Update app.py to utilize session state for ownership and actual metrics

- Replaced direct references to ownership_dict and actual_dict with session state equivalents, improving consistency in data handling.
- Enhanced the calculation of actual fantasy points and ownership metrics to ensure accurate data processing based on session state values.

app.py

@@ -122,7 +122,7 @@ with tab2:
                             working_df = working_df[working_df['BaseName'].isin(entry_names)]
 
         # Calculate metrics based on game type
-        st.write(actual_dict)
+        st.write(st.session_state['ownership_dict'])
         if type_var == 'Classic':
             working_df['stack'] = working_df.apply(
                 lambda row: Counter(
@@ -140,9 +140,9 @@ 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(actual_dict.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['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(ownership_dict.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['sorted'] = working_df[player_columns].apply(
                 lambda row: ','.join(sorted(row.values)),
                 axis=1