app.py

import streamlit as st
st.set_page_config(layout="wide")

for name in dir():
    if not name.startswith('_'):
        del globals()[name]

import pulp
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import time
from itertools import combinations

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

        credentials = {
          "type": "service_account",
          "project_id": "model-sheets-connect",
          "private_key_id": st.secrets['model_sheets_connect_pk'],
          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
          "client_email": "[email protected]",
          "client_id": "100369174533302798535",
          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
          "token_uri": "https://oauth2.googleapis.com/token",
          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
        }
        
        credentials2 = {
          "type": "service_account",
          "project_id": "sheets-api-connect-378620",
          "private_key_id": st.secrets['sheets_api_connect_pk'],
          "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQCtKa01beXwc88R\nnPZVQTNPVQuBnbwoOfc66gW3547ja/UEyIGAF112dt/VqHprRafkKGmlg55jqJNt\na4zceLKV+wTm7vBu7lDISTJfGzCf2TrxQYNqwMKE2LOjI69dBM8u4Dcb4k0wcp9v\ntW1ZzLVVuwTvmrg7JBHjiSaB+x5wxm/r3FOiJDXdlAgFlytzqgcyeZMJVKKBQHyJ\njEGg/1720A0numuOCt71w/2G0bDmijuj1e6tH32MwRWcvRNZ19K9ssyDz2S9p68s\nYDhIxX69OWxwScTIHLY6J2t8txf/XMivL/636fPlDADvBEVTdlT606n8CcKUVQeq\npUVdG+lfAgMBAAECggEAP38SUA7B69eTfRpo658ycOs3Amr0JW4H/bb1rNeAul0K\nZhwd/HnU4E07y81xQmey5kN5ZeNrD5EvqkZvSyMJHV0EEahZStwhjCfnDB/cxyix\nZ+kFhv4y9eK+kFpUAhBy5nX6T0O+2T6WvzAwbmbVsZ+X8kJyPuF9m8ldcPlD0sce\ntj8NwVq1ys52eosqs7zi2vjt+eMcaY393l4ls+vNq8Yf27cfyFw45W45CH/97/Nu\n5AmuzlCOAfFF+z4OC5g4rei4E/Qgpxa7/uom+BVfv9G0DIGW/tU6Sne0+37uoGKt\nW6DzhgtebUtoYkG7ZJ05BTXGp2lwgVcNRoPwnKJDxQKBgQDT5wYPUBDW+FHbvZSp\nd1m1UQuXyerqOTA9smFaM8sr/UraeH85DJPEIEk8qsntMBVMhvD3Pw8uIUeFNMYj\naLmZFObsL+WctepXrVo5NB6RtLB/jZYxiKMatMLUJIYtcKIp+2z/YtKiWcLnwotB\nWdCjVnPTxpkurmF2fWP/eewZ+wKBgQDRMtJg7etjvKyjYNQ5fARnCc+XsI3gkBe1\nX9oeXfhyfZFeBXWnZzN1ITgFHplDznmBdxAyYGiQdbbkdKQSghviUQ0igBvoDMYy\n1rWcy+a17Mj98uyNEfmb3X2cC6WpvOZaGHwg9+GY67BThwI3FqHIbyk6Ko09WlTX\nQpRQjMzU7QKBgAfi1iflu+q0LR+3a3vvFCiaToskmZiD7latd9AKk2ocsBd3Woy9\n+hXXecJHPOKV4oUJlJgvAZqe5HGBqEoTEK0wyPNLSQlO/9ypd+0fEnArwFHO7CMF\nycQprAKHJXM1eOOFFuZeQCaInqdPZy1UcV5Szla4UmUZWkk1m24blHzXAoGBAMcA\nyH4qdbxX9AYrC1dvsSRvgcnzytMvX05LU0uF6tzGtG0zVlub4ahvpEHCfNuy44UT\nxRWW/oFFaWjjyFxO5sWggpUqNuHEnRopg3QXx22SRRTGbN45li/+QAocTkgsiRh1\nqEcYZsO4mPCsQqAy6E2p6RcK+Xa+omxvSnVhq0x1AoGAKr8GdkCl4CF6rieLMAQ7\nLNBuuoYGaHoh8l5E2uOQpzwxVy/nMBcAv+2+KqHEzHryUv1owOi6pMLv7A9mTFoS\n18B0QRLuz5fSOsVnmldfC9fpUc6H8cH1SINZpzajqQA74bPwELJjnzrCnH79TnHG\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
          "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
          "client_id": "106625872877651920064",
          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
          "token_uri": "https://oauth2.googleapis.com/token",
          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
        }
     
