Enhance sport selection and contest file loading logic

- Updated the sport selection options in app.py to include 'MMA' and 'GOLF', improving user choice for contest types.
- Modified the load_contest_file function to handle new sports, defining position lists and DataFrame structures accordingly.
- Maintained existing functionality while expanding the application's capabilities for diverse sports contests.

app.py

@@ -57,7 +57,7 @@ with tab1:
         with search_options:
             parse_type = st.selectbox("Manual upload or DB search?", ['DB Search', 'Manual'], key='parse_type')
         with sport_options:
-            sport_select = st.selectbox("Select Sport", ['MLB', 'NBA', 'NFL'], key='sport_select')
+            sport_select = st.selectbox("Select Sport", ['MLB', 'MMA', 'GOLF'], key='sport_select')
             contest_names, contest_id_map, curr_info = grab_contest_names(db, sport_select)
             
         with date_options:

global_func/load_contest_file.py

@@ -4,6 +4,10 @@ import pandas as pd
 def load_contest_file(upload, helper = None, sport = None):
     if sport == 'MLB':
         pos_list = [' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF ']
+    elif sport == 'MMA':
+        pos_list = ['Guy', 'Dude', 'Pooba', 'Bub', 'Chief']
+    elif sport == 'GOLF':
+        pos_list = ['Guy', 'Dude', 'Pooba', 'Bub', 'Chief']
     if upload is not None:
         try:
             try:
@@ -69,11 +73,21 @@ def load_contest_file(upload, helper = None, sport = None):
             cleaned_df = df[['BaseName', 'Lineup']]
             cleaned_df['Lineup'] = cleaned_df['Lineup'].replace(pos_list, value=',', regex=True)
             check_lineups = cleaned_df.copy()
-            cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
+            if sport == 'MLB':
+                cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
+            elif sport == 'MMA':
+                cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief']] = cleaned_df['Lineup'].str.split(',', expand=True)
+            elif sport == 'GOLF':
+                cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief']] = cleaned_df['Lineup'].str.split(',', expand=True)
             cleaned_df = cleaned_df.drop(columns=['Lineup', 'Remove'])
             entry_counts = cleaned_df['BaseName'].value_counts()
             cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
-            cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'P1', 'P2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
+            if sport == 'MLB':
+                cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'P1', 'P2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
+            elif sport == 'MMA':
+                cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief']]
+            elif sport == 'GOLF':
+                cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief']]
 
             print('Made it through check_lineups')