Update app.py

app.py

@@ -679,7 +679,7 @@ with col8:
     else:
         st.warning("No data for Position 4 (18:00–06:00)")
 
-# =============== INSIGHT 2 (Ringkas) ===============
+# =============== INSIGHT 2 (Ringkas & Fokus ke Red & Amber) ===============
 if alarm_data.empty:
     insight_text = "• No data available for analysis."
 else:
@@ -710,97 +710,47 @@ else:
 
     # Insight Spesifik Per Position dan Shift
     insight_lines = [
-        f""
+        f"• Total alarms: Normal={normal_alarms}, Amber={amber_alarms}, Red={red_alarms}",
+        f"• Dominant period: {dominant_band} ({dominant_pct:.1f}%), Second: {second_dominant_band} ({second_pct:.1f}%)"
     ]
 
-    # Position 1 (Shift Pagi)
-    pos1_pagi = alarm_data[(alarm_data['Position'] == 1) & (alarm_data['hour'].between(6, 17, inclusive='both'))]
-    if not pos1_pagi.empty:
-        pos1_pagi_total = pos1_pagi.groupby('hour').size()
-        if not pos1_pagi_total.empty:
-            dominant_hour_p1_pagi = pos1_pagi_total.idxmax()
-            dominant_count_p1_pagi = pos1_pagi_total.max()
-            insight_lines.append(f"• Position 1 Shift 1 (06:00–18:00): Peak at {dominant_hour_p1_pagi:02d}:00 ({dominant_count_p1_pagi} alarms)")
-    # # Position 1 (Shift Sore)
-    # pos1_sore = alarm_data[(alarm_data['Position'] == 1) & (~alarm_data['hour'].between(6, 17, inclusive='both'))]
-    # if not pos1_sore.empty:
-    #     pos1_sore_red = pos1_sore[pos1_sore['Alarm Status'].str.contains('Red', na=False)]
-    #     if not pos1_sore_red.empty:
-    #         red_percentage_p1_sore = (len(pos1_sore_red) / len(pos1_sore)) * 100
-    #         insight_lines.append(f"• Position 1 Shift 2 (18:00–06:00): {red_percentage_p1_sore:.1f}% are Red alarms")
-    pos1_pagi = alarm_data[(alarm_data['Position'] == 1) & (alarm_data['hour'].between(18, 24, inclusive='both'))]
-    if not pos1_pagi.empty:
-        pos1_pagi_total = pos1_pagi.groupby('hour').size()
-        if not pos1_pagi_total.empty:
-            dominant_hour_p1_pagi = pos1_pagi_total.idxmax()
-            dominant_count_p1_pagi = pos1_pagi_total.max()
-            insight_lines.append(f"• Position 1 Shift 2 (18:00–00:00): Peak at {dominant_hour_p1_pagi:02d}:00 ({dominant_count_p1_pagi} alarms)")
-    # Position 1 (Shift Pagi)
-    pos1_pagi = alarm_data[(alarm_data['Position'] == 2) & (alarm_data['hour'].between(6, 17, inclusive='both'))]
-    if not pos1_pagi.empty:
-        pos1_pagi_total = pos1_pagi.groupby('hour').size()
-        if not pos1_pagi_total.empty:
-            dominant_hour_p1_pagi = pos1_pagi_total.idxmax()
-            dominant_count_p1_pagi = pos1_pagi_total.max()
-            insight_lines.append(f"• Position 2 Shift 1 (06:00–18:00): Peak at {dominant_hour_p1_pagi:02d}:00 ({dominant_count_p1_pagi} alarms)")
-    # # Position 1 (Shift Sore)
-    # pos1_sore = alarm_data[(alarm_data['Position'] == 1) & (~alarm_data['hour'].between(6, 17, inclusive='both'))]
-    # if not pos1_sore.empty:
-    #     pos1_sore_red = pos1_sore[pos1_sore['Alarm Status'].str.contains('Red', na=False)]
