Update app.py

app.py

@@ -1665,229 +1665,135 @@ 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
+# =========================================
+#        LEFT COLUMN — INSIGHTS
+# =========================================
 with col_insights:
+
     st.subheader("Insights by Advanced Analytics")
 
-    # 1. Critical Hour Analysis (2-5 AM)
+    # 1. Critical Hour Analysis (3–6 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(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 (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:
-
-    # --- Threshold FIX > 20 km/h ---
-    high_speed_threshold = 20
 
-    # --- Filter High-Speed Fatigue ---
-    high_speed_fatigue = df[df[col_speed] > high_speed_threshold]
-
-    # --- Persentase ---
-    high_speed_pct = (len(high_speed_fatigue) / len(df) * 100) if len(df) > 0 else 0
+    bg_color = (
+        "#ffcccc" if critical_pct > 50 else
+        "#ffebcc" if critical_pct > 25 else
+        "#ffffcc" if critical_pct > 10 else
+        "#e6ffe6"
+    )
 
-    # --- Teks judul + hasil warna merah ---
     st.markdown(
         f"""
-        <p style="color:#d32f2f; font-size:20px; font-weight:700; margin-bottom:4px;">
-            High-Speed Fatigue Events: {len(high_speed_fatigue)}
-        </p>
-        <p style="color:#d32f2f; font-size:15px; font-weight:500; margin-top:-6px;">
-            {high_speed_pct:.1f}% of total alerts (Speed > {high_speed_threshold} km/h)
-        </p>
+        <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
     )
 
-    # --- pesan risiko ---
-    if high_speed_pct > 20:
+    if critical_pct > 10:
         st.warning(
-            f"High risk: {high_speed_pct:.1f}% of fatigue alerts occur at high speeds. "
-            f"This increases accident severity potential."
+            f"High risk: {critical_pct:.1f}% of fatigue alerts occur during critical hours (3-6 AM)."
         )
     else:
         st.info(
-            f"{high_speed_pct:.1f}% of alerts occur at high speeds. This is within acceptable range."
+            f"{critical_pct:.1f}% of alerts occur during critical hours. This is within acceptable range."
         )
 
-else:
-    st.info("Speed data not available for High-Speed Fatigue Analysis.")
+    # 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]
+        high_speed_pct = (len(high_speed_fatigue) / len(df)) * 100 if len(df) else 0
 
+        # Title
+        st.markdown("**High-Speed Fatigue Risk (Speed > 20 km/h)**")
+
+        # Red text (no box)
+        st.markdown(
+            f"""
+            <p style="color:#d32f2f; font-size:20px; font-weight:700; margin-bottom:4px;">
+                High-Speed Fatigue Events: {len(high_speed_fatigue)}
+            </p>
+            <p style="color:#d32f2f; font-size:15px; font-weight:500; margin-top:-6px;">
+                {high_speed_pct:.1f}% of total alerts (Speed > {high_speed_threshold} km/h)
+            </p>
+            """,
+            unsafe_allow_html=True
+        )
+
+        if high_speed_pct > 20:
+            st.warning(
+                f"High risk: {high_speed_pct:.1f}% fatigue alerts occur at high speeds."
+            )
+        else:
+            st.info(
+                f"{high_speed_pct:.1f}% of alerts occur at high speeds. Within acceptable limits."
+            )
+    else:
+        st.info("Speed data not available for High-Speed Fatigue Analysis.")
 
     # 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**")
+
+        st.markdown("**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
-                st.warning(f"Shift {shift_val} has disproportionately high alerts ({shift_pct:.1f}%). Review shift scheduling and workload.")
+
+            st.metric(
+                f"Shift {shift_val} Alerts",
+                f"{shift_counts[shift_val]}",
+                f"{shift_pct:.1f}% of total alerts"
+            )
+
+            if shift_pct > 50:
+                st.warning(
+                    f"Shift {shift_val} has disproportionately high alerts ({shift_pct:.1f}%)."
+                )
             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
-# 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**")
-
-    # Warna berdasarkan ranking 1–5
-    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]
-
-        # 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
-        )
+    # 4. Operator Risk Profiling
+    if col_operator and col_operator in df.columns:
 
-        # 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}%).")
+        operator_alerts = df[col_operator].value_counts()
+        top_risk_operators = operator_alerts.head(5)
 
-else:
-    st.info("Operator data not available for Operator Risk Profiling.")
+        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]
 
-# Kolom kanan: AI Recommendations
-with col_recs:
-    st.subheader("Recommendations")
-    ai_recs = []
-    insights_found = [] # Untuk menyimpan insight yang ditemukan
+            st.markdown(f"**Operator:** {op_name}  \n**Alerts:** {count}")
 
-    # 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.")
-        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.")
-
-    # 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.")
-
-    # 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.")
-
-    # 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.",
-                "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."
-            })
-        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."
-            })
-        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."
-            })
-        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."
-            })
+            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
+            )
 
-        # 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;
-                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;
-            ">
-                <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> {rec['data_point']}
-                </div>
-                <div style="font-size: 12px; padding: 8px; background: #f1f1f1; border-radius: 5px; margin-top: 5px;">
-                    <strong>AI Reasoning:</strong> {rec['reason']}
-                </div>
-            </div>
-            """, unsafe_allow_html=True)
+            if op_pct > 5:
+                st.warning(
+                    f"Operator {op_name} has high fatigue risk ({op_pct:.1f}%)."
+                )
+            else:
+                st.info(
+                    f"Operator {op_name} risk is acceptable ({op_pct:.1f}%)."
+                )
     else:
-        st.info("No specific data points available for AI recommendations. Ensure relevant columns (hour, shift, operator, duration, speed) are present and populated.")
-
-# ================= 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.info("Operator data not available for Operator Risk Profiling.")