Update app.py

app.py

@@ -1658,204 +1658,182 @@ else:
         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")
 
-# Membagi menjadi 2 kolom
+# Membagi tampilan menjadi dua kolom
 col_insights, col_recs = st.columns(2)
 
-# ============================
-# KIRI — INSIGHTS
-# ============================
+# Kolom kiri: Insights by Advanced Analytics
 with col_insights:
     st.subheader("Insights by Advanced Analytics")
 
-    # 1. Critical Hour Analysis
+    # 1. Critical Hour Analysis (2-5 AM)
     critical_hours = [2, 3, 4, 5]
     critical_alerts = df[df['hour'].isin(critical_hours)]
     critical_pct = (len(critical_alerts) / len(df)) * 100 if len(df) > 0 else 0
 
-    st.markdown("**Critical Hour Risk (3–6 AM)**")
-    bg_color = (
-        "#ffcccc" if critical_pct > 50 else
-        "#ffebcc" if critical_pct > 25 else
-        "#ffffcc" if critical_pct > 10 else "#e6ffe6"
-    )
+    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"
+    
+    # ✅ Highlight percentage in red
     st.markdown(
-        f'<div style="background-color:{bg_color}; padding:10px; border-radius:5px;">'
-        f'Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}% of total alerts)'
-        f'</div>',
+        f'<div style="background-color: {bg_color}; padding: 10px; border-radius: 5px;">'
+        f'Critical Hour Alerts: {len(critical_alerts)} '
+        f'<span style="color:#d32f2f; font-weight:bold;">({critical_pct:.1f}% of total alerts)</span>'
+        '</div>',
         unsafe_allow_html=True
     )
-
+    
     if critical_pct > 10:
-        st.warning(f"High risk: {critical_pct:.1f}% of fatigue alerts occur during critical hours.")
+        st.warning(f"High risk: <span style='color:#d32f2f'>{critical_pct:.1f}%</span> of fatigue alerts occur during critical hours (3-6 AM). This is a known circadian dip period.", icon="⚠️")
     else:
-        st.info(f"{critical_pct:.1f}% of alerts occur during critical hours. Within acceptable range.")
+        st.info(f"<span style='color:#d32f2f'>{critical_pct:.1f}%</span> of alerts occur during critical hours. This is within acceptable range.")
 
     # 2. High-Speed Fatigue Analysis
     if col_speed and col_speed in df.columns:
-        high_speed_threshold = df[col_speed].quantile(0.75)
-        high_speed_fatigue = df[df[col_speed] >= high_speed_threshold]
-        high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100
-
+        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")
-
+        
+        # ✅ st.metric doesn't support HTML, so replace with markdown
+        st.markdown(
+            f"<div style='font-size:1.1em; margin:8px 0;'>"
+            f"High-Speed Fatigue Events: <b>{len(high_speed_fatigue)}</b> "
+            f"<span style='color:#d32f2f; font-weight:bold;'>({high_speed_pct:.1f}% of total alerts)</span>"
+            "</div>",
+            unsafe_allow_html=True
+        )
+        
         if high_speed_pct > 20:
-            st.warning(f"High risk: {high_speed_pct:.1f}% of alerts occur at high speeds.")
+            st.warning(f"High risk: <span style='color:#d32f2f'>{high_speed_pct:.1f}%</span> of fatigue alerts occur at high speeds. This increases accident severity potential.")
         else:
-            st.info(f"{high_speed_pct:.1f}% occur at high speeds. Acceptable.")
-    else:
-        st.info("Speed data not available for High-Speed Fatigue Analysis.")
-
-
-# ============================
-# KANAN — RECOMMENDATIONS
-# ============================
-with col_recs:
-    st.subheader("AI-Driven Recommendations")
+            st.info(f"<span style='color:#d32f2f'>{high_speed_pct:.1f}%</span> of alerts occur at high speeds. This is within acceptable range.")
 