-    #     if not pos1_sore_red.empty:
-    #         red_percentage_p1_sore = (len(pos1_sore_red) / len(pos1_sore)) * 100
-    #         insight_lines.append(f"• Position 1 Shift 2 (18:00–06:00): {red_percentage_p1_sore:.1f}% are Red alarms")
-    pos1_pagi = alarm_data[(alarm_data['Position'] == 2) & (alarm_data['hour'].between(18, 24, inclusive='both'))]
-    if not pos1_pagi.empty:
-        pos1_pagi_total = pos1_pagi.groupby('hour').size()
-        if not pos1_pagi_total.empty:
-            dominant_hour_p1_pagi = pos1_pagi_total.idxmax()
-            dominant_count_p1_pagi = pos1_pagi_total.max()
-            insight_lines.append(f"• Position 2 Shift 2 (18:00–00:00): Peak at {dominant_hour_p1_pagi:02d}:00 ({dominant_count_p1_pagi} alarms)")
-     # Position 1 (Shift Pagi)
-    pos1_pagi = alarm_data[(alarm_data['Position'] == 3) & (alarm_data['hour'].between(6, 17, inclusive='both'))]
-    if not pos1_pagi.empty:
-        pos1_pagi_total = pos1_pagi.groupby('hour').size()
-        if not pos1_pagi_total.empty:
-            dominant_hour_p1_pagi = pos1_pagi_total.idxmax()
-            dominant_count_p1_pagi = pos1_pagi_total.max()
-            insight_lines.append(f"• Position 3 Shift 1 (06:00–18:00): Peak at {dominant_hour_p1_pagi:02d}:00 ({dominant_count_p1_pagi} alarms)")
-    # # Position 1 (Shift Sore)
-    # pos1_sore = alarm_data[(alarm_data['Position'] == 1) & (~alarm_data['hour'].between(6, 17, inclusive='both'))]
-    # if not pos1_sore.empty:
-    #     pos1_sore_red = pos1_sore[pos1_sore['Alarm Status'].str.contains('Red', na=False)]
-    #     if not pos1_sore_red.empty:
-    #         red_percentage_p1_sore = (len(pos1_sore_red) / len(pos1_sore)) * 100
-    #         insight_lines.append(f"• Position 1 Shift 2 (18:00–06:00): {red_percentage_p1_sore:.1f}% are Red alarms")
-    pos1_pagi = alarm_data[(alarm_data['Position'] == 3) & (alarm_data['hour'].between(18, 24, inclusive='both'))]
-    if not pos1_pagi.empty:
-        pos1_pagi_total = pos1_pagi.groupby('hour').size()
-        if not pos1_pagi_total.empty:
-            dominant_hour_p1_pagi = pos1_pagi_total.idxmax()
-            dominant_count_p1_pagi = pos1_pagi_total.max()
-            insight_lines.append(f"• Position 3 Shift 2 (18:00–00:00): Peak at {dominant_hour_p1_pagi:02d}:00 ({dominant_count_p1_pagi} alarms)")
-    # Position 1 (Shift Pagi)
-    pos1_pagi = alarm_data[(alarm_data['Position'] == 4) & (alarm_data['hour'].between(6, 17, inclusive='both'))]
-    if not pos1_pagi.empty:
-        pos1_pagi_total = pos1_pagi.groupby('hour').size()
-        if not pos1_pagi_total.empty:
-            dominant_hour_p1_pagi = pos1_pagi_total.idxmax()
-            dominant_count_p1_pagi = pos1_pagi_total.max()
-            insight_lines.append(f"• Position 4 Shift 1 (06:00–18:00): Peak at {dominant_hour_p1_pagi:02d}:00 ({dominant_count_p1_pagi} alarms)")
-    # # Position 1 (Shift Sore)
-    # pos1_sore = alarm_data[(alarm_data['Position'] == 1) & (~alarm_data['hour'].between(6, 17, inclusive='both'))]
-    # if not pos1_sore.empty:
