Update app.py

app.py

@@ -1743,6 +1743,287 @@ 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 (PER INSIGHT + PER OPERATOR)
+# # =====================================================================
+# 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({
+#                 "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({
+#                 "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({
+#                 "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({
+#                 "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({
+#                 "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({
+#                 "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 → AI Rec for EACH of Top 5 Operators
+#     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({
+#                     "action": f"Coaching or mandatory rest for Operator {op_name}.",
+#                     "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)",
+#                     "reasoning": f"Operator has high fatigue alerts — requires individual intervention."
+#                 })
+#             else:
+#                 ai_recommendations.append({
+#                     "action": f"Continue general monitoring for Operator {op_name}.",
+#                     "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)",
+#                     "reasoning": f"Risk is within acceptable range — no urgent action needed."
+#                 })
+
+#     # Render each recommendation as a card
+#     for rec in ai_recommendations:
+#         # Highlight percentages in red
+#         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")
 
@@ -1872,7 +2153,7 @@ with col_insights:
         st.info("Operator data not available for Operator Risk Profiling.")
 
 # =====================================================================
-# 🔹 KOLOM KANAN — AI RECOMMENDATIONS (PER INSIGHT + PER OPERATOR)
+# 🔹 KOLOM KANAN — AI RECOMMENDATIONS
 # =====================================================================
 with col_recs:
     st.subheader("Recommendations")
@@ -1886,12 +2167,14 @@ with col_recs:
 
         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."
@@ -1905,12 +2188,14 @@ with col_recs:
 
         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."
@@ -1923,18 +2208,20 @@ with col_recs:
 
         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 → AI Rec for EACH of Top 5 Operators
+    # 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():
@@ -1942,72 +2229,89 @@ with col_recs:
 
             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}%)",
-                    "reasoning": f"Operator has high fatigue alerts — requires individual intervention."
+                    "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}%)",
-                    "reasoning": f"Risk is within acceptable range — no urgent action needed."
+                    "data_point": f"Operator {op_name}: {count} alerts ({op_pct:.1f}%)"
                 })
 
-    # Render each recommendation as a card
+    # Render each recommendation based on type
     for rec in ai_recommendations:
-        # Highlight percentages in red
-        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>)")
+        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="
-                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;
-                ">
+            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="
-                    padding: 8px;
-                    background: #f1f1f1;
-                    border-radius: 5px;
+                    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);
                 ">
-                    <strong>AI Reasoning:</strong> {reasoning_colored}
+                    <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>
-            </div>
-            """,
-            unsafe_allow_html=True
-        )
+                """,
+                unsafe_allow_html=True
+            )
 
     if not ai_recommendations:
         st.info(