     # 3. Shift Pattern Analysis
     if col_shift and col_shift in df.columns:
         shift_counts = df[col_shift].value_counts()
-        st.markdown("**Shift Pattern Risk**")
-
-        shift_color = "#d32f2f"
-
+        st.markdown(f"**Shift Pattern Risk**")
+        
         for shift_val in shift_counts.index:
             shift_pct = (shift_counts[shift_val] / len(df)) * 100
-
+            # ✅ Replace st.metric with red-percentage version
             st.markdown(
-                f"""
-                <div style="padding:12px 16px; border-radius:10px; background-color:#fff; 
-                    border:1px solid #eee; margin-bottom:10px;">
-                    <div style="font-size:16px; font-weight:600;">Shift {shift_val} Alerts</div>
-                    <div style="font-size:28px; font-weight:700;">{shift_counts[shift_val]}</div>
-                    <div style="font-size:14px; font-weight:700;">
-                        <span style="color:{shift_color};">{shift_pct:.1f}% of total alerts</span>
-                    </div>
-                </div>
-                """,
+                f"**Shift {shift_val} Alerts**<br>"
+                f"{shift_counts[shift_val]} "
+                f'<span style="color:#d32f2f; font-weight:bold;">({shift_pct:.1f}% of total alerts)</span>',
                 unsafe_allow_html=True
             )
-
+            
             if shift_pct > 50:
-                st.warning(f"Shift {shift_val} has disproportionately high alerts ({shift_pct:.1f}%).")
+                st.warning(f"Shift {shift_val} has disproportionately high alerts (<span style='color:#d32f2f'>{shift_pct:.1f}%</span>). Review shift scheduling and workload.")
             else:
-                st.info(f"Shift {shift_val} distribution is acceptable ({shift_pct:.1f}%).")
-    else:
-        st.info("Shift data not available for Shift Pattern Analysis.")
+                st.info(f"Shift {shift_val} alert distribution is acceptable (<span style='color:#d32f2f'>{shift_pct:.1f}%</span>).")
 
     # 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)
-
-        st.markdown("**High-Risk Operator Identification**")
-
-        # Warna ranking
-        colors = ["#d32f2f", "#e57373", "#ef9a9a", "#ffcdd2", "#ffe1e4"]
-
-        for idx, (op_name, count) in enumerate(top_risk_operators.items()):
+        st.markdown(f"**High-Risk Operator Identification**")
+        
+        for op_name, count in top_risk_operators.items():
             op_pct = (count / len(df)) * 100
-            color = colors[idx] if idx < len(colors) else colors[-1]
-
-            # Nama & jumlah
-            st.markdown(f"**Operator:** {op_name}  \n**Alerts:** {count}")
-
-            # Share berwarna
             st.markdown(
-                f"<b>Share:</b> <span style='color:{color}; font-weight:700'>{op_pct:.1f}% of total alerts</span>",
+                f"**Operator: {op_name}**<br>"
+                f"{count} alerts "
+                f'<span style="color:#d32f2f; font-weight:bold;">({op_pct:.1f}% of total alerts)</span>',
                 unsafe_allow_html=True
             )
-
+            
             if op_pct > 5:
-                st.warning(f"Operator {op_name} shows high fatigue tendency ({op_pct:.1f}%).")
+                st.warning(f"Operator {op_name} has high fatigue risk (<span style='color:#d32f2f'>{op_pct:.1f}%</span> of alerts). Consider coaching or rest plan.")
             else:
-                st.info(f"Operator {op_name} is within the expected alert range ({op_pct:.1f}%).")
+                st.info(f"Operator {op_name} fatigue risk is within acceptable range (<span style='color:#d32f2f'>{op_pct:.1f}%</span>).")
+
     else:
         st.info("Operator data not available for Operator Risk Profiling.")
 
-
-
 # Kolom kanan: AI Recommendations
 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:
         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 (3-6 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.")
 
     # Risk shift
     if col_shift and not df.empty:
         worst_shift = df[col_shift].value_counts().idxmax()
-        insights_found.append(f" Highest fatigue recorded in **Shift {worst_shift}** — review scheduling & workload.")
+        insights_found.append(f"Highest fatigue recorded in **Shift {worst_shift}** — review scheduling & workload.")
 
     # Worst operator
     if col_operator and not df.empty:
         worst_operator = df[col_operator].value_counts().idxmax()
-        insights_found.append(f" Operator at highest risk: **{worst_operator}** — suggested coaching or rest plan.")
+        insights_found.append(f"Operator at highest risk: **{worst_operator}** — suggested coaching or rest plan.")
 
     # Duration risk
     if "duration_sec" in df.columns and not df.empty:
         avg_duration = df["duration_sec"].mean()
         if not pd.isna(avg_duration) and avg_duration > 10:
-            insights_found.append(" Long fatigue event duration suggests slow response — improve alerting training.")
+            insights_found.append("Long fatigue event duration suggests slow response — improve alerting training.")
 
