Update app.py

app.py

@@ -255,7 +255,7 @@ html,body,[class*="css"]{font-family:'Plus Jakarta Sans',sans-serif;}
 """, unsafe_allow_html=True)
 
 # ============================================================
-# HELPER: Baca secret dari HF Secrets (tidak bocor ke UI)
+# HELPER: Baca secret dari HF Secrets
 # ============================================================
 def _read_secret(name: str) -> str:
     try:
@@ -266,6 +266,21 @@ def _read_secret(name: str) -> str:
         pass
     return os.environ.get(name, "").strip()
 
+
+def _init_api_keys():
+    """
+    Load API keys sekali ke session_state saat startup.
+    Ini memastikan keys tetap tersedia di semua Streamlit reruns,
+    termasuk di dalam @st.fragment (What-If tab) yang punya scope berbeda.
+    """
+    if "api_keys_loaded" not in st.session_state:
+        st.session_state.openrouter_key = _read_secret("OPENROUTER_API_KEY")
+        st.session_state.gemini_key     = _read_secret("GEMINI_API_KEY")
+        st.session_state.groq_key       = _read_secret("GROQ_API_KEY")
+        st.session_state.api_keys_loaded = True
+
+_init_api_keys()
+
 # ============================================================
 # SIDEBAR
 # ============================================================
@@ -282,19 +297,18 @@ with st.sidebar:
     )
     st.markdown("---")
 
-    # Baca API key langsung dari secrets
-    openrouter_key = _read_secret("OPENROUTER_API_KEY")
-    gemini_key     = _read_secret("GEMINI_API_KEY")
-    groq_key       = _read_secret("GROQ_API_KEY")
+    # Ambil dari session_state (sudah diinisialisasi di _init_api_keys)
+    openrouter_key = st.session_state.openrouter_key
+    gemini_key     = st.session_state.gemini_key
+    groq_key       = st.session_state.groq_key
 
-    # Tampilkan status key (✅/❌)
     st.caption(
         f"🔑 **OpenRouter:** {'✅' if openrouter_key else '❌'}  "
         f"**Gemini:** {'✅' if gemini_key else '❌'}  "
         f"**Groq:** {'✅' if groq_key else '❌'}"
     )
     if not any([openrouter_key, gemini_key, groq_key]):
-        st.warning("⚠️ Tidak ada API key terdeteksi. Chat akan menggunakan fallback.")
+        st.warning("⚠️ Tidak ada API key terdeteksi. Chat menggunakan Smart Fallback.")
 
     st.markdown("---")
     st.markdown(T('sidebar_stack', lang))
@@ -329,7 +343,7 @@ def load_model():
 def load_rag_index():
     try:
         from rag import load_index
-        return load_index(kb_path="knowledge_base", index_path="kb_index.pkl")
+        return load_index(kb_path="knowledge_base", index_path="/tmp/kb_index.pkl")
     except ImportError:
         st.sidebar.warning("⚠️ rag.py not found. RAG disabled.")
         return None
@@ -388,7 +402,10 @@ def risk_result(pd_score, loan_rp, lgd=0.40):
 
 def shap_summary(vals, names, n=5):
     idx = np.argsort(np.abs(vals))[-n:][::-1]
-    return '\n'.join([f"- {names[i]}: {'increases' if vals[i]>0 else 'decreases'} risk (SHAP={vals[i]:.3f})" for i in idx])
+    return '\n'.join([
+        f"- {names[i]}: {'increases' if vals[i]>0 else 'decreases'} risk (SHAP={vals[i]:.3f})"
+        for i in idx
+    ])
 
 def make_shap_png(shap_vals, feature_names):
     sv = np.array(shap_vals); names = list(feature_names); n = min(12,len(sv))
@@ -403,28 +420,95 @@ def make_shap_png(shap_vals, feature_names):
     buf = io.BytesIO(); fig.savefig(buf, format='png', dpi=130, bbox_inches='tight')
     plt.close(fig); buf.seek(0); return buf.read()
 
-def call_openrouter(messages, api_key, model="anthropic/claude-3-haiku-20240307"):
-    if not api_key: return None, "No API key"
-    try:
-        resp = requests.post("https://openrouter.ai/api/v1/chat/completions",
-            headers={"Authorization":f"Bearer {api_key}","Content-Type":"application/json",
-                     "HTTP-Referer":"https://huggingface.co","X-Title":"SME Credit Risk AI"},
-            json={"model":model,"messages":messages,"max_tokens":600,"temperature":0.75}, timeout=20)
-        if resp.status_code==200: return resp.json()['choices'][0]['message']['content'], None
-        return None, f"HTTP {resp.status_code}: {resp.text[:200]}"
-    except Exception as e: return None, str(e)
+# ============================================================
+# LLM HELPERS — OpenRouter dengan free model cascade
+# ============================================================
+
+# Model gratis OpenRouter, dicoba berurutan
+_OR_FREE_MODELS = [
+    "meta-llama/llama-3.1-8b-instruct:free",
+    "google/gemini-2.0-flash-exp:free",
+    "mistralai/mistral-7b-instruct:free",
+]
+
+def call_openrouter(messages, api_key, model=None):
+    """Coba free models berurutan sampai ada yang berhasil."""
+    if not api_key:
+        return None, "No API key"
+    models_to_try = [model] if model else _OR_FREE_MODELS
+    last_error = None
+    for m in models_to_try:
+        try:
+            resp = requests.post(
+                "https://openrouter.ai/api/v1/chat/completions",
+                headers={
+                    "Authorization": f"Bearer {api_key}",
+                    "Content-Type": "application/json",
+                    "HTTP-Referer": "https://huggingface.co",
+                    "X-Title": "SME Credit Risk AI",
+                },
+                json={"model": m, "messages": messages, "max_tokens": 600, "temperature": 0.75},
+                timeout=25,
+            )
+            if resp.status_code == 200:
+                return resp.json()['choices'][0]['message']['content'], None
+            last_error = f"HTTP {resp.status_code} ({m}): {resp.text[:150]}"
+        except requests.exceptions.Timeout:
+            last_error = f"Timeout ({m})"
+        except Exception as e:
+            last_error = f"{m}: {str(e)[:100]}"
+    return None, last_error
+
+def _call_llm(messages, prompt_flat=None):
+    """
+    Coba semua LLM dari session_state. Return (response_text, source_label, error).
+    messages = list[dict] format OpenAI
+    prompt_flat = string untuk Gemini (gabungan system+history)
+    """
+    _or  = st.session_state.get("openrouter_key", "")
+    _gem = st.session_state.get("gemini_key", "")
+    _grq = st.session_state.get("groq_key", "")
+
+    if _or:
+        r, err = call_openrouter(messages, _or)
+        if r: return r, f'🤖 OpenRouter ({_OR_FREE_MODELS[0].split("/")[-1]})', None
+    else:
+        err = None
+
+    if _gem:
+        try:
+            import google.generativeai as genai
+            genai.configure(api_key=_gem)
+            content = prompt_flat or "\n".join([
+                f"{'User' if m['role']=='user' else 'Assistant'}: {m['content']}"
+                for m in messages if m['role'] != 'system'
+            ])
+            r = genai.GenerativeModel('gemini-1.5-flash').generate_content(content)
+            return r.text, '🤖 Gemini Flash', None
+        except Exception as e:
+            err = f"Gemini: {str(e)[:100]}"
+
+    if _grq:
+        try:
+            from groq import Groq
+            r = Groq(api_key=_grq).chat.completions.create(
+                messages=messages, model='llama3-8b-8192', max_tokens=500)
+            return r.choices[0].message.content, '🤖 Groq (Llama3)', None
+        except Exception as e:
+            err = f"Groq: {str(e)[:100]}"
+
+    return None, None, err
 
