Update app.py

app.py

@@ -437,14 +437,30 @@ with st.sidebar.form("filters_form"):
         format_func=lambda x: "All" if x is None else x
     )
     filter_dict['site'] = selected_site
-
     # ---------------- Group Model Filter ✅ NOW WORKING ----------------
     all_models = sorted(df['group_model'].dropna().unique())
-    selected_model = st.selectbox(
+
+    # Define display names for specific values
+    display_map = {
+        "OB HAULLER": "OB HAULER",
+        "HAULING COAL": "COAL HAULING"
+    }
+
+    # Create display options
+    display_options = [display_map.get(model, model) for model in all_models]
+
+    # Create reverse map to get original value back
+    reverse_map = {v: k for k, v in display_map.items()}
+
+    # Create selectbox with display names
+    selected_display = st.selectbox(
         "Filter Group Model",
-        options=[None] + all_models,
+        options=[None] + display_options,
         format_func=lambda x: "All" if x is None else x
     )
+
+    # Map back to original value for filtering
+    selected_model = reverse_map.get(selected_display, selected_display) if selected_display else None
     filter_dict['group_model'] = selected_model
 
     # ---------------- Shift ----------------
@@ -1724,562 +1740,7 @@ else:
         st.error(f"Error in Top 10 Operator analysis: {str(e)}")
         st.exception(e)
 
-# # =================== 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)
-# # =====================================================================
-# with col_recs:
-#     st.subheader("Recommendations")
-
-#     # Reset list to collect recommendations per insight
-#     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 Insight → AI Rec
-#     if col_operator and col_operator in df.columns and not df.empty:
-#         worst_operator = df[col_operator].value_counts().idxmax()
-#         op_pct = (df[col_operator].value_counts()[worst_operator] / len(df)) * 100
-
-#         if op_pct > 5:
-#             ai_recommendations.append({
-#                 "action": "Coaching or mandatory rest for the identified high-risk operator.",
-#                 "data_point": f"Operator {worst_operator}: {df[col_operator].value_counts()[worst_operator]} alerts ({op_pct:.1f}%)",
-#                 "reasoning": "Operator has highest fatigue alerts — requires individual intervention."
-#             })
-#         else:
-#             ai_recommendations.append({
-#                 "action": "Continue general monitoring — no single operator dominates risk.",
-#                 "data_point": f"Top Operator: {worst_operator} — {df[col_operator].value_counts()[worst_operator]} alerts ({op_pct:.1f}%)",
-#                 "reasoning": "Risk is distributed across operators — no urgent individual 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")
-
-# # 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)
-# # =====================================================================
-# with col_recs:
-#     st.subheader("Recommendations")
-
-#     # Reset list to collect recommendations per insight
-#     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 Insight → AI Rec
-#     if col_operator and col_operator in df.columns and not df.empty:
-#         worst_operator = df[col_operator].value_counts().idxmax()
-#         op_pct = (df[col_operator].value_counts()[worst_operator] / len(df)) * 100
-
-#         if op_pct > 5:
-#             ai_recommendations.append({
-#                 "action": "Coaching or mandatory rest for the identified high-risk operator.",
-#                 "data_point": f"Operator {worst_operator}: {df[col_operator].value_counts()[worst_operator]} alerts ({op_pct:.1f}%)",
-#                 "reasoning": "Operator has highest fatigue alerts — requires individual intervention."
-#             })
-#         else:
-#             ai_recommendations.append({
-#                 "action": "Continue general monitoring — no single operator dominates risk.",
-#                 "data_point": f"Top Operator: {worst_operator} — {df[col_operator].value_counts()[worst_operator]} alerts ({op_pct:.1f}%)",
-#                 "reasoning": "Risk is distributed across operators — no urgent individual 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")