Update app.py

app.py

@@ -445,6 +445,7 @@ else:
 Front tyres: Pressure {front_pressure_avg:.1f} psi, temperature {front_temp_avg:.1f}°C → stable under current load/pressure balance. Rear tyres: Pressure {rear_pressure_avg:.1f} psi, temperature {rear_temp_avg:.1f}°C → balanced operation.
 """
 
+
 st.markdown(f"""
 <div class="insight-box">
     <div class="content">
@@ -452,8 +453,12 @@ st.markdown(f"""
     </div>
 </div>
 """, unsafe_allow_html=True)
+
+#### OBJECTICVE 2
+
 st.markdown("""
-<h3 class="objective-title">OBJECTIVE 2: Shift and Tyre Position - How Are Alarms Concentrated Across Shifts and Tyres?</h3>""", unsafe_allow_html=True)
+<h3 class="objective-title">OBJECTIVE 2: Shift and Tyre Position - How Are Alarms Concentrated Across Shifts and Tyres?</h3>
+""", unsafe_allow_html=True)
 
 # Filter semua data (termasuk alarm normal)
 alarm_data = dff.copy()
@@ -492,14 +497,14 @@ def create_radial_chart(pos_data, title, shift_hours, shift_type):
 
     fig = go.Figure()
 
-    # Tambahkan trace untuk masing-masing kategori
+    # Tambahkan trace untuk masing-masing kategori dengan hovertemplate custom
     fig.add_trace(go.Barpolar(
         r=hourly_normal.values,
         theta=theta,
         name='Normal',
         marker_color='#2E7D32',  # Hijau
         opacity=0.8,
-        hovertemplate='<b>Hour:</b> %{theta:.0f}<br><b>Count:</b> %{r}<br><b>Status:</b> Normal<extra></extra>',
+        hovertemplate='<b>Hour:</b> %{customdata}:00<br><b>Count:</b> %{r}<br><b>Status:</b> Normal<extra></extra>',
         customdata=shift_hours
     ))
     fig.add_trace(go.Barpolar(
@@ -508,7 +513,7 @@ def create_radial_chart(pos_data, title, shift_hours, shift_type):
         name='Amber',
         marker_color='#FFC107',  # Kuning
         opacity=0.8,
-        hovertemplate='<b>Hour:</b> %{theta:.0f}<br><b>Count:</b> %{r}<br><b>Status:</b> Amber<extra></extra>',
+        hovertemplate='<b>Hour:</b> %{customdata}:00<br><b>Count:</b> %{r}<br><b>Status:</b> Amber<extra></extra>',
         customdata=shift_hours
     ))
     fig.add_trace(go.Barpolar(
@@ -517,7 +522,7 @@ def create_radial_chart(pos_data, title, shift_hours, shift_type):
         name='Red',
         marker_color='#D32F2F',  # Merah
         opacity=0.8,
-        hovertemplate='<b>Hour:</b> %{theta:.0f}<br><b>Count:</b> %{r}<br><b>Status:</b> Red<extra></extra>',
+        hovertemplate='<b>Hour:</b> %{customdata}:00<br><b>Count:</b> %{r}<br><b>Status:</b> Red<extra></extra>',
         customdata=shift_hours
     ))
 
@@ -665,9 +670,9 @@ else:
 
     # 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}"
+        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)
@@ -677,7 +682,7 @@ else:
         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.")
+            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'))]
@@ -685,7 +690,7 @@ else:
         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.")
+            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'))]
@@ -694,7 +699,7 @@ else:
         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.")
+            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'))]
@@ -702,7 +707,7 @@ else:
         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.")
+            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'))]
@@ -711,7 +716,7 @@ else:
         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.")
+            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'))]
@@ -719,7 +724,7 @@ else:
         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_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)
 
@@ -1074,7 +1079,7 @@ else:
 # Strong correlation between temperature and pressure in front tyres (r = {corr_p_t_front:.2f}) vs rear (r = {corr_p_t_rear:.2f}).
 # •
 insight_text = f""" At temperatures ≥52°C, front tyres trigger {red_high_pressure_count} Red High Pressure alarms, indicating critical heat thresholds.
-• Pressure vs (T/v) shows weak correlation (r = {corr_p_tv_front:.2f}), suggesting speed alone is not primary heat factor.
+• Pressure vs (T/v) shows weak correlation (r = {corr_p_tv_front:.2f}) for front and (r = {corr_p_tv_rear:.2f}) for rear, suggesting speed alone is not primary heat factor.
 """
 
 st.markdown(f"""
@@ -1104,64 +1109,7 @@ else:
         height='520px'
     )
 
