Update app.py

app.py

@@ -544,9 +544,8 @@ if 'temuan_kode_distrik' in df_local.columns:
                 st.markdown("### Insight")
                 st.markdown(
                     f"<div class='ai-insight'>"
-                    f"In UM Area, <strong>{top['nama_perusahaan']}</strong> leads with ratio <strong>{top['avg_monthly_ratio']:.2f}</strong>, "
-                    f"and <strong>{low['nama_perusahaan']}</strong> reports least (<strong>{low['avg_monthly_ratio']:.2f}</strong>). "
-                    f"Check if low reporters have sufficient access to reporting tools or training."
+                    f"Across all companies, the finding-per-person ratio is similar. In the UM Area "
+
                     f"</div>",
                     unsafe_allow_html=True
                 )
@@ -648,7 +647,7 @@ if not avg_ratio_per_location.empty:
             f"(<strong>{top_location['avg_monthly_ratio']:.2f}</strong>). "
             f"<strong>{low_location['nama_lokasi_full']}</strong> shows the lowest activity level "
             f"(<strong>{low_location['avg_monthly_ratio']:.2f}</strong>). "
-            f"Areas with high activity (green) warrant investigation into the underlying causes of frequent findings. "
+            f"Areas with high activity (green) warrant inspection into the underlying causes of frequent findings. "
             f"Areas with low activity (red) should be reviewed to ensure reporting completeness and identify any hidden risks."
             f"</div>"
         )
@@ -878,7 +877,7 @@ with col_3c:
                 f"<div class='ai-insight'>"
                 f"<strong>Insight:</strong> Individual reporting ranges from {min_r:.2f} to {max_r:.2f} findings/month (avg: {mean_r:.2f}). "
                 f"<strong>{top_reporter}</strong> is the most active contributor. "
-                f"<strong>Recommendation:</strong> Recognize top reporters; investigate causes of low activity (<0.5/month) via 1:1 review."
+                f"<strong>Recommendation:</strong> Recognize top reporters; inspection causes of low activity (<0.5/month) via 1:1 review."
                 f"</div>",
                 unsafe_allow_html=True
             )
@@ -905,7 +904,7 @@ with col_3b:
         # Ambil subset sesuai pilihan
         if sort_opt == "Top 10":
             # 10 tercepat: ascending (kecil → besar), tetap diurut ascending → tercepat di atas
-            subset = full_sorted.head(10).copy()
+            subset = full_sorted.head(10).sort_values('avg_monthly_leadtime', ascending=True)
         else:  # "Bottom 10 Slowest"
             # 10 terlambat: descending (besar → kecil), agar terlambat di atas
             subset = full_sorted.tail(10).sort_values('avg_monthly_leadtime', ascending=False)
@@ -972,7 +971,7 @@ with col_3d:
         full_sorted = avg_leadtime_per_indiv.sort_values('avg_monthly_leadtime', ascending=True)
 
         if sort_opt == "Top 10":
-            subset = full_sorted.head(10).copy()
+            subset = full_sorted.head(10).subset = full_sorted.head(10).sort_values('avg_monthly_leadtime', ascending=True)
         else:  # "Bottom 10 Slowest"
             subset = full_sorted.tail(10).sort_values('avg_monthly_leadtime', ascending=False)