Update app.py

app.py

@@ -249,7 +249,7 @@ st.markdown("""
 <div class="main-header" style="text-align:center;">
     <h1>Michelin Mining Tyre Analytics</h1>
     <p style="font-size:12px; color:#9a9a9a; margin-top:-6px;">
-        Daily trend insights derived from 13–16 December 2023 data”
+        Daily trend insights derived from 13–16 December 2023 data
     </p>
 </div>
 """, unsafe_allow_html=True)
@@ -679,103 +679,7 @@ with col8:
     else:
         st.warning("No data for Position 4 (18:00–06:00)")
 
-# =============== INSIGHT 3 ===============
-if alarm_data.empty:
-    insight_text = "• No data available for analysis."
-else:
-    # Insight tetap sama
-    alarm_hours = alarm_data['hour']
-
-    def hour_to_band(h):
-        if 0 <= h < 6:   return "00:00–06:00 (Night)"
-        if 6 <= h < 12:  return "06:00–12:00 (Morning)"
-        if 12 <= h < 18: return "12:00–18:00 (Afternoon)"
-        return "18:00–00:00 (Evening)"
-
-    alarm_hours_df = pd.DataFrame({'hour': alarm_hours})
-    alarm_hours_df['band'] = alarm_hours_df['hour'].apply(hour_to_band)
-    band_counts = alarm_hours_df['band'].value_counts().sort_index()
-
-    top_bands = band_counts.nlargest(2)
-    dominant_band = top_bands.index[0] if len(top_bands) > 0 else "N/A"
-    second_dominant_band = top_bands.index[1] if len(top_bands) > 1 else "N/A"
-
-    dominant_pct = (top_bands.iloc[0] / band_counts.sum() * 100) if len(top_bands) > 0 else 0
-    second_pct = (top_bands.iloc[1] / band_counts.sum() * 100) if len(top_bands) > 1 else 0
 
-    # Hitung jumlah masing-masing jenis alarm
-    normal_alarms = alarm_data[alarm_data['Alarm Status'] == 'No Alarm'].shape[0]
-    red_alarms = alarm_data[alarm_data['Alarm Status'].str.contains('Red', na=False)].shape[0]
-    amber_alarms = alarm_data[alarm_data['Alarm Status'].str.contains('Amber', na=False)].shape[0]
-
-    # Insight Spesifik Per Position dan Shift
-    insight_lines = [
-        f"{dominant_band} is the dominant period ({dominant_pct:.1f}% of all data).",
-        f"{second_dominant_band} is the second-highest period ({second_pct:.1f}% of data).",
-        f"Total: Normal={normal_alarms}, Amber={amber_alarms}, Red={red_alarms}"
-    ]
-
-    # 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 (06:00–18:00): Dominant alarm at {dominant_hour_p1_pagi:02d}:00 with {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 (18:00–06:00): Red alarms account for {red_percentage_p1_sore:.1f}% of total alarms.")
-
-    # Position 3 (Shift Pagi)
-    pos3_pagi = alarm_data[(alarm_data['Position'] == 3) & (alarm_data['hour'].between(6, 17, inclusive='both'))]
-    if not pos3_pagi.empty:
-        pos3_pagi_total = pos3_pagi.groupby('hour').size()
-        if not pos3_pagi_total.empty:
-            dominant_hour_p3_pagi = pos3_pagi_total.idxmax()
-            dominant_count_p3_pagi = pos3_pagi_total.max()
-            insight_lines.append(f"Position 3 (06:00–18:00): Dominant alarm at {dominant_hour_p3_pagi:02d}:00 with {dominant_count_p3_pagi} alarms.")
-
-    # Position 3 (Shift Sore)
-    pos3_sore = alarm_data[(alarm_data['Position'] == 3) & (~alarm_data['hour'].between(6, 17, inclusive='both'))]
-    if not pos3_sore.empty:
-        pos3_sore_amber = pos3_sore[pos3_sore['Alarm Status'].str.contains('Amber', na=False)]
-        if not pos3_sore_amber.empty:
-            amber_percentage_p3_sore = (len(pos3_sore_amber) / len(pos3_sore)) * 100
-            insight_lines.append(f"Position 3 (18:00–06:00): Amber alarms account for {amber_percentage_p3_sore:.1f}% of total alarms.")
-
-    # Position 4 (Shift Pagi)
-    pos4_pagi = alarm_data[(alarm_data['Position'] == 4) & (alarm_data['hour'].between(6, 17, inclusive='both'))]
-    if not pos4_pagi.empty:
-        pos4_pagi_total = pos4_pagi.groupby('hour').size()
-        if not pos4_pagi_total.empty:
-            dominant_hour_p4_pagi = pos4_pagi_total.idxmax()
-            dominant_count_p4_pagi = pos4_pagi_total.max()
-            insight_lines.append(f"Position 4 (06:00–18:00): Dominant alarm at {dominant_hour_p4_pagi:02d}:00 with {dominant_count_p4_pagi} alarms.")
-
-    # Position 4 (Shift Sore)
-    pos4_sore = alarm_data[(alarm_data['Position'] == 4) & (~alarm_data['hour'].between(6, 17, inclusive='both'))]
-    if not pos4_sore.empty:
-        pos4_sore_amber = pos4_sore[pos4_sore['Alarm Status'].str.contains('Amber', na=False)]
-        if not pos4_sore_amber.empty:
-            amber_percentage_p4_sore = (len(pos4_sore_amber) / len(pos4_sore)) * 100
-            insight_lines.append(f"Position 4 (18:00–06:00): Amber alarms account for {amber_percentage_p4_sore:.1f}% of total alarms.")
-
-    insight_text = "\n".join(insight_lines)
-
-# =============== DISPLAY INSIGHT ===============
-st.markdown(f"""
-<div class="insight-box">
-    <div class="content">
-{insight_text}
-    </div>
-</div>
-""", unsafe_allow_html=True)
 #### OBJECTICVE 3
 st.markdown('<h3 class="objective-title">OBJECTIVE 3: Correlation — How Does Heat Influence Pressure and Which Tyres Trigger Red Alarms?</h3>', unsafe_allow_html=True)
 
