Refactor app.py to replace Google Sheets API data retrieval with MongoDB for 'prop_frame', 'betsheet_frame', and 'pick_frame', while implementing dropna calls to clean data and enhance processing for betting models.

app.py

@@ -93,23 +93,23 @@ def init_baselines():
         raw_display = pd.DataFrame(cursor)
         team_frame = raw_display.drop_duplicates(subset='Names')
         
-        sh = gc.open_by_url(master_hold)
-        worksheet = sh.worksheet('prop_frame')
-        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_frame = raw_display.copy()
+        prop_frame = raw_display.dropna(subset='Team')
         
         sh = gc.open_by_url(master_hold)
         worksheet = sh.worksheet('Prop_results')
         raw_display = pd.DataFrame(worksheet.get_all_records())
         raw_display.replace('', np.nan, inplace=True)
-        betsheet_frame = raw_display.copy()
+        betsheet_frame = raw_display.dropna(subset='proj')
         
         collection = db['Pick6_Trends']
         cursor = collection.find()
         raw_display = pd.DataFrame(cursor)
         raw_display.replace('', np.nan, inplace=True)
-        pick_frame = raw_display.copy()
+        pick_frame = raw_display.dropna(subset='Player')
         
         return pitcher_stats, hitter_stats, team_frame, prop_frame, betsheet_frame, pick_frame