Update app.py

app.py

@@ -1719,9 +1719,6 @@ else:
         st.exception(e)
 
 # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
-
-# =====================================================================
-# =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
 st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")
 
 # Membagi tampilan menjadi dua kolom
@@ -1789,3 +1786,199 @@ with col_insights:
 
     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()
+        st.markdown(f"**Shift Pattern Risk**")
+
+        for shift_val in shift_counts.index:
+            shift_pct = (shift_counts[shift_val] / len(df)) * 100
+
+            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}%). "
+                    f"Review shift scheduling and workload."
+                )
+            else:
+                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:
+
+        operator_alerts = df[col_operator].value_counts()
+        top_risk_operators = operator_alerts.head(5)
+
+        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]
+
+            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
+            )
+
+            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
+# =====================================================================
+with col_recs:
+
+    st.subheader("Recommendations")
+    ai_recs = []
+    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)."
+            )
+        else:
+            insights_found.append(
+                f"Most fatigue risk occurs at **{peak_hour}:00** — likely due to circadian drop."
+            )
+
+    # 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."
+        )
+
+    # 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."
+        )
+
+    # Duration Risk
+    if "duration_sec" in df.columns and not df.empty:
+        avg_duration = df["duration_sec"].mean()
+        if avg_duration > 10:
+            insights_found.append(
+                "Long fatigue event duration suggests slow response — improve alerting training."
+            )
+
+    # ===================== AI DECISION ENGINE =====================
+    if insights_found:
+
+        if any("circadian" in i.lower() for i in insights_found):
+            ai_recs.append({
+                "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.",
+                "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": "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": "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:
+
+            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="
+                    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>
+                """,
+                unsafe_allow_html=True
+            )
+
+    else:
+        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
+)