Update app.py

app.py

@@ -2024,6 +2024,308 @@ else:
 
 # )
 
+# # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
+# 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
+# # =====================================================================
+# with col_insights:
+#     st.subheader("Insights by Advanced Analytics")
+
+#     # ===================== 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;">'
+#         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). "
+#             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."
+#         )
+
+#     # ===================== 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) > 0 else 0
+
+#         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. "
+#                 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."
+#             )
+#     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_recommendations = []
+
+#     # 1. Critical Hour Insight → AI Rec
+#     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:
+#             ai_recommendations.append({
+#                 "type": "critical_hour",
+#                 "action": "Deploy enhanced fatigue monitoring systems during 3-6 AM.",
+#                 "data_point": f"Critical Hour Alerts: {len(critical_alerts)} ({critical_pct:.1f}%)",
+#                 "reasoning": "High percentage of alerts during circadian low period."
+#             })
+#         else:
+#             ai_recommendations.append({
+#                 "type": "critical_hour",
+#                 "action": "Monitor fatigue patterns around peak hour (Hour {peak_hour}).",
+#                 "data_point": f"Peak Hour: {peak_hour}:00 — {df['hour'].value_counts()[peak_hour]} alerts",
+#                 "reasoning": "This hour shows highest fatigue occurrence."
+#             })
+
+#     # 2. High-Speed Insight → AI Rec
+#     if col_speed and col_speed in df.columns and not df.empty:
+#         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) > 0 else 0
+
+#         if high_speed_pct > 20:
+#             ai_recommendations.append({
+#                 "type": "high_speed",
+#                 "action": "Implement speed-reduction protocols during fatigue-prone hours.",
+#                 "data_point": f"High-Speed Alerts: {len(high_speed_fatigue)} ({high_speed_pct:.1f}%)",
+#                 "reasoning": "High-speed alerts increase accident severity potential."
+#             })
+#         else:
+#             ai_recommendations.append({
+#                 "type": "high_speed",
+#                 "action": "Maintain current speed monitoring — risk level is acceptable.",
+#                 "data_point": f"High-Speed Alerts: {len(high_speed_fatigue)} ({high_speed_pct:.1f}%)",
+#                 "reasoning": "Current high-speed fatigue rate is within acceptable range."
+#             })
+
+#     # 3. Shift Pattern Insight → AI Rec
+#     if col_shift and col_shift in df.columns and not df.empty:
+#         worst_shift = df[col_shift].value_counts().idxmax()
+#         shift_pct = (df[col_shift].value_counts()[worst_shift] / len(df)) * 100
+
+#         if shift_pct > 50:
+#             ai_recommendations.append({
+#                 "type": "shift_pattern",
+#                 "action": "Review shift rotation schedules for Shift {worst_shift}.",
+#                 "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts ({shift_pct:.1f}%)",
+#                 "reasoning": "Disproportionately high fatigue alerts indicate scheduling imbalance."
+#             })
+#         else:
+#             ai_recommendations.append({
+#                 "type": "shift_pattern",
+#                 "action": "Continue monitoring all shifts — no dominant risk identified.",
+#                 "data_point": f"Shift {worst_shift}: {df[col_shift].value_counts()[worst_shift]} alerts ({shift_pct:.1f}%)",
+#                 "reasoning": "Shift distribution is balanced."
+#             })
+
+#     # 4. Operator Risk Profiling → Simple Recommendations (No AI Reasoning, No Box)
+#     if col_operator and col_operator in df.columns and not df.empty:
+#         top_operators = df[col_operator].value_counts().head(5)
+#         for op_name, count in top_operators.items():
+#             op_pct = (count / len(df)) * 100
+
+#             if op_pct > 5:
+#                 ai_recommendations.append({
+#                     "type": "operator",
+#                     "action": f"Coaching or mandatory rest for Operator {op_name}.",
+#                     "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)"
+#                 })
+#             else:
+#                 ai_recommendations.append({
+#                     "type": "operator",
+#                     "action": f"Continue general monitoring for Operator {op_name}.",
+#                     "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)"
+#                 })
+
+#     # Render each recommendation based on type
+#     for rec in ai_recommendations:
+#         if rec["type"] == "operator":
+#             # Simple format: Action + Data Point only
+#             data_point_colored = rec['data_point'].replace(
+#                 f"({rec['data_point'].split('(')[-1]}",
+#                 f"(<span style='color: red;'>{rec['data_point'].split('(')[-1]}"
+#             ).replace(")", "</span>)")
+
+#             st.markdown(
+#                 f"""
+#                 <div style="margin: 10px 0; padding: 10px; background: #f8f9fa; border-left: 4px solid #495057; border-radius: 5px;">
+#                     <strong>Action:</strong> {rec['action']}<br>
+#                     <strong>Data Point:</strong> {data_point_colored}
+#                 </div>
+#                 """,
+#                 unsafe_allow_html=True
+#             )
+#         else:
+#             # Standard format with AI Reasoning and box
+#             data_point_colored = rec['data_point'].replace(
+#                 f"({rec['data_point'].split('(')[-1]}",
+#                 f"(<span style='color: red;'>{rec['data_point'].split('(')[-1]}"
+#             ).replace(")", "</span>)")
+
+#             reasoning_colored = rec['reasoning'].replace(
+#                 f"({rec['reasoning'].split('(')[-1]}",
+#                 f"(<span style='color: red;'>{rec['reasoning'].split('(')[-1]}"
+#             ).replace(")", "</span>)")
+
+#             st.markdown(
+#                 f"""
+#                 <div style="
+#                     background: #f8f9fa;
+#                     border: 1px solid #dee2e6;
+#                     border-radius: 8px;
+#                     padding: 15px;
+#                     margin: 10px 0;
+#                     box-shadow: 0 2px 8px rgba(0,0,0,0.05);
+#                 ">
+#                     <div style="
+#                         font-weight: bold;
+#                         background: #e9ecef;
+#                         padding: 8px;
+#                         border-radius: 5px;
+#                         margin-bottom: 8px;
+#                         border-left: 4px solid #495057;
+#                     ">
+#                         AI Recommendation
+#                     </div>
+#                     <div style="padding: 8px 0;">
+#                         <strong>Action:</strong> {rec['action']}
+#                     </div>
+#                     <div style="
+#                         padding: 8px;
+#                         background: #f1f1f1;
+#                         border-radius: 5px;
+#                         margin: 8px 0;
+#                     ">
+#                         <strong>Data Point:</strong> {data_point_colored}
+#                     </div>
+#                     <div style="
+#                         padding: 8px;
+#                         background: #f1f1f1;
+#                         border-radius: 5px;
+#                     ">
+#                         <strong>AI Reasoning:</strong> {reasoning_colored}
+#                     </div>
+#                 </div>
+#                 """,
+#                 unsafe_allow_html=True
+#             )
+
+#     if not ai_recommendations:
+#         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
+# )
+
 # =================== OBJECTIVE 6: Automated Insights & AI Recommendations =====================
 st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")
 
