Update session state management in app.py for adjusted dataframes

- Refactored session state variables to use 'Adj_' prefix for adjusted dataframes, enhancing clarity in data handling.
- Updated logic for name mismatches and salary formatting to reflect the new dataframe names.
- Improved player column processing and mapping dictionary creation to utilize adjusted dataframes, streamlining data processing and presentation.

app.py

@@ -74,34 +74,34 @@ with tab1:
         
     if Contest_file and projections_file:
         st.subheader("Name Matching functions")
-        st.session_state['Contest'], st.session_state['projections_df'], st.session_state['ownership_df'], st.session_state['actual_df'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df'], st.session_state['ownership_df'], st.session_state['actual_df'])
-        st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
-        st.session_state['ownership_dict'] = dict(zip(st.session_state['ownership_df']['Player'], st.session_state['ownership_df']['Own']))
-        st.session_state['actual_dict'] = dict(zip(st.session_state['actual_df']['Player'], st.session_state['actual_df']['FPTS']))
+        st.session_state['Adj_Contest'], st.session_state['Adj_projections_df'], st.session_state['Adj_ownership_df'], st.session_state['Adj_actual_df'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df'], st.session_state['ownership_df'], st.session_state['actual_df'])
+        st.session_state['Adj_projections_df']['salary'] = (st.session_state['Adj_projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
+        st.session_state['ownership_dict'] = dict(zip(st.session_state['Adj_ownership_df']['Player'], st.session_state['Adj_ownership_df']['Own']))
+        st.session_state['actual_dict'] = dict(zip(st.session_state['Adj_actual_df']['Player'], st.session_state['Adj_actual_df']['FPTS']))
 
 
 with tab2:
     excluded_cols = ['BaseName', 'EntryCount']
-    player_columns = [col for col in st.session_state['Contest'].columns if col not in excluded_cols]
-    for col in player_columns:
-        st.session_state['Contest'][col] = st.session_state['Contest'][col].astype(str)
+    if 'Adj_Contest' in st.session_state and 'Adj_projections_df' in st.session_state:
+        player_columns = [col for col in st.session_state['Adj_Contest'].columns if col not in excluded_cols]
+        for col in player_columns:
+            st.session_state['Adj_Contest'][col] = st.session_state['Adj_Contest'][col].astype(str)
     
-    # Create mapping dictionaries
-    map_dict = {
-        'pos_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
-        'team_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
-        'salary_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
-        'proj_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
-        'own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
-        'own_percent_rank': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
-        'cpt_salary_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
-        'cpt_proj_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
-        'cpt_own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
-    }
-    # Create a copy of the dataframe for calculations
-    working_df = st.session_state['Contest'].copy()
+        # Create mapping dictionaries
+        map_dict = {
+            'pos_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['position'])),
+            'team_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['team'])),
+            'salary_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['salary'])),
+            'proj_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['median'])),
+            'own_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['ownership'])),
+            'own_percent_rank': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['ownership'].rank(pct=True))),
+            'cpt_salary_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['salary'])),
+            'cpt_proj_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['median'] * 1.5)),
+            'cpt_own_map': dict(zip(st.session_state['Adj_projections_df']['player_names'], st.session_state['Adj_projections_df']['captain ownership']))
+        }
+        # Create a copy of the dataframe for calculations
+        working_df = st.session_state['Adj_Contest'].copy() 
 
-    if 'Contest' in st.session_state and 'projections_df' in st.session_state:    
         if type_var == 'Classic':
             working_df['stack'] = working_df.apply(
                 lambda row: Counter(