feat: Refactor Archon analysis engine, enhance risk reporting, and update UI/UX with Nagari branding.

app.py

@@ -4,161 +4,171 @@ import numpy as np
 import gradio as gr
 import plotly.graph_objects as go
 from google import genai
-from transformers import pipeline
 
-# --- KONFIGURASI GENERATIVE AI ---
+# --- KONFIGURASI GENERATIVE AI (GEMINI 2026) ---
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-# Inisialisasi Client (SDK Terbaru 2026)
-client_ai = genai.Client(api_key=GOOGLE_API_KEY) if GOOGLE_API_KEY else None
+# 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 BANK NAGARI ---
-# #0514DE (Deep Blue), #82C3EB (Light Blue), #E0EDF4 (Pale), #F7BD87 (Gold), #FFFFFF (White)
+# --- PALET WARNA NAGARI (BIRU MUDA - EMAS) ---
+# 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');
-.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: 30px; border-radius: 15px; border-bottom: 6px solid #F7BD87; margin-bottom: 25px; text-align: center; }
-.card-nagari { background: #E0EDF4; border-radius: 12px; padding: 20px; border: 1px solid #82C3EB; box-shadow: 0 4px 10px rgba(5, 20, 122, 0.05); }
-.status-badge { display: inline-block; padding: 5px 15px; border-radius: 20px; font-weight: bold; color: white; margin-top: 10px; }
-.report-box { background: white; border-radius: 10px; padding: 20px; border-left: 6px solid #0514DE; line-height: 1.6; }
-.advice-box { background: #fffdf0; border: 1px solid #F7BD87; padding: 20px; border-radius: 10px; }
+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; }
 """
 
-class ArchonNagariEngine:
+class ArchonFinalMaster:
     def __init__(self):
         self.load_data()
-        try: self.classifier = pipeline("text-classification", model="archon_v1")
-        except: self.classifier = None
 
     def load_data(self):
-        # Fase 1: Foundation (Fix C0014 error handling)
-        self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date']).fillna({"raw_description": "Transaksi 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")
-
-    def analyze(self, customer_id):
-        # 1. Filter Data & Validation
-        u_txn = self.df_txn[self.df_txn['customer_id'] == customer_id].copy()
-        u_bal = self.df_bal[self.df_bal['customer_id'] == customer_id].sort_values('month')
-        u_rep = self.df_rep[self.df_rep['customer_id'] == customer_id]
+        # 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)
+        cid = str(customer_id).strip().upper()
         
-        if u_txn.empty or u_bal.empty: return None
+        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]
+
+        if u_txn.empty or u_info_list.empty:
+            return None
+
+        u_info = u_info_list.iloc[0]
 
-        # --- FASE 4: RISK SCORING (DETERMINISTIC AI) ---
+        # --- FASE 4: RISK SCORING (ADAPTIVE LOGIC) ---
         income_txn = u_txn[u_txn['transaction_type'] == 'credit']['amount'].sum()
-        cust_profile = self.df_cust[self.df_cust['customer_id'] == customer_id].iloc[0]
-        base_inc = max(income_txn, cust_profile['monthly_income'])
+        # Perbaikan: Gunakan income profil jika di mutasi tidak tercatat
+        base_income = max(income_txn, u_info['monthly_income'])
         expense = u_txn[u_txn['transaction_type'] == 'debit']['amount'].sum()
-        er = expense / base_inc
+        er = min(expense / base_income, 1.0) if base_income > 0 else 1.0 # Cap di 100% untuk kewajaran visual
         
+        # 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
         
-        # Risk Score 0.0 - 1.0 (Bobot 30/20/20/20/10)
         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")
         
-        return risk_lv, final_score, er, u_bal, u_txn, base_inc, expense
+        return risk_lv, final_score, er, u_bal, u_txn, base_income, expense
 
-    def get_deterministic_explanation(self, risk_lv, score, er, u_bal):
-        # Penjelasan adaptif berdasarkan output riil (Backup jika LLM gagal)
-        exp = f"### 📖 Interpretasi Analisis Archon\n"
+    def generate_expert_report(self, risk_lv, score, er, u_bal, expense, base_income):
+        # Penjelasan Adaptif (Non-Template)
+        msg = f"### 🛡️ Interpretasi Intelijen Archon\n"
+        
+        # Penjelasan Risk Score secara Adaptif
         if risk_lv == "HIGH":
-            exp += f"⚠️ **Level Risiko Tinggi ({score:.2f})**: Perilaku keuangan Anda memerlukan restrukturisasi segera. "
+            msg += f"⚠️ **Status Risiko Tinggi ({score:.2f})**: Berdasarkan analisis pola mutasi, Bapak/Ibu berada dalam zona risiko finansial kritis. "
         elif risk_lv == "MEDIUM":
-            exp += f"🔔 **Level Risiko Menengah ({score:.2f})**: Anda berada dalam zona peringatan dini (Early Warning). "
+            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:
-            exp += f"✅ **Level Risiko Rendah ({score:.2f})**: Resiliensi finansial Anda terpantau sangat sehat. "
+            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}. "
+        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."
+        else:
+            msg += "Stabilitas saldo terpantau positif dan memberikan cadangan kas yang cukup."
             
