Update app.py

app.py

@@ -679,7 +679,115 @@ with col8:
     else:
         st.warning("No data for Position 4 (18:00–06:00)")
 
-
+# =============== INSIGHT 2 (Ringkas) ===============
+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}%)"
+    ]
+
+    # 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")
+
+    # Position 2 (Shift Pagi)
+    pos2_pagi = alarm_data[(alarm_data['Position'] == 2) & (alarm_data['hour'].between(6, 17, inclusive='both'))]
+    if not pos2_pagi.empty:
+        pos2_pagi_total = pos2_pagi.groupby('hour').size()
+        if not pos2_pagi_total.empty:
+            dominant_hour_p2_pagi = pos2_pagi_total.idxmax()
+            dominant_count_p2_pagi = pos2_pagi_total.max()
+            insight_lines.append(f"• Position 2 Shift 1 (06:00–18:00): Peak at {dominant_hour_p2_pagi:02d}:00 ({dominant_count_p2_pagi} alarms)")
+    # Position 2 (Shift Sore)
+    pos2_sore = alarm_data[(alarm_data['Position'] == 2) & (~alarm_data['hour'].between(6, 17, inclusive='both'))]
+    if not pos2_sore.empty:
+        pos2_sore_red = pos2_sore[pos2_sore['Alarm Status'].str.contains('Red', na=False)]
+        if not pos2_sore_red.empty:
+            red_percentage_p2_sore = (len(pos2_sore_red) / len(pos2_sore)) * 100
+            insight_lines.append(f"• Position 2 Shift 2 (18:00–06:00): {red_percentage_p2_sore:.1f}% are Red 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 Shift 1 (06:00–18:00): Peak at {dominant_hour_p3_pagi:02d}:00 ({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 Shift 2 (18:00–06:00): {amber_percentage_p3_sore:.1f}% are Amber 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 Shift 1 (06:00–18:00): Peak at {dominant_hour_p4_pagi:02d}:00 ({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 Shift 2 (18:00–06:00): {amber_percentage_p4_sore:.1f}% are Amber 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)
 
@@ -1128,6 +1236,7 @@ st.markdown(f"""
 </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)
 
 # --- DATA PREP ---
@@ -1231,7 +1340,7 @@ insight_text = f"""
 try:
     import requests
     import json
-    API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta  "
+    API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
     prompt = f"""
 Role: Fleet Operations Risk Analyst
 Insights: