Update app.py

app.py

@@ -40,7 +40,7 @@ dk_player_url = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2U
 CSV_URL = 'https://docs.google.com/spreadsheets/d/1lMLxWdvCnOFBtG9dhM0zv2USuxZbkogI_2jnxFfQVVs/edit#gid=1828092624'
 
 @st.cache_resource(ttl = 600)
-def load_dk_player_model():
+def init_baselines():
     sh = gc.open_by_url(dk_player_url)
     worksheet = sh.get_worksheet(0)
     raw_display = pd.DataFrame(worksheet.get_all_records())
@@ -53,43 +53,46 @@ def load_dk_player_model():
     raw_display['11x%'] = raw_display['11x%'].str.replace('%', '').astype(float)/100
     raw_display['12x%'] = raw_display['12x%'].str.replace('%', '').astype(float)/100
     raw_display['LevX'] = raw_display['LevX'].str.replace('%', '').astype(float)/100
+    roo_data = raw_display
 
-    return raw_display
-
-@st.cache_resource(ttl = 600)
-def grab_csv_data():
     sh = gc.open_by_url(CSV_URL)
     worksheet = sh.worksheet('Site_Info')
     draftkings_data = pd.DataFrame(worksheet.get_all_records())
     draftkings_data.rename(columns={"Name": "Player"}, inplace = True)
     
-    return draftkings_data
-
-tab1, tab2 = st.tabs(["Player Overall Projections", "Optimizer"])
+    return roo_data, draftkings_data
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-hold_display = load_dk_player_model()
-csv_data = grab_csv_data()
+roo_data, draftkings_data = init_baselines()
+hold_display = roo_data
+csv_data = draftkings_data
 csv_merge = pd.merge(csv_data, hold_display, how='left', left_on=['Player'], right_on = ['Player'])
 id_dict = dict(zip(csv_merge['Player'], csv_merge['Name + ID']))
-
 lineup_display = []
 check_list = []
 rand_player = 0
 boost_player = 0
 salaryCut = 0
 
+tab1, tab2 = st.tabs(["Player Overall Projections", "Optimizer"])
+
 with tab1:
     if st.button("Reset Data", key='reset1'):
               # Clear values from *all* all in-memory and on-disk data caches:
               # i.e. clear values from both square and cube
               st.cache_data.clear()
-              hold_display = load_dk_player_model()
-              csv_data = grab_csv_data()
+              roo_data, draftkings_data = init_baselines()
+              hold_display = roo_data
+              csv_data = draftkings_data
               csv_merge = pd.merge(csv_data, hold_display, how='left', left_on=['Player'], right_on = ['Player'])
               id_dict = dict(zip(csv_merge['Player'], csv_merge['Name + ID']))
+              lineup_display = []
+              check_list = []
+              rand_player = 0
+              boost_player = 0
+              salaryCut = 0
     hold_container = st.empty()
     display = hold_display.set_index('Player')
     st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)