src/streamlit_app.py

import streamlit as st
import numpy as np
import pandas as pd
import re
from database import db

st.set_page_config(layout="wide")

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 */
    .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=600)
def init_baselines():

    collection = db["Player_Baselines"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['name', 'Team', 'Opp', 'Position', 'Salary', 'team_plays', 'team_pass', 'team_rush', 'team_tds', 'team_pass_tds', 'team_rush_tds', 'dropbacks', 'pass_yards', 'pass_tds',
                               'rush_att', 'rush_yards', 'rush_tds', 'targets', 'rec', 'rec_yards', 'rec_tds', 'PPR', 'Half_PPR', 'Own']]
    player_stats = raw_display[raw_display['Position'] != 'K']

    collection = db["DK_NFL_ROO"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
    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']
    dk_roo_raw = load_display.dropna(subset=['Median'])

    collection = db["FD_NFL_ROO"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
    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']
    fd_roo_raw = load_display.dropna(subset=['Median'])

    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 = init_baselines()
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"

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()
        player_stats, dk_roo_raw, fd_roo_raw = init_baselines()
        for key in st.session_state.keys():
            del st.session_state[key]
with app_view_site_column:
    with st.container():
        app_view_column, app_site_column = st.columns([3, 3])
        with app_view_column:
            view_var = st.selectbox("Select view", ["Simple", "Advanced"], key='view_selectbox')
        with app_site_column:
            site_var = st.selectbox("What site do you want to view?", ('Draftkings', 'Fanduel'), key='site_selectbox')

selected_tab = st.segmented_control(
    "Select Tab",
    options=["Pivot Finder", "User Upload"],
    selection_mode='single',
    default='Pivot Finder',
    width='stretch',
    label_visibility='collapsed',
    key='tab_selector'
)

if selected_tab == 'Pivot Finder':
    with st.expander("Infos and Filters"):
        st.info("Welcome to the Pivot Finder! Select a player or a set of players and run the algorithm to find the best spots to pivot for maximum relative value.")
        app_info_column, player_select_column, micro_filter_column, macro_filter_column = st.columns(4)
        with app_info_column:
            st.info(t_stamp)
            if st.button("Load/Reset Data", key='reset1'):
                st.cache_data.clear()
                player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = init_baselines()
                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"
            data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1')
            if site_var == 'Draftkings':
                if data_var1 == 'User':
                    raw_baselines = st.session_state['proj_dataframe']
                elif data_var1 != 'User':
                    raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == 'Main Slate']
                    raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
                    raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
            elif site_var == 'Fanduel':
                if data_var1 == 'User':
                    raw_baselines = st.session_state['proj_dataframe']
                elif data_var1 != 'User':
                    raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == 'Main Slate']
                    raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
                    raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
        with player_select_column:
            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)
        with micro_filter_column:
            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)
        with macro_filter_column:
            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_var == 'Draftkings':
                working_roo = raw_baselines
                working_roo.replace('', 0, inplace=True)
            if site_var == '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'] * 1.75
                flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
                flex_file['Floor'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * .15), flex_file['Floor_raw'])
                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
                flex_file['STD'] = flex_file['Median'] / 4
                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']

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

                salary_file = salary_file.div(1000)

                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', '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'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', 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'] == 'RB', 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'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].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'] * 1.75
                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
                    flex_file['Floor'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * .15), flex_file['Floor_raw'])
                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'WR', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
                    flex_file['STD'] = flex_file['Median'] / 4
                    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']
    
                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
    
                    salary_file = salary_file.div(1000)
    
                    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', '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'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', 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'] == 'RB', 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'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].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:
            if view_var == 'Simple':
                display_table = st.session_state['final_Proj'][['Position', 'Team', 'Salary', 'Median', 'Own', 'LevX']]
                st.dataframe(display_table.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
            if view_var == 'Advanced':
                display_table = st.session_state['final_Proj']
                st.dataframe(display_table.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='NFL_pivot_export.csv',
                mime='text/csv',
            )
        else:
            st.write("Run some pivots my dude/dudette")

if selected_tab == 'User Upload':
    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'.")
    col1, col2 = st.columns([1, 5])

    with col1:
        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
    
        if proj_file is not None:
                  try:
                            st.session_state['proj_dataframe'] = pd.read_csv(proj_file)
                  except:
                            st.session_state['proj_dataframe'] = pd.read_excel(proj_file)
    with col2:
        if proj_file is not None:  
                  st.dataframe(st.session_state['proj_dataframe'].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)