app.py

import numpy as np
from numpy import random
import pandas as pd
import streamlit as st
import pymongo

st.set_page_config(layout="wide")

@st.cache_resource
def init_conn():
        uri = st.secrets['mongo_uri']
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
        db = client["NHL_Database"]

        return db
    
db = init_conn()

wrong_acro = ['WSH', 'AZ']
right_acro = ['WAS', 'ARI']

game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}

team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}',
                   '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'}

player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
                   '4x%': '{:.2%}','GPP%': '{:.2%}'}

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

    .stTabs [data-baseweb="tab"] {
        height: 50px;
        white-space: pre-wrap;
        background-color: #FFD700;
        color: white;
        border-radius: 10px;
        gap: 1px;
        padding: 10px 20px;
        font-weight: bold;
        transition: all 0.3s ease;
    }

    .stTabs [aria-selected="true"] {
        background-color: #DAA520;
        color: white;
    }

    .stTabs [data-baseweb="tab"]:hover {
        background-color: #DAA520;
        cursor: pointer;
    }
</style>""", unsafe_allow_html=True)

@st.cache_resource(ttl = 599)
def player_stat_table():
    collection = db["Player_Level_ROO"] 
    cursor = collection.find()
    load_display = pd.DataFrame(cursor)
    
    load_display.replace('', np.nan, inplace=True)
    player_stats = load_display.copy()

    dk_load_display = load_display[load_display['Site'] == 'Draftkings']
    fd_load_display = load_display[load_display['Site'] == 'Fanduel']

    dk_load_display = dk_load_display.sort_values(by='Own', ascending=False)
    fd_load_display = fd_load_display.sort_values(by='Own', ascending=False)

    dk_load_display = dk_load_display.dropna(subset=['Own'])
    fd_load_display = fd_load_display.dropna(subset=['Own'])
    
    dk_roo_raw = dk_load_display
    fd_roo_raw = fd_load_display

    return player_stats, dk_roo_raw, fd_roo_raw

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

player_stats, dk_roo_raw, fd_roo_raw = player_stat_table()
opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"

st.header("NHL Pivot Finder Tool")
with st.expander("Info and Filters"):
    st.info(t_stamp)
    if st.button("Load/Reset Data", key='reset1'):
            st.cache_data.clear()
            for key in st.session_state.keys():
                del st.session_state[key]
            player_stats, dk_roo_raw, fd_roo_raw = player_stat_table()
            opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
    site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
    if site_var1 == 'Draftkings':
        raw_baselines = dk_roo_raw[dk_roo_raw['Slate'] == 'Main Slate']
        raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
    elif site_var1 == 'Fanduel':
        raw_baselines = fd_roo_raw[fd_roo_raw['Slate'] == 'Main Slate']
        raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
    check_seq = st.radio("Do you want to check a single player or the top 10 in ownership?", ('Single Player', 'Top X Owned'), key='check_seq')
    if check_seq == 'Single Player':
        player_check = st.selectbox('Select player to create comps', options = raw_baselines['Player'].unique(), key='dk_player')
    elif check_seq == 'Top X Owned':
        top_x_var = st.number_input('How many players would you like to check?', min_value = 1, max_value = 10, value = 5, step = 1)
    Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100)
    Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1)
    pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1')
    if pos_var1 == 'Specific Positions':
        pos_var_list = st.multiselect('Which positions would you like to include?', options = raw_baselines['Position'].unique(), key='pos_var_list')
    elif pos_var1 == 'All Positions':
        pos_var_list = raw_baselines.Position.values.tolist()
    split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
    if split_var1 == 'Specific Games':
        team_var1 = st.multiselect('Which teams would you like to include?', options = raw_baselines['Team'].unique(), key='team_var1')
    elif split_var1 == 'Full Slate Run':
        team_var1 = raw_baselines.Team.values.tolist()

placeholder = st.empty()
displayholder = st.empty()

if st.button('Simulate appropriate pivots'):
    with placeholder:
        if site_var1 == 'Draftkings':
                working_roo = raw_baselines
                working_roo.replace('', 0, inplace=True)
        if site_var1 == 'Fanduel':
                working_roo = raw_baselines
                working_roo.replace('', 0, inplace=True)
                
        own_dict = dict(zip(working_roo.Player, working_roo.Own))
        team_dict = dict(zip(working_roo.Player, working_roo.Team))
        opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
        pos_dict = dict(zip(working_roo.Player, working_roo.Position))
        total_sims = 1000

        if check_seq == 'Single Player':
            player_var = working_roo.loc[working_roo['Player'] == player_check]
            player_var = player_var.reset_index()
            
            working_roo = working_roo[working_roo['Position'].isin(pos_var_list)]
            working_roo = working_roo[working_roo['Team'].isin(team_var1)]
            working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
            working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]

            flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
            flex_file['Floor_raw'] = flex_file['Median'] * .25
            flex_file['Ceiling_raw'] = flex_file['Median'] * 2
            flex_file['Floor'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * .5, flex_file['Floor_raw'])
            flex_file['Floor'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * .1, flex_file['Floor_raw'])
            flex_file['Ceiling'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
            flex_file['Ceiling'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
            flex_file['STD'] = flex_file['Median'] / 3
            flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
            hold_file = flex_file.copy()
            overall_file = flex_file.copy()
            salary_file = flex_file.copy()

            overall_players = overall_file[['Player']]

