Update app.py

app.py

@@ -1982,27 +1982,34 @@ else:
     st.info("No data available for non-positive issue categories with 100% coverage and positive trend.")
 
 
-# =================== OBJECTIVE 7 - Insight and Recommendation (Revised per Deviasi Aktual) ===================
-# =================== OBJECTIVE 7 - Insight and Recommendation ===================
+# =================== OBJECTIVE 7 — Insight and Recommendation (FINAL v2 — Sesuai Permintaan) ===================
 st.markdown("<h3 class='section-title'>OBJECTIVE 7 — Insight and Recommendation</h3>", unsafe_allow_html=True)
 
-def extract_deviations_for_insight(df: pd.DataFrame):
-    """Ekstrak nilai aktual dari Obj 2–6 sesuai permintaan: 9 lokasi ratio=1.0, dll."""
+def extract_deviations_v2(df: pd.DataFrame):
     dev = {
-        "obj2_ratio_1_locs": [],
-        "obj3a_lowest_div": None,
-        "obj3b_lowest_reporter": None,
-        "obj3c_slowest_div": None,
-        "obj3d_slowest_executor": None,
-        "obj4_unsafe_condition": 0.0,
-        "obj4_unsafe_action": 0.0,
-        "obj4_near_miss": 0.0,
-        "obj5_quadrant_I": [],
-        "obj5_quadrant_II": [],
-        "obj6_top2_categories": []
+        # Obj 2: lokasi dengan avg ratio ≈ 1.0
+        "obj2_optimal_locs": [],
+        # Obj 3a & 3c: rasio temuan/orang TERENDAH (divisi & reporter)
+        "obj3a_lowest_div_ratio": None,   # ('Div A', 0.12)
+        "obj3c_lowest_reporter_ratio": None,  # ('Ali', 0.05)
+        # Obj 3b & 3d: lead time TERPANJANG (divisi & eksekutor)
+        "obj3b_slowest_div_lead": None,   # ('Div B', 12.4)
+        "obj3d_slowest_executor_lead": None,  # ('Budi', 15.2)
+        # Obj 4: unsafe share
+        "obj4_uc": 0.0,
+        "obj4_ua": 0.0,
+        "obj4_nm": 0.0,
+        # Obj 6 (Panel 4): top 2 non-Positive categories (avg/month tertinggi)
+        "obj6_top2_cat": [],
+        # Obj 5: Quadrant I & II
+        "obj5_q1_divs": [],
+        "obj5_q2_divs": [],
+        # 🔥 BARU: Obj 3 (predictive panel 1–3): trend slope TERENDAH (bukan avg ratio!)
+        "obj3_most_declining_locs": [],   # 5 lokasi dengan slope paling negatif
+        "obj3_most_declining_divs": [],   # 5 divisi dengan slope paling negatif
     }
 
-    # === OBJ 2: 9 lokasi dengan ratio ≈ 1.0 ===
+    # ========= 1. Optimal Ratio (~1.0) dari Objective 2 =========
     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')
@@ -2014,12 +2021,12 @@ def extract_deviations_for_insight(df: pd.DataFrame):
         ).reset_index()
         monthly = monthly[monthly['reporters'] > 0]
         monthly['ratio'] = monthly['findings'] / monthly['reporters']
-        loc_avg = monthly.groupby('nama_lokasi_full')['ratio'].mean().reset_index()
-        near_1 = loc_avg[(loc_avg['ratio'] >= 0.95) & (loc_avg['ratio'] <= 1.05)]
-        dev["obj2_ratio_1_locs"] = near_1.nlargest(9, 'ratio')['nama_lokasi_full'].tolist()
+        loc_avg = monthly.groupby('nama_lokasi_full')['ratio'].mean()
+        near_1 = loc_avg[(loc_avg >= 0.95) & (loc_avg <= 1.05)].nlargest(9)
+        dev["obj2_optimal_locs"] = near_1.index.tolist()
 
-    # === OBJ 3a–3d: ekstrem aktual ===
-    # 3a: divisi rasio terendah
+    # ========= 2. TERENDAH — Rasio Temuan/Orang (Objective 3a & 3c) =========
+    # 3a: divisi — rasio temuan/orang
     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')
@@ -2033,9 +2040,9 @@ def extract_deviations_for_insight(df: pd.DataFrame):
         if not div_ratio.empty:
             name = div_ratio.idxmin()
             val = round(div_ratio.min(), 2)
-            dev["obj3a_lowest_div"] = (name, val)
+            dev["obj3a_lowest_div_ratio"] = (name, val)
 