 # ============================================================
-# NARRATIVE GENERATION (with LLM fallback)
+# NARRATIVE GENERATION
 # ============================================================
-def get_narrative(shap_vals, feature_names, result, lang, raw_input,
-                  openrouter_key='', gemini_key='', groq_key=''):
-    pd_pct  = result['pd']*100
+def get_narrative(shap_vals, feature_names, result, lang, raw_input):
+    pd_pct  = result['pd'] * 100
     factors = shap_summary(shap_vals, feature_names, 5)
-    lang_str = {'id':'Bahasa Indonesia profesional','en':'Professional English','hi':'Hindi'}
+    lang_str = {'id': 'Bahasa Indonesia profesional', 'en': 'Professional English', 'hi': 'Hindi'}
     prompt = (
         f"You are a senior credit analyst for Indonesian SME lending.\n"
-        f"Reply ONLY in {lang_str.get(lang,'English')}.\n"
+        f"Reply ONLY in {lang_str.get(lang, 'English')}.\n"
         f"Applicant: PD={pd_pct:.1f}%, EL=Rp {result['el']:,.0f}, "
         f"BizAge={raw_input.get('business_age_years')}yr, Employees={raw_input.get('num_employees')}, "
         f"DigitalScore={raw_input.get('digital_presence_score')}/100, "
@@ -438,25 +522,11 @@ def get_narrative(shap_vals, feature_names, result, lang, raw_input,
         f"4) {T('n_recommendations',lang)} (2-3 specific steps). "
         f"Be data-driven and reference actual values."
     )
-    msgs = [{"role":"user","content":prompt}]
-    if openrouter_key:
-        r, _ = call_openrouter(msgs, openrouter_key)
-        if r: return r, '🤖 OpenRouter (Claude Haiku)'
-    if gemini_key:
-        try:
-            import google.generativeai as genai
-            genai.configure(api_key=gemini_key)
-            r = genai.GenerativeModel('gemini-1.5-flash').generate_content(prompt)
-            return r.text, '🤖 Gemini Flash'
-        except: pass
-    if groq_key:
-        try:
-            from groq import Groq
-            r = Groq(api_key=groq_key).chat.completions.create(messages=msgs, model='llama3-8b-8192')
-            return r.choices[0].message.content, '🤖 Groq (Llama3)'
-        except: pass
+    msgs = [{"role": "user", "content": prompt}]
+    r, src, _ = _call_llm(msgs, prompt_flat=prompt)
+    if r: return r, src
 
-    # Static fallback (ringkas, tidak template)
+    # ── Static fallback ──────────────────────────────────────
     biz=raw_input.get('business_age_years',0); emp=raw_input.get('num_employees',0)
     dig=raw_input.get('digital_presence_score',0); cf=raw_input.get('monthly_cash_flow',0)
     npwp=raw_input.get('has_npwp',0); siup=raw_input.get('has_siup',0)
@@ -553,12 +623,10 @@ def _get_top_issue(raw_input, pd_pct, lang):
 # ============================================================
 # AI CHAT — Maya persona, dengan RAG
 # ============================================================
-def get_chat_response(user_msg, history, result, raw_input, shap_vals,
-                       feature_names, lang, openrouter_key='', gemini_key='', groq_key='',
-                       rag_index=None):
-    pd_pct    = result['pd']*100
+def get_chat_response(user_msg, history, result, raw_input, shap_vals, feature_names, lang, rag_index=None):
+    pd_pct    = result['pd'] * 100
     factors   = shap_summary(shap_vals, feature_names, 5)
-    lang_full = {'id':'Bahasa Indonesia','en':'English','hi':'Hindi'}
+    lang_full = {'id': 'Bahasa Indonesia', 'en': 'English', 'hi': 'Hindi'}
 
     # RAG context
     context = ""
@@ -571,7 +639,6 @@ def get_chat_response(user_msg, history, result, raw_input, shap_vals,
         except Exception:
             context = ""
 
-    # System prompt: Maya persona, cerdas, tidak kaku
     system = (
         f"Kamu adalah Maya, AI Credit Advisor yang cerdas, hangat, dan sedikit kasual untuk UMKM Indonesia.\n"
         f"Kepribadian: ramah tapi profesional, suka pakai analogi sederhana, TIDAK kaku, bisa ngobrol bebas.\n"
@@ -589,7 +656,7 @@ def get_chat_response(user_msg, history, result, raw_input, shap_vals,
         f"{context}\n"
         f"ATURAN MENJAWAB:\n"
         f"1. Jawab BEBAS — tidak harus soal kredit. Kalau ditanya soal diri sendiri, perkenalkan sebagai Maya.\n"
-        f"2. Kalau relevan, selalu hubungkan ke data pemohon di atas dengan menyebut angka aktualnya.\n"
+        f"2. Kalau relevan, selalu hubungkan ke data pemohon dengan menyebut angka aktualnya.\n"
         f"3. Berikan saran KONKRET dan SPESIFIK, bukan generik.\n"
         f"4. Maksimal 150 kata kecuali diminta lebih panjang.\n"
         f"5. Kalau ada saran slider: [ADJUST: field=value]\n"
@@ -598,32 +665,19 @@ def get_chat_response(user_msg, history, result, raw_input, shap_vals,
         f"6. JANGAN pernah balik ke template. Jawab seperti manusia cerdas.\n"
     )
 
-    messages = [{"role":"system","content":system}]
-    for m in history[-8:]: messages.append({"role":m['role'],"content":m['content']})
-    messages.append({"role":"user","content":user_msg})
+    messages = [{"role": "system", "content": system}]
+    for m in history[-8:]: messages.append({"role": m['role'], "content": m['content']})
+    messages.append({"role": "user", "content": user_msg})
 
