Enhance stack summary management in app.py by introducing session state checks for 'origin_stack_exposures'. This update ensures accurate stack statistics are displayed based on user settings and improves data handling for stack summaries during portfolio interactions.

app.py

@@ -1362,24 +1362,32 @@ with tab2:
             if 'Stack' in display_frame.columns:
                 stack_stats = []
                 stack_columns = [col for col in display_frame.columns if col.startswith('Stack')]
-                for stack in stack_dict.values():
-                    stack_mask = display_frame['Stack'] == stack
-                    if stack_mask.any():
-                        stack_stats.append({
-                            'Stack': stack,
-                            'Lineup Count': stack_mask.sum(),
-                            'Exposure': stack_mask.sum() / len(display_frame),
-                            'Avg Median': display_frame[stack_mask]['median'].mean(),
-                            'Avg Own': display_frame[stack_mask]['Own'].mean(),
-                            'Avg Dupes': display_frame[stack_mask]['Dupes'].mean(),
-                            'Avg Finish %': display_frame[stack_mask]['Finish_percentile'].mean(),
-                            'Avg Lineup Edge': display_frame[stack_mask]['Lineup Edge'].mean(),
-                        })
-                stack_summary = pd.DataFrame(stack_stats)
-                stack_summary = stack_summary.sort_values('Lineup Count', ascending=False).drop_duplicates()
+
+                if st.session_state['settings_base'] and 'origin_stack_exposures' in st.session_state and display_frame_source == 'Portfolio':
+                    st.session_state['stack_summary'] = st.session_state['origin_stack_exposures']
+                else:
+                    for stack in stack_dict.values():
+                        stack_mask = display_frame['Stack'] == stack
+                        if stack_mask.any():
+                            stack_stats.append({
+                                'Stack': stack,
+                                'Lineup Count': stack_mask.sum(),
+                                'Exposure': stack_mask.sum() / len(display_frame),
+                                'Avg Median': display_frame[stack_mask]['median'].mean(),
+                                'Avg Own': display_frame[stack_mask]['Own'].mean(),
+                                'Avg Dupes': display_frame[stack_mask]['Dupes'].mean(),
+                                'Avg Finish %': display_frame[stack_mask]['Finish_percentile'].mean(),
+                                'Avg Lineup Edge': display_frame[stack_mask]['Lineup Edge'].mean(),
+                            })
+                    stack_summary = pd.DataFrame(stack_stats)
+                    stack_summary = stack_summary.sort_values('Lineup Count', ascending=False).drop_duplicates()
+                    st.session_state['stack_summary'] = stack_summary.copy()
+                    if 'origin_stack_exposures' not in st.session_state:
+                        st.session_state['origin_stack_exposures'] = stack_summary.copy()
+
                 st.subheader("Stack Summary")
                 st.dataframe(
-                    stack_summary.style
+                    st.session_state['stack_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}',