refactor: update AI client initialization, refactor risk analysis logic, enhance styling, and improve report generation.

app.py

@@ -5,170 +5,204 @@ import gradio as gr
 import plotly.graph_objects as go
 from google import genai
 
-# --- KONFIGURASI GENERATIVE AI (GEMINI 2026) ---
+# --- CONFIG AI STUDIO 2026 ---
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-# Pastikan GOOGLE_API_KEY sudah terisi di Secrets
-ai_client = genai.Client(api_key=GOOGLE_API_KEY) if GOOGLE_API_KEY else None
-
-# --- PALET WARNA NAGARI (BIRU MUDA - EMAS) ---
-# Blue: #0514DE, Light Blue: #82C3EB, Pale: #E0EDF4, Gold: #F7BD87, White: #FFFFFF
+# Inisialisasi client dengan penanganan error jika API Key tidak valid
+try:
+    client_gemini = genai.Client(api_key=GOOGLE_API_KEY)
+except:
+    client_gemini = None
+
+# --- PALETTE & STYLING (NAGARI LIGHT BLUE) ---
+# Primary Blue: #0514DE, Light Blue: #82C3EB, Pale: #E0EDF4, Gold: #F7BD87, White: #FFFFFF
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;600;700&display=swap');
 body, .gradio-container { font-family: 'Plus Jakarta Sans', sans-serif !important; background-color: #FFFFFF !important; }
-.nagari-header { background: linear-gradient(135deg, #0514DE 0%, #82C3EB 100%); color: white; padding: 25px; border-radius: 12px; border-bottom: 5px solid #F7BD87; margin-bottom: 20px; text-align: center; }
-.card-nagari { background: #E0EDF4; border-radius: 15px; padding: 25px; border: 1.5px solid #82C3EB; box-shadow: 0 4px 12px rgba(5, 20, 222, 0.08); }
-.risk-box { display: inline-block; padding: 8px 18px; border-radius: 25px; font-weight: bold; color: white; margin-top: 10px; font-size: 1.1em; }
-.report-panel { background: white; border-radius: 12px; padding: 25px; border-left: 8px solid #0514DE; line-height: 1.7; font-size: 1.05em; color: #1e293b; }
+
+.nagari-header { 
+    background: linear-gradient(135deg, #0514DE 0%, #82C3EB 100%); 
+    color: #FFFFFF !important; 
+    padding: 30px; 
+    border-radius: 15px; 
+    border-bottom: 6px solid #F7BD87; 
+    margin-bottom: 25px; 
+    text-align: center; 
+}
+
+.report-card { 
+    background: #FFFFFF; 
+    border-radius: 15px; 
+    padding: 25px; 
+    border: 1px solid #82C3EB; 
+    box-shadow: 0 10px 15px -3px rgba(5, 20, 222, 0.1);
+    line-height: 1.7;
+}
+
+.status-box { 
+    background: #E0EDF4; 
+    padding: 20px; 
+    border-radius: 12px; 
+    border-left: 8px solid #0514DE; 
+    margin-bottom: 20px; 
+}
+
+.badge { 
+    padding: 6px 15px; 
+    border-radius: 20px; 
+    color: white; 
+    font-weight: bold; 
+    font-size: 0.9em; 
+}
 """
 
-class ArchonFinalMaster:
+class ArchonFinalNagari:
     def __init__(self):
         self.load_data()
 
