Update app.py to switch from portfolio to Contest state for data calculations

- Changed references from 'portfolio' to 'Contest' in session state to align with the new data structure.
- Updated calculations for salary, median, and ownership metrics based on the selected game type (Classic or Showdown).
- Adjusted the display logic to reflect the changes in data handling, ensuring accurate representation of contest data.

app.py

@@ -82,7 +82,7 @@ with tab1:
 with tab2:
     if st.button('Clear data', key='reset3'):
         st.session_state.clear()
-    if 'portfolio' in st.session_state and 'projections_df' in st.session_state:
+    if 'Contest' in st.session_state and 'projections_df' in st.session_state:
         col1, col2 = st.columns([1, 8])
         excluded_cols = ['BaseName', 'EntryCount']
         with col1:
@@ -109,26 +109,26 @@ with tab2:
         }
         
         if type_var == 'Classic':
-            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
-            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
-            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['Contest']['salary'] = st.session_state['Contest'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['Contest']['median'] = st.session_state['Contest'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['Contest']['Own'] = st.session_state['Contest'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
         elif type_var == 'Showdown':
             # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
-            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
+            st.session_state['Contest']['salary'] = st.session_state['Contest'].apply(
                 lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
                           sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
                 axis=1
             )
             
             # Calculate median (CPT uses cpt_proj_map, others use proj_map)
-            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
+            st.session_state['Contest']['median'] = st.session_state['Contest'].apply(
                 lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
                           sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
                 axis=1
             )
             
             # Calculate ownership (CPT uses cpt_own_map, others use own_map)
-            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
+            st.session_state['Contest']['Own'] = st.session_state['Contest'].apply(
                 lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
                           sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
                 axis=1
@@ -137,7 +137,7 @@ with tab2:
         with col2:
             # Display the paginated dataframe first
             st.dataframe(
-                st.session_state['portfolio'].style
+                st.session_state['Contest'].style
                 .background_gradient(axis=0)
                 .background_gradient(cmap='RdYlGn')
                 .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])