Refactor map_dict handling in app.py: replace local map_dict variable with session state reference for improved consistency and clarity in data processing across various operations.

app.py

@@ -786,7 +786,7 @@ with tab2:
             if site_var == 'Draftkings':
                 if type_var == 'Classic':
                     if sport_var == 'CS2':
-                        map_dict = {
+                        st.session_state['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'])),
@@ -798,7 +798,7 @@ with tab2:
                             'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
                         }
                     elif sport_var != 'CS2':
-                        map_dict = {
+                        st.session_state['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'])),
@@ -810,7 +810,7 @@ with tab2:
                             'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
                         }
                 elif type_var == 'Showdown':
-                    map_dict = {
+                    st.session_state['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'])),
@@ -822,7 +822,7 @@ with tab2:
                         'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
                     }
             elif site_var == 'Fanduel':
-                map_dict = {
+                st.session_state['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'])),
@@ -837,53 +837,53 @@ with tab2:
                 if sport_var == 'CS2':
                     # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
                     st.session_state['working_frame']['salary'] = st.session_state['working_frame'].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:]),
+                        lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
+                                sum(st.session_state['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['working_frame']['median'] = st.session_state['working_frame'].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:]),
+                        lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) + 
+                                sum(st.session_state['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['working_frame']['Own'] = st.session_state['working_frame'].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:]),
+                        lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) + 
+                                sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
                         axis=1
                     )
 
                 elif sport_var != 'CS2':
-                    st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
-                    st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
-                    st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+                    st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
+                    st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row), axis=1)
+                    st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row), axis=1)
                     if stack_dict is not None:
                         st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].index.map(stack_dict)
             elif type_var == 'Showdown':
                 # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
                 st.session_state['working_frame']['salary'] = st.session_state['working_frame'].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:]),
+                    lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
+                            sum(st.session_state['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['working_frame']['median'] = st.session_state['working_frame'].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:]),
+                    lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) + 
+                            sum(st.session_state['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['working_frame']['Own'] = st.session_state['working_frame'].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:]),
+                    lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) + 
+                            sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
                     axis=1
                 )
-            st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], map_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
+            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)
             if 'trimming_dict_maxes' not in st.session_state:
                 st.session_state['trimming_dict_maxes'] = {
                     'Own': st.session_state['working_frame']['Own'].max(),
@@ -917,7 +917,7 @@ with tab2:
                     
                     submitted = st.form_submit_button("Submit")
                     if submitted:
-                        st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], map_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
+                        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)
                         if 'trimming_dict_maxes' not in st.session_state:
                             st.session_state['trimming_dict_maxes'] = {
                                 'Own': st.session_state['working_frame']['Own'].max(),
@@ -952,7 +952,7 @@ with tab2:
                     
                     submitted = st.form_submit_button("Submit")
                     if submitted:
-                        st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], map_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
+                        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)
                         if 'trimming_dict_maxes' not in st.session_state:
                             st.session_state['trimming_dict_maxes'] = {
                                 'Own': st.session_state['working_frame']['Own'].max(),