Update app.py

app.py

@@ -1999,7 +1999,7 @@ def extract_agentic_insights_v5(df: pd.DataFrame):
         "obj6_top2_categories": [],
     }
 
-    # === 1. 9 lokasi rasio terendah ===
+    # === 1. 9 locations with lowest finding-to-reporter ratio ===
     if {'nama_lokasi_full', 'creator_nid', 'created_at', 'kode_temuan'}.issubset(df.columns):
         calc = df[['nama_lokasi_full', 'creator_nid', 'created_at', 'kode_temuan']].copy()
         calc['created_at'] = pd.to_datetime(calc['created_at'], errors='coerce')
@@ -2015,7 +2015,7 @@ def extract_agentic_insights_v5(df: pd.DataFrame):
         lowest_9 = loc_avg.nsmallest(9)
         dev["lowest_ratio_9_locs"] = [(loc, round(ratio, 3)) for loc, ratio in lowest_9.items()]
 
-    # === 2a: divisi — rasio temuan/orang terendah (Obj 3a) ===
+    # === 2a: Division — lowest finding-to-reporter ratio (Obj 3a) ===
     if {'nama', 'creator_nid', 'created_at', 'kode_temuan'}.issubset(df.columns):
         calc = df[['nama', 'creator_nid', 'created_at', 'kode_temuan']].copy()
         calc['bulan'] = pd.to_datetime(calc['created_at']).dt.to_period('M')
@@ -2031,7 +2031,7 @@ def extract_agentic_insights_v5(df: pd.DataFrame):
             val = round(div_ratio.min(), 2)
             dev["obj3a_lowest_div"] = (name, val)
 
-    # === 2b: eksekutor — lead time terpanjang (Obj 3b) ===
+    # === 2b: Executor — longest average resolution time (Obj 3b) ===
     if 'days_to_close' in df.columns:
         valid = df[df['days_to_close'].notna() & (df['days_to_close'] >= 0)]
         exec_col = 'nama_pic' if 'nama_pic' in valid.columns else 'creator_name'
@@ -2042,7 +2042,7 @@ def extract_agentic_insights_v5(df: pd.DataFrame):
                 val = round(lead.max(), 1)
                 dev["obj3b_slowest_executor"] = (name, val)
 
-    # === 2c: reporter — frekuensi terendah (Obj 3c) ===
+    # === 2c: Reporter — lowest reporting frequency (Obj 3c) ===
     if {'creator_name', 'created_at'}.issubset(df.columns):
         calc = df[['creator_name', 'created_at']].copy()
         calc['bulan'] = pd.to_datetime(calc['created_at']).dt.to_period('M')
@@ -2054,7 +2054,7 @@ def extract_agentic_insights_v5(df: pd.DataFrame):
             val = round(avg.min(), 2)
             dev["obj3c_lowest_reporter"] = (name, val)
 
-    # === 2d: divisi — lead time terpanjang (Obj 3d) ===
+    # === 2d: Division — longest average resolution time (Obj 3d) ===
     if 'days_to_close' in df.columns and 'nama' in df.columns:
         valid = df[df['days_to_close'].notna() & (df['days_to_close'] >= 0)]
         if not valid.empty:
@@ -2064,14 +2064,14 @@ def extract_agentic_insights_v5(df: pd.DataFrame):
                 val = round(lead.max(), 1)
                 dev["obj3d_slowest_div"] = (name, val)
 
-    # === 3. Komposisi non-Positive ===
+    # === 3. Composition of non-Positive findings ===
     if 'temuan_kategori' in df.columns:
         cnt = df['temuan_kategori'].value_counts(normalize=True) * 100
         dev["obj4_unsafe_condition_pct"] = round(cnt.get("Unsafe Condition", 0), 1)
         dev["obj4_unsafe_action_pct"] = round(cnt.get("Unsafe Action", 0), 1)
         dev["obj4_near_miss_pct"] = round(cnt.get("Near Miss", 0), 1)
 
-    # === 4. Kuadran Risiko (X=20, Y=3) ===
+    # === 4. Risk Quadrants (X=20 findings/month, Y=3 days avg lead time) ===
     X_LIMIT, Y_LIMIT = 20, 3
     if {'nama', 'created_at', 'days_to_close', 'kode_temuan'}.issubset(df.columns):
         calc = df.copy()
@@ -2087,7 +2087,7 @@ def extract_agentic_insights_v5(df: pd.DataFrame):
             elif r['Finding Count'] < X_LIMIT and r['Avg Lead Time'] >= Y_LIMIT:
                 dev["obj5_q2_divs"].append(r['nama'])
 
-    # === 5. Top 2 non-Positive category ===
+    # === 5. Top 2 non-Positive categories (avg per month) ===
     if {'kategori', 'temuan_kategori', 'created_at'}.issubset(df.columns):
         nonpos = df[df['temuan_kategori'] != 'Positive']
         if not nonpos.empty:
@@ -2101,100 +2101,101 @@ def extract_agentic_insights_v5(df: pd.DataFrame):
 
 dev = extract_agentic_insights_v5(df_filtered)
 
-# === INSIGHT BUILD ===
+# === INSIGHT SUMMARY ===
 insight_lines = []
 
-# 1. 9 lokasi terendah (semua)
+# 1. 9 lowest-ratio locations
 if dev["lowest_ratio_9_locs"]:
     loc_list = ", ".join([f"<strong>{loc}</strong> ({ratio})" for loc, ratio in dev["lowest_ratio_9_locs"]])
-    insight_lines.append(f"1. Sembilan lokasi dengan rasio temuan/orang <em>terendah</em> (<0.5): {loc_list}.")
+    insight_lines.append(f"1. Nine locations with the <em>lowest</em> finding-to-reporter ratio: {loc_list}.")
 
