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import streamlit as st
st.set_page_config(layout="wide")
for name in dir():
if not name.startswith('_'):
del globals()[name]
import pulp
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import pymongo
from itertools import combinations
@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": "[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"
}
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"
}
NHL_Data = st.secrets['NHL_Data']
gc = gspread.service_account_from_dict(credentials)
gc2 = gspread.service_account_from_dict(credentials2)
return gc, gc2, NHL_Data
gcservice_account, gcservice_account2, NHL_Data = init_conn()
player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
'4x%': '{:.2%}'}
@st.cache_resource(ttl=300)
def player_stat_table():
sh = gcservice_account.open_by_url(NHL_Data)
worksheet = sh.worksheet('Player_Level_ROO')
player_frame = pd.DataFrame(worksheet.get_all_records())
worksheet = sh.worksheet('Player_Lines_ROO')
line_frame = pd.DataFrame(worksheet.get_all_records())
worksheet = sh.worksheet('Player_PowerPlay_ROO')
pp_frame = pd.DataFrame(worksheet.get_all_records())
worksheet = sh.worksheet('Timestamp')
timestamp = worksheet.acell('A1').value
return player_frame, line_frame, pp_frame, timestamp
@st.cache_data
def convert_df_to_csv(df):
return df.to_csv().encode('utf-8')
player_frame, line_frame, pp_frame, timestamp = player_stat_table()
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
tab1, tab2, tab3 = st.tabs(["Player Range of Outcomes", "Line Combo Range of Outcomes", "Power Play Range of Outcomes"])
with tab1:
col1, col2 = st.columns([1, 7])
with col1:
st.info(t_stamp)
if st.button("Load/Reset Data", key='reset1'):
st.cache_data.clear()
player_frame, line_frame, pp_frame, timestamp = player_stat_table()
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
main_var1 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var1')
split_var1 = st.radio("Would you like to view the whole slate or just specific 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 in the ROO?', options = player_frame['Team'].unique(), key='team_var1')
elif split_var1 == 'Full Slate Run':
team_var1 = player_frame.Team.values.tolist()
pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
if pos_split1 == 'Specific Positions':
pos_var1 = st.multiselect('What Positions would you like to view?', options = ['C', 'W', 'D', 'G'])
elif pos_split1 == 'All Positions':
pos_var1 = 'All'
sal_var1 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 20000), key='sal_var1')
with col2:
final_Proj = player_frame[player_frame['Site'] == str(site_var1)]
final_Proj = final_Proj[final_Proj['Type'] == 'Basic']
final_Proj = final_Proj[final_Proj['Slate'] == main_var1]
final_Proj = final_Proj[player_frame['Team'].isin(team_var1)]
final_Proj = final_Proj[final_Proj['Salary'] >= sal_var1[0]]
final_Proj = final_Proj[final_Proj['Salary'] <= sal_var1[1]]
if pos_var1 != 'All':
final_Proj = final_Proj[final_Proj['Position'].str.contains('|'.join(pos_var1))]
final_Proj = final_Proj.sort_values(by='Median', ascending=False)
if pos_var1 == 'All':
final_Proj = final_Proj.sort_values(by='Median', ascending=False)
st.dataframe(final_Proj.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
st.download_button(
label="Export Tables",
data=convert_df_to_csv(final_Proj),
file_name='NHL_player_export.csv',
mime='text/csv',
)
with tab2:
col1, col2 = st.columns([1, 7])
with col1:
st.info(t_stamp)
if st.button("Load/Reset Data", key='reset2'):
st.cache_data.clear()
player_frame, line_frame, pp_frame, timestamp = player_stat_table()
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
main_var2 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var2')
with col2:
final_line_combos = line_frame[line_frame['Site'] == str(site_var2)]
final_line_combos = final_line_combos[final_line_combos['Type'] == 'Basic']
final_line_combos = final_line_combos[final_line_combos['Slate'] == main_var2]
final_line_combos = final_line_combos.sort_values(by='Median', ascending=False)
st.dataframe(final_line_combos.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
label="Export Tables",
data=convert_df_to_csv(final_line_combos),
file_name='NHL_linecombos_export.csv',
mime='text/csv',
)
with tab3:
col1, col2 = st.columns([1, 7])
with col1:
st.info(t_stamp)
if st.button("Load/Reset Data", key='reset3'):
st.cache_data.clear()
player_frame, line_frame, pp_frame, timestamp = player_stat_table()
t_stamp = f"Last Update: " + str(timestamp) + f" CST"
site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3')
main_var3 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var3')
with col2:
final_pp_combos = pp_frame[pp_frame['Site'] == str(site_var3)]
final_pp_combos = final_pp_combos[final_pp_combos['Type'] == 'Basic']
final_pp_combos = final_pp_combos[final_pp_combos['Slate'] == main_var3]
final_pp_combos = final_pp_combos.sort_values(by='Median', ascending=False)
st.dataframe(final_pp_combos.iloc[:, :-3].style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
st.download_button(
label="Export Tables",
data=convert_df_to_csv(final_pp_combos),
file_name='NHL_powerplay_export.csv',
mime='text/csv',
) |