Enhance portfolio trimming logic in app.py: add comprehensive filtering options based on salary, finish percentile, and player selections, and update predict_dupes function to adjust 'Product Own' calculation for improved accuracy.

app.py

@@ -807,7 +807,39 @@ with tab3:
                     
                     submitted = st.form_submit_button("Trim")
                     if submitted:
+                        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]
+                        
                         st.session_state['portfolio'] = trim_portfolio(st.session_state['portfolio'], performance_type, own_type)
+                        export_file = st.session_state['portfolio'].copy()
+                        st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
 
         with col2:
             st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var)

global_func/predict_dupes.py

@@ -181,7 +181,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     portfolio['Lineup Edge'] = portfolio['Win%'] * ((.5 - portfolio['Finish_percentile']) * (Contest_Size / 2.5))
     portfolio['Lineup Edge'] = portfolio.apply(lambda row: row['Lineup Edge'] / (row['Dupes'] + 1) if row['Dupes'] > 0 else row['Lineup Edge'], axis=1)
     portfolio['Lineup Edge'] = portfolio['Lineup Edge'] - portfolio['Lineup Edge'].mean()
-    portfolio['Product Own'] = portfolio[own_columns].product(axis=1).mean() * 100
+    portfolio['Product Own'] = portfolio[own_columns].product(axis=1).mean() * 1000
     portfolio = portfolio.drop(columns=dup_count_columns)
     portfolio = portfolio.drop(columns=own_columns)
     portfolio = portfolio.drop(columns=calc_columns)