-    response=None; _or_error=None
-    if openrouter_key:
-        response, _or_error = call_openrouter(messages, openrouter_key)
-    if response is None and gemini_key:
-        try:
-            import google.generativeai as genai
-            genai.configure(api_key=gemini_key)
-            flat = system+"\n\n"+"\n".join([
-                f"{'User' if m['role']=='user' else 'Assistant'}: {m['content']}"
-                for m in messages[1:]])
-            r = genai.GenerativeModel('gemini-1.5-flash').generate_content(flat)
-            response = r.text
-        except: pass
-    if response is None and groq_key:
-        try:
-            from groq import Groq
-            r = Groq(api_key=groq_key).chat.completions.create(
-                messages=messages, model='llama3-8b-8192', max_tokens=400)
-            response = r.choices[0].message.content
-        except: pass
+    # Build flat prompt untuk Gemini
+    flat = system + "\n\n" + "\n".join([
+        f"{'User' if m['role']=='user' else 'Assistant'}: {m['content']}"
+        for m in messages[1:]
+    ])
 
-    # Fallback: natural, tidak template
+    response, _src, _last_error = _call_llm(messages, prompt_flat=flat)
+
+    # ── Smart Keyword Fallback (hanya jika semua LLM gagal) ──
     if response is None:
         low = user_msg.lower()
         def _t(i,e,h): return {'id':i,'en':e,'hi':h}[lang]
@@ -632,62 +686,80 @@ def get_chat_response(user_msg, history, result, raw_input, shap_vals,
         npwp = raw_input.get('has_npwp', 0)
         biz  = raw_input.get('business_age_years', 0)
 
-        # Pertanyaan tentang identitas Maya
-        if any(w in low for w in ['siapa','kamu','km','who are you','nama','nama kamu','you are','maya']):
+        # Salam / perkenalan / identitas
+        if any(w in low for w in ['siapa','kamu','km','who are you','nama','you are','maya','halo','hai','hei','hello','hi','perkenalan']):
             response = _t(
                 f"Hei! Aku **Maya**, AI Credit Advisor kamu 😊 Aku dirancang untuk bantu kamu pahami skor kredit dan strategi bisnis UMKM. "
                 f"Skor PD kamu sekarang **{pd_pct:.1f}%** — {'sudah bagus banget! 🎉' if pd_pct<20 else 'masih ada ruang untuk diperbaiki.'} "
                 f"Mau aku jelasin lebih detail atau ada yang mau ditanyain?",
-                f"Hey! I'm **Maya**, your AI Credit Advisor 😊 I'm here to help you understand your credit score and SME business strategy. "
+                f"Hey! I'm **Maya**, your AI Credit Advisor 😊 I help you understand your credit score and SME business strategy. "
                 f"Your PD score is **{pd_pct:.1f}%** — {'looking great! 🎉' if pd_pct<20 else 'there is room to improve.'} "
-                f"Want me to explain more or do you have a question?",
+                f"Anything you want to ask?",
                 f"नमस्ते! मैं **Maya** हूँ, आपकी AI Credit Advisor 😊 "
                 f"आपका PD स्कोर **{pd_pct:.1f}%** है। कुछ पूछना है?"
             )
 