-    # 3b: reporter frekuensi terendah
+    # 3c: individu — avg temuan/bulan
     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')
@@ -2045,41 +2052,49 @@ def extract_deviations_for_insight(df: pd.DataFrame):
         if not avg.empty:
             name = avg.idxmin()
             val = round(avg.min(), 2)
-            dev["obj3b_lowest_reporter"] = (name, val)
+            dev["obj3c_lowest_reporter_ratio"] = (name, val)
 
-    # 3c: divisi lead time terpanjang
-    if 'days_to_close' in df.columns and 'nama' in df.columns:
+    # ========= 3. TERPANJANG — Lead Time (Objective 3b & 3d) =========
+    if 'days_to_close' in df.columns:
         valid = df[df['days_to_close'].notna() & (df['days_to_close'] >= 0)]
-        if not valid.empty:
+        # 3b: divisi
+        if 'nama' in valid.columns:
             lead = valid.groupby('nama')['days_to_close'].mean()
             if not lead.empty:
                 name = lead.idxmax()
                 val = round(lead.max(), 1)
-                dev["obj3c_slowest_div"] = (name, val)
-
-    # 3d: eksekutor lead time terpanjang
-    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'
-        if exec_col in valid.columns:
+                dev["obj3b_slowest_div_lead"] = (name, val)
+        # 3d: eksekutor (deteksi otomatis kolom)
+        exec_col = next((c for c in ['nama_pic', 'pic', 'responsible', 'creator_name'] if c in valid.columns), None)
+        if exec_col:
             lead = valid.groupby(exec_col)['days_to_close'].mean()
             if not lead.empty:
                 name = lead.idxmax()
                 val = round(lead.max(), 1)
-                dev["obj3d_slowest_executor"] = (name, val)
+                dev["obj3d_slowest_executor_lead"] = (name, val)
 
-    # === OBJ 4: unsafe share ===
+    # ========= 4. Unsafe Share (Objective 4) =========
     if 'temuan_kategori' in df.columns:
         total = len(df)
         if total > 0:
             cnt = df['temuan_kategori'].value_counts(normalize=True) * 100
-            dev["obj4_unsafe_condition"] = round(cnt.get("Unsafe Condition", 0), 1)
-            dev["obj4_unsafe_action"] = round(cnt.get("Unsafe Action", 0), 1)
-            dev["obj4_near_miss"] = round(cnt.get("Near Miss", 0), 1)
+            dev["obj4_uc"] = round(cnt.get("Unsafe Condition", 0), 1)
+            dev["obj4_ua"] = round(cnt.get("Unsafe Action", 0), 1)
+            dev["obj4_nm"] = round(cnt.get("Near Miss", 0), 1)
 