-    # Generate recommendations based on found insights
+    # Recommendations based on 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.",
-                "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 (3-6 AM) indicates increased risk during these hours."
+                "data_point": f"Critical Hour Alerts: {len(critical_alerts)} "
+                              f"<span style='color:#d32f2f;'>({critical_pct:.1f}% of total alerts)</span>",
+                "reason": "High percentage of alerts during circadian low period (3–6 AM) indicates elevated risk."
             })
         if any("shift" in i.lower() for i in insights_found):
+            pct = (df[col_shift].value_counts()[worst_shift] / len(df)) * 100
             ai_recs.append({
                 "recommendation": "Review shift rotation schedules to minimize consecutive high-risk shifts.",
-                "data_point": f"Shift {worst_shift} Alerts: {df[col_shift].value_counts()[worst_shift]} ({(df[col_shift].value_counts()[worst_shift] / len(df)) * 100:.1f}% of total alerts)",
-                "reason": f"The identified high-risk shift ({worst_shift}) has the highest number of fatigue alerts, suggesting scheduling or workload issues."
+                "data_point": f"Shift {worst_shift} Alerts: {df[col_shift].value_counts()[worst_shift]} "
+                              f"<span style='color:#d32f2f;'>({pct:.1f}% of total alerts)</span>",
+                "reason": f"Shift {worst_shift} accounts for a disproportionate share of fatigue events."
             })
         if any("operator" in i.lower() for i in insights_found):
+            pct = (df[col_operator].value_counts()[worst_operator] / len(df)) * 100
             ai_recs.append({
                 "recommendation": "Initiate individual coaching or mandatory rest periods for high-risk operators.",
-                "data_point": f"Operator {worst_operator} Alerts: {df[col_operator].value_counts()[worst_operator]} ({(df[col_operator].value_counts()[worst_operator] / len(df)) * 100:.1f}% of total alerts)",
-                "reason": f"The identified high-risk operator ({worst_operator}) has the highest number of fatigue alerts, indicating a need for targeted intervention."
+                "data_point": f"Operator {worst_operator} Alerts: {df[col_operator].value_counts()[worst_operator]} "
+                              f"<span style='color:#d32f2f;'>({pct:.1f}% of total alerts)</span>",
+                "reason": f"Individual intervention needed to mitigate recurrence risk."
             })
         if any("duration" in i.lower() for i in insights_found):
             ai_recs.append({
                 "recommendation": "Review and improve alert response protocols and training.",
                 "data_point": f"Average Fatigue Event Duration: {avg_duration:.2f} seconds",
-                "reason": "Long average duration suggests potential delays in response time or alert acknowledgment, requiring protocol review."
+                "reason": "Long duration suggests delayed response — requires procedural review."
             })
         if any("high-speed" in i.lower() for i in insights_found):
             ai_recs.append({
                 "recommendation": "Implement speed management strategies in conjunction with fatigue monitoring.",
-                "data_point": f"High-Speed Fatigue Events: {len(high_speed_fatigue)} ({high_speed_pct:.1f}% of total alerts)",
-                "reason": "A significant percentage of alerts occur at high speeds, increasing accident severity risk. Speed control is crucial."
+                "data_point": f"High-Speed Fatigue Events: {len(high_speed_fatigue)} "
+                              f"<span style='color:#d32f2f;'>({high_speed_pct:.1f}% of total alerts)</span>",
+                "reason": "High-speed fatigue greatly increases collision severity risk."
             })
         if not ai_recs:
             ai_recs.append({
-                "recommendation": "Data quality is sufficient. Focus on implementing recommendations from Objectives 1-5.",
+                "recommendation": "Data quality is sufficient. Focus on implementing recommendations from Objectives 1–5.",
                 "data_point": "General Data Quality Check",
-                "reason": "No specific high-impact insights were automatically identified from the aggregated data in this section."
+                "reason": "No critical anomalies detected in aggregate metrics."
             })
 
-        # Menampilkan rekomendasi dalam format kotak yang sesuai dengan permintaan
+        # Render recommendations
         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;
@@ -1866,9 +1844,6 @@ with col_recs:
                 color: #2c3e50;
                 font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
                 box-shadow: 0 2px 8px rgba(0,0,0,0.05);
-                display: flex;
-                flex-direction: column;
-                justify-content: space-between;
             ">
                 <div style="
                     font-weight: bold;
@@ -1896,4 +1871,4 @@ 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)