         # Cara improve / turunkan skor
-        elif any(w in low for w in ['improve','better','lower','reduce','tingkatkan','kurangi','turunkan','cara','gimana','bagaimana','naik','turun','कैसे','कम','सुधार']):
+        elif any(w in low for w in ['improve','better','lower','reduce','tingkatkan','kurangi','turunkan','cara','gimana','bagaimana','naik','turun','कैसे','कम','सुधार','meningkat','naikkan','optimalkan']):
             tips = []
             if not npwp:
-                tips.append(_t("1. **Urus NPWP** — ini wajib untuk pinjaman formal, daftar di pajak.go.id",
+                tips.append(_t("1. **Urus NPWP** — wajib untuk pinjaman formal, daftar di pajak.go.id",
                                "1. **Register NPWP** — required for formal loans, apply at pajak.go.id",
                                "1. **NPWP पंजीकृत करें** — औपचारिक ऋण के लिए अनिवार्य"))
             if dig < 75:
-                tips.append(_t(f"{'2' if not npwp else '1'}. **Naikkan Digital Score** dari {dig} ke 75+ — aktif di Google Business, Tokopedia/Shopee [ADJUST: digital_presence_score=75]",
-                               f"{'2' if not npwp else '1'}. **Raise Digital Score** from {dig} to 75+ — Google Business, marketplace [ADJUST: digital_presence_score=75]",
-                               f"{'2' if not npwp else '1'}. **डिजिटल स्कोर** {dig} से 75+ करें [ADJUST: digital_presence_score=75]"))
+                n = '2' if not npwp else '1'
+                tips.append(_t(f"{n}. **Naikkan Digital Score** dari {dig} ke 75+ — aktif di Google Business, Tokopedia/Shopee [ADJUST: digital_presence_score=75]",
+                               f"{n}. **Raise Digital Score** from {dig} to 75+ — Google Business, marketplace [ADJUST: digital_presence_score=75]",
+                               f"{n}. **डिजिटल स्कोर** {dig} से 75+ करें [ADJUST: digital_presence_score=75]"))
             if cf < 25e6:
-                tips.append(_t(f"{'3' if (not npwp and dig<75) else '2'}. **Optimalkan cash flow** dari Rp {cf/1e6:.0f}jt ke 25jt+/bln — diversifikasi produk [ADJUST: monthly_cash_flow=25000000]",
-                               f"{'3' if (not npwp and dig<75) else '2'}. **Grow cash flow** from Rp {cf/1e6:.0f}M to 25M+/month [ADJUST: monthly_cash_flow=25000000]",
-                               f"{'3' if (not npwp and dig<75) else '2'}. **नकद प्रवाह** Rp {cf/1e6:.0f}M से 25M+ करें [ADJUST: monthly_cash_flow=25000000]"))
+                n = str(len(tips)+1)
+                tips.append(_t(f"{n}. **Optimalkan cash flow** dari Rp {cf/1e6:.0f}jt ke 25jt+/bln — diversifikasi produk [ADJUST: monthly_cash_flow=25000000]",
+                               f"{n}. **Grow cash flow** from Rp {cf/1e6:.0f}M to 25M+/month [ADJUST: monthly_cash_flow=25000000]",
+                               f"{n}. **नकद प्रवाह** Rp {cf/1e6:.0f}M से 25M+ करें [ADJUST: monthly_cash_flow=25000000]"))
             if not tips:
-                tips.append(_t(f"Profil kamu sudah solid dengan PD {pd_pct:.1f}%! 🎉 Coba simulasikan What-If untuk eksplorasi lebih.",
-                               f"Your profile is already solid at PD {pd_pct:.1f}%! 🎉 Try the What-If tab.",
-                               f"आपकी प्रोफ़ाइल PD {pd_pct:.1f}% के साथ पहले से मजबूत है! 🎉"))
-            response = _t(f"Untuk turunkan PD dari **{pd_pct:.1f}%**, fokus ke ini:\n\n",
-                          f"To lower PD from **{pd_pct:.1f}%**, focus on:\n\n",
-                          f"PD **{pd_pct:.1f}%** कम करने के लिए:\n\n") + '\n'.join(tips[:3])
-
-        # Kenapa / faktor
-        elif any(w in low for w in ['why','kenapa','mengapa','factor','faktor','shap','pengaruh','driver','क्यों','कारण','apa yang']):
+                tips.append(_t(f"Profil kamu sudah solid dengan PD {pd_pct:.1f}%! 🎉 Coba simulasikan di tab What-If.",
+                               f"Your profile is solid at PD {pd_pct:.1f}%! 🎉 Try the What-If tab.",
+                               f"प्रोफ़ाइल PD {pd_pct:.1f}% के साथ मजबूत है! 🎉"))
+            response = (_t(f"Untuk turunkan PD dari **{pd_pct:.1f}%**, fokus ke:\n\n",
+                           f"To lower PD from **{pd_pct:.1f}%**, focus on:\n\n",
+                           f"PD **{pd_pct:.1f}%** कम करने के लिए:\n\n") + '\n'.join(tips[:3]))
+
+        # Kenapa / SHAP / jelasin faktor / analogi
+        elif any(w in low for w in ['why','kenapa','mengapa','factor','faktor','shap','pengaruh','driver','क्यों','कारण','apa yang','jelasin','jelaskan','explain','definisi','definisikan','artinya','maksud','arti','anak','sederhana','simple','5 tahun','umur 5']):
             ti  = int(np.argsort(np.abs(shap_vals))[-1])
             ti2 = int(np.argsort(np.abs(shap_vals))[-2])
+            fn  = feature_names[ti]
+            # Analogi sederhana
+            if 'business_age' in fn:
+                analogi = _t(" — ibarat pengalaman kerja, makin lama makin dipercaya",
+                             " — like work experience, longer = more trustworthy",
+                             " — अनुभव की तरह, लंबा = अधिक भरोसेमंद")
+            elif 'cash_flow' in fn:
+                analogi = _t(" — ibarat gaji bulanan kamu, makin besar makin mudah bayar cicilan",
+                             " — like your monthly income, higher = easier to repay",
+                             " — मासिक आय की तरह, अधिक = किस्त भरना आसान")
+            elif 'digital' in fn:
+                analogi = _t(" — ibarat 'nilai reputasi online' bisnis kamu di mata bank",
+                             " — your business's 'online reputation score' in the bank's eyes",
+                             " — बैंक की नजर में व्यवसाय की ऑनलाइन प्रतिष्ठा")
+            else:
+                analogi = ""
             response = _t(
                 f"Dua faktor terbesar yang drive skor PD **{pd_pct:.1f}%** kamu:\n\n"
-                f"1. **{feature_names[ti]}** — {'⬆ meningkatkan' if shap_vals[ti]>0 else '⬇ menurunkan'} risiko (SHAP={shap_vals[ti]:.3f})\n"
-                f"2. **{feature_names[ti2]}** — {'⬆ meningkatkan' if shap_vals[ti2]>0 else '⬇ menurunkan'} risiko (SHAP={shap_vals[ti2]:.3f})\n\n"
+                f"1. **{fn}**{analogi}\n   → {'⬆ meningkatkan' if shap_vals[ti]>0 else '⬇ menurunkan'} risiko (SHAP={shap_vals[ti]:.3f})\n\n"
+                f"2. **{feature_names[ti2]}**\n   → {'⬆ meningkatkan' if shap_vals[ti2]>0 else '⬇ menurunkan'} risiko (SHAP={shap_vals[ti2]:.3f})\n\n"
                 f"{'Skor bagus! Kedua faktor ini justru mendukung kelayakan kamu. 🎉' if pd_pct<20 else 'Fokus perbaiki faktor pertama dulu — dampaknya paling besar.'}",
                 f"Two biggest drivers of your PD **{pd_pct:.1f}%**:\n\n"
-                f"1. **{feature_names[ti]}** — {'⬆ increases' if shap_vals[ti]>0 else '⬇ decreases'} risk (SHAP={shap_vals[ti]:.3f})\n"
-                f"2. **{feature_names[ti2]}** — {'⬆ increases' if shap_vals[ti2]>0 else '⬇ decreases'} risk (SHAP={shap_vals[ti2]:.3f})\n\n"
-                f"{'Great score! Both factors support your eligibility. 🎉' if pd_pct<20 else 'Focus on improving the first factor — it has the biggest impact.'}",
+                f"1. **{fn}**{analogi}\n   → {'⬆ increases' if shap_vals[ti]>0 else '⬇ decreases'} risk (SHAP={shap_vals[ti]:.3f})\n\n"
+                f"2. **{feature_names[ti2]}**\n   → {'⬆ increases' if shap_vals[ti2]>0 else '⬇ decreases'} risk (SHAP={shap_vals[ti2]:.3f})\n\n"
+                f"{'Great score! Both factors support your eligibility. 🎉' if pd_pct<20 else 'Focus on the first factor — it has the biggest impact.'}",
                 f"PD **{pd_pct:.1f}%** के दो मुख्य कारक:\n\n"
-                f"1. **{feature_names[ti]}** — {'⬆ बढ़ाता' if shap_vals[ti]>0 else '⬇ घटाता'} जोखिम\n"
+                f"1. **{fn}**{analogi} — {'⬆ बढ़ाता' if shap_vals[ti]>0 else '⬇ घटाता'} जोखिम\n"
                 f"2. **{feature_names[ti2]}** — {'⬆ बढ़ाता' if shap_vals[ti2]>0 else '⬇ घटाता'} जोखिम"
             )
 