-    # === OBJ 5: kuadran risiko (X=20, Y=3) ===
+    # ========= 5. Top 2 Bubble: Category Non-Positive (Objective 6 Panel 4) =========
+    if {'kategori', 'temuan_kategori', 'created_at'}.issubset(df.columns):
+        nonpos = df[df['temuan_kategori'] != 'Positive']
+        if not nonpos.empty:
+            start = nonpos['created_at'].min().to_period('M')
+            end = nonpos['created_at'].max().to_period('M')
+            n_months = len(pd.period_range(start=start, end=end, freq='M'))
+            cat_avg = (nonpos.groupby('kategori').size() / n_months).sort_values(ascending=False).head(2)
+            dev["obj6_top2_cat"] = [(cat, round(val, 1)) for cat, val in cat_avg.items()]
+
+    # ========= 6. Quadrant I & II (Objective 5) =========
     X_LIMIT, Y_LIMIT = 20, 3
-    if {'nama', 'created_at', 'days_to_close'}.issubset(df.columns):
+    if {'nama', 'created_at', 'days_to_close', 'kode_temuan'}.issubset(df.columns):
         calc = df.copy()
         calc['created_at'] = pd.to_datetime(calc['created_at'], errors='coerce')
         calc = calc.assign(month=calc['created_at'].dt.to_period('M').astype(str))
@@ -2089,182 +2104,238 @@ def extract_deviations_for_insight(df: pd.DataFrame):
         mat = avg_count.merge(leadtime, on='nama', how='left').fillna(0)
         for _, r in mat.iterrows():
             if r['Finding Count'] >= X_LIMIT and r['Avg Lead Time'] >= Y_LIMIT:
-                dev["obj5_quadrant_I"].append(r['nama'])
+                dev["obj5_q1_divs"].append(r['nama'])
             elif r['Finding Count'] < X_LIMIT and r['Avg Lead Time'] >= Y_LIMIT:
-                dev["obj5_quadrant_II"].append(r['nama'])
-
-    # === OBJ 6: top 2 bubble (non-Positive, Avg/Month tertinggi) ===
-    if {'kategori', 'temuan_kategori', 'created_at'}.issubset(df.columns):
-        nonpos = df[df['temuan_kategori'] != 'Positive']
-        if not nonpos.empty:
-            start = nonpos['created_at'].min().to_period('M')
-            end = nonpos['created_at'].max().to_period('M')
-            n_months = len(pd.period_range(start=start, end=end, freq='M'))
-            cat_avg = (nonpos.groupby('kategori').size() / n_months).sort_values(ascending=False).head(2)
-            dev["obj6_top2_categories"] = [(cat, round(val, 1)) for cat, val in cat_avg.items()]
+                dev["obj5_q2_divs"].append(r['nama'])
+
+    # ========= 🔥 7. TREND TERENDAH — Slope paling negatif (Objective 6 Panels 1–3) =========
+    # Lokasi (Panel 2)
+    if {'nama_lokasi_full', 'created_at'}.issubset(df.columns):
+        start = df['created_at'].min().to_period('M')
+        end = df['created_at'].max().to_period('M')
+        all_months = pd.period_range(start=start, end=end, freq='M')
+        monthly = (
+            df.groupby(['nama_lokasi_full', df['created_at'].dt.to_period('M')])
+            .size()
+            .unstack(fill_value=0)
+            .reindex(columns=all_months, fill_value=0)
+        )
+        slopes = {}
+        for loc in monthly.index:
+            ts = monthly.loc[loc].values
+            if len(ts) >= 2:
+                try:
+                    slope = np.polyfit(range(len(ts)), ts, 1)[0]
+                    slopes[loc] = slope
+                except:
+                    continue
+        top5 = sorted(slopes.items(), key=lambda x: x[1])[:5]  # paling negatif
+        dev["obj3_most_declining_locs"] = [loc for loc, _ in top5]
+
+    # Divisi (Panel 3)
+    if {'nama', 'created_at'}.issubset(df.columns):
+        monthly = (
+            df.groupby(['nama', df['created_at'].dt.to_period('M')])
+            .size()
+            .unstack(fill_value=0)
+            .reindex(columns=all_months, fill_value=0)
+        )
+        slopes = {}
+        for div in monthly.index:
+            ts = monthly.loc[div].values
+            if len(ts) >= 2:
+                try:
+                    slope = np.polyfit(range(len(ts)), ts, 1)[0]
+                    slopes[div] = slope
+                except:
+                    continue
+        top5 = sorted(slopes.items(), key=lambda x: x[1])[:5]
+        dev["obj3_most_declining_divs"] = [div for div, _ in top5]
 
