Update app.py

app.py

@@ -18,42 +18,13 @@ from time import sleep as time_sleep
 
 @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": "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"
-        }
-        
-        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
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NHL_Database"]
+
+        return db
     
-gcservice_account, gcservice_account2, NHL_Data = init_conn()
+db = init_conn()
 
 prop_table_options = ['NHL_GAME_PLAYER_SHOTS_ON_GOAL', 'NHL_GAME_PLAYER_POINTS', 'NHL_GAME_PLAYER_SHOTS_BLOCKED', 'NHL_GAME_PLAYER_ASSISTS']
 prop_format = {'L5 Success': '{:.2%}', 'L10_Success': '{:.2%}', 'L20_success': '{:.2%}', 'Matchup Boost': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
@@ -64,14 +35,11 @@ sim_all_hold = pd.DataFrame(columns=['Player', 'Prop Type', 'Prop', 'Mean_Outcom
 
 @st.cache_resource(ttl=200)
 def pull_baselines():
-    sh = gcservice_account.open_by_url(NHL_Data)
-    worksheet = sh.worksheet('Prop_Betting_Table')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
+    collection = db["Prop_Betting_Table"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(cursor)
     prop_display = raw_display[raw_display['Player'] != ""]
     prop_display['Player Blocks'].replace("", np.nan, inplace=True)
-    prop_display['SOG Edge'].replace("", np.nan, inplace=True)
-    prop_display['Assist Edge'].replace("", np.nan, inplace=True)
-    prop_display['TP Edge'].replace("", np.nan, inplace=True)
     prop_table = prop_display[['Player', 'Position', 'Team', 'Opp', 'Team_Total', 'Player SOG', 'Player Goals', 'Player Assists',
                                'Player TP', 'Player Blocks', 'Player Saves']]
     prop_table['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'],
@@ -82,26 +50,27 @@ def pull_baselines():
     for stat in stat_columns:
         prop_table[stat] = prop_table[stat].astype(float)
     
-    worksheet = sh.worksheet('prop_trends_check')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
+    collection = db["prop_trends"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(cursor)
     raw_display.replace('', np.nan, inplace=True)
     prop_trends = raw_display.dropna(subset='Player')
     prop_trends['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'],
                                   ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True)
+    prop_trends = prop_trends.drop(columns=['_id', 'index'])
     
-    worksheet = sh.worksheet('Pick6_ingest')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
+    collection = db["Pick6_ingest"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(cursor)
     raw_display.replace('', np.nan, inplace=True)
     pick_frame = raw_display.dropna(subset='Player')
     pick_frame['Player'].replace(['JJ Peterka', 'Alexander Killorn', 'Matt Boldy', 'Nick Paul', 'Alex Kerfoot'],
                                   ['John-Jason Peterka', 'Alex Killorn', 'Matthew Boldy', 'Nicholas Paul', 'Alexander Kerfoot'], inplace=True)
+    pick_frame = pick_frame.drop(columns=['_id', 'index'])
     
     team_dict = dict(zip(prop_table['Player'], prop_table['Team']))
-    
-    worksheet = sh.worksheet('Timestamp')
-    timestamp = worksheet.acell('A1').value
 
-    return prop_table, prop_trends, pick_frame, timestamp, team_dict
+    return prop_table, prop_trends, pick_frame, team_dict
 
 def calculate_poisson(row):
     mean_val = row['Mean_Outcome']
@@ -113,8 +82,7 @@ def calculate_poisson(row):
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines()
-t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+prop_display, prop_trends, pick_frame, team_dict = pull_baselines()
 
 tab1, tab2, tab3 = st.tabs(["Player Stat Table", 'Prop Trend Table', 'Stat Specific Simulations'])
 
@@ -122,7 +90,7 @@ with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines()
+              prop_display, prop_trends, pick_frame, team_dict = pull_baselines()
     prop_frame = prop_display
     st.dataframe(prop_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
     
@@ -138,8 +106,7 @@ with tab2:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+              prop_display, prop_trends, pick_frame, team_dict = pull_baselines()
     split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
     if split_var5 == 'Specific Teams':
         team_var5 = st.multiselect('Which teams would you like to include in the tables?', options = prop_trends['Team'].unique(), key='team_var5')
@@ -168,8 +135,7 @@ with tab3:
     st.info('The Over and Under percentages are a composite percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
     if st.button("Reset Data/Load Data", key='reset5'):
               st.cache_data.clear()
-              prop_display, prop_trends, pick_frame, timestamp, team_dict = pull_baselines()
-              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+              prop_display, prop_trends, pick_frame, team_dict = pull_baselines()
 
     settings_container = st.container()
     df_hold_container = st.empty()