         # Pinjaman ideal
-        elif any(w in low for w in ['pinjaman','loan','kredit','berapa','amount','besar','limit','ऋण','कितना']):
+        elif any(w in low for w in ['pinjaman','loan','kredit','berapa','amount','besar','limit','ऋण','कितना','ideal','rekomendasi pinjaman']):
             if cf > 0:
                 dur_val = raw_input.get('duration', 24)
                 safe    = cf * dur_val * 0.35
@@ -697,39 +769,116 @@ def get_chat_response(user_msg, history, result, raw_input, shap_vals,
                     f"pinjaman ideal maksimal **Rp {safe/1e6:.0f}jt** (rasio cicilan 35%).\n\n"
                     f"Pinjaman kamu sekarang Rp {cur/1e6:.0f}jt — "
                     f"{'✅ masih aman, good job!' if cur<=safe else f'⚠️ melebihi batas aman. Pertimbangkan kurangi ke Rp {safe/1e6:.0f}jt atau perpanjang tenor.'}",
-                    f"Based on your cash flow of **Rp {cf/1e6:.0f}M/month** and {int(dur_val)}-month tenure, "
+                    f"Based on your cash flow **Rp {cf/1e6:.0f}M/month** and {int(dur_val)}-month tenure, "
                     f"ideal loan is max **Rp {safe/1e6:.0f}M** (35% installment ratio).\n\n"
                     f"Current loan Rp {cur/1e6:.0f}M — "
-                    f"{'✅ within safe limits, good job!' if cur<=safe else f'⚠️ exceeds safe limit. Consider reducing to Rp {safe/1e6:.0f}M or extending tenure.'}",
-                    f"नकद प्रवाह **Rp {cf/1e6:.0f}M/माह** और {int(dur_val)} माह के आधार पर, "
-                    f"अधिकतम ऋण **Rp {safe/1e6:.0f}M** (35% किस्त अनुपात)।\n\n"
-                    f"वर्तमान ऋण Rp {cur/1e6:.0f}M — "
-                    f"{'✅ सुरक्षित सीमा में।' if cur<=safe else '⚠️ सीमा से अधिक।'}"
+                    f"{'✅ within safe limits, good job!' if cur<=safe else f'⚠️ exceeds safe limit. Consider reducing to Rp {safe/1e6:.0f}M.'}",
+                    f"नकद प्रवाह **Rp {cf/1e6:.0f}M/माह** और {int(dur_val)} माह के आधार पर अधिकतम ऋण **Rp {safe/1e6:.0f}M**।\n\n"
+                    f"वर्तमान ऋण Rp {cur/1e6:.0f}M — {'✅ सुरक्षित।' if cur<=safe else '⚠️ सीमा से अधिक।'}"
                 )
             else:
-                response = _t("Isi cash flow bulanan di form dulu ya, biar aku bisa kasih rekomendasi yang akurat! 😊",
-                              "Fill in your monthly cash flow in the form first for an accurate recommendation! 😊",
+                response = _t("Isi cash flow bulanan di form dulu ya, biar aku bisa kasih rekomendasi akurat! 😊",
+                              "Fill in your monthly cash flow first for an accurate recommendation! 😊",
                               "सटीक सुझाव के लिए पहले मासिक नकद प्रवाह भरें! 😊")
 
-        # Default: tidak ke template, jawab kontekstual
+        # Tabungan / menabung / saving
+        elif any(w in low for w in ['tabungan','saving','nabung','menabung','savings','uang','keuangan','finance','financial','menabung','simpan']):
+            response = _t(
+                f"Soal tabungan dan cash flow kamu (saat ini **Rp {cf/1e6:.0f}jt/bln**):\n\n"
+                f"💰 **3 cara praktis tingkatkan tabungan UMKM:**\n"
+                f"1. Pisahkan rekening bisnis & pribadi — biar tidak tercampur\n"
+                f"2. Sisihkan minimal 10–15% dari omzet setiap bulan secara otomatis\n"
+                f"3. Dokumentasikan semua pemasukan — ini juga naikkan digital score!\n\n"
+                f"Tabungan naik → cash flow lebih sehat → PD turun 📉 [ADJUST: monthly_cash_flow={int(cf*1.3)}]",
+                f"About savings and your cash flow (currently **Rp {cf/1e6:.0f}M/month**):\n\n"
+                f"💰 **3 practical SME savings tips:**\n"
+                f"1. Separate business & personal bank accounts\n"
+                f"2. Auto-transfer 10–15% of monthly revenue to savings\n"
+                f"3. Document all income streams — this also boosts digital score!\n\n"
+                f"More savings → healthier cash flow → lower PD 📉 [ADJUST: monthly_cash_flow={int(cf*1.3)}]",
+                f"बचत और नकद प्रवाह (**Rp {cf/1e6:.0f}M/माह**) के बारे में:\n\n"
+                f"💰 **3 व्यावहारिक तरीके:**\n"
+                f"1. व्यवसाय और व्यक्तिगत खाते अलग रखें\n"
+                f"2. मासिक राजस्व का 10–15% बचाएं\n"
+                f"3. सभी आय दस्तावेज़ीकृत करें\n\n"
+                f"अधिक बचत → PD कम 📉"
+            )
+
+        # Riwayat kredit / credit history
+        elif any(w in low for w in ['riwayat kredit','credit history','kredit history','credit record','riwayat','history kredit','riwayat pembayaran']):
+            ch_val = raw_input.get('credit_history', '-')
+            response = _t(
+                f"**Riwayat kredit** = catatan sejarah bayar utangmu — ibarat rapor keuangan.\n\n"
+                f"Status kamu: **{ch_val}**\n\n"
+                f"📊 Dampak ke skor kredit:\n"
+                f"- *All paid / Existing paid* → sinyal positif 💚\n"
+                f"- *Delayed previously* → bank waspada ⚠️\n"
+                f"- *Critical* → risiko naik signifikan 🔴\n\n"
+                f"Cara bangun riwayat bagus: bayar cicilan tepat waktu, jangan ambil pinjaman melebihi kemampuan bayar.",
+                f"**Credit history** = your track record of paying debts — your financial report card.\n\n"
+                f"Your status: **{ch_val}**\n\n"
+                f"📊 Score impact:\n"
+                f"- *All paid / Existing paid* → positive signal 💚\n"
+                f"- *Delayed previously* → bank is cautious ⚠️\n"
+                f"- *Critical* → significant risk increase ����\n\n"
+                f"Build good history: pay on time, don't borrow beyond your repayment capacity.",
+                f"**क्रेडिट इतिहास** = ऋण भुगतान का रिकॉर्ड।\n\nआपका स्तर: **{ch_val}**\n\nसमय पर भुगतान करें।"
+            )
+
+        # Tips bisnis UMKM
+        elif any(w in low for w in ['tips','bisnis','usaha','umkm','sme','digital','marketplace','online','ecommerce','strategi','business tips']):
+            response = _t(
+                f"💼 **Tips bisnis UMKM buat kamu (PD {pd_pct:.1f}%):**\n\n"
+                f"1. **Digital hadir** — daftar Google Business Profile, aktif di Tokopedia/Shopee/TikTok Shop\n"
+                f"   *(Digital score kamu {dig}/100 — masih bisa naik!)*\n"
+                f"2. **Pisah keuangan** — rekening bisnis terpisah dari pribadi\n"
+                f"3. **Dokumentasi rutin** — catat semua transaksi, ini bukti ke bank\n"
+                f"4. **Legalitas** — NPWP & SIUP/NIB buka akses ke KUR & kredit formal\n\n"
+                f"Mau aku simulasikan dampaknya ke skor di tab What-If?",
+                f"💼 **SME business tips for you (PD {pd_pct:.1f}%):**\n\n"
+                f"1. **Go digital** — Google Business Profile, Tokopedia/Shopee/TikTok Shop\n"
+                f"   *(Your digital score {dig}/100 — room to improve!)*\n"
+                f"2. **Separate finances** — dedicated business bank account\n"
+                f"3. **Document everything** — track all transactions as proof for banks\n"
+                f"4. **Get legal** — NPWP & SIUP/NIB unlock KUR & formal credit\n\n"
+                f"Want me to simulate the impact in the What-If tab?",
+                f"💼 **UMKM व्यवसाय टिप्स (PD {pd_pct:.1f}%):**\n\n"
+                f"1. डिजिटल उपस्थिति बढ़ाएं — Google Business, Marketplace\n"
+                f"2. अलग बैंक खाता रखें\n"
+                f"3. सभी लेनदेन दस्तावेज़ीकृत करें\n"
+                f"4. NPWP और SIUP/NIB प्राप्त करें"
+            )
+
+        # Default: kontekstual, bukan template kaku
         else:
             top_issue = _get_top_issue(raw_input, pd_pct, lang)
+            has_key = any([
+                st.session_state.get("openrouter_key"),
+                st.session_state.get("gemini_key"),
+                st.session_state.get("groq_key"),
+            ])
+            err_note = ""
+            if has_key and _last_error:
+                err_note = _t(f"\n\n_⚠️ LLM error: `{_last_error[:80]}` — pakai fallback_",
+                              f"\n\n_⚠️ LLM error: `{_last_error[:80]}` — using fallback_",
+                              f"\n\n_⚠️ LLM त्रुटि: `{_last_error[:80]}`_")
             response = _t(
-                f"Hei! Aku Maya, AI Advisor kamu 😊 Untuk pertanyaan ini aku butuh koneksi AI — coba tambah API key di sidebar.\n\n"
-                f"Yang bisa aku kasih tahu sekarang: PD kamu **{pd_pct:.1f}%** dan {top_issue}\n\n"
-                f"Mau tanya soal cara improve skor, faktor risiko, atau pinjaman ideal?",
-                f"Hey! I'm Maya 😊 For this I need an AI connection — try adding an API key in the sidebar.\n\n"
+                f"Pertanyaan menarik! Untuk ini aku butuh koneksi AI.\n\n"
+                f"Yang bisa aku kasih tahu: PD kamu **{pd_pct:.1f}%** dan {top_issue}\n\n"
+                f"Coba tanya yang lebih spesifik: *cara turunkan skor*, *faktor risiko*, *pinjaman ideal*, *tips tabungan*, atau *riwayat kredit*.{err_note}",
+                f"Great question! For this I need an AI connection.\n\n"
                 f"What I can share: your PD is **{pd_pct:.1f}%** and {top_issue}\n\n"
-                f"Want to ask about improving your score, risk factors, or ideal loan?",
-                f"नमस्ते! मैं Maya हूँ 😊 इसके लिए AI कनेक्शन चाहिए — sidebar में API key जोड़ें।\n\n"
+                f"Try asking specifically: *how to lower score*, *risk factors*, *ideal loan*, *savings tips*, or *credit history*.{err_note}",
+                f"अच्छा प्रश्न! इसके लिए AI कनेक्शन चाहिए।\n\n"
                 f"आपका PD **{pd_pct:.1f}%** है और {top_issue}"
             )
 