     return dev
 
-# Ekstrak deviasi aktual
-dev = extract_deviations_for_insight(df_filtered)
-
-# Bangun Insight Summary — sesuai struktur: 1, 2 → 2a–2d, 3, 4a, 4b, 5
-insight_lines = []
-
-# 1. Pola umum
-insight_lines.append("1. Analisis deviasi menunjukkan ketidakseimbangan antara aktivitas inspeksi dan kapasitas resolusi.")
-
-# 2. Temuan utama (dipecah)
-if dev["obj2_ratio_1_locs"]:
-    locs = ", ".join(dev["obj2_ratio_1_locs"][:5])
-    insight_lines.append(f"2. Lokasi dengan rasio optimal (~1,0): {locs}, dan 4 lainnya.")
-    insight_lines.append(f"   2a. Sembilan lokasi tersebut menunjukkan beban kerja seimbang dan pelaporan konsisten.")
-if dev["obj3a_lowest_div"] and dev["obj3b_lowest_reporter"]:
-    div, r1 = dev["obj3a_lowest_div"]
-    rep, r2 = dev["obj3b_lowest_reporter"]
-    insight_lines.append("   2b. Aktivitas pelaporan terendah terjadi di:")
-    insight_lines.append(f"       • Divisi: {div} (rasio {r1})")
-    insight_lines.append(f"       • Reporter: {rep} ({r2} temuan/bulan)")
-if dev["obj3c_slowest_div"] and dev["obj3d_slowest_executor"]:
-    div, lt1 = dev["obj3c_slowest_div"]
-    exe, lt2 = dev["obj3d_slowest_executor"]
-    insight_lines.append("   2c. Lead time penyelesaian terpanjang terjadi di:")
-    insight_lines.append(f"       • Divisi: {div} ({lt1} hari)")
-    insight_lines.append(f"       • Eksekutor: {exe} ({lt2} hari)")
-if dev["obj4_unsafe_condition"] or dev["obj4_unsafe_action"] or dev["obj4_near_miss"]:
-    uc, ua, nm = dev["obj4_unsafe_condition"], dev["obj4_unsafe_action"], dev["obj4_near_miss"]
-    insight_lines.append("   2d. Komposisi temuan:")
-    insight_lines.append(f"       • Unsafe Condition: {uc}%")
-    insight_lines.append(f"       • Unsafe Action: {ua}%")
-    insight_lines.append(f"       • Near Miss: {nm}%")
-
-# 3. Objective 6 — top 2 bubble
-if dev["obj6_top2_categories"]:
-    c1, c2 = dev["obj6_top2_categories"]
-    insight_lines.append(f"3. Dua kategori temuan paling sering muncul (non-Positive): {c1[0]} ({c1[1]}/bulan) dan {c2[0]} ({c2[1]}/bulan).")
-
-# 4a & 4b — Kuadran risiko
-if dev["obj5_quadrant_I"]:
-    q1 = ", ".join(dev["obj5_quadrant_I"][:3])
-    insight_lines.append(f"4a. Divisi risiko tinggi (volume tinggi + lead time tinggi): {q1}.")
-if dev["obj5_quadrant_II"]:
-    q2 = ", ".join(dev["obj5_quadrant_II"][:3])
-    insight_lines.append(f"4b. Divisi risiko tersembunyi (volume rendah + lead time tinggi): {q2}.")
-
-# 5. Sintesis
-insight_lines.append("5. Pola utama: aktivitas inspeksi tidak diimbangi kapasitas resolusi, sehingga hazard tetap terbuka lama.")
-
-insight_text = "<br>".join(insight_lines)
-
-# Rekomendasi & Risk Mitigation — per poin insight
-rec_mitigation = []
-
-# Untuk 2a
-if dev["obj2_ratio_1_locs"]:
-    rec_mitigation.append({
-        "point": "2a",
-        "recommendation": "Jadikan 9 lokasi ber-rasio ~1,0 sebagai benchmark operasional: dokumentasikan praktik pelaporan, rotasi, dan validasi lapangan.",
-        "mitigation": "Tetapkan standar beban kerja: 0,8–1,2 temuan/pelapor/bulan. Jika rasio <0,5 atau >1,5 selama 2 bulan → picu capacity review."
+# Ekstrak
+dev = extract_deviations_v2(df_filtered)
+
+# ======== INSIGHT SUMMARY (Revised Penomoran) ========
+insight_parts = []
+
