    with col1:
        player_check = st.selectbox('Select player to simulate props', options = overall_stats['Player'].unique())
        prop_type_var = st.selectbox('Select type of prop to simulate', options = ['Pass Yards', 'Pass TDs', 'Rush Yards', 'Rush TDs', 'Receptions', 'Rec Yards', 'Rec TDs', 'Fantasy', 'FD Fantasy', 'PrizePicks'])

        ou_var = st.selectbox('Select wether it is an over or under', options = ['Over', 'Under'])
        if prop_type_var == 'Pass Yards':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 100.0, max_value = 400.5, value = 250.5, step = .5)
        elif prop_type_var == 'Pass TDs':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
        elif prop_type_var == 'Rush Yards':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 155.5, value = 25.5, step = .5)
        elif prop_type_var == 'Rush TDs':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
        elif prop_type_var == 'Receptions':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 15.5, value = 5.5, step = .5)
        elif prop_type_var == 'Rec Yards':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 155.5, value = 25.5, step = .5)
        elif prop_type_var == 'Rec TDs':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 5.5, value = 1.5, step = .5)
        elif prop_type_var == 'Fantasy':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
        elif prop_type_var == 'FD Fantasy':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
        elif prop_type_var == 'PrizePicks':
            prop_var = st.number_input('Type in the prop offered (i.e 5.5)', min_value = 0.0, max_value = 50.5, value = 10.5, step = .5)
        line_var = st.number_input('Type in the line on the prop (i.e. -120)', min_value = -1000, max_value = 1000, value = -150, step = 1)
        line_var = line_var + 1

        if st.button('Simulate Prop'):
            with col2:
                   
                    with df_hold_container.container():

                        df = overall_stats

                        total_sims = 5000

                        df.replace("", 0, inplace=True)

                        player_var = df[df['Player'] == player_check]
                        player_var = player_var.reset_index()
                        
                        if prop_type_var == 'Pass Yards':
                            df['Median'] = df['pass_yards']
                        elif prop_type_var == 'Pass TDs':
                            df['Median'] = df['pass_tds']
                        elif prop_type_var == 'Rush Yards':
                            df['Median'] = df['rush_yards']
                        elif prop_type_var == 'Rush TDs':
                            df['Median'] = df['rush_tds']
                        elif prop_type_var == 'Receptions':
                            df['Median'] = df['rec']
                        elif prop_type_var == 'Rec Yards':
                            df['Median'] = df['rec_yards']
                        elif prop_type_var == 'Rec TDs':
                            df['Median'] = df['rec_tds']
                        elif prop_type_var == 'Fantasy':
                            df['Median'] = df['PPR']
                        elif prop_type_var == 'FD Fantasy':
                            df['Median'] = df['Half_PPF']
                        elif prop_type_var == 'PrizePicks':
                            df['Median'] = df['Half_PPF']

                        flex_file = df
                        flex_file['Floor'] = flex_file['Median'] * .25
                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75)
                        flex_file['STD'] = flex_file['Median'] / 4
                        flex_file = flex_file[['Player', 'Floor', 'Median', 'Ceiling', 'STD']]

                        hold_file = flex_file
                        overall_file = flex_file
                        salary_file = flex_file

                        overall_players = overall_file[['Player']]

                        for x in range(0,total_sims):
                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                        overall_file=overall_file.drop(['Player', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                        overall_file.astype('int').dtypes

                        players_only = hold_file[['Player']]

                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)

                        players_only['Mean_Outcome'] = overall_file.mean(axis=1)
                        players_only['Prop'] = prop_var
                        players_only['poisson_var'] = players_only.apply(calculate_poisson, axis=1)
                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
                        if ou_var == 'Over':
                            players_only['beat_prop'] = np.where(players_only['Prop'] <= 3, players_only['poisson_var'], overall_file[overall_file > prop_var].count(axis=1)/float(total_sims))
                        elif ou_var == 'Under':
                            players_only['beat_prop'] = np.where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], (overall_file[overall_file < prop_var].count(axis=1)/float(total_sims)))

                        players_only['implied_odds'] = np.where(line_var <= 0, (-(line_var)/((-(line_var))+100)), 100/(line_var+100))

                        players_only['Player'] = hold_file[['Player']]

                        final_outcomes = players_only[['Player', '10%', 'Mean_Outcome', '90%', 'implied_odds', 'beat_prop']]
                        final_outcomes['Bet?'] = np.where(final_outcomes['beat_prop'] - final_outcomes['implied_odds'] >= .10, "Bet", "No Bet")
                        final_outcomes = final_outcomes[final_outcomes['Player'] == player_check]
                        player_outcomes = player_outcomes[player_outcomes['Player'] == player_check]
                        player_outcomes = player_outcomes.drop(columns=['Player']).transpose()
                        player_outcomes = player_outcomes.reset_index()
                        player_outcomes.columns = ['Instance', 'Outcome']

                        x1 = player_outcomes.Outcome.to_numpy()

                        print(x1)

                        hist_data = [x1]

                        group_labels = ['player outcomes']

                        fig = px.histogram(
                                player_outcomes, x='Outcome')
                        fig.add_vline(x=prop_var, line_dash="dash", line_color="green")

                        with df_hold_container:
                            df_hold_container = st.empty()
                            format_dict = {'10%': '{:.2f}', 'Mean_Outcome': '{:.2f}','90%': '{:.2f}', 'beat_prop': '{:.2%}','implied_odds': '{:.2%}'}
                            st.dataframe(final_outcomes.style.format(format_dict), use_container_width = True)

                        with info_hold_container:
                            st.info('The Y-axis is the percent of times in simulations that the player reaches certain thresholds, while the X-axis is the threshold to be met. The Green dotted line is the prop you entered. You can hover over any spot and see the percent to reach that mark.')

                        with plot_hold_container:
                            st.dataframe(player_outcomes, use_container_width = True)
                            plot_hold_container = st.empty()
                            st.plotly_chart(fig, use_container_width=True)

if selected_tab == 'Stat Specific Simulations':
    st.info(t_stamp)
    st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
    if st.button("Reset Data/Load Data", key='reset7'):
              st.cache_data.clear()
              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
              qb_stats = overall_stats[overall_stats['Position'] == 'QB']
              qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
              non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
        
    settings_container = st.empty()
    df_hold_container = st.empty()
    export_container = st.empty()

    with settings_container.container():
        col1, col2, col3, col4 = st.columns([3, 3, 3, 3])
        with col1:
            game_select_var = st.selectbox('Select prop source', options = ['Aggregate', 'Pick6'])
        with col2:
            book_select_var = st.selectbox('Select book', options = ['ALL', 'BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL'])
            if book_select_var == 'ALL':
                book_selections = ['BET_365', 'DRAFTKINGS', 'FANDUEL', 'MGM', 'UNIBET', 'WILLIAM_HILL']
            else:
                book_selections = [book_select_var]
            if game_select_var == 'Aggregate':
                prop_df = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
            elif game_select_var == 'Pick6':
                prop_df = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                book_selections = ['Pick6']
        with col3:
            if game_select_var == 'Aggregate':
                prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS',
                                                'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS'])
            elif game_select_var == 'Pick6':
                prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'Rush + Rec Yards', 'Rush + Rec TDs', 'Passing Yards', 'Passing Attempts', 'Passing TDs', 'Rushing Attempts', 'Rushing Yards', 'Receptions', 'Receiving Yards', 'Receiving TDs'])
        with col4:
            st.download_button(
                label="Download Prop Source",
                data=convert_df_to_csv(prop_df),
                file_name='NFL_prop_source.csv',
                mime='text/csv',
                key='prop_source',
            )

    if st.button('Simulate Prop Category'):
                