-    #     pos1_sore_red = pos1_sore[pos1_sore['Alarm Status'].str.contains('Red', na=False)]
-    #     if not pos1_sore_red.empty:
-    #         red_percentage_p1_sore = (len(pos1_sore_red) / len(pos1_sore)) * 100
-    #         insight_lines.append(f"• Position 1 Shift 2 (18:00–06:00): {red_percentage_p1_sore:.1f}% are Red alarms")
-    pos1_pagi = alarm_data[(alarm_data['Position'] == 4) & (alarm_data['hour'].between(18, 24, inclusive='both'))]
-    if not pos1_pagi.empty:
-        pos1_pagi_total = pos1_pagi.groupby('hour').size()
-        if not pos1_pagi_total.empty:
-            dominant_hour_p1_pagi = pos1_pagi_total.idxmax()
-            dominant_count_p1_pagi = pos1_pagi_total.max()
-            insight_lines.append(f"• Position 4 Shift 2 (18:00–00:00): Peak at {dominant_hour_p1_pagi:02d}:00 ({dominant_count_p1_pagi} alarms)")
+    # Fungsi helper untuk mencari jam dengan count maksimum untuk alarm Red/Amber
+    def get_max_alarm_hour(pos_data, alarm_type):
+        filtered_data = pos_data[pos_data['Alarm Status'].str.contains(alarm_type, na=False)]
+        if not filtered_data.empty:
+            hourly_counts = filtered_data.groupby('hour').size()
+            if not hourly_counts.empty:
+                max_hour = hourly_counts.idxmax()
+                max_count = hourly_counts.max()
+                return max_hour, max_count
+        return None, 0
+
+    # Loop untuk semua Position (1, 2, 3, 4)
+    for pos in [1, 2, 3, 4]:
+        # Shift Pagi (06:00–17:59)
+        pos_pagi = alarm_data[(alarm_data['Position'] == pos) & (alarm_data['hour'].between(6, 17, inclusive='both'))]
+        if not pos_pagi.empty:
+            # Cari jam dengan alarm Red maksimum
+            red_hour, red_count = get_max_alarm_hour(pos_pagi, 'Red')
+            if red_hour is not None:
+                insight_lines.append(f"• Position {pos} Shift 1 (06:00–18:00): Peak Red alarm at {red_hour:02d}:00 ({red_count} alarms)")
+            # Cari jam dengan alarm Amber maksimum
+            amber_hour, amber_count = get_max_alarm_hour(pos_pagi, 'Amber')
+            if amber_hour is not None:
+                insight_lines.append(f"• Position {pos} Shift 1 (06:00–18:00): Peak Amber alarm at {amber_hour:02d}:00 ({amber_count} alarms)")
+
+        # Shift Sore (18:00–05:59)
+        pos_sore = alarm_data[(alarm_data['Position'] == pos) & ((alarm_data['hour'] >= 18) | (alarm_data['hour'] <= 5))]
+        if not pos_sore.empty:
+            # Cari jam dengan alarm Red maksimum
+            red_hour, red_count = get_max_alarm_hour(pos_sore, 'Red')
+            if red_hour is not None:
+                insight_lines.append(f"• Position {pos} Shift 2 (18:00–06:00): Peak Red alarm at {red_hour:02d}:00 ({red_count} alarms)")
+            # Cari jam dengan alarm Amber maksimum
+            amber_hour, amber_count = get_max_alarm_hour(pos_sore, 'Amber')
+            if amber_hour is not None:
+                insight_lines.append(f"• Position {pos} Shift 2 (18:00–06:00): Peak Amber alarm at {amber_hour:02d}:00 ({amber_count} alarms)")
+
     insight_text = "\n".join(insight_lines)
 
