Update app.py

app.py

@@ -519,7 +519,7 @@ if 'temuan_kode_distrik' in df_local.columns:
     col1, col2 = st.columns(2)
 
     with col1:
-        st.markdown("<h5>Unit Pembangkit: Avg Monthly Finding by Company</h5>", unsafe_allow_html=True)
+        st.markdown("<h5>Unit Pembangkit: Monthly Finding by Company</h5>", unsafe_allow_html=True)
         fig_pg = create_polar_bar_chart(avg_ratio_pg, 'PG')
         if fig_pg:
             st.plotly_chart(fig_pg, use_container_width=True)
@@ -539,7 +539,7 @@ if 'temuan_kode_distrik' in df_local.columns:
             st.warning("No data for PG area.")
 
     with col2:
-        st.markdown("<h5>Unit Maintenance: Avg Monthly Finding by Company</h5>", unsafe_allow_html=True)
+        st.markdown("<h5>Unit Maintenance: Monthly Finding by Company</h5>", unsafe_allow_html=True)
         fig_um = create_polar_bar_chart(avg_ratio_um, 'UM')
         if fig_um:
             st.plotly_chart(fig_um, use_container_width=True)
@@ -549,7 +549,7 @@ if 'temuan_kode_distrik' in df_local.columns:
                 st.markdown("### Insight")
                 st.markdown(
                     f"<div class='ai-insight'>"
-                    f"Across all companies, the finding-per-person ratio is similar. In the UM Area "
+                    f"Across all companies, the finding-per-person ratio is similar in the UM Area "
 
                     f"</div>",
                     unsafe_allow_html=True
@@ -614,20 +614,35 @@ avg_ratio_per_location = avg_ratio_per_location.dropna(subset=['avg_monthly_rati
 
 # Plot Treemap dengan gradasi warna
 if not avg_ratio_per_location.empty:
-    # Gunakan color_continuous_scale untuk gradasi warna: merah → kuning → hijau
     fig_treemap = px.treemap(
         avg_ratio_per_location,
-        path=['nama_lokasi_full'],  # Path untuk hierarki (hanya satu level di sini)
-        values='avg_monthly_ratio', # Nilai yang menentukan ukuran area
-        title='Avg Monthly Finding by Location',
-        labels={'avg_monthly_ratio': 'Avg Monthly Finding/Person Ratio', 'nama_lokasi_full': 'Location'},
-        color='avg_monthly_ratio', # Warna berdasarkan nilai rasio (bukan kategori)
+        path=['nama_lokasi_full'],
+        values='avg_monthly_ratio',
+        title='Monthly Finding by Location',
+        labels={
+            'avg_monthly_ratio': 'Monthly Finding/Person Ratio',
+            'nama_lokasi_full': 'Location'
+        },
+        color='avg_monthly_ratio',
         color_continuous_scale=[
-            [0.0, '#D32F2F'],  # Merah untuk rendah
-            [0.5, '#FFB300'],  # Kuning untuk sedang
-            [1.0, '#4CAF50']   # Hijau untuk tinggi
+            [0.0, '#D32F2F'], 
+            [0.5, '#FFB300'], 
+            [1.0, '#4CAF50']
         ]
     )
+
+    # --- Set global font size 12 ---
+    fig_treemap.update_layout(
+        font=dict(size=12)
+    )
+
+    # --- Set label font size inside the treemap ---
+    fig_treemap.update_traces(
+        textfont=dict(size=12)
+    )
+
+    st.plotly_chart(fig_treemap)
+
     # Format hover
     fig_treemap.update_traces(
         hovertemplate="<b>%{label}</b><br>Avg Ratio: %{value:.2f}<extra></extra>"
@@ -650,7 +665,7 @@ if not avg_ratio_per_location.empty:
             # f"Locations with <span style='color:#D32F2F; font-weight:bold;'>red</span> color have a low ratio, indicating lower activity levels or potentially under-reporting. "
             f"<strong>{top_location['nama_lokasi_full']}</strong> shows the highest activity level "
             f"(<strong>{top_location['avg_monthly_ratio']:.2f}</strong>). "
-            f"<strong>{low_location['nama_lokasi_full']}</strong> shows the lowest activity level "
+            f"<strong>{low_location['nama_lokasi_full']}</strong> is one of areas with the lowest activity level "
             f"(<strong>{low_location['avg_monthly_ratio']:.2f}</strong>). "
             f"Areas with high activity (green) warrant inspection into the underlying causes of frequent findings. "
             f"Areas with low activity (red) should be reviewed to ensure reporting completeness and identify any hidden risks."
@@ -805,7 +820,7 @@ col_3a, col_3c = st.columns(2)
 