-    # === Tambahkan Polygon untuk Location 1 & 2 (Contoh koordinat — ganti sesuai data Anda)
-    # Jika Anda punya kolom 'Zone' atau 'Location', gunakan itu untuk grouping
-    # Di sini saya asumsikan:
-    # - Location 1: Zona dengan nama 'Parking 1'
-    # - Location 2: Zona dengan nama 'Parking 2'
-
-    # Contoh koordinat polygon (ganti dengan nilai nyata dari data Anda jika ada)
-    # Jika tidak ada, Anda bisa definisikan manual berdasarkan range latitude/longitude
-
-    # Untuk demo, saya buat polygon manual
-    # Location 1: Parking 1
-    location1_coords = [
-        [center_lat - 0.0008, center_lon - 0.001],   # kiri bawah
-        [center_lat - 0.0008, center_lon + 0.001],   # kiri atas
-        [center_lat + 0.0008, center_lon + 0.001],   # kanan atas
-        [center_lat + 0.0008, center_lon - 0.001],   # kanan bawah
-        [center_lat - 0.0008, center_lon - 0.001]    # kembali ke awal
-    ]
-    folium.Polygon(
-        locations=location1_coords,
-        color='#1976D2',
-        fill_color='#1976D2',
-        fill_opacity=0.1,
-        weight=2,
-        popup="Location 1"
-    ).add_to(m)
-
-    # Location 2: Parking 2
-    location2_coords = [
-        [center_lat + 0.001, center_lon - 0.0015],
-        [center_lat + 0.001, center_lon + 0.0015],
-        [center_lat + 0.002, center_lon + 0.0015],
-        [center_lat + 0.002, center_lon - 0.0015],
-        [center_lat + 0.001, center_lon - 0.0015]
-    ]
-    folium.Polygon(
-        locations=location2_coords,
-        color='#FF9800',
-        fill_color='#FF9800',
-        fill_opacity=0.1,
-        weight=2,
-        popup="Location 2"
-    ).add_to(m)
-
-    # Tambahkan teks label di tengah polygon
-    folium.Marker(
-        location=[center_lat - 0.0004, center_lon],
-        icon=folium.DivIcon(html=f'<div style="font-weight:bold; font-size:14px; color:#1976D2; text-shadow: 1px 1px 2px white;">Location 1</div>'),
-        tooltip="Location 1"
-    ).add_to(m)
-
-    folium.Marker(
-        location=[center_lat + 0.0015, center_lon],
-        icon=folium.DivIcon(html=f'<div style="font-weight:bold; font-size:14px; color:#FF9800; text-shadow: 1px 1px 2px white;">Location 2</div>'),
-        tooltip="Location 2"
-    ).add_to(m)
-
-    # === Plot marker per tyre
+    # === Plot marker per tyre (tanpa polygon Location 1 & 2)
     for _, r in valid_gps.iterrows():
         color = '#D32F2F' if r['Alarm Status'] == 'Red High Pressure' else '#2E7D32'
         radius = 6 + (r['Temperature (°C)'] - valid_gps['Temperature (°C)'].min()) / (valid_gps['Temperature (°C)'].max() - valid_gps['Temperature (°C)'].min() + 1e-5) * 12
@@ -1232,7 +1180,9 @@ if not valid_gps.empty:
     else:
         front_percentage = 0
 
-    insight_text = f"""Alarm concentration is highest in {top_zone}, with {top_zone_count} alarms representing {percentage:.1f}% of total alarms. Front tyres account for {front_percentage:.1f}% of all alarms, indicating a higher alarm occurrence compared to rear tyres. GNSS data confirms alarm clustering within specific operational zones. Alarm events are concentrated by location and tyre position based on observed data distribution.
+    insight_text = f"""
+    Alarm concentration is highest in {top_zone}, with {top_zone_count} alarms representing {percentage:.1f}% of total alarms.
+    Front tyres account for {front_percentage:.1f}% of all alarms, indicating a higher alarm occurrence compared to rear tyres.
     """
 
 else:
@@ -1247,7 +1197,7 @@ st.markdown(f"""
     </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)
 