+    # Parse ADJUST instructions
     adjustments = {}
     for m in re.finditer(r'\[ADJUST:\s*(\w+)=([\d.]+)\]', response):
         adjustments[m.group(1)] = float(m.group(2))
-    clean = re.sub(r'\[ADJUST:[^\]]+\]','',response).strip()
-    return clean, adjustments, _or_error
+    clean = re.sub(r'\[ADJUST:[^\]]+\]', '', response).strip()
+    return clean, adjustments, _last_error
 
 # ============================================================
 # HEADER
@@ -836,8 +985,7 @@ if submitted:
             result    = risk_result(pd_score, loan_rp, lgd_val)
             sv        = explainer(X_in)
             shap_vals = sv[0].values
-            narrative, llm_src = get_narrative(shap_vals, feature_names, result, lang, raw,
-                                               openrouter_key, gemini_key, groq_key)
+            narrative, llm_src = get_narrative(shap_vals, feature_names, result, lang, raw)
             st.session_state.result=result; st.session_state.shap_vals=shap_vals
             st.session_state.raw_input=raw; st.session_state.narrative=narrative
             st.session_state.llm_src=llm_src; st.session_state.narrative_lang=lang
@@ -855,12 +1003,9 @@ llm_src   = st.session_state.llm_src
 if result is None: st.stop()
 
