Add NAS support in load_contest_file.py for lineup processing

- Implemented logic to handle 'NAS' sport in the lineup processing, including adjustments for lineup replacement and splitting into respective columns. This enhances the functionality of the application by accommodating the new sport option in contest file loading.

global_func/load_contest_file.py

@@ -115,6 +115,8 @@ def load_contest_file(upload, type, helper = None, sport = None):
                     cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF ', ' G ', 'G '], value=',', regex=True)
                 elif sport == 'WNBA':
                     cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' G ', 'F ', ' F ', ' UTIL '], value=',', regex=True)
+                elif sport == 'NAS':
+                    cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' D ', 'D '], value=',', regex=True)
                 print(sport)
                 check_lineups = cleaned_df.copy()
                 if sport == 'MLB':
@@ -125,6 +127,8 @@ def load_contest_file(upload, type, helper = None, sport = None):
                     cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = cleaned_df['Lineup'].str.split(',', expand=True)
                 elif sport == 'WNBA':
                     cleaned_df[['Remove', 'G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']] = cleaned_df['Lineup'].str.split(',', expand=True)
+                elif sport == 'NAS':
+                    cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = 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)
@@ -136,11 +140,15 @@ def load_contest_file(upload, type, helper = None, sport = None):
                     cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']]
                 elif sport == 'WNBA':
                     cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']]
+                elif sport == 'NAS':
+                    cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']]
             elif type == 'Showdown':
                 if sport == 'NHL':
                     cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' FLEX ', 'CPT '], value=',', regex=True)
                 if sport == 'GOLF':
                     cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' G ', 'G '], value=',', regex=True)
+                elif sport == 'NAS':
+                    cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' D ', 'D '], value=',', regex=True)
                 else:
                     cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' UTIL ', 'CPT '], value=',', regex=True)
                 print(type)