Update app.py

app.py

@@ -1712,12 +1712,12 @@ with col_insights:
     critical_alerts = df[df['hour'].isin(critical_hours)]
     critical_pct = (len(critical_alerts) / len(df)) * 100 if len(df) > 0 else 0
 
-    st.markdown(f"**Critical Hour Risk (2-5 AM)**")
+    st.markdown(f"**Critical Hour Risk (3-6 AM)**")
     # Use conditional formatting for background color
     bg_color = "#ffcccc" if critical_pct > 50 else "#ffebcc" if critical_pct > 25 else "#ffffcc" if critical_pct > 10 else "#e6ffe6"
     st.markdown(f'<div style="background-color: {bg_color}; padding: 10px; border-radius: 5px;">Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}% of total alerts)</div>', unsafe_allow_html=True)
     if critical_pct > 10:  # If more than 10% of alerts happen in critical hours
-        st.warning(f"High risk: {critical_pct:.1f}% of fatigue alerts occur during critical hours (2-5 AM). This is a known circadian dip period.")
+        st.warning(f"High risk: {critical_pct:.1f}% of fatigue alerts occur during critical hours (3-6 AM). This is a known circadian dip period.")
     else:
         st.info(f"{critical_pct:.1f}% of alerts occur during critical hours. This is within acceptable range.")
 
@@ -1780,7 +1780,7 @@ with col_recs:
         peak_hour = df["hour"].value_counts().idxmax()
         critical_hours = [2, 3, 4, 5]
         if peak_hour in critical_hours:
-            insights_found.append(f" Most fatigue risk occurs at **{peak_hour}:00** — during critical circadian low period (2-5 AM). Consider enhanced monitoring.")
+            insights_found.append(f" Most fatigue risk occurs at **{peak_hour}:00** — during critical circadian low period (3-6 AM). Consider enhanced monitoring.")
         else:
             insights_found.append(f"Most fatigue risk occurs at **{peak_hour}:00** — likely due to circadian drop.")
 
@@ -1805,9 +1805,9 @@ with col_recs:
         # Contoh rekomendasi berdasarkan insight
         if any("circadian low" in i.lower() for i in insights_found):
             ai_recs.append({
-                "recommendation": "Deploy enhanced fatigue monitoring systems (e.g., EOR) specifically during 2-5 AM shifts.",
+                "recommendation": "Deploy enhanced fatigue monitoring systems (e.g., EOR) specifically during 3-6 AM shifts.",
                 "data_point": f"Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}% of total alerts)",
-                "reason": "High percentage of alerts occurring during the known circadian low period (2-5 AM) indicates increased risk during these hours."
+                "reason": "High percentage of alerts occurring during the known circadian low period (3-6 AM) indicates increased risk during these hours."
             })
         if any("shift" in i.lower() for i in insights_found):
             ai_recs.append({