import streamlit as st st.set_page_config(layout="wide") 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": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com", "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:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?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%}'} dk_columns = [['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']] fd_columns = [['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']] @st.cache_data(ttl = 60) 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 = 60) def init_DK_seed_frame(): collection = db["DK_MLB_seed_frame"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']] DK_seed = raw_display.to_numpy() return DK_seed @st.cache_data(ttl = 59) def init_FD_seed_frame(): collection = db["FD_MLB_seed_frame"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']] FD_seed = raw_display.to_numpy() return FD_seed @st.cache_data def convert_df(array): array = pd.DataFrame(array, columns=column_names) return array.to_csv().encode('utf-8') @st.cache_data def calculate_value_frequencies(np_array): unique, counts = np.unique(np_array, return_counts=True) frequencies = counts / len(np_array) # Normalize by the number of rows combined_array = np.column_stack((unique, frequencies)) return combined_array 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 column_names = dk_columns 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 column_names = fd_columns 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': working_seed = init_DK_seed_frame() # working_seed_parse = working_seed[np.isin(working_seed[:, 2], team_var2)] # working_seed_parse = working_seed[np.isin(working_seed[:, 3], stack_var2)] data_export_display = pd.DataFrame(working_seed[0:1000], columns=column_names) st.session_state.data_export_display = data_export_display.copy() st.session_state.data_export = working_seed.copy() # st.session_state.data_export_freq = calculate_value_frequencies(st.session_state.data_export) if 'data_export' in st.session_state: st.download_button( label="Export optimals set", data=convert_df(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': working_seed = init_FD_seed_frame() working_seed_parse = working_seed[np.isin(working_seed[:, 2], team_var2)] working_seed_parse = working_seed[np.isin(working_seed[:, 3], stack_var2)] data_export_display = working_seed_parse[0:1000] st.session_state.data_export_display = data_export_display.copy() st.session_state.data_export = working_seed.copy() st.session_state.data_export_freq = calculate_value_frequencies(my_array) if 'data_export' in st.session_state: st.download_button( label="Export optimals set", data=convert_df(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")