Update player summary management in app.py to ensure accurate session state handling. On portfolio reset, copy player exposures to maintain consistency. Refactor player summary assignment logic to streamline data flow and enhance user experience.

app.py

@@ -812,6 +812,7 @@ with tab2:
             with col1:
                 if st.button('Reset Portfolio', key='reset_port'):
                     st.session_state['working_frame'] = st.session_state['base_frame'].copy()
+                    st.session_state['player_summary'] = st.session_state['origin_player_exposures'].copy()
 
             with col2:
                 with st.form(key='contest_size_form'):
@@ -1242,9 +1243,7 @@ with tab2:
             player_stats = []
             player_columns = [col for col in display_frame.columns if col not in excluded_cols]
             
-            if 'origin_player_exposures' in st.session_state and display_frame_source == 'Portfolio':
-                player_summary = st.session_state['origin_player_exposures']
-            else:
+            if 'origin_player_exposures' not in st.session_state:
                 if type_var == 'Showdown':
                     for player in player_names:
                         # Create mask for lineups where this player is Captain (first column)
@@ -1334,13 +1333,12 @@ with tab2:
                 
                 player_summary = pd.DataFrame(player_stats)
                 player_summary = player_summary.sort_values('Lineup Count', ascending=False)
-                if display_frame_source == 'Portfolio':
-                    if 'origin_player_exposures' not in st.session_state:
-                        st.session_state['origin_player_exposures'] = player_summary.copy()
+                st.session_state['player_summary'] = player_summary.copy()
+                st.session_state['origin_player_exposures'] = player_summary.copy()
             
             st.subheader("Player Summary")
             st.dataframe(
-                player_summary.style
+                st.session_state['player_summary'].style
                 .background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
                 .format({
                     'Avg Median': '{:.2f}',