Update app.py

app.py

@@ -5,23 +5,28 @@ import streamlit as st
 import gspread
 from itertools import combinations
 
-scope = ['https://www.googleapis.com/auth/spreadsheets',
-          "https://www.googleapis.com/auth/drive"]
-
-credentials = {
-  "type": "service_account",
-  "project_id": "sheets-api-connect-378620",
-  "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
-  "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"
-}
-
-gc = gspread.service_account_from_dict(credentials)
+@st.cache_resource
+def init_conn():
+          scope = ['https://www.googleapis.com/auth/spreadsheets',
+                    "https://www.googleapis.com/auth/drive"]
+          
+          credentials = {
+            "type": "service_account",
+            "project_id": "sheets-api-connect-378620",
+            "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
+            "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"
+          }
+
+          gc = gspread.service_account_from_dict(credentials)
+          return gc
+
+gcservice_account = init_conn()
 
 st.set_page_config(layout="wide")
 
@@ -39,74 +44,44 @@ player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_fi
 
 all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348'
 
-@st.cache_data
-def set_slate_teams():
-    sh = gc.open_by_url(all_dk_player_projections)
-    worksheet = sh.worksheet('Site_Info')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-
-    return raw_display
-
-@st.cache_data
+@st.cache_resource(ttl = 600)
 def player_stat_table():
-    sh = gc.open_by_url(all_dk_player_projections)
+    sh = gcservice_account.open_by_url(all_dk_player_projections)
     worksheet = sh.worksheet('Player_Projections')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-
-    return raw_display
-
-@st.cache_data
-def load_dk_player_projections():
-    sh = gc.open_by_url(all_dk_player_projections)
-    worksheet = sh.worksheet('DK_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['Median'])
-
-    return raw_display
-
-@st.cache_data
-def load_fd_player_projections():
-    sh = gc.open_by_url(all_dk_player_projections)
-    worksheet = sh.worksheet('FD_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    raw_display = load_display.dropna(subset=['Median'])
-
-    return raw_display
-
-@st.cache_data
-def load_dk_stacks():
-    sh = gc.open_by_url(all_dk_player_projections)
+    player_stats = pd.DataFrame(worksheet.get_all_records())
+    
     worksheet = sh.worksheet('DK_Stacks')
     load_display = pd.DataFrame(worksheet.get_all_records())
     raw_display = load_display
-    raw_display = raw_display.sort_values(by='Own', ascending=False)
-
-    return raw_display
-
-@st.cache_data
-def load_fd_stacks():
-    sh = gc.open_by_url(all_dk_player_projections)
+    dk_stacks_raw = raw_display.sort_values(by='Own', ascending=False)
+    
     worksheet = sh.worksheet('FD_Stacks')
     load_display = pd.DataFrame(worksheet.get_all_records())
     raw_display = load_display
-    raw_display = raw_display.sort_values(by='Own', ascending=False)
+    fd_stacks_raw = raw_display.sort_values(by='Own', ascending=False)
+    
+    worksheet = sh.worksheet('DK_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    dk_roo_raw = load_display.dropna(subset=['Median'])
+    
+    worksheet = sh.worksheet('FD_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    fd_roo_raw = load_display.dropna(subset=['Median'])
+    
+    worksheet = sh.worksheet('Site_Info')
+    site_slates = pd.DataFrame(worksheet.get_all_records())
 
-    return raw_display
+    return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates
 
 @st.cache_data
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-player_stats = player_stat_table()
-dk_stacks_raw = load_fd_stacks()
-fd_stacks_raw = load_fd_stacks()
-dk_roo_raw = load_dk_player_projections()
-fd_roo_raw = load_fd_player_projections()
-t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
-site_slates = set_slate_teams()
+player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
 opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
+t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
 
 tab1, tab2 = st.tabs(['Uploads and Info', 'Pivot Finder'])
 
@@ -132,13 +107,9 @@ with tab2:
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset1'):
               st.cache_data.clear()
-              player_stats = player_stat_table()
-              dk_stacks_raw = load_fd_stacks()
-              fd_stacks_raw = load_fd_stacks()
-              dk_roo_raw = load_dk_player_projections()
-              fd_roo_raw = load_fd_player_projections()
+              player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
+              opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
               t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
-              site_slates = set_slate_teams()
         data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1')
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
         if site_var1 == 'Draftkings':
@@ -193,8 +164,12 @@ with tab2:
                 working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
   
                 flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
-                flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Median'] * .20)
-                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.5), flex_file['Median'] * 2)
+                flex_file['Floor_raw'] = flex_file['Median'] * .20
+                flex_file['Ceiling_raw'] = flex_file['Median'] * 1.9
+                flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
+                flex_file['Floor'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * .15), flex_file['Floor_raw'])
+                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
+                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
                 flex_file['STD'] = flex_file['Median'] / 4
                 flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                 hold_file = flex_file