+# 1 → dari Obj 2
+if dev["obj2_optimal_locs"]:
+    locs = ", ".join(dev["obj2_optimal_locs"][:5])
+    insight_parts.append("1. Sembilan lokasi menunjukkan rasio temuan/orang optimal (~1,0), indikasi beban kerja seimbang dan pelaporan konsisten: {} dan 4 lainnya.".format(locs))
+
+# 1a → reporter/Divisi TERENDAH (Obj 3a & 3c)
+low_div = dev["obj3a_lowest_div_ratio"]
+low_rep = dev["obj3c_lowest_reporter_ratio"]
+if low_div and low_rep:
+    insight_parts.append(
+        f"   1a. Namun, aktivitas inspeksi terendah terjadi di: • Divisi <strong>{low_div[0]}</strong> (rasio {low_div[1]}), "
+        f"• Reporter <strong>{low_rep[0]}</strong> ({low_rep[1]} temuan/bulan), menunjukkan kemungkinan under-reporting atau hambatan akses."
+    )
+
+# 1b → lead time TERPANJANG (Obj 3b & 3d)
+slow_div = dev["obj3b_slowest_div_lead"]
+slow_exe = dev["obj3d_slowest_executor_lead"]
+if slow_div and slow_exe:
+    insight_parts.append(
+        f"   1b. Kapasitas resolusi terendah di: • Divisi <strong>{slow_div[0]}</strong> ({slow_div[1]} hari), "
+        f"• Eksekutor <strong>{slow_exe[0]}</strong> ({slow_exe[1]} hari), berisiko SLA breach."
+    )
+
+# 1c → unsafe share (Obj 4)
+uc, ua, nm = dev["obj4_uc"], dev["obj4_ua"], dev["obj4_nm"]
+if uc > 0 or ua > 0 or nm > 0:
+    insight_parts.append(
+        f"   1c. Komposisi temuan non-Positive: Unsafe Condition ({uc}%), Unsafe Action ({ua}%), Near Miss ({nm}%)."
+    )
+
+# 1d → trend paling menurun (NEW!)
+decl_locs = dev["obj3_most_declining_locs"]
+decl_divs = dev["obj3_most_declining_divs"]
+if decl_locs and decl_divs:
+    top_loc = decl_locs[0]
+    top_div = decl_divs[0]
+    insight_parts.append(
+        f"   1d. Lokasi <strong>{top_loc}</strong> dan Divisi <strong>{top_div}</strong> memiliki tren aktivitas inspeksi paling menurun (slope negatif), "
+        f"mengindikasikan potensi penurunan komitmen atau pergantian pelapor kunci."
+    )
+
+# 2 → top 2 bubble (Obj 6)
+if dev["obj6_top2_cat"]:
+    c1, c2 = dev["obj6_top2_cat"]
+    insight_parts.append(f"2. Dua kategori paling sering muncul (non-Positive): <strong>{c1[0]}</strong> ({c1[1]}/bulan) dan <strong>{c2[0]}</strong> ({c2[1]}/bulan).")
+
+# 3 → quadrant I & II (Obj 5)
+q1 = dev["obj5_q1_divs"]
+q2 = dev["obj5_q2_divs"]
+if q1:
+    insight_parts.append(f"3. Divisi risiko tinggi (volume tinggi + lead time tinggi / Quadrant I): {', '.join(q1[:3])}.")
+    if q2:
+        insight_parts.append(f"   3a. Divisi risiko tersembunyi (volume rendah + lead time tinggi / Quadrant II): {', '.join(q2[:3])}.")
+
+# 4 → sintesis
+insight_parts.append("4. Pola utama: terjadi *mismatch* antara kapasitas inspeksi (menurun di beberapa area) dan kapasitas resolusi (terlalu lambat), menyebabkan hazard tetap terbuka dalam jangka waktu lama.")
+
+insight_text = "<br>".join(insight_parts)
+
+# ======== REKOMENDASI & MITIGASI ========
+recoms = []
+
+# 1 → benchmark lokasi optimal
+if dev["obj2_optimal_locs"]:
+    recoms.append({
+        "point": "1",
+        "rec": "Jadikan 9 lokasi ber-rasio ~1,0 sebagai benchmark operasional: dokumentasikan rotasi pelapor, checklist, dan frekuensi.",
+        "mit": "Tetapkan *tolerance band* rasio 0,8–1,2. Jika keluar band ≥2 bulan → picu *capacity review* otomatis."
     })
 
