Implement GOLF-specific salary and actual_fpts calculations in app.py

- Updated salary and actual_fpts calculations to handle GOLF sport separately, summing values for all players instead of applying a multiplier for the first player.
- Retained the previous logic for other sports, ensuring consistent functionality across different sport selections.

app.py

@@ -221,18 +221,22 @@ with tab2:
                 ).most_common(1)[0][1] if any(st.session_state['map_dict']['team_map'].get(player, '') for player in row[2:]) else '',
                 axis=1
             )
-            # Modified salary calculation with 1.5x multiplier for first player
-            working_df['salary'] = working_df.apply(
-                lambda row: (st.session_state['map_dict']['salary_map'].get(row[2], 0) * 1.5) + 
-                           sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row[3:]),
-                axis=1
-            )
-            # Modified actual_fpts calculation with 1.5x multiplier for first player
-            working_df['actual_fpts'] = working_df.apply(
-                lambda row: (st.session_state['actual_dict'].get(row[2], 0) * 1.5) + 
-                           sum(st.session_state['actual_dict'].get(player, 0) for player in row[3:]),
-                axis=1
-            )
+            if sport_select == 'GOLF':
+                working_df['salary'] = working_df.apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
+                working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
+            else:
+                # Modified salary calculation with 1.5x multiplier for first player
+                working_df['salary'] = working_df.apply(
+                    lambda row: (st.session_state['map_dict']['salary_map'].get(row[2], 0) * 1.5) + 
+                            sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row[3:]),
+                    axis=1
+                )
+                # Modified actual_fpts calculation with 1.5x multiplier for first player
+                working_df['actual_fpts'] = working_df.apply(
+                    lambda row: (st.session_state['actual_dict'].get(row[2], 0) * 1.5) + 
+                            sum(st.session_state['actual_dict'].get(player, 0) for player in row[3:]),
+                    axis=1
+                )
             working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
             working_df['sorted'] = working_df[st.session_state['player_columns']].apply(
                 lambda row: ','.join(sorted(row.values)),