Refactor portfolio filtering logic in app.py: streamline conditions for portfolio adjustments based on user selections, and ensure calculations are only performed when necessary. Add stack count adjustments for export options.

app.py

@@ -799,114 +799,116 @@ with tab3:
                     stack_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0)
                     stack_selections = st.multiselect("If Specific Stacks, Which to include?", options=sorted(list(set(stack_dict.values()))), default=[])
                     stack_remove = st.multiselect("If Specific Stacks, Which to remove?", options=sorted(list(set(stack_dict.values()))), default=[])
-                
+
+                st.session_state['math_done'] = False
                 submitted = st.form_submit_button("Submit")
 
         with col2:
-            st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var)
-            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Dupes'] <= max_dupes]
-            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] >= min_salary]
-            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] <= max_salary]
-            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Finish_percentile'] <= max_finish_percentile]
-            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Lineup Edge'] >= min_lineup_edge]
-            if stack_dict is not None:
-                if stack_toggle == 'All Stacks':
-                    st.session_state['portfolio'] = st.session_state['portfolio']
-                    st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
-                else:
-                    st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Stack'].isin(stack_selections)]
-                    st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
-            if player_remove:
-                # Create mask for lineups that contain any of the removed players
-                player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
-                remove_mask = st.session_state['portfolio'][player_columns].apply(
-                    lambda row: not any(player in list(row) for player in player_remove), axis=1
-                )
-                st.session_state['portfolio'] = st.session_state['portfolio'][remove_mask]
-            
-            if player_lock:
-                # Create mask for lineups that contain all locked players
-                player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
-                
-                lock_mask = st.session_state['portfolio'][player_columns].apply(
-                    lambda row: all(player in list(row) for player in player_lock), axis=1
-                )
-                st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
-            export_file = st.session_state['portfolio'].copy()
-            st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
-            if csv_file is not None:
-                player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
-                for col in player_columns:
-                    export_file[col] = export_file[col].map(st.session_state['export_dict'])
-            with st.expander("Download options"):
+            if 'math_done' not in st.session_state or st.session_state['math_done'] == False:
+                st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var)
+                st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Dupes'] <= max_dupes]
+                st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] >= min_salary]
+                st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] <= max_salary]
+                st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Finish_percentile'] <= max_finish_percentile]
+                st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Lineup Edge'] >= min_lineup_edge]
                 if stack_dict is not None:
-                    with st.form(key='stack_form'):
-                        st.subheader("Stack Count Adjustments")
-                        st.info("This allows you to fine tune the stacks that you wish to export. If you want to make sure you don't export any of a specific stack you can 0 it out.")
-                        # Create a container for stack value inputs
-                        sort_container = st.container()
-                        with sort_container:
-                            sort_var = st.selectbox("Sort export portfolio by:", options=['median', 'Lineup Edge', 'Own'])
-                        
-                        # Get unique stack values
-                        unique_stacks = sorted(list(set(stack_dict.values())))
-                        
-                        # Create a dictionary to store stack multipliers
-                        if 'stack_multipliers' not in st.session_state:
-                            st.session_state.stack_multipliers = {stack: 0.0 for stack in unique_stacks}
-                        
-                        # Create columns for the stack inputs
-                        num_cols = 6  # Number of columns to display
-                        for i in range(0, len(unique_stacks), num_cols):
-                            cols = st.columns(num_cols)
-                            for j, stack in enumerate(unique_stacks[i:i+num_cols]):
-                                with cols[j]:
-                                    # Create a unique key for each number input
-                                    key = f"stack_count_{stack}"
-                                    # Get the current count of this stack in the portfolio
-                                    current_stack_count = len(st.session_state['portfolio'][st.session_state['portfolio']['Stack'] == stack])
-                                    # Create number input with current value and max value based on actual count
-                                    st.session_state.stack_multipliers[stack] = st.number_input(
-                                        f"{stack} count",
-                                        min_value=0.0,
-                                        max_value=float(current_stack_count),
-                                        value=0.0,
-                                        step=1.0,
-                                        key=key
-                                    )
-                        
-                        # Create a copy of the portfolio
-                        portfolio_copy = st.session_state['portfolio'].copy()
-                        
-                        # Create a list to store selected rows
-                        selected_rows = []
-                        
-                        # For each stack, select the top N rows based on the count value
-                        for stack in unique_stacks:
-                            if stack in st.session_state.stack_multipliers:
-                                count = int(st.session_state.stack_multipliers[stack])
-                                # Get rows for this stack
-                                stack_rows = portfolio_copy[portfolio_copy['Stack'] == stack]
-                                # Sort by median and take top N rows
-                                top_rows = stack_rows.nlargest(count, sort_var)
-                                selected_rows.append(top_rows)
-                        
-                        # Combine all selected rows
