Update app.py

app.py

@@ -308,15 +308,16 @@ df = load_data()
 
 # ================= HEADER =================
 st.markdown("""
-<div style="text-align:center; font-family:Arial, sans-serif;">
-    <h1 style="color:#154D9C; font-weight:bold; margin-bottom:0;">Michelin Mining Tyre Analytics</h1>
-    <p style="font-size:12px; color:#7d7d7d; margin-top:2px;">
+<div style="text-align:center; font-family:Arial, sans-serif; margin-bottom:16px;">
+    <h1 style="color:#154D9C; font-weight:bold; margin:0;">
+        Tyre Pressure Monitoring System (TPMS) Analytics for Mining Equipments
+    </h1>
+    <p style="font-size:12px; color:#7d7d7d; margin:4px 0 2px 0;">
         Daily trend insights derived from 13–16 December 2023 data
     </p>
-    <p style="font-size:10px; color:#7d7d7d; margin-top:4px;">
-        &copy; 2025 Bukit Technology. All rights reserved.
+    <p style="font-size:10px; color:#7d7d7d; margin:2px 0 0 0;">
+        &copy; 2025 Bukit Technology Digital. All rights reserved.
     </p>
-    <hr style="border:0; height:1px; background-color:#e0e0e0; margin-top:8px; margin-bottom:8px;">
 </div>
 """, unsafe_allow_html=True)
 
@@ -1456,33 +1457,33 @@ else:
 # ================= OBJECTIVE 6 =================
 st.markdown('<h3 class="objective-title">OBJECTIVE 6: Insights & Mitigation — How Can Red Pressure Alarms Be Reduced?</h3>', unsafe_allow_html=True)
 
-# --- DATA PREP (dengan penanganan NaN/empty yang aman) ---
+# --- DATA PREP (aman terhadap NaN, empty, atau division-by-zero) ---
 # Front tyre stats — fallback ke "—" jika NaN
 front_pressure_avg = dff[dff['Position'].isin([1, 2])]['Pressure (psi)'].mean()
 front_temp_avg = dff[dff['Position'].isin([1, 2])]['Temperature (°C)'].mean()
 front_pressure_avg_str = f"{front_pressure_avg:.1f}" if pd.notna(front_pressure_avg) else "—"
 front_temp_avg_str = f"{front_temp_avg:.1f}" if pd.notna(front_temp_avg) else "—"
 
-# Hourly alarm stats
-hourly_counts = dff[dff['is_alarm'] == 1]['hour'].value_counts().reindex(range(24), fill_value=0)
+# Hourly alarm stats — hanya Red & Amber (ignore No Alarm)
+alarm_df = dff[dff['Alarm Type'].isin(['Red', 'Amber'])]
+hourly_counts = alarm_df['hour'].value_counts().reindex(range(24), fill_value=0)
 total_alarms = hourly_counts.sum()
+
+dominant_hour = None
+dominant_percentage = 0.0
 if total_alarms > 0 and not hourly_counts.empty:
     dominant_hour = int(hourly_counts.idxmax())
     dominant_percentage = (hourly_counts[dominant_hour] / total_alarms) * 100
-else:
-    dominant_hour = None
-    dominant_percentage = 0.0
 
-# Zone alarm stats
-zone_counts = dff[dff['is_alarm'] == 1]['Zone'].value_counts()
+# Zone alarm stats — hanya Red & Amber
+zone_counts = alarm_df['Zone'].value_counts()
+top_zone = "—"
+top_zone_percentage = 0.0
 if not zone_counts.empty and total_alarms > 0:
     top_zone = str(zone_counts.index[0])
     top_zone_percentage = (zone_counts.iloc[0] / total_alarms) * 100
-else:
-    top_zone = "—"
-    top_zone_percentage = 0.0
 
-# Correlation — pastikan tidak NaN
+# Correlation — aman terhadap NaN
 def safe_corr(x, y):
     valid = x.notna() & y.notna()
     if valid.sum() < 2:
@@ -1493,69 +1494,68 @@ def safe_corr(x, y):
 front_df = dff[dff['Position'].isin([1, 2])]
 rear_df = dff[dff['Position'].isin([3, 4])]
 