        with df_hold_container.container():
            if prop_type_var == 'All Props':
                if game_select_var == 'Aggregate':
                    prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                    sim_vars = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS', 'NFL_GAME_PLAYER_PASSING_ATTEMPTS', 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS',
                                'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NFL_GAME_PLAYER_RECEIVING_YARDS', 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
                elif game_select_var == 'Pick6':
                    prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                    sim_vars = ['Rush + Rec Yards', 'Rush + Rec TDs', 'Passing Yards', 'Passing Attempts', 'Passing TDs', 'Rushing Attempts', 'Rushing Yards', 'Receptions', 'Receiving Yards', 'Receiving TDs']
                
                player_df = overall_stats.copy()
                
                for prop in sim_vars:
                        
                    for books in book_selections:
                        prop_df = prop_df_raw[prop_df_raw['book'] == books]
                        prop_df = prop_df[prop_df['prop_type'] == prop]
                        prop_df = prop_df[~((prop_df['over_prop'] < 15) & (prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS'))]
                        prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                        prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
                        prop_df['Over'] = 1 / prop_df['over_line']
                        prop_df['Under'] = 1 / prop_df['under_line']

                        prop_dict = dict(zip(prop_df.Player, prop_df.Prop))
                        prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type))
                        book_dict = dict(zip(prop_df.Player, prop_df.book))
                        over_dict = dict(zip(prop_df.Player, prop_df.Over))
                        under_dict = dict(zip(prop_df.Player, prop_df.Under))
                        trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
                        trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending Under']))

                        player_df['book'] = player_df['Player'].map(book_dict)
                        player_df['Prop'] = player_df['Player'].map(prop_dict)
                        player_df['prop_type'] = player_df['Player'].map(prop_type_dict)
                        player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
                        player_df['Trending Under'] = player_df['Player'].map(trending_under_dict)

                        df = player_df.reset_index(drop=True)

                        team_dict = dict(zip(df.Player, df.Team))
                        
                        total_sims = 1000

                        df.replace("", 0, inplace=True)
                        
                        if prop == "NFL_GAME_PLAYER_PASSING_YARDS" or prop == "Passing Yards":
                            df['Median'] = df['pass_yards']
                        elif prop == "NFL_GAME_PLAYER_RUSHING_YARDS" or prop == "Rushing Yards":
                            df['Median'] = df['rush_yards']
                        elif prop == "NFL_GAME_PLAYER_PASSING_ATTEMPTS" or prop == "Passing Attempts":
                            df['Median'] = df['pass_att']
                        elif prop == "NFL_GAME_PLAYER_PASSING_TOUCHDOWNS" or prop == "Passing TDs":
                            df['Median'] = df['pass_tds']
                        elif prop == "NFL_GAME_PLAYER_RUSHING_ATTEMPTS" or prop == "Rushing Attempts":
                            df['Median'] = df['rush_att']
                        elif prop == "NFL_GAME_PLAYER_RECEIVING_RECEPTIONS" or prop == "Receptions":
                            df['Median'] = df['rec']
                        elif prop == "NFL_GAME_PLAYER_RECEIVING_YARDS" or prop == "Receiving Yards":
                            df['Median'] = df['rec_yards']
                        elif prop == "NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS" or prop == "Receiving TDs":
                            df['Median'] = df['rec_tds']
                        elif prop == "Rush + Rec Yards":
                            df['Median'] = df['rush_yards'] + df['rec_yards']
                        elif prop == "Rush + Rec TDs":
                            df['Median'] = df['rush_tds'] + df['rec_tds']
                            
                        flex_file = df.copy()
                        flex_file['Floor'] = flex_file['Median'] * .25
                        flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75)
                        flex_file['STD'] = flex_file['Median'] / 4
                        flex_file['Prop'] = flex_file['Player'].map(prop_dict)
                        flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]

                        hold_file = flex_file.copy()
                        overall_file = flex_file.copy()
                        prop_file = flex_file.copy()
                                