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

            salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)

            salary_file = salary_file.div(1000)

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

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

            for x in range(0,total_sims):
                maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
                raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                players_only[x] = raw_lineups_file[x].rank(ascending=False)

            players_only=players_only.drop(['Player'], axis=1)

            salary_2x_check = (overall_file - (salary_file*2))
            salary_3x_check = (overall_file - (salary_file*3))
            salary_4x_check = (overall_file - (salary_file*4))

            players_only['Average_Rank'] = players_only.mean(axis=1)
            players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
            players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
            players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
            players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
            players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
            players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
            players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)

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

            final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]

            final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
            final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
            final_Proj['Own'] = final_Proj['Player'].map(own_dict)
            final_Proj['Team'] = final_Proj['Player'].map(team_dict)
            final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
            final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
            final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
            final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
            final_Proj['LevX'] = 0
            final_Proj['LevX'] = np.where(final_Proj['Position'] == 'C', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
            final_Proj['LevX'] = np.where(final_Proj['Position'] == 'W', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
            final_Proj['LevX'] = np.where(final_Proj['Position'] == 'D', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
            final_Proj['LevX'] = np.where(final_Proj['Position'] == 'G', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
            final_Proj['CPT_Own'] = final_Proj['Own'] / 4

            final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
            final_Proj = final_Proj.set_index('Player')
            st.session_state.final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)

        elif check_seq == 'Top X Owned':
            if pos_var1 == 'Specific Positions':    
                raw_baselines = raw_baselines[raw_baselines['Position'].isin(pos_var_list)]
            player_check = raw_baselines['Player'].head(top_x_var).tolist()
            final_proj_list = []
            for players in player_check:
                players_pos = pos_dict[players]
                player_var = working_roo.loc[working_roo['Player'] == players]
                player_var = player_var.reset_index()
                working_roo_temp = working_roo[working_roo['Position'] == players_pos]
                working_roo_temp = working_roo_temp[working_roo_temp['Team'].isin(team_var1)]
                working_roo_temp = working_roo_temp.loc[(working_roo_temp['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo_temp['Salary'] <= player_var['Salary'][0] + Salary_var)]
                working_roo_temp = working_roo_temp.loc[(working_roo_temp['Median'] >= player_var['Median'][0] - Median_var) & (working_roo_temp['Median'] <= player_var['Median'][0] + Median_var)]
                
                flex_file = working_roo_temp[['Player', 'Position', 'Salary', 'Median']]
                flex_file['Floor_raw'] = flex_file['Median'] * .25
                flex_file['Ceiling_raw'] = flex_file['Median'] * 2
                flex_file['Floor'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * .5, flex_file['Floor_raw'])
                flex_file['Floor'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * .1, flex_file['Floor_raw'])
                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'G', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'D', flex_file['Median'] * 1.75, flex_file['Ceiling_raw'])
                flex_file['STD'] = flex_file['Median'] / 3
                flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                hold_file = flex_file.copy()
                overall_file = flex_file.copy()
                salary_file = flex_file.copy()
                
                overall_players = overall_file[['Player']]

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

                salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)

                salary_file = salary_file.div(1000)

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

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

                for x in range(0,total_sims):
                    maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
                    raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
                    players_only[x] = raw_lineups_file[x].rank(ascending=False)

                players_only=players_only.drop(['Player'], axis=1)

                salary_2x_check = (overall_file - (salary_file*2))
                salary_3x_check = (overall_file - (salary_file*3))
                salary_4x_check = (overall_file - (salary_file*4))

                players_only['Average_Rank'] = players_only.mean(axis=1)
                players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
                players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
                players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
                players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
                players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
                players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
                players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)

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

                final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]

                final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
                final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
                final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                final_Proj['Team'] = final_Proj['Player'].map(team_dict)
                final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
                final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
                final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
                final_Proj['LevX'] = 0
                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'C', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'W', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'D', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'G', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                final_Proj['CPT_Own'] = final_Proj['Own'] / 4
                final_Proj['Pivot_source'] = players

                final_Proj = final_Proj[['Player', 'Pivot_source', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
                
                final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
                final_proj_list.append(final_Proj)
                st.write(f'finished run for {players}')
        
            # Concatenate all the final_Proj dataframes
            final_Proj_combined = pd.concat(final_proj_list)
            final_Proj_combined = final_Proj_combined.sort_values(by='LevX', ascending=False)
            final_Proj_combined = final_Proj_combined[final_Proj_combined['Player'] != final_Proj_combined['Pivot_source']]
            st.session_state.final_Proj = final_Proj_combined.reset_index(drop=True)  # Assign the combined dataframe back to final_Proj
    placeholder.empty()

with displayholder.container():
    if 'final_Proj' in st.session_state:
        st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)

        st.download_button(
            label="Export Tables",
            data=convert_df_to_csv(st.session_state.final_Proj),
            file_name='NHL_pivot_export.csv',
            mime='text/csv',
        )
    else:
        st.write("Run some pivots my dude/dudette")