update

app.py

@@ -6,17 +6,18 @@ import plotly.graph_objects as go
 from google import genai
 from transformers import pipeline
 
-# --- AI SETUP ---
+# --- KONFIGURASI GENERATIVE AI ---
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 client = genai.Client(api_key=GOOGLE_API_KEY)
 
-# --- STYLE BANK NAGARI (BIRU & EMAS) ---
+# --- CUSTOM CSS: THEME BANK NAGARI BLUE & GOLD ---
 custom_css = """
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;700&display=swap');
-.gradio-container { font-family: 'Inter', sans-serif !important; background-color: #f1f5f9 !important; }
-.nagari-header { background: linear-gradient(135deg, #003366 0%, #004080 100%); color: white; padding: 25px; border-radius: 12px; border-bottom: 5px solid #FFD700; margin-bottom: 20px; text-align: center; }
-.card-nagari { background: white; border-radius: 12px; padding: 20px; box-shadow: 0 4px 15px rgba(0,0,0,0.05); border-left: 6px solid #FFD700; }
-.explanation-box { background: #e0f2fe; padding: 15px; border-radius: 8px; font-size: 0.9em; border: 1px solid #bae6fd; }
+@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: #f8fafc !important; }
+.nagari-header { background: linear-gradient(135deg, #003366 0%, #004d99 100%); color: white; padding: 30px; border-radius: 15px; border-bottom: 6px solid #FFD700; margin-bottom: 25px; text-align: center; box-shadow: 0 4px 20px rgba(0,51,102,0.2); }
+.metric-card { background: white; border-radius: 12px; padding: 20px; border-left: 6px solid #FFD700; box-shadow: 0 4px 6px rgba(0,0,0,0.05); }
+.advice-card { background: #fffdf0; border: 1px solid #fde68a; padding: 20px; border-radius: 12px; font-style: italic; color: #1e293b; }
+.interpretation-panel { background: #f1f5f9; padding: 20px; border-radius: 10px; border: 1px solid #e2e8f0; font-size: 0.95em; line-height: 1.6; }
 """
 
 class ArchonNagariEngine:
@@ -26,24 +27,28 @@ class ArchonNagariEngine:
         except: self.classifier = None
 
     def load_data(self):
-        # Fix C0014: fillna memastikan tidak ada crash saat kalkulasi statistik
-        self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date']).fillna({"raw_description": "Transaksi Umum"})
+        # Fase 1: Foundation (Fix C0014 & data mapping)
+        self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date']).fillna({"raw_description": "Transaksi Umum", "counterparty": "Merchant"})
+        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. Validasi & Filter
         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]
+        u_info = self.df_cust[self.df_cust['customer_id'] == customer_id].iloc[0]
         
         if u_txn.empty or u_bal.empty: return None
 
-        # --- FASE 4: RISK SCORING (WEIGHTED) ---
-        income = u_txn[u_txn['transaction_type'] == 'credit']['amount'].sum()
+        # --- FASE 4: RISK SCORING (WEIGHTED 30/20/20/20/10) ---
+        income_txn = u_txn[u_txn['transaction_type'] == 'credit']['amount'].sum()
+        # Perbaikan Logic: Gunakan income profil jika income mutasi tidak normal
+        base_income = max(income_txn, u_info['monthly_income'])
         expense = u_txn[u_txn['transaction_type'] == 'debit']['amount'].sum()
-        er = expense / income if income > 0 else 1.1
+        er = expense / base_income
         
-        # Scoring berdasarkan Bobot Manajemen
         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
@@ -52,36 +57,38 @@ class ArchonNagariEngine:
         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, score, er, u_bal, u_txn
+        return risk_lv, score, er, u_bal, u_txn, base_income, expense
 