 # Auto-regenerate narrative if language switched
-if (result is not None and raw_input is not None
-        and st.session_state.narrative_lang != lang):
+if result is not None and raw_input is not None and st.session_state.narrative_lang != lang:
     with st.spinner(T('spinner', lang)):
-        narrative, llm_src = get_narrative(
-            shap_vals, feature_names, result, lang, raw_input,
-            openrouter_key, gemini_key, groq_key)
+        narrative, llm_src = get_narrative(shap_vals, feature_names, result, lang, raw_input)
         st.session_state.narrative      = narrative
         st.session_state.llm_src        = llm_src
         st.session_state.narrative_lang = lang
@@ -898,7 +1043,8 @@ t1,t2,t3,t4,t5 = st.tabs([T('tab_shap',lang),T('tab_narrative',lang),
 # ── TAB 1: SHAP ──────────────────────────────────────────────
 with t1:
     st.markdown(f'<div class="sec-title">{T("shap_title",lang)}</div>', unsafe_allow_html=True)
-    if st.session_state.get("shap_png"): st.image(st.session_state.shap_png, use_container_width=True)
+    if st.session_state.get("shap_png"):
+        st.image(st.session_state.shap_png, use_container_width=True)
     st.caption(T('shap_caption',lang))
     top_idx = np.argsort(np.abs(shap_vals))[-10:][::-1]
     st.dataframe(pd.DataFrame({
@@ -919,50 +1065,60 @@ with t2:
 with t3:
     st.markdown(f'<div class="sec-title">{T("chat_title",lang)}</div>', unsafe_allow_html=True)
     chips = [T('chat_chip1',lang),T('chat_chip2',lang),T('chat_chip3',lang),T('chat_chip4',lang)]
-    chip_cols=st.columns(len(chips)); chip_clicked=None
+    chip_cols = st.columns(len(chips)); chip_clicked = None
     for i,(col,chip) in enumerate(zip(chip_cols,chips)):
         with col:
-            if st.button(chip, key=f"chip_{i}", use_container_width=True): chip_clicked=chip
+            if st.button(chip, key=f"chip_{i}", use_container_width=True): chip_clicked = chip
     st.markdown("---")
     if st.session_state.chat_history:
         html=""
         for msg in st.session_state.chat_history:
-            content=re.sub(r'\*\*(.+?)\*\*',r'<strong>\1</strong>',msg['content'].replace('\n','<br>'))
-            cls="chat-bubble-user" if msg['role']=='user' else "chat-bubble-ai"
-            icon="👤" if msg['role']=='user' else "🤖"
-            html+=f'<div class="{cls}">{icon} {content}</div>'
+            content = re.sub(r'\*\*(.+?)\*\*',r'<strong>\1</strong>',msg['content'].replace('\n','<br>'))
+            cls  = "chat-bubble-user" if msg['role']=='user' else "chat-bubble-ai"
+            icon = "👤" if msg['role']=='user' else "🤖"
+            html += f'<div class="{cls}">{icon} {content}</div>'
         st.markdown(html, unsafe_allow_html=True)
     else:
         st.info(f"👋 {T('chat_welcome',lang)}")
-    user_input=st.chat_input(T('chat_input',lang))
-    to_process=chip_clicked or user_input
+
+    user_input = st.chat_input(T('chat_input',lang))
+    to_process = chip_clicked or user_input
     if to_process:
         st.session_state.chat_history.append({'role':'user','content':to_process})
         with st.spinner("🤔 ..."):
-            ai_resp,adjustments,_debug_err=get_chat_response(
-                to_process,st.session_state.chat_history,result,raw_input,shap_vals,
-                feature_names,lang,openrouter_key,gemini_key,groq_key,
+            ai_resp, adjustments, _debug_err = get_chat_response(
+                to_process, st.session_state.chat_history,
+                result, raw_input, shap_vals, feature_names, lang,
                 rag_index=rag_index)
-            if _debug_err and openrouter_key:
-                st.caption(f"⚠️ OpenRouter: {_debug_err[:80]} — {T('or_fallback',lang)}")
+            # Tampilkan warning hanya jika ada key tapi LLM tetap gagal
+            if _debug_err and any([
+                st.session_state.get("openrouter_key"),
+                st.session_state.get("gemini_key"),
+                st.session_state.get("groq_key"),
+            ]):
+                st.warning(f"⚠️ Semua LLM gagal, menggunakan Smart Fallback. Error: `{_debug_err[:100]}`")
         st.session_state.chat_history.append({'role':'assistant','content':ai_resp})
-        adj_map={'digital_presence_score':'wi_dig','business_age_years':'wi_biz',
-                 'num_employees':'wi_emp','monthly_cash_flow':'wi_cash','duration':'wi_dur','loan_rp':'wi_loan'}
+        adj_map = {
+            'digital_presence_score':'wi_dig','business_age_years':'wi_biz',
+            'num_employees':'wi_emp','monthly_cash_flow':'wi_cash',
+            'duration':'wi_dur','loan_rp':'wi_loan'
+        }
         if adjustments:
-            for field,val in adjustments.items():
-                if field in adj_map: st.session_state[adj_map[field]]=val
+            for field, val in adjustments.items():
+                if field in adj_map: st.session_state[adj_map[field]] = val
             st.success(f"💡 {T('chat_updated',lang)}")
         st.rerun()
     if st.session_state.chat_history:
         if st.button(T('chat_clear',lang)):
-            st.session_state.chat_history=[]; st.rerun()
+            st.session_state.chat_history = []; st.rerun()
 