        NHL_Data = st.secrets['NHL_Data']

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

        return gc, gc2, NHL_Data
    
gcservice_account, gcservice_account2, NHL_Data = init_conn()

expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'}

@st.cache_resource(ttl = 599)
def grab_baseline_stuff():
    try:
        sh = gcservice_account.open_by_url(NHL_Data)
    except:
        sh = gcservice_account2.open_by_url(NHL_Data)
    worksheet = sh.worksheet('Player_Data_Master')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.replace('  -   ', 0, inplace=True)
    raw_display.replace('', np.nan, inplace=True)
    raw_display = raw_display.dropna(subset=' Clean Name ')
    dk_raw_proj = raw_display[[' Clean Name ', ' Team ', ' Opp ', ' Line ', ' PP Unit ', ' Position ', ' DK Salary ', ' Final DK Projection ', ' DK uploadID ', 'DK_Own', ' MainSlateDK ']]
    dk_raw_proj = dk_raw_proj.set_axis(['Player', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'Salary', 'Median', 'player_id', 'Own', 'MainSlateDK'], axis=1)
    dk_raw_proj = dk_raw_proj.dropna(subset='Salary')
    fd_raw_proj = raw_display[[' Clean Name ', ' Team ', ' Opp ', ' Line ', ' PP Unit ', ' FD Position ', ' FD Salary ', ' Final FD Projection ', ' FD uploadID ', 'FD_Own', ' MainSlateFD ']]
    fd_raw_proj = fd_raw_proj.set_axis(['Player', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'Salary', 'Median', 'player_id', 'Own', 'MainSlateFD'], axis=1)
    dk_raw_proj['Own'] = dk_raw_proj['Own'].astype(float)
    fd_raw_proj['Own'] = fd_raw_proj['Own'].astype(float)
    dk_raw_proj['player_id'] = dk_raw_proj['player_id'].astype(str)
    fd_raw_proj['player_id'] = fd_raw_proj['player_id'].astype(str)
    dk_raw_proj['Name_ID'] = dk_raw_proj['Player'] + ' (' + dk_raw_proj['player_id'].str[:-2] + ')'
    fd_raw_proj['Name_ID'] = fd_raw_proj['player_id'].str[:-2] + ':' + fd_raw_proj['Player']
    dk_raw_proj = dk_raw_proj.sort_values(by='Median', ascending=False)
    fd_raw_proj = fd_raw_proj.sort_values(by='Median', ascending=False)
    dk_raw_proj['Player'] = dk_raw_proj['Player'].str.strip()
    fd_raw_proj['Player'] = fd_raw_proj['Player'].str.strip()
    dk_ids = dict(zip(dk_raw_proj['Player'], dk_raw_proj['Name_ID']))
    fd_ids = dict(zip(fd_raw_proj['Player'], fd_raw_proj['Name_ID']))
    
    worksheet = sh.worksheet('Timestamp')
    timestamp = worksheet.acell('A1').value
    
    worksheet = sh.worksheet('Player_Lines_ROO')
    line_frame = pd.DataFrame(worksheet.get_all_records())

    return dk_raw_proj, fd_raw_proj, dk_ids, fd_ids, timestamp, line_frame

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

dk_raw_proj, fd_raw_proj, dkid_dict, fdid_dict, timestamp, line_frame = grab_baseline_stuff()
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
opp_dict = dict(zip(dk_raw_proj.Team, dk_raw_proj.Opp))

tab1, tab2 = st.tabs(['Optimizer', 'Uploads and Info'])
        
with tab1:
    col1, col2 = st.columns([1, 5])
    with col1:
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset1'):
              st.cache_data.clear()
              dk_raw_proj, fd_raw_proj, dk_ids, fd_ids, timestamp, line_frame = grab_baseline_stuff()
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
              for key in st.session_state.keys():
                  del st.session_state[key]
              
        slate_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='slate_var1')
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
        if slate_var1 != 'User':
            mainvar1 = st.radio("Main slate or Secondary?", ('Main Slate', 'Secondary'), key='mainvar1')
        if site_var1 == 'Draftkings':
              if slate_var1 == 'User':
                  init_baselines = proj_dataframe
                  
