Update app.py

app.py

@@ -1667,216 +1667,261 @@ st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")
 # Membagi tampilan menjadi dua kolom
 col_insights, col_recs = st.columns(2)
 
-# Kolom kiri: Insights by Advanced Analytics
+# =====================================================================
+# 🔹 KOLOM KIRI — INSIGHTS BY ADVANCED ANALYTICS
+# =====================================================================
 with col_insights:
     st.subheader("Insights by Advanced Analytics")
 
-    # 1. Critical Hour Analysis (2-5 AM)
+    # ===================== 1. Critical Hour Analysis =====================
     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(f"**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'<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)
+    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;">'
+        f'Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}% of total alerts)</div>',
+        unsafe_allow_html=True
+    )
+
     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.")
+        st.warning(
+            f"High risk: {critical_pct:.1f}% of fatigue alerts occur during critical hours (3-6 AM). "
+            f"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.")
+        st.info(
+            f"{critical_pct:.1f}% of alerts occur during critical hours. This is within acceptable range."
+        )
 
-    # 2. High-Speed Fatigue Analysis (Environmental Risk)
+    # ===================== 2. High-Speed Fatigue Analysis =====================
     if col_speed and col_speed in df.columns:
+
         high_speed_threshold = 20
-        high_speed_fatigue = df[df[col_speed] >= high_speed_threshold] if high_speed_threshold > 0 else pd.DataFrame()
+        high_speed_fatigue = df[df[col_speed] >= high_speed_threshold]
         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)**")
-        # 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)
+
+        st.markdown(f"**High-Speed Fatigue Risk (Speed > {high_speed_threshold} km/h)**")
+        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.")
+            st.warning(
+                f"High risk: {high_speed_pct:.1f}% of fatigue alerts occur at high speeds. "
+                f"This increases accident severity potential."
+            )
         else:
-            st.info(f"{high_speed_pct:.1f}% of alerts occur at high speeds. This is within acceptable range.")
+            st.info(
+                f"{high_speed_pct:.1f}% of alerts occur at high speeds. This is within acceptable range."
+            )
+
     else:
         st.info("Speed data not available for High-Speed Fatigue Analysis.")
-        # 3.Objective
-          if col_shift and col_shift in df.columns:
-        shift_counts = df[col_shift].value_counts()
 
+    # ===================== 3. Shift Pattern Analysis =====================
+    if col_shift and col_shift in df.columns:
+
+        shift_counts = df[col_shift].value_counts()
         st.markdown(f"**Shift Pattern Risk**")
+
         for shift_val in shift_counts.index:
             shift_pct = (shift_counts[shift_val] / len(df)) * 100
-            # 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)
+
+            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.")
+                st.warning(
+                    f"Shift {shift_val} has disproportionately high alerts ({shift_pct:.1f}%). "
+                    f"Review shift scheduling and workload."
+                )
             else:
-                st.info(f"Shift {shift_val} alert distribution is acceptable ({shift_pct:.1f}%).")
+                st.info(
+                    f"Shift {shift_val} alert distribution is acceptable ({shift_pct:.1f}%)."
+                )
+
     else:
         st.info("Shift data not available for Shift Pattern Analysis.")
 
+    # ===================== 4. Operator Risk Profiling =====================
+    if col_operator and col_operator in df.columns:
 
-    # 4. Operator Risk Profiling
-# 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
-
-    st.markdown("**High-Risk Operator Identification**")
+        operator_alerts = df[col_operator].value_counts()
+        top_risk_operators = operator_alerts.head(5)
 
-    # Warna berdasarkan ranking 1–5
-    colors = ["#d32f2f", "#e57373", "#ef9a9a", "#ffcdd2", "#ffe1e4"]
+        st.markdown("**High-Risk Operator Identification**")
+        colors = ["#d32f2f", "#e57373", "#ef9a9a", "#ffcdd2", "#ffe1e4"]
 
-    for idx, (op_name, count) in enumerate(top_risk_operators.items()):
-        op_pct = (count / len(df)) * 100
-        color = colors[idx] if idx < len(colors) else colors[-1]
+        for idx, (op_name, count) in enumerate(top_risk_operators.items()):
+            op_pct = (count / len(df)) * 100
+            color = colors[idx] if idx < len(colors) else colors[-1]
 
-        # Teks normal untuk nama dan jumlah alert
-        st.markdown(f"**Operator:** {op_name}  \n**Alerts:** {count}")
-
-        # Hanya 'Share' yang berwarna sesuai ranking
-        st.markdown(
-            f"<span style='font-weight:600'>Share:</span> "
-            f"<span style='color:{color}; font-weight:700'>{op_pct:.1f}% of total alerts</span>",
-            unsafe_allow_html=True
-        )
-
-        # Risk message (tetap gunakan component Streamlit agar konsisten)
-        if op_pct > 5:
-            st.warning(f"Operator {op_name} has high fatigue risk ({op_pct:.1f}%). Consider coaching or rest plan.")
-        else:
-            st.info(f"Operator {op_name} fatigue risk is within acceptable range ({op_pct:.1f}%).")
+            st.markdown(
+                f"**Operator:** {op_name}  \n**Alerts:** {count}"
+            )
+            st.markdown(
+                f"<span style='font-weight:600'>Share:</span> "
+                f"<span style='color:{color}; font-weight:700'>{op_pct:.1f}% of total alerts</span>",
+                unsafe_allow_html=True
+            )
 
-else:
-    st.info("Operator data not available for Operator Risk Profiling.")
+            if op_pct > 5:
+                st.warning(
+                    f"Operator {op_name} has high fatigue risk ({op_pct:.1f}%). "
+                    f"Consider coaching or rest plan."
+                )
+            else:
+                st.info(
+                    f"Operator {op_name} fatigue risk is within acceptable range ({op_pct:.1f}%)."
+                )
 
+    else:
+        st.info("Operator data not available for Operator Risk Profiling.")
 