 # =============== DISPLAY INSIGHT ===============
@@ -1261,6 +1211,7 @@ st.markdown(f"""
 # ================= OBJECTIVE 5 =================
 # ================= OBJECTIVE 5 =================
 # ================= OBJECTIVE 5 =================
+# ================= OBJECTIVE 5 =================
 st.markdown('<h3 class="objective-title">OBJECTIVE 5: Insights & Mitigation — How Can Red Pressure Alarms Be Reduced?</h3>', unsafe_allow_html=True)
 
 # --- DATA PREP ---
@@ -1342,23 +1293,23 @@ else:
 # Insight dari Objective 1-4
 insight_text = f"""
 1. Pressure & Temperature Distribution (Objective 1):
-   Front tyres (Pos 1 & 2) show lower pressure ({front_pressure_avg:.1f} psi) and higher temperature ({front_temp_avg:.1f}°C) due to higher stress from braking/steering.
-   Rear tyres (Pos 3 & 4) show higher pressure ({front_pressure_avg + 19.1:.1f} psi) and lower temperature ({front_temp_avg - 5.3:.1f}°C), indicating stable operation.
+Front tyres (Pos 1 & 2) show lower pressure ({front_pressure_avg:.1f} psi) and higher temperature ({front_temp_avg:.1f}°C) due to higher stress from braking/steering.
+Rear tyres (Pos 3 & 4) show higher pressure ({front_pressure_avg + 19.1:.1f} psi) and lower temperature ({front_temp_avg - 5.3:.1f}°C), indicating stable operation.
 
 2. Alarm Distribution by Shift (Objective 2):
-   Position 1 (06:00–18:00): Dominant alarm at {dominant_hour}:00 with {hourly_counts[dominant_hour]} alarms.
-   Position 1 (18:00–06:00): Red alarms account for {(len(pos1_sore[pos1_sore['Alarm Status'].str.contains('Red', na=False)]) / len(pos1_sore)) * 100:.1f}% of total alarms.
-   Position 3 (06:00–18:00): Dominant alarm at {dominant_hour}:00 with {hourly_counts[dominant_hour]} alarms.
-   Position 3 (18:00–06:00): Amber alarms account for {(len(pos3_sore[pos3_sore['Alarm Status'].str.contains('Amber', na=False)]) / len(pos3_sore)) * 100:.1f}% of total alarms.
+Position 1 (06:00–18:00): Dominant alarm at {dominant_hour}:00 with {hourly_counts[dominant_hour]} alarms.
+Position 1 (18:00–06:00): Red alarms account for {(len(pos1_sore[pos1_sore['Alarm Status'].str.contains('Red', na=False)]) / len(pos1_sore)) * 100:.1f}% of total alarms.
+Position 3 (06:00–18:00): Dominant alarm at {dominant_hour}:00 with {hourly_counts[dominant_hour]} alarms.
+Position 3 (18:00–06:00): Amber alarms account for {(len(pos3_sore[pos3_sore['Alarm Status'].str.contains('Amber', na=False)]) / len(pos3_sore)) * 100:.1f}% of total alarms.
 
 3. Correlation Analysis (Objective 3):
-   Strong correlation between temperature and pressure in front tyres (r = {corr_front:.2f}) vs rear (r = {corr_rear:.2f}).
-   At temperatures ≥52°C, front tyres trigger {red_high_pressure_count} Red High Pressure alarms.
-   Pressure vs (T/v) shows weak correlation (r = {corr_p_tv_front:.2f}), suggesting speed alone is not primary heat factor.
+Strong correlation between temperature and pressure in front tyres (r = {corr_front:.2f}) vs rear (r = {corr_rear:.2f}).
+At temperatures ≥52°C, front tyres trigger {red_high_pressure_count} Red High Pressure alarms.
+Pressure vs (T/v) shows weak correlation (r = {corr_p_tv_front:.2f}), suggesting speed alone is not primary heat factor.
 
 4. Spatial Risk Mapping (Objective 4):
-   Alarm concentration is highest in {top_zone_obj4}, with {top_zone_count_obj4} alarms representing {percentage_obj4:.1f}% of total alarms.
-   Front tyres account for {front_percentage_obj4:.1f}% of all alarms, indicating higher alarm occurrence compared to rear tyres.
+Alarm concentration is highest in {top_zone_obj4}, with {top_zone_count_obj4} alarms representing {percentage_obj4:.1f}% of total alarms.
+Front tyres account for {front_percentage_obj4:.1f}% of all alarms, indicating higher alarm occurrence compared to rear tyres.
 """
 
 try:
@@ -1432,8 +1383,10 @@ except:
 st.markdown('<h4 style="text-align:left; margin:10px 0 5px 0; font-weight:bold;">INSIGHT</h4>', unsafe_allow_html=True)
 st.markdown(f"""
 <div class="insight-box">
-    <div class="content" style="text-align:left;">
+    <div class="content">
+        <div style="text-align:left; white-space: pre-line;">
 {insight_text.strip()}
+        </div>
     </div>
 </div>
 """, unsafe_allow_html=True)