 # ── TAB 4: What-If ────────────────────────────────────────────
 with t4:
     @st.fragment
     def render_whatif():
-        _lang=st.session_state.get("lang_sel","id")
-        result_f=st.session_state.result; raw_input_f=st.session_state.raw_input
+        _lang = st.session_state.get("lang_sel","id")
+        result_f   = st.session_state.result
+        raw_input_f = st.session_state.raw_input
         if result_f is None or raw_input_f is None:
             st.info(T('wi_form_first',_lang)); return
         st.markdown(f'<div class="sec-title">{T("wi_title",_lang)}</div>', unsafe_allow_html=True)
@@ -973,44 +1129,51 @@ with t4:
         d_cash = int((st.session_state.wi_cash or raw_input_f.get('monthly_cash_flow',15e6))//1e6)
         d_dur  = int(st.session_state.wi_dur  or raw_input_f.get('duration',24))
         d_loan = int((st.session_state.wi_loan or raw_input_f.get('loan_rp',50e6))//1e6)
-        wc1,wc2=st.columns(2)
+        wc1,wc2 = st.columns(2)
         with wc1:
-            wi_loan=st.slider(T('wi_loan',_lang),5,500,d_loan,5)
-            wi_dur =st.slider(T('wi_duration',_lang),4,72,d_dur)
-            wi_cash=st.slider(T('wi_cashflow',_lang),1,100,d_cash)
+            wi_loan = st.slider(T('wi_loan',_lang),5,500,d_loan,5)
+            wi_dur  = st.slider(T('wi_duration',_lang),4,72,d_dur)
+            wi_cash = st.slider(T('wi_cashflow',_lang),1,100,d_cash)
         with wc2:
-            wi_dig =st.slider(T('wi_digital',_lang),1,100,d_dig)
-            wi_biz =st.slider(T('wi_bizage',_lang),1,20,d_biz)
-            wi_emp =st.slider(T('wi_employees',_lang),1,50,d_emp)
+            wi_dig  = st.slider(T('wi_digital',_lang),1,100,d_dig)
+            wi_biz  = st.slider(T('wi_bizage',_lang),1,20,d_biz)
+            wi_emp  = st.slider(T('wi_employees',_lang),1,50,d_emp)
         try:
-            wi_raw={**raw_input_f,'duration':wi_dur,'digital_presence_score':wi_dig,
-                    'business_age_years':float(wi_biz),'monthly_cash_flow':float(wi_cash*1e6),'num_employees':wi_emp}
-            X_wi=preprocess(wi_raw,scaler,feature_names)
-            wi_pd=float(ensemble.predict_proba(X_wi)[0][1])
-            wi_res=risk_result(wi_pd,wi_loan*1e6,result_f['lgd'])
-            d_pd=wi_pd-result_f['pd']; d_el=wi_res['el']-result_f['el']
-            r1,r2,r3=st.columns(3)
+            wi_raw = {
+                **raw_input_f,
+                'duration':wi_dur,'digital_presence_score':wi_dig,
+                'business_age_years':float(wi_biz),
+                'monthly_cash_flow':float(wi_cash*1e6),
+                'num_employees':wi_emp
+            }
+            X_wi   = preprocess(wi_raw, scaler, feature_names)
+            wi_pd  = float(ensemble.predict_proba(X_wi)[0][1])
+            wi_res = risk_result(wi_pd, wi_loan*1e6, result_f['lgd'])
+            d_pd   = wi_pd - result_f['pd']
+            d_el   = wi_res['el'] - result_f['el']
+            r1,r2,r3 = st.columns(3)
             with r1: st.metric(T('kpi_pd',_lang),f"{wi_pd*100:.1f}%",f"{d_pd*100:+.1f}pp",delta_color="inverse")
             with r2: st.metric(T('kpi_el',_lang),f"Rp {wi_res['el']/1e6:.2f}jt",f"Rp {d_el/1e6:+.2f}jt",delta_color="inverse")
             with r3:
-                icon_m={'risk-low':'🟢','risk-med':'🟡','risk-high':'🔴'}
-                label_m={'risk-low':T('wi_approved',_lang),'risk-med':T('wi_review',_lang),'risk-high':T('wi_highrisk',_lang)}
+                icon_m  = {'risk-low':'🟢','risk-med':'🟡','risk-high':'🔴'}
+                label_m = {'risk-low':T('wi_approved',_lang),'risk-med':T('wi_review',_lang),'risk-high':T('wi_highrisk',_lang)}
                 st.metric(T('wi_status',_lang),f"{icon_m[wi_res['css']]} {label_m[wi_res['css']]}")
-            fig2,ax=plt.subplots(figsize=(7,2.5))
-            bars=ax.barh(['Original','What-If'],[result_f['pd']*100,wi_pd*100],
-                         color=[result_f['color'],wi_res['color']],height=0.4)
+            fig2,ax = plt.subplots(figsize=(7,2.5))
+            bars = ax.barh(['Original','What-If'],[result_f['pd']*100,wi_pd*100],
+                           color=[result_f['color'],wi_res['color']],height=0.4)
             ax.axvline(20,color='#f7971e',ls='--',lw=1.5,label='20%')
             ax.axvline(50,color='#e74c3c',ls='--',lw=1.5,label='50%')
             for bar,v in zip(bars,[result_f['pd']*100,wi_pd*100]):
-                ax.text(bar.get_width()+0.5,bar.get_y()+bar.get_height()/2,f'{v:.1f}%',va='center',fontweight='bold',fontsize=11)
+                ax.text(bar.get_width()+0.5,bar.get_y()+bar.get_height()/2,
+                        f'{v:.1f}%',va='center',fontweight='bold',fontsize=11)
             ax.set_xlim(0,110); ax.legend(fontsize=8); ax.grid(axis='x',alpha=0.3)
             ax.set_title(T('wi_chart_title',_lang),fontweight='bold')
             plt.tight_layout()
-            buf2=io.BytesIO(); fig2.savefig(buf2,format='png',dpi=130,bbox_inches='tight')
+            buf2 = io.BytesIO(); fig2.savefig(buf2,format='png',dpi=130,bbox_inches='tight')
             plt.close(fig2); buf2.seek(0); st.image(buf2.read(),use_container_width=True)
-            if wi_pd<result_f['pd']-0.005:
+            if wi_pd < result_f['pd']-0.005:
                 st.success(f"✅ PD {T('wi_pd_down',_lang)} **{(result_f['pd']-wi_pd)*100:.1f}pp** ({T('wi_pd_from',_lang)} {result_f['pd']*100:.1f}% → {wi_pd*100:.1f}%)")
-            elif wi_pd>result_f['pd']+0.005:
+            elif wi_pd > result_f['pd']+0.005:
                 st.warning(f"⚠️ PD {T('wi_pd_up',_lang)} **{(wi_pd-result_f['pd'])*100:.1f}pp** ({T('wi_pd_from',_lang)} {result_f['pd']*100:.1f}% → {wi_pd*100:.1f}%)")
             else:
                 st.info(f"↔️ {T('wi_no_change',_lang)}")
@@ -1023,7 +1186,8 @@ with t4:
                 if wi_emp<5:   tips.append(_t("👥 Tambah karyawan = skala bisnis lebih sehat","👥 More employees signals healthy scale","👥 अधिक कर्मचारी = स्वस्थ पैमाना"))
                 if not tips:   tips.append(_t("🌟 Profil sudah optimal!","🌟 Profile already well-optimized!","🌟 प्रोफ़ाइल पहले से अनुकूलित!"))
                 for tip in tips: st.markdown(tip)
-        except Exception as e: st.error(f"Error: {e}")
+        except Exception as e:
+            st.error(f"Error: {e}")
     render_whatif()
 
 # ── TAB 5: Formula ────────────────────────────────────────────
@@ -1031,15 +1195,17 @@ with t5:
     st.markdown(f'<div class="sec-title">{T("form_formula_title",lang)}</div>', unsafe_allow_html=True)
     st.latex(r"EL = PD \times LGD \times EAD")
     st.latex(rf"EL = {result['pd']:.4f} \times {result['lgd']:.2f} \times Rp\,{result['ead']:,.0f} = Rp\,{result['el']:,.0f}")
-    cf1,cf2=st.columns(2)
+    cf1,cf2 = st.columns(2)
     with cf1: st.markdown(T('formula_def',lang))
     with cf2:
-        cat_lbl=(T('formula_low',lang) if result['pd']<.2 else T('formula_medium',lang) if result['pd']<.5 else T('formula_high',lang))
-        st.markdown(f"| {T('formula_komponen',lang)} | {T('formula_nilai',lang)} |\n|--|--|\n"
-                    f"| PD | {result['pd']*100:.1f}% ({cat_lbl}) |\n"
-                    f"| LGD | {result['lgd']*100:.0f}% |\n"
-                    f"| EAD | Rp {result['ead']/1e6:.0f}jt |\n"
-                    f"| **EL** | **Rp {result['el']/1e6:.2f}jt** |")
+        cat_lbl = (T('formula_low',lang) if result['pd']<.2 else T('formula_medium',lang) if result['pd']<.5 else T('formula_high',lang))
+        st.markdown(
+            f"| {T('formula_komponen',lang)} | {T('formula_nilai',lang)} |\n|--|--|\n"
+            f"| PD | {result['pd']*100:.1f}% ({cat_lbl}) |\n"
+            f"| LGD | {result['lgd']*100:.0f}% |\n"
+            f"| EAD | Rp {result['ead']/1e6:.0f}jt |\n"
+            f"| **EL** | **Rp {result['el']/1e6:.2f}jt** |"
+        )
 
 # ============================================================
 # DOWNLOAD REPORT