refactor: Reorganize Archon engine logic, simplify risk scoring and NBO, and revamp the report UI with new styles and an enhanced AI advisor.

app.py

@@ -6,157 +6,145 @@ import plotly.graph_objects as go
 from google import genai
 from datetime import timedelta
 
-# --- CONFIG AI ---
+# --- INITIALIZATION ---
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 client_ai = genai.Client(api_key=GOOGLE_API_KEY) if GOOGLE_API_KEY else None
 
-# --- UI STYLE BANK NAGARI ---
+# --- UI STYLE BANK NAGARI PREMIUM ---
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;600;700;800&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%); padding: 30px; border-radius: 12px; border-bottom: 6px solid #F7BD87; text-align: center; margin-bottom: 20px; }
-.nagari-header h1 { color: #FFFFFF !important; font-weight: 800 !important; margin: 0; }
-.audit-card { background: white; border-radius: 12px; padding: 25px; border: 1px solid #E2E8F0; line-height: 1.8; }
-.status-card { background: #E0EDF4; padding: 20px; border-radius: 12px; border-left: 8px solid #0514DE; }
+.nagari-header { background: linear-gradient(135deg, #0514DE 0%, #82C3EB 100%); padding: 35px; border-radius: 15px; border-bottom: 6px solid #F7BD87; text-align: center; margin-bottom: 25px; }
+.nagari-header h1 { color: #FFFFFF !important; font-weight: 800 !important; margin: 0; font-size: 2.2em; }
+.report-card { background: #FFFFFF; border-radius: 12px; padding: 25px; border: 1.5px solid #E0EDF4; box-shadow: 0 4px 15px rgba(5, 20, 222, 0.05); margin-bottom: 20px; }
+.section-header { color: #0514DE; border-bottom: 2px solid #F7BD87; padding-bottom: 5px; margin-bottom: 15px; font-weight: 700; }
+.status-pill { padding: 10px 20px; border-radius: 30px; color: white; font-weight: 800; display: inline-block; font-size: 1.1em; }
 """
 
-class ArchonEngine:
+class ArchonNarrativeEngine:
     def __init__(self):
         self.load_data()
 
     def load_data(self):
-        # FASE 1: DATA FOUNDATION
+        # FASE 1: Foundation
         self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date']).sort_values('date')
         self.df_cust = pd.read_csv('customers.csv')
         self.df_bal = pd.read_csv('balances_revised.csv', parse_dates=['month']).sort_values('month')
-        self.df_rep = pd.read_csv('repayments_revised.csv', parse_dates=['due_date'])
+        self.df_rep = pd.read_csv('repayments_revised.csv', parse_dates=['due_date']).fillna("on_time")
 
-    def run_analysis(self, customer_id):
+    def run_full_pipeline(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_df = self.df_cust[self.df_cust['customer_id'] == cid]
+        u_info = self.df_cust[self.df_cust['customer_id'] == cid].iloc[0] if cid in self.df_cust['customer_id'].values else None
 
-        if u_txn.empty or u_info_df.empty: return None
-        u_info = u_info_df.iloc[0]
+        if u_txn.empty or u_info is None: return None
 
-        # --- FASE 2: TRANSACTION INTELLIGENCE ---
-        # 1. Expense Type
-        essential_cats = {'groceries', 'utilities', 'transport', 'healthcare', 'education'}
-        u_txn['expense_type'] = u_txn['raw_description'].apply(lambda x: 'essential' if any(k in x.lower() for k in essential_cats) else 'discretionary')
-        
-        # 2. Risk Spending Flag (Rolling 30D Median)
-        u_txn = u_txn.set_index('date').sort_index()
-        u_txn['rolling_median'] = u_txn['amount'].rolling('30D').median()
-        u_txn['risk_spending_flag'] = ((u_txn['expense_type'] == 'discretionary') & (u_txn['amount'] > u_txn['rolling_median'])).astype(int)
-        u_txn = u_txn.reset_index()
-
-        # 3. Behavior Signal (Normal, Impulsive, Recurring, Anomaly)
-        q75 = u_txn['amount'].quantile(0.75)
-        def get_behavior(row):
-            if row['expense_type'] == 'discretionary' and row['amount'] > q75: return 'impulsive'
-            return 'normal'
-        u_txn['behavior_signal'] = u_txn.apply(get_behavior, axis=1)
-
-        # --- FASE 3 & 4: RISK LABELING ---
-        income = u_txn[u_txn['transaction_type'] == 'credit']['amount'].sum()
+        # --- FASE 2 & 3: INTELLIGENCE ---
+        income_txn = u_txn[u_txn['transaction_type'] == 'credit']['amount'].sum()
+        ref_income = max(income_txn, u_info['monthly_income'])
         expense = u_txn[u_txn['transaction_type'] == 'debit']['amount'].sum()
-        ref_inc = max(income, u_info['monthly_income'])
-        er = min(expense / ref_inc, 1.0)
+        er = min(expense / ref_income, 1.0)
         