-    def get_smart_advice(self, risk_lv, er, cust_id, u_txn):
-        # FASE 5: NBO DENGAN DATA KONTEKSTUAL
-        merchants = u_txn[u_txn['transaction_type'] == 'debit'].tail(3)['raw_description'].tolist()
+    def get_generative_advice(self, risk_lv, er, cust_id, u_txn):
+        # FASE 5: NBO (Smart Prompting)
+        fav_merchant = u_txn[u_txn['transaction_type'] == 'debit']['raw_description'].mode().iloc[0] if not u_txn.empty else "transaksi harian"
         prompt = f"""
-        Halo Gemini, bertindaklah sebagai Virtual Advisor Bank Nagari. 
-        Nasabah {cust_id} berisiko {risk_lv} dengan rasio belanja {er:.1%}. 
-        Transaksi terakhir mereka di: {', '.join(merchants)}.
-        Beri saran finansial yang SANGAT personal (bukan template!), gunakan sapaan 'Bapak/Ibu'. 
-        Hubungkan saranmu dengan merchant transaksi terakhir mereka tadi. Maks 3 kalimat.
+        Identitas: Anda adalah Senior Personal Banker di Bank Nagari.
+        Analisis Nasabah {cust_id}: Risiko {risk_lv}, Rasio Belanja {er:.1%}.
+        Konteks: Nasabah sering bertransaksi di '{fav_merchant}'.
+        Tugas: Berikan saran finansial yang SANGAT personal, hangat (sapa Bapak/Ibu), dan tidak kaku. 
+        Hubungkan saran Anda dengan kebiasaan belanja mereka di '{fav_merchant}' agar terasa nyata. 
+        Maksimal 3 kalimat. Hindari kata-kata bot seperti 'berdasarkan data'.
         """
         try:
             resp = client.models.generate_content(model="gemini-1.5-flash", contents=prompt)
             return resp.text
-        except: return "Bapak/Ibu, kami menyarankan efisiensi pada pos pengeluaran bulan ini agar resiliensi finansial tetap terjaga."
+        except: return "Bapak/Ibu, mari kita tinjau kembali pola pengeluaran bulan ini agar rencana masa depan Anda tetap terjaga dengan aman."
 
-    def create_viz(self, u_bal, u_txn):
-        # Grafik Inflow vs Outflow
+    def create_plots(self, u_bal, u_txn):
+        # Grafik 1: Inflow vs Outflow (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='#10b981'))
         fig1.add_trace(go.Bar(x=cf.index, y=cf.get('debit', 0), name='Pengeluaran', marker_color='#003366'))
-        fig1.update_layout(title="Arus Kas Bulanan", barmode='group', template='plotly_white')
+        fig1.update_layout(title="Laporan Arus Kas Bulanan", barmode='group', template='plotly_white')
 
-        # Grafik Tren Saldo
+        # Grafik 2: Balance History
         fig2 = go.Figure()
-        fig2.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], name='Rata-rata Saldo', line=dict(color='#FFD700', width=4)))
-        fig2.update_layout(title="Tren Saldo Nasabah", template='plotly_white')
+        fig2.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], name='Saldo Rata-rata', line=dict(color='#FFD700', width=4)))
+        fig2.add_trace(go.Bar(x=u_bal['month'], y=u_bal['min_balance'], name='Saldo Minimum', marker_color='#94a3b8', opacity=0.3))
+        fig2.update_layout(title="Tren Pertumbuhan Saldo", template='plotly_white')
         return fig1, fig2
 
 # --- UI LOGIC ---
@@ -89,46 +96,46 @@ engine = ArchonNagariEngine()
 
 def run_app(cust_id):
     res = engine.analyze(cust_id)
-    if not res: return "### ❌ ID Tidak Valid", "Mohon gunakan ID C0001 - C0120", None, None, ""
+    if not res: return "## ❌ ID Tidak Terdaftar", "Gunakan ID C0001-C0120", None, None, ""
     
-    risk_lv, score, er, u_bal, u_txn = res
-    advice = engine.get_smart_advice(risk_lv, er, cust_id, u_txn)
-    f1, f2 = engine.create_viz(u_bal, u_txn)
+    risk_lv, score, er, u_bal, u_txn, inc, exp = res
+    advice = engine.get_generative_advice(risk_lv, er, cust_id, u_txn)
+    f1, f2 = engine.create_plots(u_bal, u_txn)
     
