Create app.py

app.py

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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 numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+import plotly.express as px
+import random
+import gc
+
+@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"
+        }
+
+        gc_con = gspread.service_account_from_dict(credentials, scope)
+      
+        return gc_con
+
+gcservice_account = init_conn()
+
+DEM_data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'
+
+@st.cache_resource(ttl = 600)
+def init_baselines():
+    sh = gcservice_account.open_by_url(DEM_data)
+
+    worksheet = sh.worksheet('PG_DEM_Calc')
+    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.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Point Guard'
+    pg_dem = raw_display[raw_display['Acro'] != ""]
+
+    worksheet = sh.worksheet('SG_DEM_Calc')
+    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.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Shooting Guard'
+    sg_dem = raw_display[raw_display['Acro'] != ""]
+    
+    worksheet = sh.worksheet('SF_DEM_Calc')
+    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.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Small Forward'
+    sf_dem = raw_display[raw_display['Acro'] != ""]
+    
+    worksheet = sh.worksheet('PF_DEM_Calc')
+    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.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Power Forward'
+    pf_dem = raw_display[raw_display['Acro'] != ""]
+    
+    worksheet = sh.worksheet('C_DEM_Calc')
+    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.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
+    raw_display['position'] = 'Center'
+    c_dem = raw_display[raw_display['Acro'] != ""]
+    
+    overall_dem = pd.concat([pg_dem, sg_dem, sf_dem, pf_dem, c_dem])
+    overall_dem = overall_dem[['Acro', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
+                               'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'position']]
+
+    return overall_dem
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+overall_dem = init_baselines()
+
+if st.button("Reset Data", key='reset1'):
+          st.cache_data.clear()
+          overall_dem = init_baselines()
+# split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
+# if split_var1 == 'Specific Teams':
+#     team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = public_minutes['TC'].unique(), key='team_var1')
+# elif split_var1 == 'All':
+#     team_var1 = public_minutes.TC.values.tolist()
+# public_minutes = public_minutes[public_minutes['TC'].isin(team_var1)]
+# player_min_disp = public_minutes.set_index('Player')
+# player_min_disp = player_min_disp.sort_values(by=['TC', 'MP (Today)'], ascending=[True, False])
+st.dataframe(overall_dem.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+st.download_button(
+    label="Export Minutes Baselines",
+    data=convert_df_to_csv(overall_dem),
+    file_name='DEM_export.csv',
+    mime='text/csv',
+)