     def load_data(self):
-        # Fase 1: Foundation (Cek presensi file)
-        try:
-            self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date']).fillna("Umum")
-            self.df_cust = pd.read_csv('customers.csv')
-            self.df_bal = pd.read_csv('balances_revised.csv', parse_dates=['month']).fillna(0)
-            self.df_rep = pd.read_csv('repayments_revised.csv', parse_dates=['due_date']).fillna("on_time")
-        except Exception as e:
-            print(f"Error Loading CSV: {e}")
-
-    def analyze_engine(self, customer_id):
-        # 1. Bersihkan input ID (atasi spasi/case sensitive)
+        # Proteksi maksimal terhadap error ID (C0014, dll)
+        # Menggunakan 'df.copy()' untuk menghindari setting-with-copy warning
+        self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date']).fillna("Transaksi")
+        self.df_cust = pd.read_csv('customers.csv').fillna(0)
+        self.df_bal = pd.read_csv('balances_revised.csv', parse_dates=['month']).fillna(0)
+        self.df_rep = pd.read_csv('repayments_revised.csv', parse_dates=['due_date']).fillna("on_time")
+
+    def analyze_logic(self, customer_id):
         cid = str(customer_id).strip().upper()
-        
         u_txn = self.df_txn[self.df_txn['customer_id'] == cid].copy()
         u_bal = self.df_bal[self.df_bal['customer_id'] == cid].sort_values('month')
         u_rep = self.df_rep[self.df_rep['customer_id'] == cid]
-        u_info_list = self.df_cust[self.df_cust['customer_id'] == cid]
+        u_info_df = self.df_cust[self.df_cust['customer_id'] == cid]
 
-        if u_txn.empty or u_info_list.empty:
-            return None
+        if u_txn.empty or u_info_df.empty: return None
 
-        u_info = u_info_list.iloc[0]
+        u_info = u_info_df.iloc[0]
 
-        # --- FASE 4: RISK SCORING (ADAPTIVE LOGIC) ---
+        # --- FASE 4: RISK SCORING (DETERMINISTIC) ---
         income_txn = u_txn[u_txn['transaction_type'] == 'credit']['amount'].sum()
-        # Perbaikan: Gunakan income profil jika di mutasi tidak tercatat
-        base_income = max(income_txn, u_info['monthly_income'])
+        # Perbaikan logic ratio: Gunakan gaji bulanan jika mutasi kosong
+        ref_income = max(income_txn, u_info['monthly_income'])
         expense = u_txn[u_txn['transaction_type'] == 'debit']['amount'].sum()
-        er = min(expense / base_income, 1.0) if base_income > 0 else 1.0 # Cap di 100% untuk kewajaran visual
+        er = min(expense / ref_income, 1.0) if ref_income > 0 else 1.0
         
-        # Scoring Parameters (Fase 4 - Bobot 30/20/20/20/10)
         er_s = 1.0 if er > 0.8 else (0.5 if er > 0.5 else 0.0)
         bt_s = 1.0 if len(u_bal) >= 2 and u_bal.iloc[-1]['avg_balance'] < u_bal.iloc[-2]['avg_balance'] else 0.0
         od_s = 1.0 if (u_bal['min_balance'] <= 0).any() else 0.0
         mp_s = 1.0 if (u_rep['status'] == 'late').any() else 0.0
         
-        final_score = (0.3 * er_s) + (0.2 * bt_s) + (0.2 * od_s) + (0.2 * mp_s) + 0.1
-        risk_lv = "HIGH" if final_score >= 0.7 else ("MEDIUM" if final_score >= 0.4 else "LOW")
+        score = (0.3 * er_s) + (0.2 * bt_s) + (0.2 * od_s) + (0.2 * mp_s) + 0.1
+        risk_lv = "HIGH" if score >= 0.7 else ("MEDIUM" if score >= 0.4 else "LOW")
         
-        return risk_lv, final_score, er, u_bal, u_txn, base_income, expense
+        return risk_lv, score, er, u_bal, u_txn, expense, ref_income
 
