Update app.py

app.py

@@ -1122,7 +1122,7 @@ try:
     # ================================
     # 12. DISPLAY TABLE
     # ================================
-    st.subheader("Operator Hazard Summary Table (8 Weeks Observed)")
+    st.subheader("Operator Risk Summary Table (8 Weeks Observed)")
     table_display = (
         risk_matrix[[
             "Operator Name",
@@ -1546,7 +1546,7 @@ else:
                 for insight in ob_insights:
                     st.markdown(f"""
                     <div class="ai-insight-box">
-                        <div class="ai-insight-title">Hazard Summary</div>
+                        <div class="ai-insight-title">Risk Summary</div>
                         <p>{insight}</p>
                     </div>
                     """, unsafe_allow_html=True)
@@ -1573,7 +1573,7 @@ else:
                 for insight in coal_insights:
                     st.markdown(f"""
                     <div class="ai-insight-box">
-                        <div class="ai-insight-title">Hazard Summary</div>
+                        <div class="ai-insight-title">Risk Summary</div>
                         <p>{insight}</p>
                     </div>
                     """, unsafe_allow_html=True)
@@ -1657,9 +1657,6 @@ else:
     except Exception as e:
         st.error(f"Error in Top 10 Operator analysis: {str(e)}")
         st.exception(e)  # optionally show full traceback during dev
-
-
-
 # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
 st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")
 
@@ -1685,7 +1682,7 @@ with col_insights:
 
     # 2. High-Speed Fatigue Analysis (Environmental Risk)
     if col_speed and col_speed in df.columns:
-        high_speed_threshold = df[col_speed].quantile(0.75) if not df[col_speed].dropna().empty else 0
+        high_speed_threshold = 20
         high_speed_fatigue = df[df[col_speed] >= high_speed_threshold] if high_speed_threshold > 0 else pd.DataFrame()
         high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100 if len(df) > 0 else 0
         
@@ -1813,6 +1810,18 @@ with col_recs:
             })
 
         for rec in ai_recs:
+            # Ambil data_point dan ganti teks persentase di dalamnya menjadi warna merah
+            data_point_text = rec['data_point']
+            # Ganti pola persentase (X.X%) dengan span warna merah
+            import re
+            # Cari pola seperti "1.6%", "21.2%", dll.
+            data_point_colored = re.sub(r'(\d+\.?\d*%)', r'<span style="color: red;">\1</span>', data_point_text)
+
+            # Ambil reason dan lakukan hal yang sama
+            reason_text = rec['reason']
+            reason_colored = re.sub(r'(\d+\.?\d*%)', r'<span style="color: red;">\1</span>', reason_text)
+
+            # Tampilkan rekomendasi dengan teks persentase berwarna merah
             st.markdown(f"""
             <div style="
                 background: #f8f9fa;
@@ -1841,10 +1850,10 @@ with col_recs:
                     <strong>Action:</strong> {rec['recommendation']}
                 </div>
                 <div style="font-size: 12px; padding: 8px; background: #e9ecef; border-radius: 5px; margin-top: 5px;">
-                    <strong>Data Point:</strong> {rec['data_point']}
+                    <strong>Data Point:</strong> {data_point_colored}
                 </div>
                 <div style="font-size: 12px; padding: 8px; background: #f1f1f1; border-radius: 5px; margin-top: 5px;">
-                    <strong>AI Reasoning:</strong> {rec['reason']}
+                    <strong>AI Reasoning:</strong> {reason_colored}
                 </div>
             </div>
             """, unsafe_allow_html=True)