                        overall_players = overall_file[['Player']]

                        for x in range(0,total_sims):    
                            prop_file[x] = prop_file['Prop']

                        prop_file = prop_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)

                        for x in range(0,total_sims):
                            overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                        overall_file=overall_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)

                        players_only = hold_file[['Player']]

                        player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)

                        prop_check = (overall_file - prop_file)

                        players_only['Mean_Outcome'] = overall_file.mean(axis=1)
                        players_only['Book'] = players_only['Player'].map(book_dict)
                        players_only['Prop'] = players_only['Player'].map(prop_dict)
                        players_only['Trending Over'] = players_only['Player'].map(trending_over_dict)
                        players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
                        players_only['poisson_var'] = players_only.apply(calculate_poisson, axis=1)
                        players_only['10%'] = overall_file.quantile(0.1, axis=1)
                        players_only['90%'] = overall_file.quantile(0.9, axis=1)
                        players_only['Over'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], prop_check[prop_check > 0].count(axis=1)/float(total_sims))
                        players_only['Imp Over'] = players_only['Player'].map(over_dict)
                        players_only['Over%'] = players_only[["Over", "Imp Over", "Trending Over"]].mean(axis=1)
                        players_only['Under'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], prop_check[prop_check < 0].count(axis=1)/float(total_sims))
                        players_only['Imp Under'] = players_only['Player'].map(under_dict)
                        players_only['Under%'] = players_only[["Under", "Imp Under", "Trending Under"]].mean(axis=1)
                        players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                        players_only['prop_threshold'] = .10
                        players_only = players_only[players_only['Mean_Outcome'] > 0]
                        players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
                        players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
                        players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
                        players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
                        players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
                        players_only['Edge'] = players_only['Bet_check']
                        players_only['Prop Type'] = prop

                        players_only['Player'] = hold_file[['Player']]
                        players_only['Team'] = players_only['Player'].map(team_dict)

                        leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
                        sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                        
                        final_outcomes = sim_all_hold
                        st.write(f'finished {prop} for {books}')
                    
            elif prop_type_var != 'All Props':

                player_df = overall_stats.copy()

                if game_select_var == 'Aggregate':
                    prop_df_raw = prop_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                elif game_select_var == 'Pick6':
                    prop_df_raw = pick_frame[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                    
                for books in book_selections:
                    prop_df = prop_df_raw[prop_df_raw['book'] == books]
                    
                    if prop_type_var == "NFL_GAME_PLAYER_PASSING_YARDS":
                        prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_YARDS']
                    elif prop_type_var == "Passing Yards":
                        prop_df = prop_df[prop_df['prop_type'] == 'Passing Yards']
                    elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_YARDS":
                        prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_YARDS']
                    elif prop_type_var == "Rushing Yards":
                        prop_df = prop_df[prop_df['prop_type'] == 'Rushing Yards']
                    elif prop_type_var == "NFL_GAME_PLAYER_PASSING_ATTEMPTS":
                        prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_ATTEMPTS']
                    elif prop_type_var == "Passing Attempts":
                        prop_df = prop_df[prop_df['prop_type'] == 'Passing Attempts']
                    elif prop_type_var == "NFL_GAME_PLAYER_PASSING_TOUCHDOWNS":
                        prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_PASSING_TOUCHDOWNS']
                    elif prop_type_var == "Passing TDs":
                        prop_df = prop_df[prop_df['prop_type'] == 'Passing TDs']
                    elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_ATTEMPTS":
                        prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RUSHING_ATTEMPTS']
                    elif prop_type_var == "Rushing Attempts":
                        prop_df = prop_df[prop_df['prop_type'] == 'Rushing Attempts']
                    elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_RECEPTIONS":
                        prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_RECEPTIONS']
                    elif prop_type_var == "Receptions":
                        prop_df = prop_df[prop_df['prop_type'] == 'Receptions']
                    elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_YARDS":
                        prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_YARDS']
                    elif prop_type_var == "Receiving Yards":
                        prop_df = prop_df[prop_df['prop_type'] == 'Receiving Yards']
                    elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS":
                        prop_df = prop_df[prop_df['prop_type'] == 'NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS']
                    elif prop_type_var == "Receiving TDs":
                        prop_df = prop_df[prop_df['prop_type'] == 'Receiving TDs']
                    elif prop_type_var == "Rush + Rec Yards":
                        prop_df = prop_df[prop_df['prop_type'] == 'Rush + Rec Yards']
                    elif prop_type_var == "Rush + Rec TDs":
                        prop_df = prop_df[prop_df['prop_type'] == 'Rush + Rec TDs']