@@ -1277,56 +1227,9 @@ else:
     corr_rear = 0
 
 # Insight dari Objective 1-4
-# Insight 1: Fisika & Mekanikal
-front_pressure_avg_obj1 = dff[dff['Position'].isin([1, 2])]['Pressure (psi)'].mean()
-rear_pressure_avg_obj1 = dff[dff['Position'].isin([3, 4])]['Pressure (psi)'].mean()
-front_temp_avg_obj1 = dff[dff['Position'].isin([1, 2])]['Temperature (°C)'].mean()
-rear_temp_avg_obj1 = dff[dff['Position'].isin([3, 4])]['Temperature (°C)'].mean()
-
-# Insight 2: Shift & Alarm Distribution
-pos1_pagi = dff[(dff['Position'] == 1) & (dff['hour'].between(6, 17, inclusive='both'))]
-pos1_sore = dff[(dff['Position'] == 1) & (~dff['hour'].between(6, 17, inclusive='both'))]
-pos3_pagi = dff[(dff['Position'] == 3) & (dff['hour'].between(6, 17, inclusive='both'))]
-pos3_sore = dff[(dff['Position'] == 3) & (~dff['hour'].between(6, 17, inclusive='both'))]
-
-# Insight 3: Korelasi
-corr_p_t_front = safe_corr(front_df['Temperature (°C)'], front_df['Pressure (psi)'])
-corr_p_t_rear = safe_corr(rear_df['Temperature (°C)'], rear_df['Pressure (psi)'])
-
-high_temp_front = front_df[front_df['Temperature (°C)'] >= 52]
-red_high_pressure_count = high_temp_front[high_temp_front['Alarm Status'] == 'Red High Pressure'].shape[0]
-
-if not front_df.empty and (front_df['Speed (km/h)'] > 0).any():
-    front_df_filtered = front_df[front_df['Speed (km/h)'] > 0].copy()
-    front_df_filtered['Temp_Speed_Ratio'] = front_df_filtered['Temperature (°C)'] / front_df_filtered['Speed (km/h)']
-    corr_p_tv_front = safe_corr(front_df_filtered['Pressure (psi)'], front_df_filtered['Temp_Speed_Ratio'])
-else:
-    corr_p_tv_front = 0.0
-
-# Insight 4: Spatial
-if not valid_gps.empty:
-    zone_counts_obj4 = valid_gps[valid_gps['is_alarm'] == 1]['Zone'].value_counts()
-    top_zone_obj4 = zone_counts_obj4.index[0] if not zone_counts_obj4.empty else "N/A"
-    top_zone_count_obj4 = zone_counts_obj4.iloc[0] if not zone_counts_obj4.empty else 0
-    total_alarms_obj4 = valid_gps[valid_gps['is_alarm'] == 1].shape[0]
-    percentage_obj4 = (top_zone_count_obj4 / total_alarms_obj4) * 100 if total_alarms_obj4 > 0 else 0
-    front_alarms_obj4 = valid_gps[(valid_gps['is_alarm'] == 1) & (valid_gps['Position'].isin([1, 2]))].shape[0]
-    rear_alarms_obj4 = valid_gps[(valid_gps['is_alarm'] == 1) & (valid_gps['Position'].isin([3, 4]))].shape[0]
-    total_alarms_obj4_total = front_alarms_obj4 + rear_alarms_obj4
-    if total_alarms_obj4_total > 0:
-        front_percentage_obj4 = (front_alarms_obj4 / total_alarms_obj4_total) * 100
-    else:
-        front_percentage_obj4 = 0
-else:
-    top_zone_obj4 = "N/A"
-    percentage_obj4 = 0
-    front_percentage_obj4 = 0
-
-# Gabungkan semua 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_obj1:.1f} psi) and higher temperature ({front_temp_avg_obj1:.1f}°C) due to higher stress from braking/steering.
-   • Rear tyres (Pos 3 & 4) show higher pressure ({rear_pressure_avg_obj1:.1f} psi) and lower temperature ({rear_temp_avg_obj1:.1f}°C), indicating stable operation.
+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.
@@ -1335,7 +1238,7 @@ insight_text = f"""
    • 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_p_t_front:.2f}) vs rear (r = {corr_p_t_rear:.2f}).
+   • 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.
 