-    def generate_expert_report(self, risk_lv, score, er, u_bal, expense, base_income):
-        # Penjelasan Adaptif (Non-Template)
-        msg = f"### 🛡️ Interpretasi Intelijen Archon\n"
+    def get_deterministic_report(self, risk_lv, score, er, u_bal, expense, income):
+        # Backup Advisor: Penjelasan Adaptif Berbasis Data (Anti-Template)
+        msg = f"### 📑 HASIL AUDIT ARCHON ({risk_lv})\n"
+        msg += f"Berdasarkan analisis bobot Fase 4 (Risk Scoring), Bapak/Ibu memiliki skor resiliensi **{score:.2f}**.\n\n"
+        
+        msg += f"**Analisis Arus Kas:**\n"
+        msg += f"- Pengeluaran aktual bulan ini sebesar Rp{expense:,.0f} ({er:.1%} dari total daya beli).\n"
+        if er > 0.8: msg += "- ⚠️ Pengeluaran melampaui batas aman perbankan. Segera tinjau pos pengeluaran lifestyle.\n"
+        else: msg += "- ✅ Struktur pengeluaran Anda masih dalam batas terkendali.\n\n"
         
-        # Penjelasan Risk Score secara Adaptif
-        if risk_lv == "HIGH":
-            msg += f"⚠️ **Status Risiko Tinggi ({score:.2f})**: Berdasarkan analisis pola mutasi, Bapak/Ibu berada dalam zona risiko finansial kritis. "
-        elif risk_lv == "MEDIUM":
-            msg += f"🔔 **Status Risiko Menengah ({score:.2f})**: Anda terpantau dalam pantauan dini (Early Warning). Kondisi ini memerlukan penyesuaian anggaran segera. "
-        else:
-            msg += f"✅ **Status Risiko Rendah ({score:.2f})**: Struktur keuangan Bapak/Ibu sangat sehat dan resilien terhadap fluktuasi ekonomi. "
-
-        # Analisis Rasio
-        msg += f"\n\n**Analisis Rasio Pengeluaran ({er:.1%}):**\n"
-        msg += f"Bapak/Ibu menghabiskan Rp{expense:,.0f} dari total daya serap pendapatan sebesar Rp{base_income:,.0f}. "
-        if er > 0.8:
-            msg += "Angka ini sudah melampaui batas aman perbankan (80%). Prioritaskan pemangkasan biaya gaya hidup."
-        else:
-            msg += "Alokasi pengeluaran Anda masih dalam batas terkendali dan sehat."
-
-        # Analisis Saldo
         latest_avg = u_bal.iloc[-1]['avg_balance'] if not u_bal.empty else 0
-        msg += f"\n\n**Analisis Kesehatan Saldo:**\n"
-        msg += f"Saldo rata-rata terakhir berada di angka Rp{latest_avg:,.0f}. "
+        msg += f"**Analisis Saldo:**\n"
+        msg += f"- Saldo rata-rata saat ini Rp{latest_avg:,.0f}. "
         if len(u_bal) > 1 and latest_avg < u_bal.iloc[-2]['avg_balance']:
-            msg += "Waspada: Terjadi tren penurunan saldo yang konsisten dalam dua periode terakhir."
+            msg += "Terdeteksi tren penurunan saldo dibanding periode sebelumnya."
         else:
-            msg += "Stabilitas saldo terpantau positif dan memberikan cadangan kas yang cukup."
+            msg += "Pertumbuhan saldo terpantau stabil."
             
         return msg
 
-    def get_generative_advice(self, risk_lv, er, cust_id, u_txn):
-        # FASE 5: NBO (GENERATIVE AI - BACKUP)
-        if not ai_client: return None
-        merchants = u_txn.tail(2)['raw_description'].tolist()
-        prompt = f"Advisor Bank Nagari: Nasabah {cust_id} risiko {risk_lv}, pengeluaran {er:.1%}. Transaksi terakhir di {merchants}. Beri 1 saran finansial hangat & solutif untuk Bapak/Ibu (Maks 3 kalimat)."
+    def get_llm_advice(self, risk_lv, er, cust_id, u_txn):
+        # FASE 5: NBO GENERATIVE (GEMINI)
+        if not client_gemini: return None
+        merchants = u_txn.tail(3)['raw_description'].tolist()
+        prompt = f"""
+        Identitas: Senior Banker Bank Nagari. 
+        Analisis Nasabah {cust_id}: Risiko {risk_lv}, Pengeluaran {er:.1%}.
+        Transaksi Terakhir: {merchants}.
+        Berikan saran finansial yang sangat hangat, empatik, dan SANGAT PERSONAL dalam Bahasa Indonesia. 
+        Hubungkan saran Anda dengan transaksi terakhir mereka di {merchants[0] if merchants else 'merchant'}. 
+        Gunakan sapaan Bapak/Ibu. Maksimal 3 kalimat pendek.
+        """
         try:
-            resp = ai_client.models.generate_content(model="gemini-1.5-flash", contents=prompt)
+            resp = client_gemini.models.generate_content(model="gemini-1.5-flash", contents=prompt)
             return resp.text
-        except: return None
+        except:
+            return None
 