-corr_front = safe_corr(front_df['Pressure (psi)'], front_df['Temperature (°C)'])
-corr_rear = safe_corr(rear_df['Speed (km/h)'], rear_df['Temperature (°C)'])
+corr_pressure_temp_front = safe_corr(front_df['Pressure (psi)'], front_df['Temperature (°C)'])
+corr_speed_temp_rear = safe_corr(rear_df['Speed (km/h)'], rear_df['Temperature (°C)'])
 
-# Format korelasi: batas 2 desimal & hindari -0.00 → 0.00
-corr_front_str = f"{corr_front:+.2f}".replace("-0.00", "0.00")
-corr_rear_str = f"{corr_rear:+.2f}".replace("-0.00", "0.00")
+# Format korelasi: 2 desimal, hindari -0.00
+corr_pressure_temp_front_str = f"{corr_pressure_temp_front:+.2f}".replace("-0.00", "0.00")
+corr_speed_temp_rear_str = f"{corr_speed_temp_rear:+.2f}".replace("-0.00", "0.00")
 
-# Insight text — aman dari N/A/NaN
+# Position 1 alarm count (Red + Amber only)
+pos1_alarm_count = alarm_df[alarm_df['Position'] == 1].shape[0]
+
+# ================= INSIGHTS (dynamic, fact-based) =================
 insight_lines = []
 
-line1 = f"1. Front tyres (Pos 1 & 2) average pressure: {front_pressure_avg_str} psi, temperature: {front_temp_avg_str}°C."
-if pd.notna(front_pressure_avg) and front_pressure_avg > 125:  # sesuaikan threshold jika perlu
-    line1 += " Values suggest risk of over-inflation under operational load (Objective 1)."
+line1 = f"1. Front tyres (Pos 1 & 2): avg pressure {front_pressure_avg_str} psi, avg temperature {front_temp_avg_str}°C."
+if pd.notna(front_pressure_avg) and front_pressure_avg > 115:
+    line1 += " Exceeds ideal inflation range (100–110 psi), indicating over-pressure risk."
 insight_lines.append(line1)
 
 if dominant_hour is not None:
     insight_lines.append(
-        f"2. Peak alarm in Position 1 (238 occurrences)"
+        f"2. Peak Red/Amber alarm concentration at {dominant_hour:02d}:00 ({dominant_percentage:.1f}% of total)."
     )
 else:
-    insight_lines.append("2. No clear hourly alarm peak detected.")
+    insight_lines.append("2. No statistically dominant hourly alarm peak.")
 
 insight_lines.append(
-    f"3. Front tyres show pressure–temperature correlation r = {corr_front_str}; "
-    f" Temperature–speed correlation to pressure is weak."
+    f"3. Front pressure–temperature correlation: r = {corr_pressure_temp_front_str}; "
+    f"Rear speed–temperature correlation: r = {corr_speed_temp_rear_str}."
 )
 
-if top_zone != "—":
-    insight_lines.append(f"4. Zone Parking 2 for {top_zone_percentage:.1f}% of alarms, confirmed as high-risk zone.")
+if top_zone != "—" and top_zone_percentage >= 10.0:
+    insight_lines.append(
+        f"4. Zone {top_zone} accounts for {top_zone_percentage:.1f}% of alarms — highest-risk location."
+    )
 else:
-    insight_lines.append("4. No dominant alarm zone identified.")
+    insight_lines.append("4. Alarm distribution appears relatively uniform across zones.")
 
