Enhance caching and data handling in app.py for improved performance

- Added caching decorators to several functions to optimize data retrieval and processing, enhancing application performance.
- Introduced a new function to highlight specific rows in the dataframe based on conditions, improving data visualization for users.
- These changes aim to streamline data management and enhance user experience by ensuring efficient data handling and clearer presentation of information.

app.py

@@ -10,6 +10,7 @@ from datetime import datetime
 
 # Just setting a note here to say that I should attempt to do some memory allocation savings and swap to numpy soon
 
+@st.cache_resource
 def init_conn():
         
         uri = st.secrets['mongo_uri']
@@ -17,7 +18,8 @@ def init_conn():
         db = client['Contest_Information']
 
         return db
-    
+
+@st.cache_data
 def grab_contest_names(db, sport, type):
     if type == 'Classic':
         db_type = 'reg'
@@ -33,6 +35,7 @@ def grab_contest_names(db, sport, type):
     
     return contest_names, curr_info
 
+@st.cache_data
 def grab_contest_player_info(db, sport, type, contest_date, contest_name, contest_id_map):
     if type == 'Classic':
         db_type = 'reg'
@@ -56,6 +59,7 @@ def grab_contest_player_info(db, sport, type, contest_date, contest_name, contes
     
     return player_info, info_maps
 
+@st.cache_data
 def export_contest_file(db, sport, type, contest_date, contest_id, contest_data):
     if type == 'Classic':
         db_type = 'reg'
@@ -103,12 +107,20 @@ from global_func.create_stack_comparison import create_stack_comparison
 from global_func.create_size_comparison import create_size_comparison
 from global_func.create_general_comparison import create_general_comparison
 
+@st.cache_data
 def is_valid_input(file):
     if isinstance(file, pd.DataFrame):
         return not file.empty
     else:
         return file is not None  # For Streamlit uploader objects
 
+@st.cache_data
+def highlight_row_condition(row):
+    if row['BaseName'] == 'Backtesting_upload':
+        return ['background-color: lightgreen'] * len(row)
+    else:
+        return [''] * len(row)
+
 player_exposure_format = {'Exposure Overall': '{:.2%}', 'Exposure Top 1%': '{:.2%}', 'Exposure Top 5%': '{:.2%}', 'Exposure Top 10%': '{:.2%}', 'Exposure Top 20%': '{:.2%}'}
 dupe_format = {'uniques%': '{:.2%}', 'under_5%': '{:.2%}', 'under_10%': '{:.2%}'}
 
@@ -496,6 +508,7 @@ with tab2:
         end_idx = min((st.session_state.current_page) * rows_per_page, total_rows)
         st.dataframe(
             st.session_state['display_contest_info'].iloc[start_idx:end_idx].style
+            .apply(highlight_row_condition, axis=1)
             .background_gradient(axis=0)
             .background_gradient(cmap='RdYlGn')
             .format(precision=2),