@@ -1223,7 +1127,87 @@ st.markdown(f"""
     </div>
 </div>
 """, unsafe_allow_html=True)
-# ================= OBJECTIVE 5 =================
+# =============== INSIGHT 3 ===============
+if alarm_data.empty:
+    insight_text = "• No data available for analysis."
+else:
+    # Insight tetap sama
+    alarm_hours = alarm_data['hour']
+
+    def hour_to_band(h):
+        if 0 <= h < 6:   return "00:00–06:00 (Night)"
+        if 6 <= h < 12:  return "06:00–12:00 (Morning)"
+        if 12 <= h < 18: return "12:00–18:00 (Afternoon)"
+        return "18:00–00:00 (Evening)"
+
+    alarm_hours_df = pd.DataFrame({'hour': alarm_hours})
+    alarm_hours_df['band'] = alarm_hours_df['hour'].apply(hour_to_band)
+    band_counts = alarm_hours_df['band'].value_counts().sort_index()
+
+    top_bands = band_counts.nlargest(2)
+    dominant_band = top_bands.index[0] if len(top_bands) > 0 else "N/A"
+    second_dominant_band = top_bands.index[1] if len(top_bands) > 1 else "N/A"
+
+    dominant_pct = (top_bands.iloc[0] / band_counts.sum() * 100) if len(top_bands) > 0 else 0
+    second_pct = (top_bands.iloc[1] / band_counts.sum() * 100) if len(top_bands) > 1 else 0
+
+    # Hitung jumlah masing-masing jenis alarm
+    normal_alarms = alarm_data[alarm_data['Alarm Status'] == 'No Alarm'].shape[0]
+    red_alarms = alarm_data[alarm_data['Alarm Status'].str.contains('Red', na=False)].shape[0]
+    amber_alarms = alarm_data[alarm_data['Alarm Status'].str.contains('Amber', na=False)].shape[0]
+
+    # Insight Spesifik Per Position dan Shift
+    insight_lines = [
+        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}%)"
+    ]
+
+    # Front Tyres (Position 1 & 2)
+    front_data = alarm_data[alarm_data['Position'].isin([1, 2])]
+    front_pagi = front_data[front_data['hour'].between(6, 17, inclusive='both')]
+    front_sore = front_data[~front_data['hour'].between(6, 17, inclusive='both')]
+
+    if not front_pagi.empty:
+        front_pagi_total = front_pagi.groupby('hour').size()
+        if not front_pagi_total.empty:
+            dominant_hour_front_pagi = front_pagi_total.idxmax()
+            dominant_count_front_pagi = front_pagi_total.max()
+            insight_lines.append(f"• Front Shift 1 (06:00–18:00): Peak at {dominant_hour_front_pagi:02d}:00 ({dominant_count_front_pagi} alarms)")
+    if not front_sore.empty:
+        front_sore_total = front_sore.groupby('hour').size()
+        if not front_sore_total.empty:
+            dominant_hour_front_sore = front_sore_total.idxmax()
+            dominant_count_front_sore = front_sore_total.max()
+            insight_lines.append(f"• Front Shift 2 (18:00–06:00): Peak at {dominant_hour_front_sore:02d}:00 ({dominant_count_front_sore} alarms)")
+
+    # Rear Tyres (Position 3 & 4)
+    rear_data = alarm_data[alarm_data['Position'].isin([3, 4])]
+    rear_pagi = rear_data[rear_data['hour'].between(6, 17, inclusive='both')]
+    rear_sore = rear_data[~rear_data['hour'].between(6, 17, inclusive='both')]
+
+    if not rear_pagi.empty:
+        rear_pagi_total = rear_pagi.groupby('hour').size()
+        if not rear_pagi_total.empty:
+            dominant_hour_rear_pagi = rear_pagi_total.idxmax()
+            dominant_count_rear_pagi = rear_pagi_total.max()
+            insight_lines.append(f"• Rear Shift 1 (06:00–18:00): Peak at {dominant_hour_rear_pagi:02d}:00 ({dominant_count_rear_pagi} alarms)")
+    if not rear_sore.empty:
+        rear_sore_total = rear_sore.groupby('hour').size()
+        if not rear_sore_total.empty:
+            dominant_hour_rear_sore = rear_sore_total.idxmax()
+            dominant_count_rear_sore = rear_sore_total.max()
+            insight_lines.append(f"• Rear Shift 2 (18:00–06:00): Peak at {dominant_hour_rear_sore:02d}:00 ({dominant_count_rear_sore} alarms)")
+
+    insight_text = "\n".join(insight_lines)
+
+# =============== DISPLAY INSIGHT ===============
+st.markdown(f"""
+<div class="insight-box">
+    <div class="content">
+{insight_text}
+    </div>
+</div>
+""", unsafe_allow_html=True)
 # ================= 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)
@@ -1305,24 +1289,25 @@ else:
     front_percentage_obj4 = 0
 
 # 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.
-
-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.
-
-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.
-
-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.
+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.
+
+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.
+
+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.
+
+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.
 """
 
 try: