Update app.py

app.py

@@ -570,7 +570,7 @@ if 'temuan_kode_distrik' in df_local.columns:
                 st.markdown("### Insight")
                 insight_text = (
                     f"<div class='ai-insight'>"
-                    f"In UM Area, all companies show almost the same average finding per person ration"
+                    f"In UM Area, all companies show almost the same average finding per person ratio"
                     # f"(<strong>{top_company_um['avg_monthly_ratio']:.2f}</strong>), warranting a focused safety audit. "
                     # f"<strong>{low_company_um['nama_perusahaan']}</strong> shows the lowest ratio "
                     # f"(<strong>{low_company_um['avg_monthly_ratio']:.2f}</strong>), which could reflect strong safety practices or requires verification of reporting completeness."
@@ -668,11 +668,11 @@ if not avg_ratio_per_location.empty:
 
         st.markdown("### Insight")
         insight_text = (
-            f"<div class='ai-insight'>"
-            f"The treemap visualizes the average finding-to-person ratio per location using a <strong>color gradient</strong>, indicating reporting activity levels. "
-            f"Locations with <span style='color:#4CAF50; font-weight:bold;'>green</span> color have a high ratio, indicating high reporting activity or exposure. "
-            f"Those with <span style='color:#FFB300; font-weight:bold;'>yellow</span> color have a medium ratio, indicating moderate reporting. "
-            f"Locations with <span style='color:#D32F2F; font-weight:bold;'>red</span> color have a low ratio, indicating lower activity levels or potentially under-reporting. "
+            # f"<div class='ai-insight'>"
+            # f"The treemap visualizes the average finding-to-person ratio per location using a <strong>color gradient</strong>, indicating reporting activity levels. "
+            # f"Locations with <span style='color:#4CAF50; font-weight:bold;'>green</span> color have a high ratio, indicating high reporting activity or exposure. "
+            # f"Those with <span style='color:#FFB300; font-weight:bold;'>yellow</span> color have a medium ratio, indicating moderate reporting. "
+            # f"Locations with <span style='color:#D32F2F; font-weight:bold;'>red</span> color have a low ratio, indicating lower activity levels or potentially under-reporting. "
             f"<strong>{top_location['nama_lokasi_full']}</strong> shows the highest activity level "
             f"(<strong>{top_location['avg_monthly_ratio']:.2f}</strong>). "
             f"<strong>{low_location['nama_lokasi_full']}</strong> shows the lowest activity level "
@@ -1216,7 +1216,7 @@ try:
             hover_name="Division",
             size=[12] * len(risk_matrix),
             size_max=15,
-            title="Audit Findings Risk Matrix: Avg Monthly Count vs Lead Time"
+            # title="Audit Findings Risk Matrix: Avg Monthly Count vs Lead Time"
         )
         # Background quadrant (same as original)
         fig.add_shape(type="rect", x0=X_LIMIT, x1=max_x, y0=Y_LIMIT, y1=max_y,
@@ -1250,7 +1250,7 @@ try:
         # ============================
         # 9. Summary Table
         # ============================
-        st.markdown("<h3 style='font-size:12px; margin-bottom:6px;'>Summary (Avg Monthly Count vs Avg Lead Time)</h3>",unsafe_allow_html=True)
+        # st.markdown("<h3 style='font-size:12px; margin-bottom:6px;'>Summary (Avg Monthly Count vs Avg Lead Time)</h3>",unsafe_allow_html=True)
 
         st.dataframe(
             risk_matrix.sort_values("Finding Count", ascending=False),
@@ -1464,7 +1464,7 @@ def predict_creators(df):
                     reason = f"Slope = {slope:.3f}, Coverage = {coverage*100:.1f}%. Avg: {avg_rate:.2f}/mo."
                     results.append({
                         'Creator': creator,
-                        'Avg Reports/Month': round(avg_rate, 2),
+                        'Reports/Month': round(avg_rate, 2),
                         'Coverage (%)': round(coverage * 100, 1),
                         'Trend Slope': round(slope, 3),
                         'Trend': ascii_sparkline_pln(ts.values.tolist()),
@@ -1512,7 +1512,7 @@ def predict_locations(df):
                     reason = f"Slope = {slope:.3f}, Coverage = {coverage*100:.1f}%. Avg: {avg_rate:.2f}/mo."
                     results.append({
                         'Location': lokasi,
-                        'Avg Reports/Month': round(avg_rate, 2),
+                        'Reports/Month': round(avg_rate, 2),
                         'Coverage (%)': round(coverage * 100, 1),
                         'Trend Slope': round(slope, 3),
                         'Trend': ascii_sparkline_pln(ts.values.tolist()),
@@ -1560,7 +1560,7 @@ def predict_divisions(df):
                     reason = f"Slope = {slope:.3f}, Coverage = {coverage*100:.1f}%. Avg: {avg_rate:.2f}/mo."
                     results.append({
                         'Division': div,
-                        'Avg Reports/Month': round(avg_rate, 2),
+                        'Reports/Month': round(avg_rate, 2),
                         'Coverage (%)': round(coverage * 100, 1),
                         'Trend Slope': round(slope, 3),
                         'Trend': ascii_sparkline_pln(ts.values.tolist()),
@@ -1640,7 +1640,7 @@ df_category = predict_categories(df_filtered)
 st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
 st.markdown("<div class='predictive-header'>1. Which Reporters Are Predicted to Have Less Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
 if not df_creator.empty:
-    cols = ['Creator', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
+    cols = ['Creator', 'Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
 
     # 🔥 Rename hanya untuk DISPLAY, bukan data asli
     df_display = df_creator[cols].rename(columns={
@@ -1676,7 +1676,7 @@ st.markdown("</div>", unsafe_allow_html=True)
 st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
 st.markdown("<div class='predictive-header'>2. Which Locations Are Predicted to Have Less Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
 if not df_location.empty:
-    cols = ['Location', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
+    cols = ['Location', 'Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
 
     # # 🔥 Rename hanya untuk DISPLAY, bukan data asli
     df_display = df_location[cols].rename(columns={
@@ -1712,7 +1712,7 @@ st.markdown("</div>", unsafe_allow_html=True)
 st.markdown("<div class='predictive-panel'>", unsafe_allow_html=True)
 st.markdown("<div class='predictive-header'>3. Which Divisions Are Predicted to Have Less Future Inspections? (Top 10 Most Declining)</div>", unsafe_allow_html=True)
 if not df_division.empty:
-    cols = ['Division', 'Avg Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
+    cols = ['Division', 'Reports/Month', 'Coverage (%)', 'Trend Slope', 'Trend']
 
     # # 🔥 Rename hanya untuk DISPLAY, bukan data asli
     df_display = df_division[cols].rename(columns={
@@ -1823,7 +1823,7 @@ if not df_category.empty:
 
     # Layout
     fig.update_layout(
-        title=dict(text="<b>Issue Category Trend vs Frequency (Non-Positive)</b>", x=0.5, y=0.95),
+        # title=dict(text="<b>Issue Category Trend vs Frequency (Non-Positive)</b>", x=0.5, y=0.95),
         xaxis=dict(
             title="Category",
             tickangle=45,