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

@st.cache_resource
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

gcservice_account = init_conn()

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

@st.cache_resource(ttl = 300)
def init_stat_load():
    sh = gcservice_account.open_by_url(all_dk_player_projections)
    worksheet = sh.worksheet('DK_Build_Up')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    dk_raw = raw_display.sort_values(by='Median', ascending=False)
    
    worksheet = sh.worksheet('FD_Build_Up')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
    raw_display.replace("", 'Welp', inplace=True)
    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
    raw_display = raw_display.loc[raw_display['Median'] > 0]
    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
    fd_raw = raw_display.sort_values(by='Median', ascending=False)

    return dk_raw, fd_raw

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

dk_raw, fd_raw = init_stat_load()
opp_dict = dict(zip(dk_raw.Team, dk_raw.Opp))
t_stamp = "Fix this later"

tab1, tab2 = st.tabs(['Uploads and Info', 'Pivot Finder'])

with tab1:
    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', '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:
                            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)
        
with tab2:
    col1, col2 = st.columns([1, 9])
    with col1:
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset1'):
              st.cache_data.clear()
              dk_raw, fd_raw = init_stat_load()
              opp_dict = dict(zip(dk_raw.Team, dk_raw.Opp))
              t_stamp = "Fix this later"
              for key in st.session_state.keys():
                  del st.session_state[key]
        data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1')
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
        if site_var1 == 'Draftkings':
              if data_var1 == 'User':
                  raw_baselines = proj_dataframe
              elif data_var1 != 'User':
                  raw_baselines = dk_raw
        elif site_var1 == 'Fanduel':
              if data_var1 == 'User':
                  raw_baselines = proj_dataframe
              elif data_var1 != 'User':
                  raw_baselines = fd_raw
        player_check = st.selectbox('Select player to create comps', options = dk_raw['Player'].unique(), key='dk_player')
        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 = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var_list')
        elif pos_var1 == 'All Positions':
            pos_var_list = ['PG', 'SG', 'SF', 'PF', 'C']
        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()
        
    with col2:
        proj_container = st.empty()
        display_container = st.empty()
        display_dl_container = st.empty()
        hold_container = st.empty()
        if site_var1 == 'Draftkings':
            raw_baselines = dk_raw
        elif site_var1 == 'Fanduel':
            raw_baselines = fd_raw
        st.session_state.proj_display = raw_baselines.copy()
        if st.button('Simulate appropriate pivots'):
            with hold_container:
                
                working_roo = raw_baselines
                working_roo = working_roo[working_roo['Team'].isin(team_var1)]
                own_dict = dict(zip(working_roo.Player, working_roo.Own))
                min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
                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()
                
                if pos_var1 == 'Specific Positions':
                    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', 'Minutes']]
                flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
                flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .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.Median.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 = 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.set_index('Player')
                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
                
                st.session_state.final_Proj = final_Proj
                
                hold_container = st.empty()
        
        with proj_container:
             proj_container = st.empty()
             if 'proj_display' in st.session_state:
                 st.dataframe(st.session_state.proj_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
        
        with display_container:
             display_container = st.empty()
             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)
        
        with display_dl_container:
                 display_dl_container = st.empty()
                 if 'final_Proj' in st.session_state:
                     st.download_button(
                        label="Export Tables",
                        data=convert_df_to_csv(st.session_state.final_Proj),
                        file_name='Custom_NBA_export.csv',
                        mime='text/csv',
                     )