                    prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type', 'Trending Over', 'Trending Under']]
                    prop_df = prop_df.rename(columns={"over_prop": "Prop"})
                    prop_df['Over'] = 1 / prop_df['over_line']
                    prop_df['Under'] = 1 / prop_df['under_line']

                    prop_dict = dict(zip(prop_df.Player, prop_df.Prop))
                    prop_type_dict = dict(zip(prop_df.Player, prop_df.prop_type))
                    book_dict = dict(zip(prop_df.Player, prop_df.book))
                    over_dict = dict(zip(prop_df.Player, prop_df.Over))
                    under_dict = dict(zip(prop_df.Player, prop_df.Under))
                    trending_over_dict = dict(zip(prop_df.Player, prop_df['Trending Over']))
                    trending_under_dict = dict(zip(prop_df.Player, prop_df['Trending Under']))

                    player_df['book'] = player_df['Player'].map(book_dict)
                    player_df['Prop'] = player_df['Player'].map(prop_dict)
                    player_df['prop_type'] = player_df['Player'].map(prop_type_dict)
                    player_df['Trending Over'] = player_df['Player'].map(trending_over_dict)
                    player_df['Trending Under'] = player_df['Player'].map(trending_under_dict)

                    df = player_df.reset_index(drop=True)

                    team_dict = dict(zip(df.Player, df.Team))
                    
                    total_sims = 1000

                    df.replace("", 0, inplace=True)
                    
                    if prop_type_var == "NFL_GAME_PLAYER_PASSING_YARDS" or prop_type_var == "Passing Yards":
                        df['Median'] = df['pass_yards']
                    elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_YARDS" or prop_type_var == "Rushing Yards":
                        df['Median'] = df['rush_yards']
                    elif prop_type_var == "NFL_GAME_PLAYER_PASSING_ATTEMPTS" or prop_type_var == "Passing Attempts":
                        df['Median'] = df['pass_att']
                    elif prop_type_var == "NFL_GAME_PLAYER_PASSING_TOUCHDOWNS" or prop_type_var == "Passing TDs":
                        df['Median'] = df['pass_tds']
                    elif prop_type_var == "NFL_GAME_PLAYER_RUSHING_ATTEMPTS" or prop_type_var == "Rushing Attempts":
                        df['Median'] = df['rush_att']
                    elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_RECEPTIONS" or prop_type_var == "Receptions":
                        df['Median'] = df['rec']
                    elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_YARDS" or prop_type_var == "Receiving Yards":
                        df['Median'] = df['rec_yards']
                    elif prop_type_var == "NFL_GAME_PLAYER_RECEIVING_TOUCHDOWNS" or prop_type_var == "Receiving TDs":
                        df['Median'] = df['rec_tds']
                    elif prop_type_var == "Rush + Rec Yards":
                        df['Median'] = df['rush_yards'] + df['rec_yards']
                    elif prop_type_var == "Rush + Rec TDs":
                        df['Median'] = df['rush_tds'] + df['rec_tds']