 # ─── 3a: Reporter by Division (Rasio Temuan/Orang) ───────────────────────────
 with col_3a:
-    st.markdown("<h5 style='text-align:center;'>3a. Avg Finding/Person Ratio by Division (Reporter)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5 style='text-align:center;'>3a. Finding/Person Ratio by Division (Reporter)</h5>", unsafe_allow_html=True)
     if avg_ratio_per_nama.empty:
         st.warning("No data for division-level reporter analysis.")
     else:
@@ -825,7 +840,7 @@ with col_3a:
         fig = px.bar(
             subset, x='avg_monthly_ratio', y='nama', orientation='h',
             title=f'{sort_opt} Divisions',
-            labels={'avg_monthly_ratio': 'Avg Monthly Ratio', 'nama': 'Division'},
+            labels={'avg_monthly_ratio': 'Monthly Ratio', 'nama': 'Division'},
             color='color', color_discrete_map={c: c for c in subset['color'].unique()},
             text=subset['avg_monthly_ratio'].apply(lambda x: f'{x:.2f}')
         )
@@ -839,8 +854,9 @@ with col_3a:
             best, worst = full_sorted.iloc[0]['nama'], full_sorted.iloc[-1]['nama']
             st.markdown(
                 f"<div class='ai-insight'>"
-                f"<strong>Insight:</strong> Division reporting efficiency ranges from {min_r:.2f} to {max_r:.2f} (avg: {mean_r:.2f}). "
-                f"<strong>{best}</strong> leads; <strong>{worst}</strong> lags. "
+                f"<strong>Insight:</strong> Division reporting ratio ranges from {min_r:.2f} to {max_r:.2f} (avg: {mean_r:.2f}). "
+                f"<strong>{best}</strong> leads."
+                 # <strong>{worst}</strong> lags.
                 f"<strong>Recommendation:</strong> Benchmark processes from {best}; assess capacity/tooling gaps in {worst}."
                 f"</div>",
                 unsafe_allow_html=True
@@ -849,7 +865,7 @@ with col_3a:
 
 # ─── 3c: Reporter by Individual ──────────────────────────────────────────────
 with col_3c:
-    st.markdown("<h5 style='text-align:center;'>3c. Avg Monthly Findings per Reporter (Individual)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5 style='text-align:center;'>3c. Monthly Findings per Reporter (Individual)</h5>", unsafe_allow_html=True)
     if avg_rate_per_creator.empty:
         st.warning("No data for individual reporter analysis.")
     else:
@@ -867,7 +883,7 @@ with col_3c:
         fig = px.bar(
             subset, x='avg_monthly_rate', y='creator_name', orientation='h',
             title=f'{sort_opt} Reporters',
-            labels={'avg_monthly_rate': 'Avg Monthly Findings', 'creator_name': 'Reporter'},
+            labels={'avg_monthly_rate': 'Monthly Findings', 'creator_name': 'Reporter'},
             color='color', color_discrete_map={c: c for c in subset['color'].unique()},
             text=subset['avg_monthly_rate'].apply(lambda x: f'{x:.2f}')
         )
@@ -893,7 +909,7 @@ col_3b, col_3d = st.columns(2)
 