@@ -1351,12 +1254,10 @@ try:
     prompt = f"""
 Role: Fleet Operations Risk Analyst
 Insights:
-- High-risk zone: {top_zone_obj4} ({percentage_obj4:.1f}% of alarms)
+- High-risk zone: {top_zone} ({percentage_obj4:.1f}% of alarms)
 - Front tyres: {front_percentage_obj4:.1f}% of total alarms
 - Peak alarm hour: {dominant_hour}:00 ({dominant_percentage:.1f}%)
 - Front tyre pressure–temperature correlation r = {corr_p_t_front:.2f}
-- At temperatures ≥52°C, {red_high_pressure_count} Red High Pressure alarms
-- Strong correlation between temperature and pressure in front tyres (r = {corr_p_t_front:.2f})
 Task:
 Generate:
 1. Business Recommendations
@@ -1382,11 +1283,11 @@ Rules:
     if recommendation_text == "":
         recommendation_text = f"""1. Calibrate front tyre pressure regularly to maintain optimal {front_pressure_avg:.1f} psi and prevent over-inflation.
 <br>
-2. Implement operational restrictions during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) in {top_zone_obj4} to reduce alarm frequency.
+2. Implement operational restrictions during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) in {top_zone} to reduce alarm frequency.
 <br>
 3. Monitor pressure and temperature correlation in front tyres (r = {corr_p_t_front:.2f}) to prevent overheating and premature wear.
 <br>
-4. Restrict vehicle access to {top_zone_obj4} until pavement maintenance is completed, as it contributes to {percentage_obj4:.1f}% of alarms."""
+4. Restrict vehicle access to {top_zone} until pavement maintenance is completed, as it contributes to {percentage_obj4:.1f}% of alarms."""
     if risk_mitigation_text == "":
         risk_mitigation_text = f"""1. Adjust front tyre load distribution to reduce {front_temp_avg:.1f}°C temperature and prevent overheating.
 <br>
@@ -1394,49 +1295,50 @@ Rules:
 <br>
 3. Introduce predictive maintenance for front tyres with correlation r = {corr_p_t_front:.2f} to prevent unplanned downtime.
 <br>
-4. Implement real-time monitoring in {top_zone_obj4} where {percentage_obj4:.1f}% of alarms are concentrated."""
+4. Implement real-time monitoring in {top_zone} where {percentage_obj4:.1f}% of alarms are concentrated."""
 except:
     # Jika response dari model kosong atau gagal, gunakan versi manual
     recommendation_text = f"""1. Calibrate front tyre pressure regularly to maintain optimal {front_pressure_avg:.1f} psi and prevent over-inflation.
 <br>
-2. Implement operational restrictions during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) in {top_zone_obj4} to reduce alarm frequency.
+2. Implement operational restrictions during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) in {top_zone} to reduce alarm frequency.
 <br>
 3. Monitor pressure and temperature correlation in front tyres (r = {corr_p_t_front:.2f}) to prevent overheating and premature wear.
 <br>
-4. Restrict vehicle access to {top_zone_obj4} until pavement maintenance is completed, as it contributes to {percentage_obj4:.1f}% of alarms."""
+4. Restrict vehicle access to {top_zone} until pavement maintenance is completed, as it contributes to {percentage_obj4:.1f}% of alarms."""
     # Risk Mitigation
     risk_mitigation_text = f"""1. Adjust front tyre load distribution to reduce {front_temp_avg:.1f}°C temperature and prevent overheating.
 <br>
 2. Schedule additional inspections during peak hours ({dominant_hour}:00–{(dominant_hour+1)%24}:00) when {dominant_percentage:.1f}% of alarms occur.
 <br>
 3. Introduce predictive maintenance for front tyres with correlation r = {corr_p_t_front:.2f} to prevent unplanned downtime.
-4. Implement real-time monitoring in {top_zone_obj4} where {percentage_obj4:.1f}% of alarms are concentrated."""
+<br>
+4. Implement real-time monitoring in {top_zone} where {percentage_obj4:.1f}% of alarms are concentrated."""
 
 # ============== SUBHEADER + BOX 1: INSIGHT ==============
-st.markdown('<h4 style="text-align:center; margin:10px 0 5px 0; font-weight:bold;">INSIGHT</h4>', unsafe_allow_html=True)
+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">
+    <div class="content" style="text-align:left;">
 {insight_text.strip()}
     </div>
 </div>
 """, unsafe_allow_html=True)
 
 # ============== SUBHEADER + BOX 2: RECOMMENDATION ==============
-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:left; margin:15px 0 5px 0; font-weight:bold;">RECOMMENDATION</h4>', unsafe_allow_html=True)
 st.markdown(f"""
 <div class="insight-box">
-    <div class="content">
+    <div class="content" style="text-align:left;">
 {recommendation_text.strip()}
     </div>
 </div>
 """, unsafe_allow_html=True)
 
 # ============== SUBHEADER + BOX 3: RISK MITIGATION ==============
-st.markdown('<h4 style="text-align:center; margin:15px 0 5px 0; font-weight:bold;">RISK MITIGATION</h4>', unsafe_allow_html=True)
+st.markdown('<h4 style="text-align:left; margin:15px 0 5px 0; font-weight:bold;">RISK MITIGATION</h4>', unsafe_allow_html=True)
 st.markdown(f"""
 <div class="insight-box">
-    <div class="content">
+    <div class="content" style="text-align:left;">
 {risk_mitigation_text.strip()}
     </div>
 </div>