app.py

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

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

import numpy as np
import pandas as pd
import streamlit as st
import gspread
import random
import gc

def init_conn():
          scope = ['https://www.googleapis.com/auth/spreadsheets',
                    "https://www.googleapis.com/auth/drive"]
          
          credentials = {
            "type": "service_account",
            "project_id": "sheets-api-connect-378620",
            "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
            "private_key": "-----BEGIN PRIVATE KEY-----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\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"
          }

          gc = gspread.service_account_from_dict(credentials)
          return gc

gspreadcon = init_conn()

dk_player_url = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
solver_conn = 'https://docs.google.com/spreadsheets/d/1H7kdaxVF7Bv3kb1DSa_3Dq6OaC9ajq9UAQfVyDluXzk/edit#gid=0'

@st.cache_resource(ttl = 600)
def init_baslines():
    sh = gspreadcon.open_by_url(dk_player_url)
    worksheet = sh.worksheet('DK_Salaries')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"name": "Player"}, inplace = True)
    raw_display['player_id_name'] = raw_display['Player'] + " (" + raw_display['player_id'].astype(str) + ")"
    dk_ids = dict(zip(raw_display.Player, raw_display.player_id_name))
    
    return dk_ids

dk_ids = init_baslines()

freq_format = {'Proj Own': '{:.2%}', 'Freq': '{:.2%}'}

tab1, tab2 = st.tabs(['Uploads', 'Manage Portfolio'])

with tab1:
    with st.container():          
          col1, col2 = st.columns([3, 3])
          
          with col1:
                    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'. Upload your projections first to avoid an error message.")
                    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)
                                        proj_dataframe = proj_dataframe.dropna(subset='Median')
                                        proj_dataframe['Player'] = proj_dataframe['Player'].str.strip()
                                        try:
                                            proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float)
                                        except:
                                            pass
                                        
                              except:
                                        proj_dataframe = pd.read_excel(proj_file)
                                        proj_dataframe = proj_dataframe.dropna(subset='Median')
                                        proj_dataframe['Player'] = proj_dataframe['Player'].str.strip()
                                        try:
                                            proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float)
                                        except:
                                            pass
                                
                              st.table(proj_dataframe.head(10))
                              player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary))
                              player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median))
                              player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own))
                              
          with col2:
                    st.info("The Portfolio file must contain only columns in order and explicitly named: 'PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', and 'UTIL'. Upload your projections first to avoid an error message.")
                    portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader')

                    if portfolio_file is not None:
                            try:
                                      portfolio_dataframe = pd.read_csv(portfolio_file)
                                      
                            except:
                                      portfolio_dataframe = pd.read_excel(portfolio_file)
                              
                            portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
                            split_portfolio = portfolio_dataframe
                            split_portfolio[['PG', 'PG_ID']] = split_portfolio.PG.str.split("(", n=1, expand = True)
                            split_portfolio[['SG', 'SG_ID']] = split_portfolio.SG.str.split("(", n=1, expand = True)
                            split_portfolio[['SF', 'SF_ID']] = split_portfolio.SF.str.split("(", n=1, expand = True)
                            split_portfolio[['PF', 'PF_ID']] = split_portfolio.PF.str.split("(", n=1, expand = True)
                            split_portfolio[['C', 'C_ID']] = split_portfolio.C.str.split("(", n=1, expand = True)
                            split_portfolio[['G', 'G_ID']] = split_portfolio.G.str.split("(", n=1, expand = True)
                            split_portfolio[['F', 'F_ID']] = split_portfolio.F.str.split("(", n=1, expand = True)
                            split_portfolio[['UTIL', 'UTIL_ID']] = split_portfolio.UTIL.str.split("(", n=1, expand = True)
  
                            split_portfolio['PG'] = split_portfolio['PG'].str.strip()
                            split_portfolio['SG'] = split_portfolio['SG'].str.strip()
                            split_portfolio['SF'] = split_portfolio['SF'].str.strip()
                            split_portfolio['PF'] = split_portfolio['PF'].str.strip()
                            split_portfolio['C'] = split_portfolio['C'].str.strip()
                            split_portfolio['G'] = split_portfolio['G'].str.strip()
                            split_portfolio['F'] = split_portfolio['F'].str.strip()
                            split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip()
                            
                            split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
                                      split_portfolio['SG'].map(player_salary_dict),
                                      split_portfolio['SF'].map(player_salary_dict),
                                      split_portfolio['PF'].map(player_salary_dict),
                                      split_portfolio['C'].map(player_salary_dict),
                                      split_portfolio['G'].map(player_salary_dict),
                                      split_portfolio['F'].map(player_salary_dict),
                                      split_portfolio['UTIL'].map(player_salary_dict)])
                            
