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James McCool
Remove PDF export functionality from app.py and update requirements.txt to reflect changes. The `convert_df_to_pdf` function has been deleted, along with related download options for PDF. The requirements.txt file has been updated to remove the reportlab dependency, streamlining the application for CSV exports only.
c9d235d
| 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 | |
| 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" | |
| } | |
| gc_con = gspread.service_account_from_dict(credentials, scope) | |
| return gc_con | |
| gcservice_account = init_conn() | |
| NHL_data = st.secrets['NHL_Data'] | |
| percentages_format = {'Shots': '{:.2%}', 'HDCF': '{:.2%}', 'Goals': '{:.2%}', 'Assists': '{:.2%}', 'Blocks': '{:.2%}', | |
| 'L14_Shots': '{:.2%}', 'L14_HDCF': '{:.2%}', 'L14_Goals': '{:.2%}', 'L14_Assists': '{:.2%}', | |
| 'L14_Blocks': '{:.2%}', 'Max Goal%': '{:.2%}', 'L14 Max Goal%': '{:.2%}'} | |
| matchups_format = {'HDCF%': '{:.2%}', 'o_HDCA%': '{:.2%}', 'HDCF_m%': '{:.2%}'} | |
| def init_baselines(): | |
| parse_hold = pd.DataFrame(columns=['Line', 'SK1', 'SK2', 'SK3', 'Cost', 'Team Total', 'Shots', 'HDCF', 'Goals', 'Assists', 'Blocks', | |
| 'L14_Shots', 'L14_HDCF', 'L14_Goals', 'L14_Assists', 'L14_Blocks', 'Max Goal%']) | |
| sh = gcservice_account.open_by_url(NHL_data) | |
| worksheet = sh.worksheet('Player_Level_ROO') | |
| 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 = raw_display[raw_display['Opp'] != ""] | |
| team_frame = raw_display[['Team', 'Opp']] | |
| team_list = team_frame['Team'].unique() | |
| team_dict = dict(zip(team_frame['Team'], team_frame['Opp'])) | |
| worksheet = sh.worksheet('Matchups') | |
| 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 = raw_display[raw_display['Opp'] != ""] | |
| matchups = raw_display[['Team', 'Opp', 'FL1$', 'FL2$', 'FL3$', 'Team Total', 'Game Pace', 'SF', 'o_SA', 'SF_m', 'HDCF', | |
| 'o_HDCA', 'HDCF_m', 'HDCF%', 'o_HDCA%', 'HDCF_m%', 'HDSF+']] | |
| data_cols = matchups.columns.drop(['Team', 'Opp']) | |
| matchups[data_cols] = matchups[data_cols].apply(pd.to_numeric, errors='coerce') | |
| matchups = matchups.dropna(subset='FL1$') | |
| matchups = matchups.sort_values(by='HDCF_m', ascending=False) | |
| worksheet = sh.worksheet('Marketshares') | |
| 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 = raw_display[raw_display['Line'] != ""] | |
| overall_ms = raw_display[['Line', 'SK1', 'SK2', 'SK3', 'Cost', 'Team Total', 'Shots', 'HDCF', 'Goals', 'Assists', 'Blocks', | |
| 'L14_Shots', 'L14_HDCF', 'L14_Goals', 'L14_Assists', 'L14_Blocks']] | |
| pat = '|'.join(team_list) | |
| s = overall_ms['Line'].str.extract('('+ pat + ')', expand=False) | |
| overall_ms['Max Goal%'] = overall_ms.groupby(s)['Goals'].transform('max') | |
| overall_ms['L14 Max Goal%'] = overall_ms.groupby(s)['L14_Goals'].transform('max') | |
| data_cols = overall_ms.columns.drop(['Line', 'SK1', 'SK2', 'SK3']) | |
| overall_ms[data_cols] = overall_ms[data_cols].apply(pd.to_numeric, errors='coerce') | |
| overall_ms['Proj Goal'] = overall_ms['Goals'] * overall_ms['Team Total'] | |
| overall_ms['L14 Proj Goal'] = overall_ms['L14_Goals'] * overall_ms['Team Total'] | |
| overall_ms = overall_ms[['Line', 'SK1', 'SK2', 'SK3', 'Cost', 'Team Total', 'Shots', 'HDCF', 'Goals', 'Max Goal%', 'Proj Goal', | |
| 'Assists', 'Blocks', 'L14_Shots', 'L14_HDCF', 'L14_Goals', 'L14 Max Goal%', 'L14 Proj Goal', 'L14_Assists', 'L14_Blocks']] | |
| overall_ms = overall_ms.sort_values(by='Shots', ascending=False) | |
| return matchups, overall_ms, team_frame, team_list, team_dict | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| matchups, overall_ms, team_frame, team_list, team_dict = init_baselines() | |
| col1, col2 = st.columns([1, 9]) | |
| with col1: | |
| if st.button("Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| matchups, overall_ms, team_frame, team_list, team_dict = init_baselines() | |
| split_var1 = st.radio("View matchups or line marketshares?", ('Slate Matchups', 'Line Marketshares'), key='split_var1') | |
| if split_var1 == "Line Marketshares": | |
| team_var = st.radio("View all teams or specific teams?", ('All Teams', 'Specific Teams'), key='team_var') | |
| if team_var == "All Teams": | |
| team_split = team_frame.Team.values.tolist() | |
| elif team_var == "Specific Teams": | |
| team_split = st.multiselect('Which teams would you like to include in the tables?', options = team_frame['Team'].unique(), key='team_var1') | |
| with col2: | |
| if split_var1 == 'Slate Matchups': | |
| display_table = matchups | |
| display_table = display_table.set_index('Team') | |
| st.dataframe(display_table.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(matchups_format, precision=2), height=500, use_container_width = True) | |
| elif split_var1 == 'Line Marketshares': | |
| display_table = overall_ms | |
| display_parsed = display_table[display_table['Line'].str.contains('|'.join(team_split))] | |
| display_parsed = display_parsed.set_index('Line') | |
| st.dataframe(display_parsed.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), height=500, use_container_width = True) | |
| st.download_button( | |
| label="Export Marketshares (CSV)", | |
| data=convert_df_to_csv(display_table), | |
| file_name='Marketshares_export.csv', | |
| mime='text/csv', | |
| ) |