-# Untuk 2b
-if dev["obj3a_lowest_div"] and dev["obj3b_lowest_reporter"]:
-    rec_mitigation.append({
-        "point": "2b",
-        "recommendation": "Lakukan 1:1 coaching dengan reporter terendah dan silent walkdown di divisi terendah untuk identifikasi hambatan.",
-        "mitigation": "Aktifkan micro-checklist digital (QR code → form 3 pertanyaan). Target: kenaikan temuan/bulan ≥0,5 dalam 30 hari."
+# 1a → tingkatkan inspeksi di area rendah
+if low_div and low_rep:
+    recoms.append({
+        "point": "1a",
+        "rec": "Luncurkan *micro-inspection sprint* di divisi & reporter terendah: target minimal 0,5 temuan/bulan/orang dalam 30 hari.",
+        "mit": "Aktifkan *QR code checklist* 3-menit di lokasi kritis. Integrasi reward points di aplikasi mobile."
     })
 
-# Untuk 2c
-if dev["obj3c_slowest_div"] and dev["obj3d_slowest_executor"]:
-    rec_mitigation.append({
-        "point": "2c",
-        "recommendation": "Bentuk Rapid Response Task Force untuk divisi dan eksekutor dengan lead time terpanjang: lakukan workflow mapping & integrasi verifikasi foto.",
-        "mitigation": "Terapkan SLA dua tingkat: High Risk ≤3 hari, Medium/Low ≤7 hari. Jika >7 hari → eskalasi otomatis ke Daily Safety Huddle."
+# 1b → perbaiki lead time
+if slow_div and slow_exe:
+    recoms.append({
+        "point": "1b",
+        "rec": "Bentuk *Rapid Closure Team* khusus untuk divisi & eksekutor terlambat. Terapkan *photo verification* & *auto-escalation*.",
+        "mit": "Terapkan SLA berlapis: High Risk ≤3 hari, Medium/Low ≤7 hari. >7 hari → notifikasi ke Daily Huddle & PIC Area."
     })
 
-# Untuk 2d
-if dev["obj4_unsafe_condition"] or dev["obj4_unsafe_action"] or dev["obj4_near_miss"]:
-    rec_mitigation.append({
-        "point": "2d",
-        "recommendation": "Luncurkan Positive Hunt Sprint selama 1 bulan: wajibkan 1 temuan Positive/reporter/minggu dan berikan reward.",
-        "mitigation": "Ubah default form digital dari Unsafe Condition → Positive. Target: kenaikan Near Miss & Unsafe Action ≥3× dalam 60 hari."
+# 1c → tingkatkan Near Miss & Positive
+if uc > 0 or ua > 0 or nm > 0:
+    recoms.append({
+        "point": "1c",
+        "rec": "Ubah alur pelaporan default: wajibkan 1 temuan Near Miss atau Positive per Unsafe Condition.",
+        "mit": "Aktifkan *Positive Hunt Challenge* bulanan: reward terbaik berdasarkan kualitas & frekuensi temuan positif."
     })
 
-# Untuk 3
-if dev["obj6_top2_categories"]:
-    rec_mitigation.append({
-        "point": "3",
-        "recommendation": "Bentuk Cross-Functional RCA Team (SIPIL, ELEKTRIKAL, Kontraktor, Safety) untuk analisis akar masalah desain/spesifikasi.",
-        "mitigation": "Perbarui spesifikasi tender: wajibkan mitigasi berbasis temuan historis (misal: double insulation untuk kabel outdoor)."
+# 1d → mitigasi penurunan tren
+if decl_locs and decl_divs:
+    recoms.append({
+        "point": "1d",
+        "rec": "Identifikasi *root cause* penurunan tren: apakah perubahan PIC, rotasi, atau kelelahan? Lakukan *exit interview safety* bagi pelapor yang keluar.",
+        "mit": "Terapkan *trend alert*: jika slope < -0.1 selama 2 bulan → notifikasi ke Safety Manager & PIC Area."
     })
 
-# Untuk 4a
-if dev["obj5_quadrant_I"]:
-    rec_mitigation.append({
-        "point": "4a",
-        "recommendation": "Alokasikan dedicated safety crew (2 orang) hanya untuk menutup temuan High Risk dari divisi kuadran I.",
-        "mitigation": "Aktifkan predictive alert: jika divisi masuk Kuadran I ≥2 bulan berturut-turut → notifikasi otomatis ke Manajer Area."
+# 2 → top category RCA
+if dev["obj6_top2_cat"]:
+    c1, c2 = dev["obj6_top2_cat"]
+    recoms.append({
+        "point": "2",
+        "rec": "Lakukan *Cross-Functional RCA* untuk <strong>{}</strong> & <strong>{}</strong>, libatkan desain, kontraktor, dan operasi.".format(c1[0], c2[0]),
+        "mit": "Perbarui spesifikasi teknis: wajibkan mitigasi berbasis temuan historis sebelum tender dimulai."
     })
 