                            split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
                                      split_portfolio['SG'].map(player_proj_dict),
                                      split_portfolio['SF'].map(player_proj_dict),
                                      split_portfolio['PF'].map(player_proj_dict),
                                      split_portfolio['C'].map(player_proj_dict),
                                      split_portfolio['G'].map(player_proj_dict),
                                      split_portfolio['F'].map(player_proj_dict),
                                      split_portfolio['UTIL'].map(player_proj_dict)])
                            
                            split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
                                      split_portfolio['SG'].map(player_own_dict),
                                      split_portfolio['SF'].map(player_own_dict),
                                      split_portfolio['PF'].map(player_own_dict),
                                      split_portfolio['C'].map(player_own_dict),
                                      split_portfolio['G'].map(player_own_dict),
                                      split_portfolio['F'].map(player_own_dict),
                                      split_portfolio['UTIL'].map(player_own_dict)])
                            
                            st.table(split_portfolio.head(10))
                            
                            split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
                                      split_portfolio['SG'].map(player_salary_dict),
                                      split_portfolio['SF'].map(player_salary_dict),
                                      split_portfolio['PF'].map(player_salary_dict),
                                      split_portfolio['C'].map(player_salary_dict),
                                      split_portfolio['G'].map(player_salary_dict),
                                      split_portfolio['F'].map(player_salary_dict),
                                      split_portfolio['UTIL'].map(player_salary_dict)])
                            
                            split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
                                      split_portfolio['SG'].map(player_proj_dict),
                                      split_portfolio['SF'].map(player_proj_dict),
                                      split_portfolio['PF'].map(player_proj_dict),
                                      split_portfolio['C'].map(player_proj_dict),
                                      split_portfolio['G'].map(player_proj_dict),
                                      split_portfolio['F'].map(player_proj_dict),
                                      split_portfolio['UTIL'].map(player_proj_dict)])
                                                            
                            
                            split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
                                      split_portfolio['SG'].map(player_own_dict),
                                      split_portfolio['SF'].map(player_own_dict),
                                      split_portfolio['PF'].map(player_own_dict),
                                      split_portfolio['C'].map(player_own_dict),
                                      split_portfolio['G'].map(player_own_dict),
                                      split_portfolio['F'].map(player_own_dict),
                                      split_portfolio['UTIL'].map(player_own_dict)])
                                 
                            display_portfolio = split_portfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'Salary', 'Projection', 'Ownership']]
                            st.session_state.display_portfolio = display_portfolio
                            st.session_state.export_portfolio = st.session_state.display_portfolio.replace(dk_ids)
                            hold_portfolio = display_portfolio.sort_values(by='Projection', ascending=False)
                            
                            st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_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'] / len(st.session_state.display_portfolio)
                            st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(player_own_dict) / 100
                            st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
                            
                            gc.collect() 
                            
with tab2:
    with st.container():
        hold_container = st.empty()
        col1, col2, col3, col4, col5, col6 = st.columns([2, 2, 2, 2, 2, 2])
        with col1:
            if st.button("Load/Reset Data", key='reset1'):
                for key in st.session_state.keys():
                      del st.session_state[key]
                display_portfolio = hold_portfolio
                st.session_state.display_portfolio = display_portfolio
                st.session_state.export_portfolio = st.session_state.display_portfolio.replace(dk_ids)
                st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_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'] / len(st.session_state.display_portfolio)
                st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(player_own_dict) / 100
                st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
        with col2:
            if st.button("Trim Lineups", key='trim1'):
                max_proj = 10000
                max_own = display_portfolio['Ownership'].iloc[0]
                x = 0
                for index, row in display_portfolio.iterrows():
                    if row['Ownership'] > max_own:
                        display_portfolio.drop(index, inplace=True)
                    elif row['Ownership'] <= max_own:
                        max_own = row['Ownership']
                st.session_state.display_portfolio = display_portfolio
                st.session_state.export_portfolio = st.session_state.display_portfolio.replace(dk_ids)
                st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_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'] / len(st.session_state.display_portfolio)
                st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(player_own_dict) / 100
                st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
        with col3:
            if proj_file is not None:
                player_check = st.selectbox('Select player to create comps', options = proj_dataframe['Player'].unique(), key='dk_player')
        with col4:
            if proj_file is not None:
                pos_var_list = st.multiselect('Which positions would you like to include?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var_list')
        with col5:
            if st.button('Simulate appropriate pivots'):
                with hold_container:
                    
                    working_roo = proj_dataframe
                    working_roo = working_roo[working_roo['Position'].str.contains('|'.join(pos_var_list))]
                    working_roo.rename(columns={"Minutes Proj": "Minutes_Proj"}, inplace = True)
                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
                    min_dict = dict(zip(working_roo.Player, working_roo.Minutes_Proj))
                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
                    total_sims = 1000
                    
                    player_var = working_roo.loc[working_roo['Player'] == player_check]
                    player_var = player_var.reset_index()
                    
                    working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - 300) & (working_roo['Salary'] <= player_var['Salary'][0] + 300)]
                    working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - 3) & (working_roo['Median'] <= player_var['Median'][0] + 3)]
                    