     def create_viz(self, u_bal, u_txn):
-        # Plot 1: Arus Kas (Nagari Light Blue Palette)
+        # Plot 1: Cashflow (Fase 6)
         u_txn['m'] = u_txn['date'].dt.to_period('M').dt.to_timestamp()
         cf = u_txn.groupby(['m', 'transaction_type'])['amount'].sum().unstack().fillna(0)
         fig1 = go.Figure()
-        fig1.add_trace(go.Bar(x=cf.index, y=cf.get('credit', 0), name='Pemasukan', marker_color='#82C3EB'))
-        fig1.add_trace(go.Bar(x=cf.index, y=cf.get('debit', 0), name='Pengeluaran', marker_color='#0514DE'))
-        fig1.update_layout(title="Laporan Arus Kas", barmode='group', template='plotly_white')
+        fig1.add_trace(go.Bar(x=cf.index, y=cf.get('credit', 0), name='Inflow', marker_color='#82C3EB'))
+        fig1.add_trace(go.Bar(x=cf.index, y=cf.get('debit', 0), name='Outflow', marker_color='#0514DE'))
+        fig1.update_layout(title="Arus Kas Masuk & Keluar", barmode='group', template='plotly_white')
 
-        # Plot 2: Saldo
+        # Plot 2: Balance History
         fig2 = go.Figure()
-        fig2.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], name='Saldo Rata-rata', line=dict(color='#F7BD87', width=4)))
-        fig2.update_layout(title="Kesehatan Pertumbuhan Saldo", template='plotly_white')
+        fig2.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], name='Rata-rata Saldo', line=dict(color='#F7BD87', width=4)))
+        fig2.update_layout(title="Grafik Pertumbuhan Saldo", template='plotly_white')
         return fig1, fig2
 
-# --- UI EXECUTION ---
-engine = ArchonFinalMaster()
+# --- UI LOGIC ---
+engine = ArchonFinalNagari()
 
-def run_archon(cust_id):
-    res = engine.analyze_engine(cust_id)
-    if not res:
-        return "## ❌ ID Tidak Valid atau Tidak Ditemukan", "Pastikan ID menggunakan format C0001 - C0120.", None, None
+def execute(cust_id):
+    res = engine.analyze_logic(cust_id)
+    if not res: return "## ⚠️ Nasabah Tidak Ditemukan", "Cek kembali Customer ID Anda.", None, None
     
-    risk_lv, score, er, u_bal, u_txn, inc, exp = res
-    report_md = engine.generate_expert_report(risk_lv, score, er, u_bal, exp, inc)
-    gen_advice = engine.get_generative_advice(risk_lv, er, cust_id, u_txn)
+    risk_lv, score, er, u_bal, u_txn, exp, inc = res
+    audit_report = engine.get_deterministic_report(risk_lv, score, er, u_bal, exp, inc)
+    ai_advice = engine.get_llm_advice(risk_lv, er, cust_id, u_txn)
     p1, p2 = engine.create_viz(u_bal, u_txn)
     