              elif slate_var1 != 'User':
                  init_baselines = dk_raw_proj
                  if mainvar1 == 'Main Slate':
                      init_baselines = init_baselines.loc[init_baselines['MainSlateDK'] == ' Main ']
                  if mainvar1 != 'Main Slate':
                      init_baselines = init_baselines.loc[init_baselines['MainSlateDK'] != ' Main ']
        elif site_var1 == 'Fanduel':
              if slate_var1 == 'User':
                  init_baselines = proj_dataframe
              elif slate_var1 != 'User':
                  init_baselines = fd_raw_proj
                  if mainvar1 == 'Main Slate':
                      init_baselines = init_baselines.loc[init_baselines['MainSlateFD'] == ' Main ']
                  if mainvar1 != 'Main Slate':
                      init_baselines = init_baselines.loc[init_baselines['MainSlateFD'] != ' Main ']
        contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'GPP'), key='contest_var1')
        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 in the optimization?', options = init_baselines['Team'].unique(), key='team_var1')
        elif split_var1 == 'Full Slate Run':
            team_var1 = init_baselines.Team.values.tolist()
        lock_var1 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = init_baselines['Player'].unique(), key='lock_var1')
        avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = init_baselines['Player'].unique(), key='avoid_var1')
        linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 1, step = 1, key='linenum_var1')
        if site_var1 == 'Draftkings':
            min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1')
            max_sal1 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal1')
        elif site_var1 == 'Fanduel':
            min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 54900, value = 54000, step = 100, key='min_sal1')
            max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 55000, value = 55000, step = 100, key='max_sal1')
    with col2:
        init_baselines = init_baselines[init_baselines['Team'].isin(team_var1)]
        init_baselines = init_baselines[~init_baselines['Player'].isin(avoid_var1)]
        ownframe = init_baselines.copy()
        raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Line', 'PP Unit', 'Median', 'Own']]
        raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
        raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var1), 1, 0)
        st.session_state.export_baselines = raw_baselines.copy()
        st.session_state.display_baselines = raw_baselines.copy()
        st.session_state.display_lines = line_frame[line_frame['Slate'] == mainvar1]
        
        display_container = st.empty()
        display_dl_container = st.empty()
        optimize_container = st.empty()
        download_container = st.empty()
        freq_container = st.empty()
        
        if st.button('Optimize'):
            max_proj = 1000
            max_own = 1000
            total_proj = 0
            total_own = 0
            
            lineup_display = []
            check_list = []
            lineups = []
            portfolio = pd.DataFrame()
            x = 1
    
            with st.spinner('Wait for it...'):
                with optimize_container:
                    while x <= linenum_var1:
                        sorted_lineup = []
                        p_used = []
                        cvar = 0
                        firvar = 0
                        secvar = 0
                        thirvar = 0

                        raw_proj_file = raw_baselines
                        raw_flex_file = raw_proj_file.dropna(how='all')
                        raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
                        flex_file = raw_flex_file
                        flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
                        flex_file['name_var'] = flex_file['Player']
                        flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var1), 1, 0)
                        player_ids = flex_file.index

                        overall_players = flex_file[['Player']]
                        overall_players['player_var_add'] = flex_file.index
                        overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)

                        player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
                        total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
                        player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
                        player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))

                        player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
                        player_team = dict(zip(flex_file['Player'], flex_file['Team']))
                        player_pos = dict(zip(flex_file['Player'], flex_file['Position']))
                        player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
                        player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
                        player_line = dict(zip(flex_file['Player'], flex_file['Line']))
                        player_ppunit = dict(zip(flex_file['Player'], flex_file['PP Unit']))

                        obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
                        total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1
                        total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1

                        if site_var1 == 'Draftkings':
                            
                            for flex in flex_file['lock'].unique():
                                sub_idx = flex_file[flex_file['lock'] == 1].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1)

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] != "RIP"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "G"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "C"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "W"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "C"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "W"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "D"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2

                        elif site_var1 == 'Fanduel':
                            
                            for flex in flex_file['lock'].unique():
                                sub_idx = flex_file[flex_file['lock'] == 1].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1)

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] != "RIP"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "G"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "C"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "W"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "C"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "W"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2

                            for flex in flex_file['Position'].unique():
                                sub_idx = flex_file[flex_file['Position'] == "D"].index
                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2

                        player_count = []
                        player_trim = []
                        lineup_list = []
                        
                        if contest_var1 == 'Cash':
                            obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
                            total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
                        elif contest_var1 != 'Cash':
                            obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
                            total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
                        

