Refactor variable usage in app.py for clarity and consistency

- Updated variable assignments in app.py to use local variables for ownership_dict and actual_dict instead of session state, enhancing code readability and maintainability.
- Ensured that the changes maintain the integrity of data processing during name matching operations.

app.py

@@ -75,7 +75,7 @@ with tab1:
         if 'Contest' not in st.session_state and 'projections_df' not in st.session_state:
             if contest_base is not None and projections is not None:
                 st.subheader("Name Matching functions")
-                st.session_state['Contest'], st.session_state['projections_df'], st.session_state['ownership_dict'], st.session_state['actual_dict'] = find_name_mismatches(contest_base, projections, ownership_df, fpts_df)
+                st.session_state['Contest'], st.session_state['projections_df'], ownership_dict, actual_dict = find_name_mismatches(contest_base, projections, ownership_df, fpts_df)
                 st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
 
 with tab2:
@@ -138,9 +138,9 @@ with tab2:
             )
             working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
             working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
-            working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
+            working_df['actual_fpts'] = working_df.apply(lambda row: sum(actual_dict.get(player, 0) for player in row), axis=1)
             working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
-            working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
+            working_df['actual_own'] = working_df.apply(lambda row: sum(ownership_dict.get(player, 0) for player in row), axis=1)
             working_df['sorted'] = working_df[player_columns].apply(
                 lambda row: ','.join(sorted(row.values)),
                 axis=1