Add functions to load and save base frames using compressed storage in session state. Update Manage Portfolio tab to utilize these functions for managing working frames and default base frame.

app.py

@@ -645,6 +645,20 @@ def optimize_dataframe_dtypes(df):
     
     return df
 
+def load_base_frame(base_name):
+    """Load a base frame from compressed storage"""
+    if base_name in st.session_state['base_frame_names']:
+        base_bytes = st.session_state['base_frame_names'][base_name]
+        return pd.read_parquet(io.BytesIO(base_bytes))
+    else:
+        raise KeyError(f"Base frame '{base_name}' not found")
+
+def save_base_frame(base_name, dataframe):
+    """Save a base frame to compressed storage"""
+    buffer = io.BytesIO()
+    dataframe.to_parquet(buffer, compression='gzip')
+    st.session_state['base_frame_names'][base_name] = buffer.getvalue()
+
 def create_memory_efficient_mappings(projections_df, site_var, type_var, sport_var):
     """Create mappings with optimized data types"""
     # Optimize projections data types first
@@ -1752,7 +1766,8 @@ if selected_tab == 'Manage Portfolio':
                     if st.session_state['base_frame_names']:
                         base_choice = st.selectbox("Base Choice", options=list(st.session_state['base_frame_names'].keys()), index=0)
                         if st.button("Load Selected Base"):
-                            st.session_state['working_frame'] = st.session_state['base_frame_names'][base_choice].copy()
+                            # Load from compressed storage
+                            st.session_state['working_frame'] = load_base_frame(base_choice)
                             st.rerun()
                     else:
                         st.info("No saved base frames available")
@@ -1765,7 +1780,8 @@ if selected_tab == 'Manage Portfolio':
                     new_base_name = st.text_input("New Base Name", value='New Base')
                     if st.button("Save Current as Base"):
                         if new_base_name and new_base_name not in st.session_state['base_frame_names']:
-                            st.session_state['base_frame_names'][new_base_name] = st.session_state['working_frame'].copy()
+                            # Store using compressed storage
+                            save_base_frame(new_base_name, st.session_state['working_frame'])
                             st.success(f"Base '{new_base_name}' saved successfully!")
                         elif new_base_name in st.session_state['base_frame_names']:
                             st.error("Base name already exists")
@@ -1814,8 +1830,8 @@ if selected_tab == 'Manage Portfolio':
             # Create the final base frame with dupe predictions
             final_base_frame = predict_dupes(processed_frame, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
             
-            # Set up the Default base and working frame
-            st.session_state['base_frame_names']['Default'] = final_base_frame.copy()
+            # Set up the Default base and working frame using memory-efficient storage
+            save_base_frame('Default', final_base_frame)
             st.session_state['working_frame'] = final_base_frame.copy()
             
             # st.session_state['highest_owned_teams'] = st.session_state['projections_df'][~st.session_state['projections_df']['position'].isin(['P', 'SP'])].groupby('team')['ownership'].sum().sort_values(ascending=False).head(3).index.tolist()
@@ -2762,7 +2778,9 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
                         
                         # 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_names']['Default'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
+                        # Load Default base from compressed storage for reassess_edge
+                        default_base = load_base_frame('Default')
+                        st.session_state['working_frame'] = reassess_edge(st.session_state['working_frame'], default_base, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
                         team_dict = dict(zip(st.session_state['portfolio_inc_proj']['player_names'], st.session_state['portfolio_inc_proj']['team']))
                         if 'Stack' in st.session_state['working_frame'].columns:
                             st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].apply(
@@ -2800,7 +2818,9 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['export_base'] = parsed_frame.reset_index(drop=True)
                         
                         # 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_names']['Default'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
+                        # Load Default base from compressed storage for reassess_edge
+                        default_base = load_base_frame('Default')
+                        st.session_state['export_base'] = reassess_edge(st.session_state['export_base'], default_base, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
                         team_dict = dict(zip(st.session_state['portfolio_inc_proj']['player_names'], st.session_state['portfolio_inc_proj']['team']))
                         if 'Stack' in st.session_state['export_base'].columns:
                             st.session_state['export_base']['Stack'] = st.session_state['export_base'].apply(