-                        portfolio_copy = pd.concat(selected_rows)
-                        
-                        # Update export_file with filtered data
-                        export_file = portfolio_copy.copy()
-                        for col in export_file.columns:
-                            if col not in excluded_cols:
-                                export_file[col] = export_file[col].map(st.session_state['export_dict'])
-                        
-                        submitted = st.form_submit_button("Submit")
-                        if submitted:
-                            st.write('Export portfolio updated!')
+                    if stack_toggle == 'All Stacks':
+                        st.session_state['portfolio'] = st.session_state['portfolio']
+                        st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
+                    else:
+                        st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Stack'].isin(stack_selections)]
+                        st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
+                if player_remove:
+                    # Create mask for lineups that contain any of the removed players
+                    player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+                    remove_mask = st.session_state['portfolio'][player_columns].apply(
+                        lambda row: not any(player in list(row) for player in player_remove), axis=1
+                    )
+                    st.session_state['portfolio'] = st.session_state['portfolio'][remove_mask]
+                
+                if player_lock:
+                    # Create mask for lineups that contain all locked players
+                    player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+                    
+                    lock_mask = st.session_state['portfolio'][player_columns].apply(
+                        lambda row: all(player in list(row) for player in player_lock), axis=1
+                    )
+                    st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
+                export_file = st.session_state['portfolio'].copy()
+                st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
+                if csv_file is not None:
+                    player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+                    for col in player_columns:
+                        export_file[col] = export_file[col].map(st.session_state['export_dict'])
+                with st.expander("Download options"):
+                    if stack_dict is not None:
+                        with st.form(key='stack_form'):
+                            st.subheader("Stack Count Adjustments")
+                            st.info("This allows you to fine tune the stacks that you wish to export. If you want to make sure you don't export any of a specific stack you can 0 it out.")
+                            # Create a container for stack value inputs
+                            sort_container = st.container()
+                            with sort_container:
+                                sort_var = st.selectbox("Sort export portfolio by:", options=['median', 'Lineup Edge', 'Own'])
+                            
+                            # Get unique stack values
+                            unique_stacks = sorted(list(set(stack_dict.values())))
+                            
+                            # Create a dictionary to store stack multipliers
+                            if 'stack_multipliers' not in st.session_state:
+                                st.session_state.stack_multipliers = {stack: 0.0 for stack in unique_stacks}
+                            
+                            # Create columns for the stack inputs
+                            num_cols = 6  # Number of columns to display
+                            for i in range(0, len(unique_stacks), num_cols):
+                                cols = st.columns(num_cols)
+                                for j, stack in enumerate(unique_stacks[i:i+num_cols]):
+                                    with cols[j]:
+                                        # Create a unique key for each number input
+                                        key = f"stack_count_{stack}"
+                                        # Get the current count of this stack in the portfolio
+                                        current_stack_count = len(st.session_state['portfolio'][st.session_state['portfolio']['Stack'] == stack])
+                                        # Create number input with current value and max value based on actual count
+                                        st.session_state.stack_multipliers[stack] = st.number_input(
+                                            f"{stack} count",
+                                            min_value=0.0,
+                                            max_value=float(current_stack_count),
+                                            value=0.0,
+                                            step=1.0,
+                                            key=key
+                                        )
+                            
+                            # Create a copy of the portfolio
+                            portfolio_copy = st.session_state['portfolio'].copy()
+                            
+                            # Create a list to store selected rows
+                            selected_rows = []
+                            
+                            # For each stack, select the top N rows based on the count value
+                            for stack in unique_stacks:
+                                if stack in st.session_state.stack_multipliers:
+                                    count = int(st.session_state.stack_multipliers[stack])
+                                    # Get rows for this stack
+                                    stack_rows = portfolio_copy[portfolio_copy['Stack'] == stack]
+                                    # Sort by median and take top N rows
+                                    top_rows = stack_rows.nlargest(count, sort_var)
+                                    selected_rows.append(top_rows)
+                            
+                            # Combine all selected rows
+                            portfolio_copy = pd.concat(selected_rows)
+                            
+                            # Update export_file with filtered data
+                            export_file = portfolio_copy.copy()
+                            for col in export_file.columns:
+                                if col not in excluded_cols:
+                                    export_file[col] = export_file[col].map(st.session_state['export_dict'])
+                            
+                            submitted = st.form_submit_button("Submit")
+                            if submitted:
+                                st.write('Export portfolio updated!')
+                st.session_state['math_done'] = True
                 
             st.download_button(label="Download Portfolio", data=export_file.to_csv(index=False), file_name="portfolio.csv", mime="text/csv")
             
-
             # Add pagination controls below the dataframe
             total_rows = len(st.session_state['portfolio'])
             rows_per_page = 500