removed use of gspread

app.py

@@ -42,14 +42,15 @@ def init_conn():
 
         uri = st.secrets['mongo_uri']
         client = MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
-        db = client["Props_DB"]
+        dfs_db = client["NFL_Database"]
+        props_db = client["Props_DB"]
 
         gc = gspread.service_account_from_dict(credentials)
         gc2 = gspread.service_account_from_dict(credentials2)
 
-        return gc, gc2, NFL_Data, db
+        return gc, gc2, NFL_Data, props_db, dfs_db
     
-gcservice_account, gcservice_account2, NFL_Data, db = init_conn()
+gcservice_account, gcservice_account2, NFL_Data, props_db, dfs_db = init_conn()
 
 game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'}
 american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'}
@@ -63,37 +64,44 @@ def calculate_poisson(row):
 
 @st.cache_resource(ttl=600)
 def init_baselines():
-    sh = gcservice_account.open_by_url(NFL_Data)
-    worksheet = sh.worksheet('Game_Betting')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('#DIV/0!', np.nan, inplace=True)
-    game_model = raw_display.copy()
+    collection = dfs_db["Game_Betting_Model"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    game_model = raw_display[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
     
-    worksheet = sh.worksheet('Prop_Table')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('', np.nan, inplace=True)
-    overall_stats = raw_display.dropna()
+    collection = dfs_db["Player_Stats"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    overall_stats = raw_display[['Player', 'Position', 'Team', 'Opp', 'rush_att', 'rec', 'dropbacks', 'rush_yards', 'rush_tds', 'rec_yards', 'rec_tds', 'pass_att', 'pass_yards', 'pass_tds', 'PPR', 'Half_PPR']]
     
-    worksheet = sh.worksheet('prop_frame')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('', np.nan, inplace=True)
-    prop_trends = raw_display.copy()
+    collection = dfs_db["Prop_Trends"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    prop_trends = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
+                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
     
-    worksheet = sh.worksheet('DK_ROO')
-    timestamp = worksheet.acell('U2').value
+    collection = dfs_db["DK_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    timestamp = load_display['timestamp'][0]
     
-    worksheet = sh.worksheet('prop_frame')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('', np.nan, inplace=True)
-    raw_display.replace('#DIV/0!', np.nan, inplace=True)
-    prop_frame = raw_display.copy()
+    collection = dfs_db["Prop_Trends"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    prop_frame = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
+                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge']]
     
-    worksheet = sh.worksheet('Pick6_ingest')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display.replace('', np.nan, inplace=True)
-    pick_frame = raw_display.dropna(subset='Player')
+    collection = dfs_db['Pick6_ingest']
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    pick_frame = raw_display[['Player', 'over_prop', 'over_line', 'under_prop', 'under_line', 'book', 'prop_type', 'No Vig', 'Team', 'L3 Success', 'L6_Success', 'L10_success', 'L6 Avg', 'Projection',
+                               'Proj Diff', 'Implied Over', 'Trending Over', 'Over Edge', 'Implied Under', 'Trending Under', 'Under Edge', 'last_name', 'P6_name', 'Full_name']]
 
-    collection = db["NFL_Props"] 
+    collection = props_db["NFL_Props"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))