                        total_score.solve()
                        for v in total_score.variables():
                            if v.varValue > 0:
                                lineup_list.append(v.name)
                        df = pd.DataFrame(lineup_list)
                        df['Names'] = df[0].map(player_match)
                        df['Cost'] = df['Names'].map(player_sal)
                        df['Proj'] = df['Names'].map(player_proj)
                        df['Own'] = df['Names'].map(player_own)
                        df['Line'] = df['Names'].map(player_line)
                        total_cost = sum(df['Cost'])
                        total_own = sum(df['Own'])
                        total_proj = sum(df['Proj'])
                        lineup_raw = pd.DataFrame(lineup_list)
                        lineup_raw['Names'] = lineup_raw[0].map(player_match)
                        lineup_raw['value'] = lineup_raw[0].map(player_index_match)
                        lineup_final = lineup_raw.sort_values(by=['value'])
                        del lineup_final[lineup_final.columns[0]]
                        del lineup_final[lineup_final.columns[1]]
                        lineup_final = lineup_final.reset_index(drop=True)
                        
                        lineup_test = lineup_final
                        lineup_final = lineup_final.T
                        lineup_final['Cost'] = total_cost
                        lineup_final['Proj'] = total_proj
                        lineup_final['Own'] = total_own
                    
                        lineup_test['Team'] = lineup_test['Names'].map(player_team)
                        lineup_test['Position'] = lineup_test['Names'].map(player_pos)
                        lineup_test['Line'] = lineup_test['Names'].map(player_line)
                        lineup_test['Salary'] = lineup_test['Names'].map(player_sal)
                        lineup_test['Proj'] = lineup_test['Names'].map(player_proj)
                        lineup_test['Own'] = lineup_test['Names'].map(player_own)
                        lineup_test = lineup_test.set_index('Names')
                        lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0)

                        lineup_display.append(lineup_test)

                        with col2:
                            with st.container():
                                st.table(lineup_test)

                        max_proj = total_proj
                        max_own = total_own

                        check_list.append(total_proj)

                        portfolio = pd.concat([portfolio, lineup_final], ignore_index = True)

                        x += 1
    
                    if site_var1 == 'Draftkings':
                        portfolio.rename(columns={0: "C1", 1: "C2", 2: "W1", 3: "W2", 4: "W3", 5: "D1", 6: "D2", 7: "G", 8: "UTIL"}, inplace = True)
                    elif site_var1 == 'Fanduel':
                        portfolio.rename(columns={0: "C1", 1: "C2", 2: "W1", 3: "W2", 4: "D1", 5: "D2", 6: "UTIL1", 7: "UTIL2", 8: "G"}, inplace = True)
                    portfolio = portfolio.dropna()
                    portfolio = portfolio.reset_index()
                    portfolio['Lineup_num'] = portfolio['index'] + 1
                    portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True)
                    portfolio = portfolio.set_index('Lineup')
                    portfolio = portfolio.drop(columns=['index'])
                    
                    st.session_state.portfolio = portfolio.drop_duplicates()
                    
                    st.session_state.final_outcomes = portfolio
                    
                    if site_var1 == 'Draftkings':
                        final_outcomes = portfolio[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'Cost', 'Proj', 'Own']]
                        
                        final_outcomes_export = pd.DataFrame()
                        final_outcomes_export['C1'] = final_outcomes['C1']
                        final_outcomes_export['C2'] = final_outcomes['C2']
                        final_outcomes_export['W1'] = final_outcomes['W1']
                        final_outcomes_export['W2'] = final_outcomes['W2']
                        final_outcomes_export['W3'] = final_outcomes['W3']
                        final_outcomes_export['D1'] = final_outcomes['D1']
                        final_outcomes_export['D2'] = final_outcomes['D2']
                        final_outcomes_export['G'] = final_outcomes['G']
                        final_outcomes_export['UTIL'] = final_outcomes['UTIL']
                        final_outcomes_export['Salary'] = final_outcomes['Cost']
                        final_outcomes_export['Own'] = final_outcomes['Own']
                        final_outcomes_export['Proj'] = final_outcomes['Proj']
                        
                        final_outcomes_export['C1'].replace(dkid_dict, inplace=True)
                        final_outcomes_export['C2'].replace(dkid_dict, inplace=True)
                        final_outcomes_export['W1'].replace(dkid_dict, inplace=True)
                        final_outcomes_export['W2'].replace(dkid_dict, inplace=True)
                        final_outcomes_export['W3'].replace(dkid_dict, inplace=True)
                        final_outcomes_export['D1'].replace(dkid_dict, inplace=True)
                        final_outcomes_export['D2'].replace(dkid_dict, inplace=True)
                        final_outcomes_export['G'].replace(dkid_dict, inplace=True)
                        final_outcomes_export['UTIL'].replace(dkid_dict, inplace=True)
                        