-    # Gabungkan laporan deterministik dengan Gemini
-    final_analysis = report_md
-    if gen_advice:
-        final_analysis += f"\n\n---\n**💡 Saran Tambahan Virtual Advisor:**\n{gen_advice}"
+    # GABUNGKAN DALAM SATU PANEL (Audit + LLM)
+    final_output = audit_report
+    if ai_advice:
+        final_output += f"\n\n---\n**💡 SARAN VIRTUAL ADVISOR (AI):**\n{ai_advice}"
+    else:
+        final_report += f"\n\n---\n*Koneksi AI Advisor sedang tidak stabil. Menggunakan laporan audit internal.*"
     
+    # Status Badge
     color = "#ef4444" if risk_lv == "HIGH" else ("#f59e0b" if risk_lv == "MEDIUM" else "#10b981")
     status_html = f"""
-    <div class='card-nagari'>
-        <h2 style='color: #0514DE; margin:0;'>Hasil Analisis Archon</h2>
-        <div class='risk-box' style='background:{color}'>{risk_lv} RISK</div>
-        <p style='margin-top:15px; font-size:1.1em;'><b>Risk Score:</b> {score:.2f} / 1.00</p>
-        <p style='font-size:1.1em;'><b>Expense Ratio:</b> {er:.1%}</p>
+    <div class='status-box'>
+        <h2 style='color: #0514DE; margin:0;'>Archon Analysis Result</h2>
+        <span class='badge' style='background:{color}'>{risk_lv} RISK LEVEL</span>
+        <p style='margin-top:15px;'><b>Risk Score:</b> {score:.2f} | <b>Expense Ratio:</b> {er:.1%}</p>
     </div>
     """
-    return status_html, final_analysis, p1, p2
+    return status_html, final_output, p1, p2
 
 with gr.Blocks(css=custom_css) as demo:
-    gr.HTML("<div class='nagari-header'><h1>🏦 ARCHON-AI: BANK NAGARI</h1><p>Advisor Resiliensi Finansial & Pengelolaan Risiko Cerdas</p></div>")
+    gr.HTML("""
+    <div class='nagari-header'>
+        <h1 style='margin:0; font-weight:700;'>🛡️ **ARCHON-AI**: BANK NAGARI</h1>
+        <p style='margin:0; opacity:0.9;'>Sistem Intelijen Resiliensi Finansial & Manajemen Risiko</p>
+    </div>
+    """)
     
     with gr.Row():
         with gr.Column(scale=1):
-            id_in = gr.Textbox(label="Input Customer ID", placeholder="C0001")
-            btn = gr.Button("PROSES ANALISIS", variant="primary")
+            id_in = gr.Textbox(label="Input Customer ID", placeholder="C0001 - C0120")
+            btn = gr.Button("ANALYZE CUSTOMER", variant="primary")
             out_status = gr.HTML()
             gr.Markdown("---")
-            gr.Markdown("ℹ️ **Interpretasi**: Skor risiko menggabungkan analisis saldo, pengeluaran, dan riwayat kredit nasabah secara otomatis.")
+            gr.Markdown("ℹ️ **Audit Info**: Analisis dilakukan secara hibrida menggunakan data mutasi rekening dan profil pendapatan bulanan nasabah.")
         
         with gr.Column(scale=2):
             with gr.Tabs():
-                with gr.TabItem("Arus Kas Bulanan"): plot_cf = gr.Plot()
-                with gr.TabItem("Tren Saldo"): plot_bal = gr.Plot()
-            out_report = gr.Markdown(elem_classes="report-panel")
-
-    btn.click(fn=run_archon, inputs=id_in, outputs=[out_status, out_report, plot_cf, plot_bal])
+                with gr.TabItem("Analysis Summary"):
+                    out_report = gr.Markdown(elem_classes="report-card")
+                with gr.TabItem("Cashflow Charts"):
+                    plot_cf = gr.Plot()
+                with gr.TabItem("Balance History"):
+                    plot_bal = gr.Plot()
+
+    btn.click(fn=execute, inputs=id_in, outputs=[out_status, out_report, plot_cf, plot_bal])
 
 demo.launch()