+        # --- FASE 4: RISK SCORING (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
-        vol_s = 0.5 # Default stability score
         
-        # Formula: 0.3*ER + 0.2*BT + 0.2*OD + 0.2*MP + 0.1*VOL
-        risk_score = (0.3 * er_s) + (0.2 * bt_s) + (0.2 * od_s) + (0.2 * mp_s) + (0.1 * vol_s)
-        risk_lv = "HIGH" if risk_score >= 0.7 else ("MEDIUM" if risk_score >= 0.4 else "LOW")
-
-        # --- FASE 5: NBO ENGINE ---
-        disc_ratio = u_txn[u_txn['expense_type'] == 'discretionary']['amount'].sum() / expense if expense > 0 else 0
-        if risk_lv == "HIGH" or mp_s == 1:
-            action, reason = "Restructuring Suggestion", "repayment_risk_detected"
-        elif er > 0.6:
-            action, reason = "Spending Control", "high_discretionary_spending"
-        elif risk_lv == "LOW" and disc_ratio < 0.3:
-            action, reason = "Promote Investment", "surplus_balance"
-        else:
-            action, reason = "Financial Education", "stable_cashflow"
-
-        return risk_lv, risk_score, er, disc_ratio, u_bal, u_txn, action, reason
-
-    def get_report(self, risk_lv, score, er, dr, u_bal, action, reason, cid, u_txn):
-        # FASE 6: EXPLAINABLE SUMMARY (RAPI POIN-POIN)
-        report = f"### 📊 HASIL AUDIT FINANCIAL RESILIENCE\n\n"
-        report += f"**1. Parameter Risiko Utama (Fase 4)**\n"
-        report += f"* **Risk Level**: **{risk_lv}** (Skor: {score:.2f})\n"
-        report += f"* **Expense Ratio**: {er:.1%}. Penggunaan dana terpantau {'kritis' if er > 0.8 else 'stabil'}.\n"
-        report += f"* **Discretionary Ratio**: {dr:.1%}. Porsi belanja gaya hidup nasabah.\n\n"
+        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")
+        
+        # --- FASE 5: NBO ---
+        if risk_lv == "HIGH": action, nbo_desc = "Restructuring Suggestion", "Fokus pada penyelamatan kredit."
+        elif er > 0.6: action, nbo_desc = "Spending Control", "Batasi pengeluaran non-esensial."
+        else: action, nbo_desc = "Promote Saving/Investment", "Optimalkan surplus dana nasabah."
+
+        return risk_lv, score, er, u_bal, u_txn, expense, ref_income, action, nbo_desc
+
+    def build_narrative_report(self, risk_lv, score, er, u_bal, expense, income, action, nbo_desc, cid, u_txn):
+        # FASE 6: EXPLAINABLE SUMMARY & RINGKASAN AI
+        report = f"<div class='report-card'>"
+        report += f"<h3 class='section-header'>🌟 EXECUTIVE SUMMARY (RINGKASAN AI)</h3>"
+        
+        # Ringkasan Naratif Otomatis
+        report += f"<p>Berdasarkan analisis menyeluruh terhadap data mutasi dan profil Bapak/Ibu ({cid}), sistem Archon menetapkan tingkat resiliensi finansial Anda berada pada kategori <b>{risk_lv}</b>. "
+        report += f"Hal ini disebabkan oleh kombinasi antara rasio pengeluaran sebesar {er:.1%} dan stabilitas saldo yang memerlukan perhatian khusus.</p>"
         
