import streamlit as st import numpy as np import pandas as pd import streamlit as st import gspread import pymongo st.set_page_config(layout="wide") @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": st.secrets['model_sheets_connect_pk'], "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" } credentials2 = { "type": "service_account", "project_id": "sheets-api-connect-378620", "private_key_id": st.secrets['sheets_api_connect_pk'], "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" } uri = st.secrets['mongo_uri'] client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) db = client["NBA_DFS"] NBA_Data = st.secrets['NBA_Data'] gc = gspread.service_account_from_dict(credentials) gc2 = gspread.service_account_from_dict(credentials2) return gc, gc2, db, NBA_Data gcservice_account, gcservice_account2, db, NBA_Data = init_conn() dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] @st.cache_data(ttl=300) def load_overall_stats(): try: sh = gcservice_account.open_by_url(NBA_Data) except: sh = gcservice_account2.open_by_url(NBA_Data) 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['Salary'] > 0] 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) worksheet = sh.worksheet('Secondary_DK_Build') 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_sec = raw_display.sort_values(by='Median', ascending=False) worksheet = sh.worksheet('Secondary_FD_Build') 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_sec = raw_display.sort_values(by='Median', ascending=False) worksheet = sh.worksheet('Player_Level_ROO') raw_display = pd.DataFrame(worksheet.get_all_records()) 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') roo_raw = raw_display.sort_values(by='Median', ascending=False) timestamp = raw_display['timestamp'].values[0] return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp @st.cache_data(ttl = 300) def init_DK_lineups(): collection = db["DK_NBA_seed_frame"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] DK_seed = raw_display.head(10000).to_numpy() return DK_seed @st.cache_data(ttl = 300) def init_FD_lineups(): collection = db["FD_NBA_seed_frame"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] FD_seed = raw_display.head(10000).to_numpy() return FD_seed def convert_df_to_csv(df): return df.to_csv().encode('utf-8') @st.cache_data def convert_df(array): array = pd.DataFrame(array, columns=column_names) return array.to_csv().encode('utf-8') dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(timestamp) + f" CST" tab1, tab2 = st.tabs(['Range of Outcomes', 'Uploads and Info']) with tab1: 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, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(timestamp) + f" CST" for key in st.session_state.keys(): del st.session_state[key] site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2') if site_var2 == 'Draftkings': site_baselines = roo_raw[roo_raw['site'] == 'Draftkings'] elif site_var2 == 'Fanduel': site_baselines = roo_raw[roo_raw['site'] == 'Fanduel'] slate_split = st.radio("Are you viewing the main slate or the secondary slate?", ('Main Slate', 'Secondary'), key='slate_split') if slate_split == 'Main Slate': raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate'] elif slate_split == 'Secondary': raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary'] split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2') if split_var2 == 'Specific Games': team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2') elif split_var2 == 'Full Slate Run': team_var2 = raw_baselines.Team.values.tolist() pos_var2 = st.selectbox('View specific position?', options = ['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2') with col2: display_container_1 = st.empty() display_dl_container_1 = st.empty() display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)] display_proj = display_proj.drop(columns=['site', 'version', 'slate', 'timestamp']) st.session_state.display_proj = display_proj with display_container_1: display_container = st.empty() if 'display_proj' in st.session_state: if pos_var2 == 'All': st.session_state.display_proj = st.session_state.display_proj elif pos_var2 != 'All': st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)] st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True) with display_dl_container_1: display_dl_container = st.empty() if 'display_proj' in st.session_state: st.download_button( label="Export Tables", data=convert_df_to_csv(st.session_state.display_proj), file_name='NBA_ROO_export.csv', mime='text/csv', ) with tab2: col1, col2 = st.columns([1, 7]) with col1: if st.button("Load/Reset Data", key='reset2'): st.cache_data.clear() dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats() dk_lineups = init_DK_lineups() fd_lineups = init_FD_lineups() t_stamp = f"Last Update: " + str(timestamp) + f" CST" for key in st.session_state.keys(): del st.session_state[key] slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the 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 player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') if player_var1 == 'Specific Players': player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique()) elif player_var1 == 'Full Slate': player_var2 = dk_raw.Player.values.tolist() elif site_var1 == 'Fanduel': raw_baselines = fd_raw column_names = fd_columns player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') if player_var1 == 'Specific Players': player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique()) elif player_var1 == 'Full Slate': player_var2 = dk_raw.Player.values.tolist() if st.button("Prepare data export", key='data_export'): data_export = st.session_state.working_seed.copy() st.download_button( label="Export optimals set", data=convert_df(data_export), file_name='NBA_optimals_export.csv', mime='text/csv', ) with col2: if st.button("Load Lineups", key='load_data'): if site_var1 == 'Draftkings': if 'working_seed' in st.session_state: st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:,0:15], player_var2)] st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:151, :], columns=column_names) elif 'working_seed' not in st.session_state: st.session_state.working_seed = dk_lineups.copy() st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:,0:15], player_var2)] st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:151, :], columns=column_names) elif site_var1 == 'Fanduel': if 'working_seed' in st.session_state: st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:,0:16], player_var2)] st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:151, :], columns=column_names) elif 'working_seed' not in st.session_state: st.session_state.working_seed = fd_lineups.copy() st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:,0:16], player_var2)] st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:151, :], columns=column_names) with st.container(): if 'data_export_display' in st.session_state: st.dataframe(st.session_state.data_export_display.style.format(precision=2), use_container_width = True)