Enhance player selection functionality in app.py for improved filtering

- Updated player selection logic to utilize unique player names, ensuring that NaN values are excluded from the options presented to users.
- Implemented a new filtering mechanism for player selection, allowing users to filter lineups based on specific players, enhancing the overall user experience.

app.py

@@ -247,24 +247,26 @@ with tab2:
                     stack_size_parse_var = st.selectbox("Do you want to view a specific stack size(s)?", ['All', 'Specific'], key = 'stack_size_parse_var')
                     stack_size_names = st.multiselect("Select stack sizes", options=working_df['stack_size'].unique(), default=[], key = 'stack_size_names')
                 with player_var:
+                    unique_players = pd.unique(working_df[player_columns].values.ravel('K'))
+                    unique_players = [p for p in unique_players if p != 'nan']  # Remove any NaN values
                     player_parse_var = st.selectbox("Do you want to view lineups with specific player(s)?", ['All', 'Specific'], key = 'player_parse_var')
-                    player_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[], key = 'player_names')
+                    player_names = st.multiselect("Select players", options=unique_players, default=[], key = 'player_names')
                 submitted = st.form_submit_button("Submit")
                 if submitted:
                     if 'player_frame' in st.session_state:
                         del st.session_state['player_frame']
                     if 'stack_frame' in st.session_state:
                         del st.session_state['stack_frame']
-                    # Apply entry name filter if specific entries are selected
+                        
                     if entry_parse_var == 'Specific' and entry_names:
                         working_df = working_df[working_df['BaseName'].isin(entry_names)]
                     if stack_parse_var == 'Specific' and stack_names:
                         working_df = working_df[working_df['stack'].isin(stack_names)]
                     if stack_size_parse_var == 'Specific' and stack_size_names:
                         working_df = working_df[working_df['stack_size'].isin(stack_size_names)]
-                        
-                    # if player_parse_var == 'Specific' and player_names:
-                    #     working_df = working_df[working_df['BaseName'].isin(player_names)]
+                    if player_parse_var == 'Specific' and player_names:
+                        mask = working_df[player_columns].apply(lambda row: any(player in row.values for player in player_names), axis=1)
+                        working_df = working_df[mask]
                     if low_entries_var and high_entries_var:
                         working_df = working_df[working_df['EntryCount'].between(low_entries_var, high_entries_var)]