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 plotly.express as px
import random
import gc

@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": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
          "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"
        }

        gc_con = gspread.service_account_from_dict(credentials, scope)
      
        return gc_con

gcservice_account = init_conn()

DEM_data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'

@st.cache_resource(ttl = 600)
def init_baselines():
    sh = gcservice_account.open_by_url(DEM_data)

    worksheet = sh.worksheet('PG_DEM_Calc')
    raw_display = pd.DataFrame(worksheet.get_values())
    raw_display.columns = raw_display.iloc[0]
    raw_display = raw_display[1:]
    raw_display = raw_display.reset_index(drop=True)
    raw_display.replace('%', '', inplace=True)
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Point Guard'
    pg_dem = raw_display[raw_display['Acro'] != ""]

    worksheet = sh.worksheet('SG_DEM_Calc')
    raw_display = pd.DataFrame(worksheet.get_values())
    raw_display.columns = raw_display.iloc[0]
    raw_display = raw_display[1:]
    raw_display = raw_display.reset_index(drop=True)
    raw_display.replace('%', '', inplace=True)
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Shooting Guard'
    sg_dem = raw_display[raw_display['Acro'] != ""]
    
    worksheet = sh.worksheet('SF_DEM_Calc')
    raw_display = pd.DataFrame(worksheet.get_values())
    raw_display.columns = raw_display.iloc[0]
    raw_display = raw_display[1:]
    raw_display = raw_display.reset_index(drop=True)
    raw_display.replace('%', '', inplace=True)
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Small Forward'
    sf_dem = raw_display[raw_display['Acro'] != ""]
    
    worksheet = sh.worksheet('PF_DEM_Calc')
    raw_display = pd.DataFrame(worksheet.get_values())
    raw_display.columns = raw_display.iloc[0]
    raw_display = raw_display[1:]
    raw_display = raw_display.reset_index(drop=True)
    raw_display.replace('%', '', inplace=True)
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Power Forward'
    pf_dem = raw_display[raw_display['Acro'] != ""]
    
    worksheet = sh.worksheet('C_DEM_Calc')
    raw_display = pd.DataFrame(worksheet.get_values())
    raw_display.columns = raw_display.iloc[0]
    raw_display = raw_display[1:]
    raw_display = raw_display.reset_index(drop=True)
    raw_display.replace('%', '', inplace=True)
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Center'
    c_dem = raw_display[raw_display['Acro'] != ""]
    
    overall_dem = pd.concat([pg_dem, sg_dem, sf_dem, pf_dem, c_dem])
    overall_dem = overall_dem[['Acro', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
                               'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'position']]
    overall_dem = overall_dem.reset_index()

    return overall_dem

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

overall_dem = init_baselines()

if st.button("Reset Data", key='reset1'):
          st.cache_data.clear()
          overall_dem = init_baselines()
# split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
# if split_var1 == 'Specific Teams':
#     team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = public_minutes['TC'].unique(), key='team_var1')
# elif split_var1 == 'All':
#     team_var1 = public_minutes.TC.values.tolist()
# public_minutes = public_minutes[public_minutes['TC'].isin(team_var1)]
# player_min_disp = public_minutes.set_index('Player')
# player_min_disp = player_min_disp.sort_values(by=['TC', 'MP (Today)'], ascending=[True, False])
st.dataframe(overall_dem.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
    label="Export Minutes Baselines",
    data=convert_df_to_csv(overall_dem),
    file_name='DEM_export.csv',
    mime='text/csv',
)