-        report += f"**2. Analisis Perilaku & Saldo (Fase 2 & 3)**\n"
-        latest_avg = u_bal.iloc[-1]['avg_balance'] if not u_bal.empty else 0
-        report += f"* **Kondisi Saldo**: Rp{latest_avg:,.0f}. {'Tren menurun' if len(u_bal) > 1 and latest_avg < u_bal.iloc[-2]['avg_balance'] else 'Tren positif'}.\n"
-        report += f"* **Sinyal Dominan**: {u_txn['behavior_signal'].mode()[0].upper()}\n\n"
+        report += f"<h3 class='section-header'>🔍 MENGAPA SKOR ANDA {score:.2f}?</h3>"
+        report += f"<ul>"
+        report += f"<li><b>Rasio Pengeluaran ({er:.1%})</b>: Artinya, dari setiap Rp1.000.000 yang Bapak/Ibu terima, sebanyak Rp{int(er*1000000):,} habis digunakan. {'Angka ini kritis karena melebihi batas aman 80%.' if er > 0.8 else 'Kondisi ini sehat karena di bawah batas aman.'}</li>"
         
-        report += f"**3. Rekomendasi Aksi (NBO Engine)**\n"
-        report += f"* **Recommended Action**: **{action}**\n"
-        report += f"* **Action Reason**: {reason.replace('_', ' ').title()}\n"
+        if len(u_bal) > 1:
+            trend = "menurun" if u_bal.iloc[-1]['avg_balance'] < u_bal.iloc[-2]['avg_balance'] else "meningkat"
+            report += f"<li><b>Tren Saldo</b>: Saldo rata-rata Anda terpantau <b>{trend}</b>. Tren ini digunakan bank untuk melihat kemampuan Bapak/Ibu dalam menghadapi kebutuhan mendadak di masa depan.</li>"
+        
+        report += f"</ul>"
 
+        report += f"<h3 class='section-header'>🎯 REKOMENDASI TINDAKAN (NBO)</h3>"
+        report += f"<p>Sistem merekomendasikan aksi: <b>{action}</b>. <br><i>Mengapa?</i> {nbo_desc}</p>"
+        
+        # Gemini Section
         if client_ai:
             try:
-                tx = u_txn.tail(2)['raw_description'].tolist()
-                prompt = f"Advisor Bank Nagari: Nasabah {cid} ({risk_lv} risk). Saran hangat personal (Bapak/Ibu) terkait {tx}, maks 2 kalimat."
-                advice = client_ai.models.generate_content(model="gemini-1.5-flash", contents=prompt).text
-                report += f"\n---\n**💡 SARAN VIRTUAL ADVISOR:**\n_{advice}_"
+                tx = u_txn.tail(1)['raw_description'].iloc[0]
+                prompt = f"Advisor Bank Nagari: Nasabah {cid} risiko {risk_lv}, pengeluaran {er:.1%}. Terakhir belanja di {tx}. Beri saran hangat personal (Bapak/Ibu) maks 2 kalimat."
+                resp = client_ai.models.generate_content(model="gemini-1.5-flash", contents=prompt)
+                report += f"<div style='background:#f0f9ff; padding:15px; border-radius:10px; margin-top:10px; border:1px solid #bae6fd;'><b>💡 Pesan Personal Advisor:</b><br>_{resp.text}_</div>"
             except: pass
             
+        report += "</div>"
         return report
 
 # --- UI LOGIC ---
-engine = ArchonEngine()
+engine = ArchonNarrativeEngine()
 
-def run_app(cust_id):
-    data = engine.run_analysis(cust_id)
-    if not data: return "## ❌ ID Tidak Valid", "Gunakan C0001 - C0120", None, None
+def process(cust_id):
+    res = engine.run_full_pipeline(cust_id)
+    if not res: return "## ❌ ID Tidak Ditemukan", "Coba gunakan C0001 - C0120", None, None
     
-    risk_lv, score, er, dr, u_bal, u_txn, action, reason = data
-    report = engine.get_report(risk_lv, score, er, dr, u_bal, action, reason, cust_id, u_txn)
+    risk_lv, score, er, u_bal, u_txn, exp, inc, action, nbo_desc = res
+    narrative = engine.build_narrative_report(risk_lv, score, er, u_bal, exp, inc, action, nbo_desc, cust_id, u_txn)
     