-    # Penjelasan Detail (Explainable Summary)
+    # INTERPRETASI DATA YANG ELEGAN (Fase 6)
     interp = f"""
-    ### 📖 Interpretasi Data Archon
-    * **Risk Score ({score:.2f})**: Dihitung dari 5 parameter (Gaji, Tren Saldo, Overdraft, Cicilan, Volatilitas). Skor di atas 0.7 masuk kategori Risiko Tinggi.
-    * **Expense Ratio ({er:.1%})**: Artinya Bapak/Ibu menghabiskan {er:.1%} dari pendapatan untuk belanja. Idealnya di bawah 50%.
-    * **Grafik Arus Kas**: Jika batang Biru (Pengeluaran) sering lebih tinggi dari Hijau (Pemasukan), ini sinyal bahaya bagi resiliensi keuangan.
+    ### 🛡️ Ringkasan Intelijen Keuangan
+    * **Indeks Risiko ({score:.2f})**: Merupakan hasil evaluasi komprehensif terhadap 5 variabel vital. Status **{risk_lv}** mengindikasikan perlunya { 'tindakan preventif segera' if risk_lv=='HIGH' else 'pemantauan berkala' }.
+    * **Efisiensi Anggaran ({er:.1%})**: Bapak/Ibu mengalokasikan {er:.1%} dari total pemasukan untuk pengeluaran. Kami merekomendasikan batas ideal pengeluaran di angka 50% untuk resiliensi jangka panjang.
+    * **Analisis Visual**: Grafik Arus Kas menunjukkan perbandingan likuiditas. Jika batang Biru dominan, disarankan untuk melakukan restrukturisasi anggaran pada pos pengeluaran gaya hidup.
     """
     
     report_html = f"""
-    <div style='padding: 20px; border-radius: 10px; background: white; border-left: 10px solid {"#ef4444" if risk_lv=="HIGH" else "#f59e0b"};'>
-        <h2 style='color: #003366; margin: 0;'>🛡️ STATUS: {risk_lv}</h2>
+    <div class='metric-card' style='border-left-color: {"#ef4444" if risk_lv=="HIGH" else "#f59e0b"};'>
+        <h2 style='color: #003366; margin:0;'>Status Resilience: {risk_lv}</h2>
         <p style='margin: 5px 0;'>ID Nasabah: {cust_id} | Skor: {score:.2f}</p>
     </div>
     """
     return report_html, advice, f1, f2, interp
 
-with gr.Blocks() as demo:
-    gr.HTML("<div class='nagari-header'><h1>🏦 ARCHON-AI: BANK NAGARI</h1><p>Financial Resilience & Intelligent NBO Engine</p></div>")
+with gr.Blocks(css=custom_css) as demo:
+    gr.HTML("<div class='nagari-header'><h1>🏦 ARCHON-AI: BANK NAGARI</h1><p>Pusat Intelijen Risiko & Resiliensi Finansial Nasabah</p></div>")
     
     with gr.Row():
         with gr.Column(scale=1):
-            with gr.Column(elem_classes="card-nagari"):
-                id_in = gr.Textbox(label="Masukkan Customer ID", placeholder="C0001")
-                btn = gr.Button("PROSES ANALISIS", variant="primary")
+            with gr.Column(elem_classes="metric-card"):
+                id_in = gr.Textbox(label="Customer ID", placeholder="Masukkan ID (C0001)")
+                btn = gr.Button("MULAI ANALISIS", variant="primary")
                 out_status = gr.HTML()
             gr.Markdown("---")
-            out_interp = gr.Markdown(elem_classes="explanation-box")
+            out_interp = gr.Markdown(elem_classes="interpretation-panel")
         
         with gr.Column(scale=2):
             with gr.Tabs():
-                with gr.TabItem("Analisis Arus Kas"): plot_1 = gr.Plot()
-                with gr.TabItem("Tren Saldo"): plot_2 = gr.Plot()
-            out_advice = gr.Textbox(label="Virtual Advisor (Gemini 1.5 Flash)", lines=5)
+                with gr.TabItem("Arus Kas Bulanan"): plot_1 = gr.Plot()
+                with gr.TabItem("Riwayat Saldo"): plot_2 = gr.Plot()
+            out_advice = gr.Markdown(elem_classes="advice-card")
 
     btn.click(fn=run_app, inputs=id_in, outputs=[out_status, out_advice, plot_1, plot_2, out_interp])
 
-demo.launch(css=custom_css)
+demo.launch()