-insight_text = "<br>".join(insight_lines)
-
-# ============== ACTIONS (Recommendation & Risk Mitigation Digabung - Berdasarkan Data & Logika) ==============
-action_lines = []
+if pos1_alarm_count > 0:
+    insight_lines.append(
+        f"5. Position 1 recorded {pos1_alarm_count} Red/Amber alarms — highest of any wheel position."
+    )
 
-# 1. Front tyre pressure & temp — fokus anomali
-if pd.notna(front_pressure_avg):
-    # Jika pressure jauh dari ideal (misal 100-110 psi), tambahkan warning
-    ideal_low = 100
-    ideal_high = 110
-    if front_pressure_avg < ideal_low or front_pressure_avg > ideal_high:
-        action_lines.append(f"1. Calibrate front tyre pressure: current {front_pressure_avg_str} psi deviates from optimal range ({ideal_low}–{ideal_high} psi). Front temperature {front_temp_avg_str}°C suggests heat buildup; correlate with load/terrain.")
-    else:
-        action_lines.append(f"• Monitor front tyre pressure: current {front_pressure_avg_str} psi is within operational range.")
-# 2. Peak alarm di Position 1 — fokus mekanikal
-action_lines.append(
-    f"2. Position 1 triggers 238 alarms — inspect for uneven load distribution, misalignment, or brake drag."
-)
+insight_text = "<br>".join(insight_lines)
 
-# 3. Correlation: front tinggi (r=+0.99), rear rendah (r=+0.01)
-action_lines.append(
-    f"3. Strong front pressure–temp correlation (r = {corr_front_str}) confirms heat-driven pressure rise — monitor load cycles."
-)
-action_lines.append(
-    f"4. Low rear speed–temp correlation (r = {corr_rear_str}) indicates rear tyres operate under stable conditions. {top_zone_percentage:.1f}% of alarms in Parking 2 — inspect road surface, debris, or operational practices in this zone."
-)
+# ================= SUMMARY (HARD-CODED — EXACTLY AS PROVIDED) =================
+summary_text = """
+1. Front tyres are predominantly exposed to over-pressure risk, while rear tyres are prone to under-pressure events linked to ambient temperature variation, particularly during morning and evening operations.<br>
+2. Overall, over-pressure alarms significantly exceed under-pressure alarms, and pressure behaviour is not correlated with vehicle speed, suggesting that temperature rise is driven by operational and environmental factors rather than driving behaviour.
+"""
 
-action_text = "<br>".join(action_lines)
+# ================= RECOMMENDATION (HARD-CODED — EXACTLY AS PROVIDED) =================
+recommendation_text = """
+1. Adjust cold inflation pressures based on actual axle loads, particularly for front tyres.<br>
+2. Apply shift-based pressure management to account for ambient temperature changes affecting rear tyres.<br>
+3. Strengthen leak detection and valve inspections for tyres with recurring under-pressure events.<br>
+4. Optimise TPMS alarm thresholds to reduce nuisance alarms and prevent alarm fatigue.<br>
+5. Address non-speed temperature drivers through haul road improvement, payload control, and reduced idle under load.<br>
+6. Use TPMS trends to enable proactive tyre health monitoring and maintenance planning.
+"""
 
-# ============== RENDER ==============
-st.markdown('<h4 style="text-align:center; margin:10px 0 5px 0; font-weight:bold;">SUMMARY</h4>', unsafe_allow_html=True)
+# ================= RENDER =================
+st.markdown('<h4 style="text-align:center; margin:10px 0 5px 0; font-weight:bold;">INSIGHTS</h4>', unsafe_allow_html=True)
 st.markdown(f"""
 <div class="insight-box">
     <div class="content" style="text-align:left;">
@@ -1564,11 +1564,20 @@ st.markdown(f"""
 </div>
 """, unsafe_allow_html=True)
 
-st.markdown('<h4 style="text-align:center; margin:15px 0 5px 0; font-weight:bold;">RECOMMENDATION</h4>', unsafe_allow_html=True)
+st.markdown('<h4 style="text-align:center; margin:15px 0 5px 0; font-weight:bold;">SUMMARY</h4>', unsafe_allow_html=True)
+st.markdown(f"""
+<div class="insight-box">
+    <div class="content" style="text-align:left;">
+{summary_text}
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+st.markdown('<h4 style="text-align:center; margin:15px 0 5px 0; font-weight:bold;">RECOMMENDATION & RISK MITIGATION</h4>', unsafe_allow_html=True)
 st.markdown(f"""
 <div class="insight-box">
     <div class="content" style="text-align:left;">
-{action_text}
+{recommendation_text}
     </div>
 </div>
 """, unsafe_allow_html=True)