testing the reoptimization stuff

app.py

@@ -3042,6 +3042,10 @@ if selected_tab == 'Manage Portfolio':
                     
                     if opt_reg_submitted:
                         st.session_state['settings_base'] = False
+                        
+                        # Store original for comparison (player columns only)
+                        original_frame = st.session_state['working_frame'][st.session_state['player_columns']].copy()
+                        
                         # Run optimization on working_frame
                         optimized_frame = optimize_lineup(
                             working_frame=st.session_state['working_frame'],
@@ -3056,6 +3060,11 @@ if selected_tab == 'Manage Portfolio':
                             optimize_by=optimize_by
                         )
                         
+                        # Store changes mask in session state for highlighting (no columns added)
+                        st.session_state['optimization_changes_mask'] = (
+                            original_frame != optimized_frame[st.session_state['player_columns']]
+                        )
+                        
                         # Recalculate lineup metrics
                         optimized_frame = calculate_lineup_metrics(
                             optimized_frame, 
@@ -3103,6 +3112,10 @@ if selected_tab == 'Manage Portfolio':
                     
                     elif opt_exp_submitted:
                         st.session_state['settings_base'] = False
+                        
+                        # Store original for comparison (player columns only)
+                        original_frame = st.session_state['export_base'][st.session_state['player_columns']].copy()
+                        
                         # Run optimization on export_base
                         optimized_frame = optimize_lineup(
                             working_frame=st.session_state['export_base'],
@@ -3117,6 +3130,11 @@ if selected_tab == 'Manage Portfolio':
                             optimize_by=optimize_by
                         )
                         
+                        # Store changes mask in session state for highlighting (no columns added)
+                        st.session_state['optimization_changes_mask'] = (
+                            original_frame != optimized_frame[st.session_state['player_columns']]
+                        )
+                        
                         # Recalculate lineup metrics for export
                         optimized_frame = calculate_lineup_metrics(
                             optimized_frame, 
@@ -3161,6 +3179,11 @@ if selected_tab == 'Manage Portfolio':
                                 axis=1
                             )
                         st.session_state['export_merge'] = st.session_state['export_base'].copy()
+                
+                # Clear highlighting button (outside the form)
+                if st.button("Clear Optimization Highlighting", key='clear_opt_highlight'):
+                    if 'optimization_changes_mask' in st.session_state:
+                        del st.session_state['optimization_changes_mask']
             
         with st.container():
             if 'export_base' not in st.session_state:
@@ -3207,15 +3230,15 @@ if selected_tab == 'Manage Portfolio':
                     select_custom_index = st.multiselect("Select rows to add (based on first column):", options=st.session_state['display_frame'].index, default=[])
                     if st.button("Add selected to Custom Export"):
                         st.session_state['export_base'] = pd.concat([st.session_state['export_base'], st.session_state['display_frame'].loc[select_custom_index]])
-                    st.session_state['export_base'] = st.session_state['export_base'].drop_duplicates()
-                    st.session_state['export_base'] = st.session_state['export_base'].reset_index(drop=True)
+                        st.session_state['export_base'] = st.session_state['export_base'].drop_duplicates()
+                        st.session_state['export_base'] = st.session_state['export_base'].reset_index(drop=True)
 
                 with remove_rows_col:
                     remove_custom_index = st.multiselect("Remove rows (based on first column):", options=st.session_state['display_frame'].index, default=[])
                     if st.button("Remove selected from Display"):
                         st.session_state['display_frame'] = st.session_state['display_frame'].drop(remove_custom_index)
-                    st.session_state['display_frame'] = st.session_state['display_frame'].drop_duplicates()
-                    st.session_state['display_frame'] = st.session_state['display_frame'].reset_index(drop=True)
+                        st.session_state['display_frame'] = st.session_state['display_frame'].drop_duplicates()
+                        st.session_state['display_frame'] = st.session_state['display_frame'].reset_index(drop=True)
                     
 
             total_rows = len(st.session_state['display_frame'])
@@ -3246,10 +3269,24 @@ if selected_tab == 'Manage Portfolio':
 
             # Get the subset of data for the current page
             current_page_data = st.session_state['display_frame'].iloc[start_idx:end_idx]
+            
+            # Define highlight function for optimization changes
+            def highlight_optimization_changes(df):
+                styles = pd.DataFrame('', index=df.index, columns=df.columns)
+                if 'optimization_changes_mask' in st.session_state:
+                    mask = st.session_state['optimization_changes_mask']
+                    for col in mask.columns:
+                        if col in styles.columns:
+                            common_idx = mask.index.intersection(df.index)
+                            for idx in common_idx:
+                                if mask.loc[idx, col]:
+                                    styles.loc[idx, col] = 'background-color: #90EE90'
+                return styles
+            
             # Display the paginated dataframe first
             st.dataframe(
                 current_page_data.style
-                .background_gradient(axis=0)
+                .apply(highlight_optimization_changes, axis=None)
                 .background_gradient(cmap='RdYlGn')
                 .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])
                 .format(freq_format, precision=2),