                        st.session_state.final_outcomes_export = final_outcomes_export.copy()
                    elif site_var1 == 'Fanduel':
                        final_outcomes = portfolio[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'Cost', 'Proj', 'Own']]
                        
                        final_outcomes_export = pd.DataFrame()
                        final_outcomes_export['C1'] = final_outcomes['C1']
                        final_outcomes_export['C2'] = final_outcomes['C2']
                        final_outcomes_export['W1'] = final_outcomes['W1']
                        final_outcomes_export['W2'] = final_outcomes['W2']
                        final_outcomes_export['D1'] = final_outcomes['D1']
                        final_outcomes_export['D2'] = final_outcomes['D2']
                        final_outcomes_export['UTIL1'] = final_outcomes['UTIL1']
                        final_outcomes_export['UTIL2'] = final_outcomes['UTIL2']
                        final_outcomes_export['G'] = final_outcomes['G']
                        final_outcomes_export['Salary'] = final_outcomes['Cost']
                        final_outcomes_export['Own'] = final_outcomes['Own']
                        final_outcomes_export['Proj'] = final_outcomes['Proj']
                        
                        final_outcomes_export['C1'].replace(fdid_dict, inplace=True)
                        final_outcomes_export['C2'].replace(fdid_dict, inplace=True)
                        final_outcomes_export['W1'].replace(fdid_dict, inplace=True)
                        final_outcomes_export['W2'].replace(fdid_dict, inplace=True)
                        final_outcomes_export['D1'].replace(fdid_dict, inplace=True)
                        final_outcomes_export['D2'].replace(fdid_dict, inplace=True)
                        final_outcomes_export['UTIL1'].replace(fdid_dict, inplace=True)
                        final_outcomes_export['UTIL2'].replace(fdid_dict, inplace=True)
                        final_outcomes_export['G'].replace(fdid_dict, inplace=True)
                        
                        st.session_state.FD_final_outcomes_export = final_outcomes_export.copy()
                    
                    st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.portfolio.iloc[:,0:8].values, return_counts=True)),
                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                    st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
                    st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(player_pos)
                    st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(player_sal)
                    st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(player_own) / 100
                    st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(linenum_var1)
                    st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(player_team)
      
                    st.session_state.player_freq = st.session_state.player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
                    st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
    
        with display_container:
            display_container = st.empty()
            if 'display_baselines' in st.session_state:
                tab1, tab2 = st.tabs(['Line Combo ROO', 'Player Projections'])
                with tab1:
                    st.dataframe(st.session_state.display_lines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
                with tab2:
                    st.dataframe(st.session_state.display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)

        with display_dl_container:
                    display_dl_container = st.empty()
                    if 'export_baselines' in st.session_state:
                        st.download_button(
                            label="Export Projections",
                            data=convert_df_to_csv(st.session_state.export_baselines),
                            file_name='NHL_proj_export.csv',
                            mime='text/csv',
                        )        
                
        with optimize_container:
                    optimize_container = st.empty()
                    if 'final_outcomes' in st.session_state:
                        st.dataframe(st.session_state.final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
        
        with download_container:
            download_container = st.empty()
            if site_var1 == 'Draftkings':
                if 'final_outcomes_export' in st.session_state:
                    st.download_button(
                        label="Export Optimals",
                        data=convert_df_to_csv(st.session_state.final_outcomes_export),
                        file_name='NHL_optimals_export.csv',
                        mime='text/csv',
                    )
            elif site_var1 == 'Fanduel':
                if 'FD_final_outcomes_export' in st.session_state:
                    st.download_button(
                        label="Export Optimals",
                        data=convert_df_to_csv(st.session_state.FD_final_outcomes_export),
                        file_name='FD_NHL_optimals_export.csv',
                        mime='text/csv',
                    )
        
        with freq_container:
            freq_container = st.empty()
            if 'player_freq' in st.session_state:
                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)

with tab2:
    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Line', 'PP Unit', 'Own', and 'player_id'. The player_id is the draftkings or fanduel ID associated with the player for upload.")
    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:
                            proj_dataframe = pd.read_csv(proj_file)
                  except:
                            proj_dataframe = pd.read_excel(proj_file)
    with col2:
        if proj_file is not None:  
                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)