 # ─── 3b: Executor by Division (Lead Time) ────────────────────────────────────
 with col_3b:
-    st.markdown("<h5 style='text-align:center;'>3b. Avg Lead Time by Division (Executor)</h5>", unsafe_allow_html=True)
+    st.markdown("<h5 style='text-align:center;'>3b. Monthly Lead Time by Division (Executor)</h5>", unsafe_allow_html=True)
     if avg_leadtime_nama.empty:
         st.warning("No data for division-level executor analysis.")
     else:
@@ -929,7 +945,7 @@ with col_3b:
             y='nama', 
             orientation='h',
             title=sort_opt,
-            labels={'avg_monthly_leadtime': 'Avg Lead Time (Days)', 'nama': 'Division'},
+            labels={'avg_monthly_leadtime': 'Monthly Lead Time (Days)', 'nama': 'Division'},
             color='color', 
             color_discrete_map={c: c for c in subset['color'].unique()},
             text=subset['avg_monthly_leadtime'].apply(lambda x: f'{x:.1f}')
@@ -961,7 +977,7 @@ with col_3b:
 # ─── 3d: Executor by Individual ──────────────────────────────────────────────
 with col_3d:
     st.markdown(
-        f"<h5 style='text-align:center;'>3d. Avg Lead Time per Executor ({EXECUTOR_INDIV_COL})</h5>", 
+        f"<h5 style='text-align:center;'>3d. Monthly Lead Time per Executor (Individual)</h5>", 
         unsafe_allow_html=True
     )
     if avg_leadtime_per_indiv.empty:
@@ -996,7 +1012,7 @@ with col_3d:
             y=id_col, 
             orientation='h',
             title=sort_opt,
-            labels={'avg_monthly_leadtime': 'Avg Lead Time (Days)', id_col: 'Executor'},
+            labels={'avg_monthly_leadtime': 'Monthly Lead Time (Days)', id_col: 'Executor'},
             color='color', 
             color_discrete_map={c: c for c in subset['color'].unique()},
             text=subset['avg_monthly_leadtime'].apply(lambda x: f'{x:.1f}')
@@ -1032,7 +1048,7 @@ try:
 except ImportError:
     WORDCLOUD_AVAILABLE = False
 
-st.markdown("<h3 class='section-title'>OBJECTIVE 4 - What Unsafe Issues Appear Most Often?</h3>", 
+st.markdown("<h3 class='section-title'>OBJECTIVE 4 - Unsafe Issues: Which One is the Most Often?</h3>", 
             unsafe_allow_html=True)
 
 # 🔹 Fungsi untuk membuat judul seragam
@@ -1220,7 +1236,7 @@ try:
         operator_lead = (
             df_local_matrix.groupby('nama')['lead_time_days']
             .mean()
-            .reset_index(name='Average Lead Time')
+            .reset_index(name='Lead Time')
         )
         # ============================
         # 6. Merge Risk Matrix
@@ -1307,7 +1323,7 @@ import numpy as np
 import pandas as pd
 
 # =================== OBJECTIVE 6 - Predictive Dashboard & Early Warning Signals ===================
-st.markdown("<h3 class='section-title'>OBJECTIVE 6 — Predictive Dashboard & Early Warning Signals</h3>", unsafe_allow_html=True)
+st.markdown("<h3 class='section-title'>OBJECTIVE 6 — Prediction for Early Warning Signals: Which Ones Have Less Future Inspections?</h3>", unsafe_allow_html=True)
 
 # ✅ Enhanced CSS + Minimal Sortable JS
 st.markdown("""
@@ -1915,7 +1931,7 @@ if not df_category.empty:
         # title=dict(text="<b>Issue Category Trend vs Frequency (Non-Positive)</b>", x=0.5, y=0.95),
         xaxis=dict(
             title="Category",
-            tickangle=45,
+            tickangle=135,
             showgrid=True,
             gridcolor="#e0e0e0",
             gridwidth=1,
@@ -1940,7 +1956,6 @@ if not df_category.empty:
     fig.add_annotation(
         xref="paper", yref="paper",
         x=0.05, y=0.95,
-        text="Trend<br>(Slope)",
         showarrow=False,
         font=dict(size=12, color="#003DA5"),
         textangle=-90,
@@ -1949,7 +1964,7 @@ if not df_category.empty:
     fig.add_annotation(
         xref="paper", yref="paper",
         x=0.5, y=0.05,
-        text="Category",
+        text="",
         showarrow=False,
         font=dict(size=12, color="#003DA5"),
         align="center"
@@ -1957,7 +1972,7 @@ if not df_category.empty:
     fig.add_annotation(
         xref="paper", yref="paper",
         x=0.95, y=0.95,
-        text="<b>Legend of Frequency</b><br>• 10<br>•• 20<br>••• 30<br>•••• 40",
+        text="<b>Number of Findings</b><br>• 10<br>•• 20<br>••• 30<br>•••• 40",
         showarrow=False,
         font=dict(size=12, color="#003DA5"),
         align="left"
@@ -1965,7 +1980,7 @@ if not df_category.empty:
     fig.add_annotation(
         xref="paper", yref="paper",
         x=0.05, y=0.1,
-        text="<b>Semakin tinggi = semakin sering ditemukan deviasi</b><br><b>Semakin besar = semakin banyak ditemukan deviasi</b><br><b>Non-positive</b>",
+        text="<b>Semakin tinggi = semakin sering ditemukan deviasi</b><br><b>Semakin besar = semakin banyak ditemukan deviasi</b><br><b></b>",
         showarrow=False,
         font=dict(size=12, color="#003DA5"),
         align="left"
@@ -2146,7 +2161,7 @@ import math
 #   OBJECTIVE 7 — INSIGHT & RECOMMENDATION (LLM FIRST, RULE-BASED IF FAIL)
 # =====================================================================
 