                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes_Proj']]
                    flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes_Proj'] * .25)
                    flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes_Proj'] * .25)
                    flex_file['STD'] = (flex_file['Median']/4)
                    flex_file = flex_file[['Player', 'Position', 'Salary', '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):    
                        salary_file[x] = salary_file['Salary']
      
                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
                    salary_file.astype('int').dtypes
      
                    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)
                    overall_file.astype('int').dtypes
      
                    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,hold_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)
                    players_only.astype('int').dtypes
    
                    salary_2x_check = (overall_file - (salary_file*4))
                    salary_3x_check = (overall_file - (salary_file*5))
                    salary_4x_check = (overall_file - (salary_file*6))
                    gpp_check = (overall_file - ((salary_file*5)+10))
    
                    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['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
                    players_only['GPP%'] = salary_4x_check[gpp_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+%', '3x%', '4x%', '5x%', 'GPP%']]
      
                    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+%', '3x%', '4x%', '5x%', 'GPP%']]
    
                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
                    final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
                    final_Proj['Own'] = final_Proj['Own'].astype('float')
                    final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
                    final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
                    final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
                    final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
                    final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
    
                    final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
                    final_Proj['Player_swap'] = player_check
                    st.session_state.final_Proj = final_Proj
                    
                    hold_container = st.empty()
        with col6:
            if 'final_Proj' in st.session_state:
                player_swap = st.selectbox('Select player to swap to:', options = st.session_state.final_Proj['Player'].unique(), key='dk_swap')
                if st.button('Make swaps'):
                    with hold_container:
                        if pos_var_list == "PG":
                            st.session_state.display_portfolio['PG'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['G'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['UTIL'].replace(player_check, player_swap, inplace=True)
                        elif pos_var_list == "SG":
                            st.session_state.display_portfolio['SG'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['G'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['UTIL'].replace(player_check, player_swap, inplace=True)
                        elif pos_var_list == "SF":
                            st.session_state.display_portfolio['SF'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['F'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['UTIL'].replace(player_check, player_swap, inplace=True)
                        elif pos_var_list == "PF":
                            st.session_state.display_portfolio['PF'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['F'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['UTIL'].replace(player_check, player_swap, inplace=True)
                        elif pos_var_list == "C":
                            st.session_state.display_portfolio['C'].replace(player_check, player_swap, inplace=True)
                            st.session_state.display_portfolio['UTIL'].replace(player_check, player_swap, inplace=True)
                        split_portfolio = st.session_state.display_portfolio
                        
                        split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
                                  split_portfolio['SG'].map(player_salary_dict),
                                  split_portfolio['SF'].map(player_salary_dict),
                                  split_portfolio['PF'].map(player_salary_dict),
                                  split_portfolio['C'].map(player_salary_dict),
                                  split_portfolio['G'].map(player_salary_dict),
                                  split_portfolio['F'].map(player_salary_dict),
                                  split_portfolio['UTIL'].map(player_salary_dict)])
                        
                        split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
                                  split_portfolio['SG'].map(player_proj_dict),
                                  split_portfolio['SF'].map(player_proj_dict),
                                  split_portfolio['PF'].map(player_proj_dict),
                                  split_portfolio['C'].map(player_proj_dict),
                                  split_portfolio['G'].map(player_proj_dict),
                                  split_portfolio['F'].map(player_proj_dict),
                                  split_portfolio['UTIL'].map(player_proj_dict)])
                        
                        split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
                                  split_portfolio['SG'].map(player_own_dict),
                                  split_portfolio['SF'].map(player_own_dict),
                                  split_portfolio['PF'].map(player_own_dict),
                                  split_portfolio['C'].map(player_own_dict),
                                  split_portfolio['G'].map(player_own_dict),
                                  split_portfolio['F'].map(player_own_dict),
                                  split_portfolio['UTIL'].map(player_own_dict)])
                             
                        st.session_state.display_portfolio = split_portfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'Salary', 'Projection', 'Ownership']]
                        st.session_state.export_portfolio = st.session_state.display_portfolio.replace(dk_ids)
                        hold_portfolio = display_portfolio.sort_values(by='Projection', ascending=False)
                        
                        st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_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'] / len(st.session_state.display_portfolio)
                        st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(player_own_dict) / 100
                        st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
                        
                        gc.collect()
                
    with st.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(precision=2), use_container_width = True)
        
        col1, col2 = st.columns([7, 2])         
        with col1:
            if 'display_portfolio' in st.session_state:
                st.dataframe(st.session_state.display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
                st.download_button(
                    label="Export Full Frame",
                    data=st.session_state.export_portfolio.to_csv().encode('utf-8'),
                    file_name='portfolio_export.csv',
                    mime='text/csv',
                )
        with col2:
            if 'player_freq' in st.session_state:
                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)