-# 2. Gabungan 2a–2d → satu insight agentic AI
+# 2. Combined lowest-performer insights (3a–3d)
 parts = []
 if dev["obj3a_lowest_div"]:
-    parts.append(f"divisi <strong>{dev['obj3a_lowest_div'][0]}</strong> (rasio {dev['obj3a_lowest_div'][1]})")
+    parts.append(f"division <strong>{dev['obj3a_lowest_div'][0]}</strong> (ratio: {dev['obj3a_lowest_div'][1]})")
 if dev["obj3c_lowest_reporter"]:
-    parts.append(f"reporter <strong>{dev['obj3c_lowest_reporter'][0]}</strong> ({dev['obj3c_lowest_reporter'][1]}/bln)")
+    parts.append(f"reporter <strong>{dev['obj3c_lowest_reporter'][0]}</strong> ({dev['obj3c_lowest_reporter'][1]} findings/month)")
 if dev["obj3d_slowest_div"]:
-    parts.append(f"divisi <strong>{dev['obj3d_slowest_div'][0]}</strong> ({dev['obj3d_slowest_div'][1]} hari)")
+    parts.append(f"division <strong>{dev['obj3d_slowest_div'][0]}</strong> (avg. resolution: {dev['obj3d_slowest_div'][1]} days)")
 if dev["obj3b_slowest_executor"]:
-    parts.append(f"eksekutor <strong>{dev['obj3b_slowest_executor'][0]}</strong> ({dev['obj3b_slowest_executor'][1]} hari)")
+    parts.append(f"executor <strong>{dev['obj3b_slowest_executor'][0]}</strong> (avg. resolution: {dev['obj3b_slowest_executor'][1]} days)")
 
 if parts:
     joined = "; ".join(parts)
     insight_lines.append(
-        f"2. Sistem mendeteksi <em>agency-capacity mismatch</em>: {joined}. "
-        "Ini menunjukkan ketidakseimbangan antara kapasitas pelaporan dan kapasitas resolusi — dua pilar utama ekosistem <em>agentic safety</em>."
+        f"2. Agentic AI detection flags uneven operational capacity: {joined}. "
+        "This suggests imbalance in reporting engagement and resolution efficiency across divisions and individuals."
     )
 
-# 3. Komposisi non-Positive
+# 3. Non-Positive composition
 uc, ua, nm = dev["obj4_unsafe_condition_pct"], dev["obj4_unsafe_action_pct"], dev["obj4_near_miss_pct"]
 if uc + ua + nm > 0:
     insight_lines.append(
-        f"3. Komposisi non-Positive: Unsafe Condition ({uc}%), Unsafe Action ({ua}%), Near Miss ({nm}%). "
-        "Proporsi Near Miss masih rendah — indikasi <em>under-reporting of close calls</em>."
+        f"3. Non-Positive finding composition: Unsafe Condition ({uc}%), Unsafe Action ({ua}%), Near Miss ({nm}%)."
     )
 
-# 4. Kuadran risiko
+# 4. Risk Quadrants
 if dev["obj5_q1_divs"] or dev["obj5_q2_divs"]:
-    q1 = ", ".join([f"<strong>{d}</strong>" for d in dev["obj5_q1_divs"][:3]])
-    q2 = ", ".join([f"<strong>{d}</strong>" for d in dev["obj5_q2_divs"][:3]])
+    q1 = ", ".join([f"<strong>{d}</strong>" for d in dev["obj5_q1_divs"][:5]])  # cap for readability
+    q2 = ", ".join([f"<strong>{d}</strong>" for d in dev["obj5_q2_divs"][:5]])
     q1_str = q1 if q1 else "—"
     q2_str = q2 if q2 else "—"
-    insight_lines.append(f"4. Divisi risiko tinggi (Kuadran I): {q1_str}. Divisi risiko tersembunyi (Kuadran II): {q2_str}.")
+    insight_lines.append(f"4. High-risk divisions (Quadrant I): {q1_str}. Hidden-risk divisions (Quadrant II): {q2_str}.")
 
