Enhance trimming options in app.py: add informational prompt for filtering before trimming and update portfolio copy logic to ensure correct data handling during download, improving user guidance and functionality.

app.py

@@ -805,7 +805,7 @@ with tab3:
                     
                     submitted = st.form_submit_button("Submit")
             with st.expander('Trimming Options'):
-
+                st.info("Make sure you filter before trimming if you want to filter, trimming before a filter will reset your portfolio")
                 with st.form(key='trim_form'):
                     performance_type = st.selectbox("Select Sorting variable", ['median', 'Finish_percentile'])
                     own_type = st.selectbox("Select trimming variable", ['Own', 'Weighted Own'])
@@ -845,6 +845,7 @@ with tab3:
                         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)
+                        st.session_state['download_portfolio'] = export_file
 
         with col2:
             st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var)
@@ -920,7 +921,10 @@ with tab3:
                                     )
                         
                         # Create a copy of the portfolio
-                        portfolio_copy = st.session_state['portfolio'].copy()
+                        if 'download_portfolio' not in st.session_state:
+                            portfolio_copy = st.session_state['portfolio'].copy()
+                        else:
+                            portfolio_copy = st.session_state['download_portfolio'].copy()
                         
                         # Create a list to store selected rows
                         selected_rows = []

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['Weighted Own'] = portfolio['Own'] * ((portfolio[own_columns].max(axis=1) - portfolio[own_columns].min(axis=1)) / 100)
+    portfolio['Weighted Own'] = (portfolio['Own'] * ((portfolio[own_columns].max(axis=1) - portfolio[own_columns].min(axis=1)) / 100)) * 200
     portfolio = portfolio.drop(columns=dup_count_columns)
     portfolio = portfolio.drop(columns=own_columns)
     portfolio = portfolio.drop(columns=calc_columns)