-st.markdown("<h3 class='section-title'>OBJECTIVE 7 — Insight and Recommendation</h3>", unsafe_allow_html=True)
+st.markdown("<h3 class='section-title'>OBJECTIVE 7 — Risk Mitigation: What are the Insights and Recommendations?</h3>", unsafe_allow_html=True)
 
 
 # ============================================================
@@ -2354,7 +2369,7 @@ if dev["obj3b_slowest_executor"]:
     parts.append(f"executor <strong>{dev['obj3b_slowest_executor'][0]}</strong> ({dev['obj3b_slowest_executor'][1]} days)")
 
 if parts:
-    insight_lines.append("2. Agentic AI detection flags uneven operational capacity: " + "; ".join(parts))
+    insight_lines.append("2. Uneven operational capacity: " + "; ".join(parts))
 
 uc, ua, nm = dev["obj4_unsafe_condition_pct"], dev["obj4_unsafe_action_pct"], dev["obj4_near_miss_pct"]
 if uc + ua + nm > 0:
@@ -2369,7 +2384,7 @@ if dev["obj6_top2_categories"]:
     c1, c2 = dev["obj6_top2_categories"]
     insight_lines.append(f"5. Top recurring non-Positive categories: <strong>{c1[0]}</strong> ({c1[1]}/mo) & <strong>{c2[0]}</strong> ({c2[1]}/mo).")
 
-insight_text = "<br>".join(insight_lines)
+insight_text = "<br><br>".join(insight_lines)
 
 
 # ============================================================
@@ -2387,7 +2402,7 @@ else:
         recs.append({"point":"1","rec":"Launch spot-inspection sprint at across the 9 lowest-ratio locations.","mit":"Enable 3-min QR checklist  + automated WhatsApp reminders."})
 
     if parts:
-        recs.append({"point":"2","rec":"real-time monitoring of finding/reporter ratios and resolution lead times per division/individual.","mit":"Trigger coaching alerts to Area PICs & Division"})
+        recs.append({"point":"2","rec":"Real-time monitoring of finding/reporter ratios and resolution lead times per division/individual.","mit":"Trigger coaching alerts to Area PICs & Division"})
 
     if uc + ua + nm > 0:
         recs.append({"point":"3","rec":"Enforce photo-based validation for Unsafe Condition/Action/Near Miss submissions to ensure accurate categorization.","mit":"System blocks submission if photo evidence or category justification is missing."})
@@ -2408,7 +2423,7 @@ else:
 st.markdown(
     f"""
     <div class="card" style="background-color:#f8f9fa;border-left:4px solid #003DA5;padding:16px;margin-bottom:20px;border-radius:4px;box-shadow:0 2px 4px rgba(0,0,0,0.05);">
-        <h4 style="margin-top:0;color:#FF6B6B;">Insight Summary</h4>
+        <h4 style="margin-top:0;color:#FF6B6B;">Summary</h4>
         <p style="margin-bottom:0;line-height:1.6;font-size:0.98em;">{insight_text}</p>
     </div>
     """,