Refine error message and enhance duplication frame metrics in app.py

- Updated the error message for contest retrieval to clarify the conditions under which no contests are found.
- Improved the duplication frame by calculating percentages for 'uniques', 'under_5', and 'under_10' relative to 'EntryCount', enhancing data analysis and clarity.

app.py

@@ -88,7 +88,7 @@ with tab1:
             try:
                 contest_names, curr_info = grab_contest_names(db, sport_select, type_var)
             except:
-                st.error("No contests found for this sport, type, and date range")
+                st.error("No contests found for this sport and/or game type")
                 st.stop()
             
         with date_options:
@@ -428,7 +428,10 @@ with tab2:
                     st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
 
             with tab5:
-                st.session_state['duplication_frame'] = working_df[['BaseName', 'EntryCount', 'dupes', 'uniques', 'under_5', 'under_10']]
-                st.session_state['duplication_frame']['average_dupes'] = st.session_state['duplication_frame'].groupby('BaseName')['dupes'].mean()
-                st.session_state['duplication_frame'] = st.session_state['duplication_frame'][['BaseName', 'EntryCount', 'average_dupes', 'uniques', 'under_5', 'under_10']].drop_duplicates(subset='BaseName', keep='first')
+                dupe_frame = working_df[['BaseName', 'EntryCount', 'dupes', 'uniques', 'under_5', 'under_10']]
+                dupe_frame['average_dupes'] = dupe_frame['dupes'].mean()
+                dupe_frame['uniques%'] = dupe_frame['uniques'] / dupe_frame['EntryCount']
+                dupe_frame['under_5%'] = dupe_frame['under_5'] / dupe_frame['EntryCount']
+                dupe_frame['under_10%'] = dupe_frame['under_10'] / dupe_frame['EntryCount']
+                st.session_state['duplication_frame'] = dupe_frame[['BaseName', 'EntryCount', 'average_dupes', 'uniques', 'uniques%', 'under_5', 'under_5%', 'under_10', 'under_10%']].drop_duplicates(subset='BaseName', keep='first')
                 st.dataframe(st.session_state['duplication_frame'].style.background_gradient(cmap='RdYlGn_r', axis=1).format(precision=2), hide_index=True)