-        exp += f"\n\n**Rincian Metrik:**\n"
-        exp += f"- **Expense Ratio ({er:.1%})**: Anda menggunakan {er:.1%} pendapatan untuk belanja. "
-        exp += f"{'Sangat boros (>80%), kurangi pengeluaran lifestyle.' if er > 0.8 else 'Cukup aman, namun tetap waspada.'}\n"
-        
-        latest_bal = u_bal.iloc[-1]['avg_balance']
-        exp += f"- **Tren Saldo**: Saldo rata-rata Anda Rp{latest_bal:,.0f}. "
-        exp += f"{'Tren menurun dibanding bulan lalu.' if len(u_bal) > 1 and latest_bal < u_bal.iloc[-2]['avg_balance'] else 'Saldo terpantau stabil/naik.'}"
-        
-        return exp
-
-    def get_gemini_advice(self, risk_lv, er, cust_id, u_txn):
-        # FASE 5: NBO GENERATIVE
-        if not client_ai: return None
-        merchants = u_txn.tail(3)['raw_description'].tolist()
-        prompt = f"""
-        Identitas: Senior Wealth Manager Bank Nagari.
-        Analisis Nasabah {cust_id}: Risiko {risk_lv}, Rasio Pengeluaran {er:.1%}.
-        Riwayat Belanja: {', '.join(merchants)}.
-        Tugas: Berikan saran finansial spesifik & empati untuk Bapak/Ibu. Hubungkan dengan pengeluaran di {merchants[0]}. Maks 3 kalimat.
-        """
+        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)."
         try:
-            resp = client_ai.models.generate_content(model="gemini-1.5-flash", contents=prompt)
+            resp = ai_client.models.generate_content(model="gemini-1.5-flash", contents=prompt)
             return resp.text
         except: return None
 
     def create_viz(self, u_bal, u_txn):
-        # Plot 1: Cashflow (Fase 6)
+        # Plot 1: Arus Kas (Nagari Light Blue Palette)
         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="Inflow vs Outflow Bulanan", barmode='group', template='plotly_white')
+        fig1.update_layout(title="Laporan Arus Kas", barmode='group', template='plotly_white')
 
-        # Plot 2: Balance Trend
+        # Plot 2: Saldo
         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 Saldo (Fase 6)", template='plotly_white')
+        fig2.update_layout(title="Kesehatan Pertumbuhan Saldo", template='plotly_white')
         return fig1, fig2
 
-# --- UI LOGIC ---
-engine = ArchonNagariEngine()
+# --- UI EXECUTION ---
+engine = ArchonFinalMaster()
 
 def run_archon(cust_id):
-    res = engine.analyze(cust_id)
-    if not res: return "## ❌ ID Tidak Valid", "Data tidak ditemukan.", None, None
+    res = engine.analyze_engine(cust_id)
+    if not res:
+        return "## ❌ ID Tidak Valid atau Tidak Ditemukan", "Pastikan ID menggunakan format C0001 - C0120.", None, None
     
     risk_lv, score, er, u_bal, u_txn, inc, exp = res
-    det_exp = engine.get_deterministic_explanation(risk_lv, score, er, u_bal)
-    gen_advice = engine.get_gemini_advice(risk_lv, er, cust_id, u_txn)
-    f1, f2 = engine.create_plots(u_bal, u_txn)
+    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)
+    p1, p2 = engine.create_viz(u_bal, u_txn)
     
-    # Gabungkan penjelasan Deterministik dengan Gemini (Jika Gemini ok)
-    final_report = det_exp
+    # Gabungkan laporan deterministik dengan Gemini
+    final_analysis = report_md
     if gen_advice:
-        final_report += f"\n\n---\n**💡 Rekomendasi Virtual Advisor:**\n{gen_advice}"
-    else:
-        final_report += f"\n\n---\n*Saran Virtual Advisor sedang dalam sinkronisasi sistem.*"
-        
+        final_analysis += f"\n\n---\n**💡 Saran Tambahan Virtual Advisor:**\n{gen_advice}"
+    
     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 AI</h2>
-        <span class='status-badge' style='background:{color}'>{risk_lv} RISK</span>
-        <p style='margin-top:15px;'><b>Risk Score:</b> {score:.2f} / 1.00</p>
-        <p><b>Expense Ratio:</b> {er:.1%}</p>
+        <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>
     """
-    return status_html, final_report, f1, f2
+    return status_html, final_analysis, p1, p2
 
 with gr.Blocks(css=custom_css) as demo:
-    gr.HTML("<div class='nagari-header'><h1>🛡️ ARCHON-AI: FINANCIAL ADVISOR</h1><p>Sistem Intelijen Resiliensi Finansial & Manajemen Risiko</p></div>")
+    gr.HTML("<div class='nagari-header'><h1>🏦 ARCHON-AI: BANK NAGARI</h1><p>Advisor Resiliensi Finansial & Pengelolaan Risiko Cerdas</p></div>")
     
     with gr.Row():
         with gr.Column(scale=1):
-            id_in = gr.Textbox(label="Customer ID", placeholder="Masukkan ID (C0001 - C0120)")
-            btn = gr.Button("PROSES DATA", variant="primary")
+            id_in = gr.Textbox(label="Input Customer ID", placeholder="C0001")
+            btn = gr.Button("PROSES ANALISIS", variant="primary")
             out_status = gr.HTML()
             gr.Markdown("---")
-            gr.Markdown("ℹ️ **Info Metrik**: Skor risiko didasarkan pada perpaduan saldo, rasio belanja, dan kedisiplinan cicilan nasabah.")
+            gr.Markdown("ℹ️ **Interpretasi**: Skor risiko menggabungkan analisis saldo, pengeluaran, dan riwayat kredit nasabah secara otomatis.")
         
         with gr.Column(scale=2):
             with gr.Tabs():
-                with gr.TabItem("Analisis Cashflow"): plot_1 = gr.Plot()
-                with gr.TabItem("Tren Pertumbuhan Saldo"): plot_2 = gr.Plot()
-            out_report = gr.Markdown(elem_classes="report-box")
+                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_1, plot_2])
+    btn.click(fn=run_archon, inputs=id_in, outputs=[out_status, out_report, plot_cf, plot_bal])
 
 demo.launch()