 
-# Kolom kanan: AI Recommendations
+# =====================================================================
+# 🔹 KOLOM KANAN — AI RECOMMENDATIONS
+# =====================================================================
 with col_recs:
+
     st.subheader("Recommendations")
     ai_recs = []
     insights_found = []
 
-    # Peak hour
+    # 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)."
+            )
         else:
-            insights_found.append(f"Most fatigue risk occurs at **{peak_hour}:00** — likely due to circadian drop.")
+            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:
+    # Risk Shift
+    if col_shift and col_shift in df.columns 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
+    if col_operator and col_operator in df.columns 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
+    # 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.")
+        if avg_duration > 10:
+            insights_found.append(
+                "Long fatigue event duration suggests slow response — improve alerting training."
+            )
 
-    # Generate recommendations based on found insights
+    # ===================== AI DECISION ENGINE =====================
     if insights_found:
-        if any("circadian low" in i.lower() for i in insights_found):
+
+        if any("circadian" 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."
+                "recommendation": "Deploy enhanced fatigue monitoring systems during 3-6 AM.",
+                "data_point": f"Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}%)",
+                "reason": "High percentage of alerts during circadian low period."
             })
+
         if any("shift" in i.lower() for i in insights_found):
             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."
+                "recommendation": "Review shift rotation schedules.",
+                "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts",
+                "reason": "This shift shows highest fatigue alerts."
             })
+
         if any("operator" in i.lower() for i in insights_found):
             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."
+                "recommendation": "Coaching or mandatory rest for the identified high-risk operator.",
+                "data_point": f"Operator {worst_operator}: {df[col_operator].value_counts()[worst_operator]} alerts",
+                "reason": "Operator has highest fatigue alerts."
             })
+
         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."
-            })
-        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."
-            })
-        if not ai_recs:
-            ai_recs.append({
-                "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."
+                "recommendation": "Improve fatigue alert response training.",
+                "data_point": f"Avg Duration: {avg_duration:.1f} sec",
+                "reason": "Long fatigue event duration indicates slow response."
             })
 
+        # Render all recommendations
+        import re
+
         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;
-                border: 1px solid #dee2e6;
-                border-radius: 8px;
-                padding: 15px;
-                margin: 10px 0;
-                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;
-            ">
+
+            data_point_colored = re.sub(
+                r'(\d+\.?\d*%)',
+                r'<span style="color: red;">\1</span>',
+                rec['data_point']
+            )
+
+            reason_colored = re.sub(
+                r'(\d+\.?\d*%)',
+                r'<span style="color: red;">\1</span>',
+                rec['reason']
+            )
+
+            st.markdown(
+                f"""
                 <div style="
-                    font-weight: bold;
-                    color: #2c3e50;
-                    margin-bottom: 8px;
-                    font-size: 14px;
-                    background: #e9ecef;
-                    padding: 8px;
-                    border-radius: 5px;
-                    border-left: 4px solid #495057;
-                ">AI Recommendation</div>
-                <div style="padding-top: 8px; font-size: 14px; margin-bottom: 10px;">
-                    <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> {data_point_colored}
-                </div>
-                <div style="font-size: 12px; padding: 8px; background: #f1f1f1; border-radius: 5px; margin-top: 5px;">
-                    <strong>AI Reasoning:</strong> {reason_colored}
+                    background: #f8f9fa;
+                    border: 1px solid #dee2e6;
+                    border-radius: 8px;
+                    padding: 15px;
+                    margin: 10px 0;
+                ">
+                    <div style="font-weight: bold; background: #e9ecef; padding: 8px; border-radius: 5px;">
+                        AI Recommendation
+                    </div>
+                    <div style="padding-top: 8px;"><strong>Action:</strong> {rec['recommendation']}</div>
+                    <div style="padding: 8px; background: #e9ecef; border-radius: 5px;">
+                        <strong>Data Point:</strong> {data_point_colored}
+                    </div>
+                    <div style="padding: 8px; background: #f1f1f1; border-radius: 5px;">
+                        <strong>AI Reasoning:</strong> {reason_colored}
+                    </div>
                 </div>
-            </div>
-            """, unsafe_allow_html=True)
+                """,
+                unsafe_allow_html=True
+            )
+
     else:
-        st.info("No specific data points available for AI recommendations. Ensure relevant columns (hour, shift, operator, duration, speed) are present and populated.")
+        st.info(
+            "No specific data points available for AI recommendations. "
+            "Ensure relevant columns are present (hour, shift, operator, duration, speed)."
+        )
 
 # ================= 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
+)