-# 5. Top 2 kategori
+# 5. Top non-Positive categories
 if dev["obj6_top2_categories"]:
     c1, c2 = dev["obj6_top2_categories"]
-    insight_lines.append(f"5. Dua kategori non-Positive paling sering: <strong>{c1[0]}</strong> ({c1[1]}/bulan) dan <strong>{c2[0]}</strong> ({c2[1]}/bulan).")
+    insight_lines.append(
+        f"5. Top two recurring non-Positive categories: <strong>{c1[0]}</strong> ({c1[1]}/month) and <strong>{c2[0]}</strong> ({c2[1]}/month)."
+    )
 
 insight_text = "<br>".join(insight_lines)
 
-# === REKOMENDASI & MITIGASI (Agentic AI Style) ===
+# === RECOMMENDATIONS & MITIGATION (Agentic AI Style) ===
 recs = []
 
-# 1
+# Rec 1: Low-ratio locations
 if dev["lowest_ratio_9_locs"]:
     recs.append({
         "point": "1",
-        "rec": "Luncurkan <em>Agency Activation Sprint</em> di 9 lokasi terendah: PIC Area wajib lakukan 1 inspeksi spot/minggu.",
-        "mit": "Aktifkan QR code checklist 3-menit + notifikasi WhatsApp otomatis. Target: rasio ≥0.5 dalam 45 hari."
+        "rec": "Launch <em>Agency Activation Sprint</em> across the 9 lowest-ratio locations: Area PICs to conduct ≥1 spot inspection/week.",
+        "mit": "Deploy 3-minute QR-code checklists + automated WhatsApp reminders. Target: ratio ≥ 0.5 within 45 days."
     })
 
-# 2 → gabungan 2a–2d
+# Rec 2: Capacity imbalance
 if parts:
     recs.append({
         "point": "2",
-        "rec": "Aktifkan <em>Agentic Capacity Dashboard</em>: monitor rasio & lead time per individu/divisi secara real-time.",
-        "mit": "Jika deviasi >20% dari baseline → picu <em>auto-coaching alert</em> ke PIC Area & Safety Manager, disertai best practice dari division benchmark."
+        "rec": "Activate <em>Agentic Capacity Dashboard</em>: real-time monitoring of finding/reporter ratios and resolution lead times per division/individual.",
+        "mit": "Auto-trigger <em>coaching alerts</em> to Area PICs & Safety Managers if deviation >20% from divisional baseline, with peer benchmarking insights."
     })
 
-# 3
+# Rec 3: Category validation
 if uc + ua + nm > 0:
     recs.append({
         "point": "3",
-        "rec": "Luncurkan <em>Near Miss Amplifier</em>: setiap laporan Unsafe Condition wajib diikuti ≥1 Near Miss terkait.",
-        "mit": "Sistem blokir submit jika tidak memenuhi. Tampilkan <em>Near Miss Leaderboard</em> di dashboard tim."
+        "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."
     })
 
-# 4
+# Rec 4: Quadrant-based intervention
 if dev["obj5_q1_divs"] or dev["obj5_q2_divs"]:
     recs.append({
         "point": "4",
-        "rec": "Alokasikan <em>dedicated safety crew</em> untuk Kuadran I; terapkan <em>One Finding, One Day</em> untuk Kuadran II.",
-        "mit": "Jika masuk Kuadran I/II ≥2 bulan berturut-turut → sistem auto-generate <em>executive escalation report</em> ke VP Operasi."
+        "rec": "Assign <em>dedicated safety crews</em> to Quadrant I divisions; enforce <em>One Finding, One Day</em> closure policy for Quadrant II.",
+        "mit": "Auto-generate executive escalation reports to VP Operations if any division remains in QI/QII for ≥2 consecutive months."
     })
 
-# 5
+# Rec 5: Top categories RCA
 if dev["obj6_top2_categories"]:
     c1, c2 = dev["obj6_top2_categories"]
     recs.append({
         "point": "5",
-        "rec": f"Bentuk <em>RCA Task Force</em> lintas fungsi (SIPIL, ELEKTRIKAL, K3, Kontraktor) untuk {c1[0]} & {c2[0]}.",
-        "mit": "Revisi spesifikasi teknis & template tender: semua penawaran wajib menyertakan mitigasi berbasis temuan historis."
+        "rec": f"Form cross-functional <em>RCA Task Force</em> (Civil, Electrical, HSE, Contractors) for <strong>{c1[0]}</strong> and <strong>{c2[0]}</strong>.",
+        "mit": "Update technical specifications & tender templates: all bids must include mitigations based on historical findings for these categories."
     })
 
-# === TAMPILKAN ===
+# === RENDER OUTPUT ===
 st.markdown(
     f"""
     <div class="card" style="
@@ -2212,7 +2213,7 @@ st.markdown(
     unsafe_allow_html=True
 )
 
-# Tabel rekomendasi
+# Recommendations table
 if recs:
     rows = []
     for r in recs:
@@ -2237,7 +2238,7 @@ if recs:
             <thead>
                 <tr style="background-color:#e8f5ee;">
                     <th style="padding:10px; text-align:center; border:1px solid #ccc;">#</th>
-                    <th style="padding:10px; text-align:left; border:1px solid #ccc;">Recommended Actions</th>
+                    <th style="padding:10px; text-align:left; border:1px solid #ccc;">Recommended Action</th>
                     <th style="padding:10px; text-align:left; border:1px solid #ccc;">Risk Mitigation Strategy</th>
                 </tr>
             </thead>