                    flex_file = df.copy()
                    flex_file['Floor'] = flex_file['Median'] * .25
                    flex_file['Ceiling'] = flex_file['Median'] + (flex_file['Median'] * 1.75)
                    flex_file['STD'] = flex_file['Median'] / 4
                    flex_file['Prop'] = flex_file['Player'].map(prop_dict)
                    flex_file = flex_file[['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD']]

                    hold_file = flex_file.copy()
                    overall_file = flex_file.copy()
                    prop_file = flex_file.copy()
                            
                    overall_players = overall_file[['Player']]

                    for x in range(0,total_sims):    
                        prop_file[x] = prop_file['Prop']

                    prop_file = prop_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)

                    for x in range(0,total_sims):
                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])

                    overall_file=overall_file.drop(['Player', 'book', 'Prop', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)

                    players_only = hold_file[['Player']]

                    player_outcomes = pd.merge(players_only, overall_file, left_index=True, right_index=True)

                    prop_check = (overall_file - prop_file)

                    players_only['Mean_Outcome'] = overall_file.mean(axis=1)
                    players_only['Book'] = players_only['Player'].map(book_dict)
                    players_only['Prop'] = players_only['Player'].map(prop_dict)
                    players_only['Trending Over'] = players_only['Player'].map(trending_over_dict)
                    players_only['Trending Under'] = players_only['Player'].map(trending_under_dict)
                    players_only['poisson_var'] = players_only.apply(calculate_poisson, axis=1)
                    players_only['10%'] = overall_file.quantile(0.1, axis=1)
                    players_only['90%'] = overall_file.quantile(0.9, axis=1)
                    players_only['Over'] = np_where(players_only['Prop'] <= 3, players_only['poisson_var'], prop_check[prop_check > 0].count(axis=1)/float(total_sims))
                    players_only['Imp Over'] = players_only['Player'].map(over_dict)
                    players_only['Over%'] = players_only[["Over", "Imp Over", "Trending Over"]].mean(axis=1)
                    players_only['Under'] = np_where(players_only['Prop'] <= 3, 1 - players_only['poisson_var'], prop_check[prop_check < 0].count(axis=1)/float(total_sims))
                    players_only['Imp Under'] = players_only['Player'].map(under_dict)
                    players_only['Under%'] = players_only[["Under", "Imp Under", "Trending Under"]].mean(axis=1)
                    players_only['Prop_avg'] = players_only['Prop'].mean() / 100
                    players_only['prop_threshold'] = .10
                    players_only = players_only[players_only['Mean_Outcome'] > 0]
                    players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
                    players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
                    players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
                    players_only['Bet_suggest'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], "Over" , "Under")
                    players_only['Bet?'] = np.where(players_only['Bet_check'] >= players_only['prop_threshold'], players_only['Bet_suggest'], "No Bet")
                    players_only['Edge'] = players_only['Bet_check']
                    players_only['Prop Type'] = prop_type_var

                    players_only['Player'] = hold_file[['Player']]
                    players_only['Team'] = players_only['Player'].map(team_dict)

                    leg_outcomes = players_only[['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Trending Over', 'Over%', 'Imp Under', 'Trending Under', 'Under%', 'Bet?', 'Edge']]
                    sim_all_hold = pd.concat([sim_all_hold, leg_outcomes], ignore_index=True)
                    
                    final_outcomes = sim_all_hold
                    st.write(f'finished {prop_type_var} for {books}')
            
            final_outcomes = final_outcomes.dropna()
            if game_select_var == 'Pick6':
                final_outcomes = final_outcomes.drop_duplicates(subset=['Player', 'Prop Type'])
            final_outcomes = final_outcomes.sort_values(by='Edge', ascending=False)

            with df_hold_container:
                df_hold_container = st.empty()
                st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
            with export_container:
                export_container = st.empty()
                st.download_button(
                    label="Export Projections",
                    data=convert_df_to_csv(final_outcomes),
                    file_name='NFL_prop_proj.csv',
                    mime='text/csv',
                    key='prop_proj',
                )