Refactor 'Manage Portfolio' logic to directly use reassess_edge for both working and export frames, streamlining data processing and enhancing efficiency.

app.py

@@ -1614,18 +1614,7 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['working_frame']['salary'] = st.session_state['working_frame']['salary'].astype('uint16')
                         
                         # st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
-                        # st.session_state['working_frame'] = reassess_edge(st.session_state['working_frame'], st.session_state['base_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
-                        # Store the number of rows in the modified frame
-                        num_modified_rows = len(st.session_state['working_frame'])
-                        
-                        # Concatenate the modified frame with the base frame
-                        combined_frame = pd.concat([st.session_state['working_frame'].drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity']), st.session_state['base_frame'].drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity'])], ignore_index=True)
-                        
-                        # Run predict_dupes on the combined frame
-                        updated_combined_frame = predict_dupes(combined_frame, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
-                        
-                        # Extract the first N rows (which correspond to our modified frame)
-                        st.session_state['working_frame'] = updated_combined_frame.head(num_modified_rows).copy()
+                        st.session_state['working_frame'] = reassess_edge(st.session_state['working_frame'], st.session_state['base_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
                     elif exp_submitted:
                         st.session_state['settings_base'] = False
@@ -1721,19 +1710,7 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['export_base']['salary'] = st.session_state['export_base']['salary'].astype('uint16')
                         
                         # st.session_state['export_base'] = predict_dupes(st.session_state['export_base'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
-                        # st.session_state['export_base'] = reassess_edge(st.session_state['export_base'], st.session_state['base_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
-                        num_modified_rows = len(st.session_state['export_base'])
-                        print(num_modified_rows)
-                        
-                        # Concatenate the modified frame with the base frame
-                        combined_frame = pd.concat([st.session_state['export_base'].drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity']), st.session_state['base_frame'].drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity'])], ignore_index=True)
-                        print(len(combined_frame))
-                        
-                        # Run predict_dupes on the combined frame
-                        updated_combined_frame = predict_dupes(combined_frame, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
-                        print(len(updated_combined_frame))
-                        # Extract the first N rows (which correspond to our modified frame)
-                        st.session_state['export_base'] = updated_combined_frame.head(num_modified_rows).copy()
+                        st.session_state['export_base'] = reassess_edge(st.session_state['export_base'], st.session_state['base_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
                         st.session_state['export_merge'] = st.session_state['export_base'].copy()
                         
         with st.container():

global_func/reassess_edge.py

@@ -24,7 +24,7 @@ def reassess_edge(modified_frame: pd.DataFrame, base_frame: pd.DataFrame, maps_d
     num_modified_rows = len(modified_frame)
     
     # Concatenate the modified frame with the base frame
-    combined_frame = pd.concat([modified_frame, base_frame], ignore_index=True)
+    combined_frame = pd.concat([modified_frame.drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity']), base_frame.drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity'])], ignore_index=True)
     
     # Run predict_dupes on the combined frame
     updated_combined_frame = predict_dupes(combined_frame, maps_dict, site_var, type_var, Contest_Size, strength_var, sport_var, max_salary)