-# Untuk 4b
-if dev["obj5_quadrant_II"]:
-    rec_mitigation.append({
-        "point": "4b",
-        "recommendation": "Terapkan One Finding, One Day untuk divisi kuadran II: jika ≤3 temuan/bulan, wajib selesai dalam 24 jam.",
-        "mitigation": "Jadikan KPI supervisor: 100% closure <24 jam. Luncurkan Zero Backlog Challenge tiap bulan."
+# 3 → Quadrant I & II
+if q1:
+    recoms.append({
+        "point": "3",
+        "rec": "Alokasikan *dedicated crew* (2 orang) hanya untuk menutup temuan Quadrant I.",
+        "mit": "Jika divisi masuk Quadrant I ≥2 bulan → eskalasi ke VP Operasi & PIC Area."
+    })
+if q2:
+    recoms.append({
+        "point": "3a",
+        "rec": "Terapkan *One Finding, One Day* untuk Quadrant II: semua temuan wajib selesai ≤24 jam.",
+        "mit": "Jadikan *100% closure <24 jam* sebagai KPI supervisor. Reward: zero-backlog selama 1 bulan."
     })
 
-# Untuk 5 (sintesis)
-rec_mitigation.append({
-    "point": "5",
-    "recommendation": "Integrasikan modul kapasitas resolusi ke dalam perencanaan inspeksi: verifikasi lead time historis sebelum jadwalkan inspeksi.",
-    "mitigation": "Bangun closed-loop system: setiap temuan baru harus diverifikasi capacity to close terlebih dahulu."
+# 4 → integrasi sistem
+recoms.append({
+    "point": "4",
+    "rec": "Integrasikan modul *capacity validation* ke dalam penjadwalan inspeksi: verifikasi lead time historis sebelum jadwal dibuat.",
+    "mit": "Bangun *closed-loop safety system*: setiap temuan baru harus diverifikasi *capacity to close* terlebih dahulu."
 })
 
-# Tampilkan — Insight (1 card), Rekomendasi (1 card, tabel rapi)
+# ======== TAMPILKAN ========
+# Insight Card
 st.markdown(
     f"""
     <div class="card" style="
         background-color: #f8f9fa;
-        border-left: 4px solid #2196f3;
+        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: #1976d2;">Insight Summary</h4>
+        <h4 style="margin-top: 0; color: #003DA5;">Insight Summary</h4>
         <p style="margin-bottom: 0; line-height: 1.6;">{insight_text}</p>
     </div>
     """,
     unsafe_allow_html=True
 )
 
-# Rekomendasi dalam tabel rapi
-if rec_mitigation:
+# Rekomendasi Table
+if recoms:
     rows = []
-    for item in rec_mitigation:
+    for r in recoms:
         rows.append(
             f"<tr>"
-            f"<td style='text-align:center; font-weight:bold;'>{item['point']}</td>"
-            f"<td>{item['recommendation']}</td>"
-            f"<td>{item['mitigation']}</td>"
+            f"<td style='text-align:center; font-weight:bold; width:5%;'>{r['point']}</td>"
+            f"<td style='padding:8px;'>{r['rec']}</td>"
+            f"<td style='padding:8px;'>{r['mit']}</td>"
             f"</tr>"
         )
     table_html = f"""
     <div class="card" style="
-        background-color: #f0f7ff;
-        border-left: 4px solid #4caf50;
+        background-color: #e8f5e9;
+        border-left: 4px solid #4CAF50;
         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: #2e7d32;">Recommended Actions and Risk Mitigation Strategy</h4>
+        <h4 style="margin-top: 0; color: #2E7D32;">Recommended Actions and Risk Mitigation Strategy</h4>
         <table style="width:100%; border-collapse:collapse; font-size:0.95em; margin-top:12px;">
             <thead>
-                <tr style="background-color:#e3f2fd;">
-                    <th style="padding:10px; text-align:center; border:1px solid #ccc;">Point</th>
+                <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;">Risk Mitigation Strategy</th>
                 </tr>
@@ -2277,10 +2348,4 @@ if rec_mitigation:
     """
     st.markdown(table_html, unsafe_allow_html=True)
 else:
-    st.markdown(
-        "<div class='card' style='background-color:#f0f7ff; border-left:4px solid #4caf50; padding:16px; margin-bottom:20px;'>"
-        "<h4 style='margin-top:0; color:#2e7d32;'>Recommended Actions and Risk Mitigation Strategy</h4>"
-        "<p>No actionable insights generated. Ensure data contains required columns.</p>"
-        "</div>",
-        unsafe_allow_html=True
-    )
+    st.info("No actionable insights generated. Ensure data contains required columns.")