@@ -2031,7 +2333,7 @@ st.subheader("OBJECTIVE 6: Instant Insights & Recommendations")
 col_insights, col_recs = st.columns(2)
 
 # =====================================================================
-# 🔹 KOLOM KIRI — INSIGHTS BY ADVANCED ANALYTICS
+# 🔹 KOLOM KIRI — INSIGHTS BY ADVANCED ANALYTICS (TANPA SEMUA KOTAK BIRU)
 # =====================================================================
 with col_insights:
     st.subheader("Insights by Advanced Analytics")
@@ -2146,14 +2448,17 @@ with col_insights:
                     f"Consider coaching or rest plan."
                 )
             else:
-                st.info(
-                    f"Operator {op_name} fatigue risk is within acceptable range ({op_pct:.1f}%)."
+                # ❌ HILANGKAN TEKS "is within acceptable range" DAN KOTAK BIRU
+                # Hanya tampilkan nama operator + alert count — tanpa tambahan teks
+                st.markdown(
+                    f"<span style='color: #2c3e50;'>Operator {op_name}: {count} alerts ({op_pct:.1f}%)</span>",
+                    unsafe_allow_html=True
                 )
     else:
         st.info("Operator data not available for Operator Risk Profiling.")
 
 # =====================================================================
-# 🔹 KOLOM KANAN — AI RECOMMENDATIONS
+# 🔹 KOLOM KANAN — AI RECOMMENDATIONS (TIDAK BERUBAH)
 # =====================================================================
 with col_recs:
     st.subheader("Recommendations")