-    # Graphs
+    # Graphs with Interpretation
+    color = "#0514DE"
     f1 = go.Figure()
-    f1.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], name='Avg Balance', line=dict(color='#0514DE', width=4)))
-    f1.update_layout(title="Tren Pertumbuhan Saldo", template="plotly_white", margin=dict(t=40, b=40, l=40, r=40))
+    f1.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], name='Rata-rata Saldo', line=dict(color='#F7BD87', width=4)))
+    f1.update_layout(title="Kesehatan Pertumbuhan Saldo", template="plotly_white")
     
     f2 = go.Figure()
     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)
-    f2.add_trace(go.Bar(x=cf.index, y=cf.get('credit', 0), name='Income', marker_color='#82C3EB'))
-    f2.add_trace(go.Bar(x=cf.index, y=cf.get('debit', 0), name='Expense', marker_color='#0514DE'))
-    f2.update_layout(title="Arus Kas Bulanan", barmode='group', template='plotly_white', margin=dict(t=40, b=40, l=40, r=40))
+    f2.add_trace(go.Bar(x=cf.index, y=cf.get('credit', 0), name='Pemasukan', marker_color='#82C3EB'))
+    f2.add_trace(go.Bar(x=cf.index, y=cf.get('debit', 0), name='Pengeluaran', marker_color='#0514DE'))
+    f2.update_layout(title="Laporan Arus Kas", barmode='group', template='plotly_white')
 
-    color = "#ef4444" if risk_lv == "HIGH" else ("#f59e0b" if risk_lv == "MEDIUM" else "#10b981")
-    status_html = f"""<div class='status-card'><h2 style='color: #0514DE; margin:0;'>Ringkasan AI</h2><div style='background:{color}; color:white; padding:5px 15px; border-radius:20px; font-weight:bold; display:inline-block; margin-top:10px;'>{risk_lv} RISK</div></div>"""
+    status_color = "#ef4444" if risk_lv == "HIGH" else ("#f59e0b" if risk_lv == "MEDIUM" else "#10b981")
+    status_html = f"""<div style='text-align:center;'><span class='status-pill' style='background:{status_color}'>{risk_lv} RISK</span><br><p style='margin-top:10px;'>Risk Score: <b>{score:.2f}</b></p></div>"""
     
-    return status_html, report, f1, f2
+    return status_html, narrative, f1, f2
 
 with gr.Blocks(css=custom_css) as demo:
     gr.HTML("<div class='nagari-header'><h1>ARCHON-AI</h1></div>")
+    
     with gr.Row():
         with gr.Column(scale=1):
             id_in = gr.Textbox(label="Customer ID", placeholder="C0001")
             btn = gr.Button("ANALYZE CUSTOMER", variant="primary")
-            status_out = gr.HTML()
+            out_status = gr.HTML()
+        
         with gr.Column(scale=2):
             with gr.Tabs():
-                with gr.Tab("Audit Report"):
-                    report_out = gr.Markdown(elem_classes="audit-card")
-                with gr.Tab("Visual Analytics"):
-                    plot_cf = gr.Plot(label="Cashflow")
-                    plot_bal = gr.Plot(label="Balance")
-
-    btn.click(fn=run_app, inputs=id_in, outputs=[status_out, report_out, plot_cf, plot_bal])
+                with gr.Tab("📋 Executive Briefing"):
+                    out_report = gr.HTML()
+                with gr.Tab("📈 Visual Analytics"):
+                    gr.Markdown("### Interpretasi Arus Kas\nBatang biru (Pengeluaran) tidak boleh sering melebihi batang muda (Pemasukan).")
+                    plot_cf = gr.Plot()
+                    gr.Markdown("---")
+                    gr.Markdown("### Tren Saldo\nGrafik yang naik menunjukkan nasabah memiliki daya tahan terhadap krisis ekonomi.")
+                    plot_bal = gr.Plot()
+
+    btn.click(fn=process, inputs=id_in, outputs=[out_status, out_report, plot_cf, plot_bal])
 
 demo.launch()