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 pymongo

@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"
        }
        uri = "mongodb+srv://multichem:[email protected]/?retryWrites=true&w=majority&appName=TestCluster"
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
        db = client["testing_db"]

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

gcservice_account, client, db = init_conn()

MLB_Data = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=340831852'

percentages_format = {'Exposure': '{:.2%}'}

@st.cache_data(ttl = 600)
def init_baselines():
    sh = gcservice_account.open_by_url(MLB_Data)
    
    worksheet = sh.worksheet('DK_Projections')
    load_display = pd.DataFrame(worksheet.get_all_records())
    load_display.replace('', np.nan, inplace=True)
    
    dk_raw = load_display.dropna(subset=['Median'])
    
    worksheet = sh.worksheet('FD_Projections')
    load_display = pd.DataFrame(worksheet.get_all_records())
    load_display.replace('', np.nan, inplace=True)
    
    fd_raw = load_display.dropna(subset=['Median'])

    return dk_raw, fd_raw

@st.cache_data(ttl = 600)
def init_DK_seed_frame():
    collection = db["DK_MLB_seed_frame"] 
    cursor = collection.find()
    
    raw_display = pd.DataFrame(list(cursor))
    DK_seed = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'salary', 'proj']]

    return DK_seed

@st.cache_data(ttl = 600)
def init_FD_seed_frame():
    collection = db["FD_MLB_seed_frame"] 
    cursor = collection.find()
    
    raw_display = pd.DataFrame(list(cursor))
    FD_seed = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'salary', 'proj']]

    return FD_seed

@st.cache_data
def convert_df_from_parquet(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    working_file = df.read_parquet()
    return working_file.to_csv().encode('utf-8')

dk_raw, fd_raw = init_baselines()

tab1, tab2 = st.tabs(['Data Export', 'Contest Sims'])
with tab1:
    col1, col2 = st.columns([1, 7])
    with col1:
        if st.button("Load/Reset Data", key='reset1'):
              st.cache_data.clear()
              for key in st.session_state.keys():
                  del st.session_state[key]
              dk_raw, fd_raw = init_baselines()
              
        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
        if site_var1 == 'Draftkings':
            raw_baselines = dk_raw
            
            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
            if team_var1 == 'Specific Teams':
                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
            elif team_var1 == 'Full Slate':
                    team_var2 = dk_raw.Team.values.tolist()
            
            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
            if stack_var1 == 'Specific Stack Sizes':
                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
            elif stack_var1 == 'Full Slate':
                    stack_var2 = [5, 4, 3, 2, 1, 0]
                    
        elif site_var1 == 'Fanduel':
            raw_baselines = fd_raw
            
            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
            if team_var1 == 'Specific Teams':
                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique())
            elif team_var1 == 'Full Slate':
                    team_var2 = fd_raw.Team.values.tolist()
            
            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
            if stack_var1 == 'Specific Stack Sizes':
                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0])
            elif stack_var1 == 'Full Slate':
                    stack_var2 = [4, 3, 2, 1, 0]
            
    with col2:
        if st.button("Load Seed Frame", key='seed_frame_load'):
            if site_var1 == 'Draftkings':
                
                DK_seed = init_DK_seed_frame()
                DK_seed_parse = DK_seed[DK_seed['Team'].isin(team_var2)]
                DK_seed_parse = DK_seed_parse[DK_seed_parse['Team_count'].isin(stack_var2)]
                data_export_display = DK_seed_parse.head(1000)
                st.session_state.data_export_display = data_export_display.copy()
                st.session_state.data_export_expo = DK_seed.copy()
                st.session_state.data_export = DK_seed.to_parquet()
                st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export_expo.iloc[:,0:9].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
                st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(len(st.session_state.data_export_expo['Team']))
                
                if 'data_export' in st.session_state:
                        st.download_button(
                            label="Export optimals set",
                            data=convert_df_from_parquet(st.session_state.data_export),
                            file_name='MLB_optimals_export.csv',
                            mime='text/csv',
                        )
                with st.container():
                    if 'data_export_display' in st.session.state:
                        st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
                with st.container():
                    if 'data_export_freq' in st.session.state:
                        st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
            
            elif site_var1 == 'Fanduel':
                FD_seed = init_FD_seed_frame()
                FD_seed_parse = FD_seed[FD_seed['Team'].isin(team_var2)]
                FD_seed_parse = FD_seed_parse[FD_seed_parse['Team_count'].isin(stack_var2)]
                data_export_display = FD_seed_parse.head(1000)
                st.session_state.data_export_display = data_export_display.copy()
                st.session_state.data_export_expo = FD_seed.copy()
                st.session_state.data_export = FD_seed.to_parquet()
                st.session_state.data_export_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.data_export_expo.iloc[:,0:8].values, return_counts=True)),
                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                st.session_state.data_export_freq['Freq'] = st.session_state.data_export_freq['Freq'].astype(int)
                st.session_state.data_export_freq['Exposure'] = st.session_state.data_export_freq['Freq']/(len(st.session_state.data_export_expo['Team']))
                
                if 'data_export' in st.session_state:
                        st.download_button(
                            label="Export optimals set",
                            data=convert_df_from_parquet(st.session_state.data_export),
                            file_name='MLB_optimals_export.csv',
                            mime='text/csv',
                        )
                with st.container():
                    if 'data_export_display' in st.session.state:
                        st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
                with st.container():
                    if 'data_export_freq' in st.session.state:
                        st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
            
with tab2:
    col1, col2 = st.columns([1, 7])
    with col1:
        if st.button("Load/Reset Data", key='reset2'):
              st.cache_data.clear()
              for key in st.session_state.keys():
                  del st.session_state[key]
              DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
    
    with col2:
        st.write("Things will go here")