Update app.py

app.py

@@ -1676,23 +1676,26 @@ with col_insights:
     critical_pct = (len(critical_alerts) / len(df)) * 100 if len(df) > 0 else 0
 
     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
+    if critical_pct > 10:
         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.")
 
     # 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  # Handle empty series
+        high_speed_threshold = df[col_speed].quantile(0.75) if not df[col_speed].dropna().empty else 0
         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
         
         st.markdown(f"**High-Speed Fatigue Risk (Speed > {high_speed_threshold:.0f} km/h)**")
-        st.metric("High-Speed Fatigue Events", f"{len(high_speed_fatigue)}", f"{high_speed_pct:.1f}% of total alerts")
-        if high_speed_pct > 20:  # If more than 20% of alerts happen at high speed
+        # Ganti st.metric dengan HTML custom
+        st.markdown(f"""
+        <div style="font-size: 24px; font-weight: bold;">{len(high_speed_fatigue)}</div>
+        <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {high_speed_pct:.1f}% of total alerts</div>
+        """, unsafe_allow_html=True)
+        if high_speed_pct > 20:
             st.warning(f"High risk: {high_speed_pct:.1f}% of fatigue alerts occur at high speeds. This increases accident severity potential.")
         else:
             st.info(f"{high_speed_pct:.1f}% of alerts occur at high speeds. This is within acceptable range.")
@@ -1702,13 +1705,16 @@ with col_insights:
     # 3. Shift Pattern Analysis
     if col_shift and col_shift in df.columns:
         shift_counts = df[col_shift].value_counts()
-        # shift_alerts_by_hour = df.groupby([col_shift, 'hour']).size().reset_index(name='alerts') # Tidak digunakan dalam tampilan ini
-        
+
         st.markdown(f"**Shift Pattern Risk**")
         for shift_val in shift_counts.index:
             shift_pct = (shift_counts[shift_val] / len(df)) * 100
-            st.metric(f"Shift {shift_val} Alerts", f"{shift_counts[shift_val]}", f"{shift_pct:.1f}% of total alerts")
-            if shift_pct > 50:  # If one shift has more than 50% of alerts
+            # Ganti st.metric dengan HTML custom
+            st.markdown(f"""
+            <div style="font-size: 24px; font-weight: bold;">{shift_counts[shift_val]}</div>
+            <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {shift_pct:.1f}% of total alerts</div>
+            """, unsafe_allow_html=True)
+            if shift_pct > 50:
                 st.warning(f"Shift {shift_val} has disproportionately high alerts ({shift_pct:.1f}%). Review shift scheduling and workload.")
             else:
                 st.info(f"Shift {shift_val} alert distribution is acceptable ({shift_pct:.1f}%).")
@@ -1718,13 +1724,17 @@ with col_insights:
     # 4. Operator Risk Profiling
     if col_operator and col_operator in df.columns:
         operator_alerts = df[col_operator].value_counts()
-        top_risk_operators = operator_alerts.head(5)  # Top 5 operators by alerts
-        
+        top_risk_operators = operator_alerts.head(5)
+
         st.markdown(f"**High-Risk Operator Identification**")
         for op_name, count in top_risk_operators.items():
             op_pct = (count / len(df)) * 100
-            st.metric(f"Operator: {op_name}", f"{count} alerts", f"{op_pct:.1f}% of total alerts")
-            if op_pct > 5:  # If an operator has more than 5% of all alerts
+            # Ganti st.metric dengan HTML custom
+            st.markdown(f"""
+            <div style="font-size: 24px; font-weight: bold;">{count} alerts</div>
+            <div style="color: red; font-size: 14px; margin-top: -5px;">↑ {op_pct:.1f}% of total alerts</div>
+            """, unsafe_allow_html=True)
+            if op_pct > 5:
                 st.warning(f"Operator {op_name} has high fatigue risk ({op_pct:.1f}% of alerts). Consider coaching or rest plan.")
             else:
                 st.info(f"Operator {op_name} fatigue risk is within acceptable range ({op_pct:.1f}%).")
@@ -1736,7 +1746,7 @@ with col_insights:
 with col_recs:
     st.subheader("Recommendations")
     ai_recs = []
-    insights_found = [] # Untuk menyimpan insight yang ditemukan
+    insights_found = []
 
     # Peak hour
     if "hour" in df.columns and not df.empty:
@@ -1765,7 +1775,6 @@ with col_recs:
 
     # Generate recommendations based on found insights
     if insights_found:
-        # 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 3-6 AM shifts.",
@@ -1803,10 +1812,7 @@ with col_recs:
                 "reason": "No specific high-impact insights were automatically identified from the aggregated data in this section."
             })
 
-        # Menampilkan rekomendasi dalam format kotak yang sesuai dengan permintaan
         for rec in ai_recs:
-            # Gunakan div dengan class khusus untuk membuat kotak rekomendasi di kolom kanan
-            # Gaya diambil dari .insight-box untuk konsistensi dan menghindari warna ungu
             st.markdown(f"""
             <div style="
                 background: #f8f9fa;
@@ -1847,4 +1853,4 @@ with col_recs:
 
 # ================= FOOTER ===========================
 st.markdown("---")
-st.markdown('<div class="footer">FatigueAnalyzer - Transforming Mining Safety with Intelligent Analytics | Contact: info@bukittechnology.com</div>', unsafe_allow_html=True)
+st.markdown('<div class="footer">FatigueAnalyzer - Transforming Mining Safety with Intelligent Analytics | Contact: info@bukittechnology.com</div>', unsafe_allow_html=True)