Update app.py

app.py

@@ -239,43 +239,19 @@ if fetch_data:
             completeness_df = pd.DataFrame({"Field": completeness.index, "Completeness (%)": completeness.values})
             completeness_table = completeness_df.set_index("Field")
         
-            # Sidebar Quick Stats
-            quick_stats_df = pd.DataFrame({
+            ## Sidebar Quick Stats (styled like Top Subjects)
+            quick_stats = pd.DataFrame({
                 "Metric": ["Total Records", "Incomplete Records", "Overall Completeness (%)"],
                 "Value": [len(metadata_df), incomplete_count, round(overall_percent, 1)]
             })
             
-            # Card-like background container
-            st.sidebar.markdown("""
-                <div style='
-                    background-color: #2b2b2b;
-                    padding: 1.5rem;
-                    border-radius: 12px;
-                    margin-bottom: 1.5rem;
-                    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.3);
-                '>
-                    <h4 style='color: #FFA500; margin-bottom: 1rem;'>Quick Stats</h4>
-                </div>
-            """, unsafe_allow_html=True)
-            
-            # Reset index to hide row numbers
-            quick_stats_df_reset = quick_stats_df.reset_index(drop=True)
-            
-            # Style with orange gradient
-            styled_stats = (
-                quick_stats_df_reset.style
-                    .background_gradient(cmap="Oranges", subset=["Value"])
-                    .format({"Value": "{:.1f}"})
-            )
-            
-            # Display styled dataframe without index
-            st.sidebar.dataframe(
-                styled_stats,
-                use_container_width=False,
-                height=240
-            )
-
-
+            # Styled Quick Stats Chart
+            with st.sidebar.expander("📊 Quick Stats", expanded=True):
+                st.dataframe(
+                    quick_stats.style.background_gradient(cmap="Oranges").format({"Value": "{:.1f}"}),
+                    use_container_width=True,
+                    height=240
+                )
 
             # Calculate Top 10 Subjects
             if 'subject' in metadata_df.columns: