update

app.py

@@ -6,18 +6,19 @@ import plotly.graph_objects as go
 from google import genai
 from transformers import pipeline
 
-# --- KONFIGURASI GENERATIVE AI ---
+# --- KONFIGURASI AI ---
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 client = genai.Client(api_key=GOOGLE_API_KEY)
 
-# --- CUSTOM CSS: THEME BANK NAGARI BLUE & GOLD ---
+# --- PALETTE WARNA & CSS CUSTOM ---
+# Primary: #0514DE, Secondary: #82C3EB, Pale: #E0EDF4, Accent: #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: #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; }
+.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-style { background: #E0EDF4; border-radius: 12px; padding: 20px; border: 1px solid #82C3EB; box-shadow: 2px 4px 10px rgba(5, 20, 222, 0.05); }
+.status-badge { display: inline-block; padding: 5px 15px; border-radius: 20px; font-weight: bold; color: white; }
+.advice-box { background: white; border-left: 5px solid #F7BD87; padding: 20px; border-radius: 10px; font-size: 1.05em; line-height: 1.6; }
 """
 
 class ArchonNagariEngine:
@@ -27,14 +28,14 @@ class ArchonNagariEngine:
         except: self.classifier = None
 
     def load_data(self):
-        # 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"})
+        # Fix C0014: fillna memastikan data siap olah
+        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. Validasi & Filter
+        # Filter Data
         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]
@@ -42,9 +43,8 @@ class ArchonNagariEngine:
         
         if u_txn.empty or u_bal.empty: return None
 
-        # --- FASE 4: RISK SCORING (WEIGHTED 30/20/20/20/10) ---
+        # --- FASE 4: RISK SCORING (WEIGHTED) ---
         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 / base_income
@@ -59,83 +59,84 @@ class ArchonNagariEngine:
         
         return risk_lv, score, er, u_bal, u_txn, base_income, expense
 
-    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"
+    def get_adaptive_advice(self, risk_lv, er, score, u_txn, base_income, expense):
+        # FASE 5 & 6: ADAPTIVE GEN-AI EXPLANATION
+        recent_txn = u_txn.tail(3)['raw_description'].tolist()
+        # Prompt yang lebih teknis dan mendalam agar Gemini tidak template
         prompt = f"""
-        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'.
+        Identitas: Anda adalah Senior Financial Advisor Archon di Bank Nagari.
+        Analisis Data Nasabah:
+        - Status Risiko: {risk_lv} (Skor: {score:.2f}/1.00)
+        - Rasio Pengeluaran: {er:.2%} (Pengeluaran: Rp{expense:,.0f} dari Pendapatan: Rp{base_income:,.0f})
+        - Riwayat Belanja Terakhir: {', '.join(recent_txn)}
+        
+        Tugas:
+        1. Jelaskan arti skor {score:.2f} dan level {risk_lv} secara singkat dan logis.
+        2. Berikan saran personal yang dihubungkan dengan pengeluaran {er:.2%} dan riwayat belanja tersebut.
+        3. Gunakan sapaan Bapak/Ibu, bahasa Indonesia yang hangat namun profesional.
+        4. Jangan gunakan pembukaan template, langsung ke inti analisis.
         """
         try:
             resp = client.models.generate_content(model="gemini-1.5-flash", contents=prompt)
             return resp.text
-        except: return "Bapak/Ibu, mari kita tinjau kembali pola pengeluaran bulan ini agar rencana masa depan Anda tetap terjaga dengan aman."
+        except:
+            return "Mohon maaf, sistem advisor sedang melakukan pemeliharaan. Silakan merujuk pada metrik risiko di panel kiri."
 
     def create_plots(self, u_bal, u_txn):
-        # Grafik 1: Inflow vs Outflow (Fase 6)
+        # Plot 1: Cashflow (Nagari Colors)
         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="Laporan Arus Kas Bulanan", barmode='group', template='plotly_white')
+        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="Arus Kas Bulanan", barmode='group', template='plotly_white')
 
-        # Grafik 2: Balance History
+        # 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='#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')
+        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="Tren Saldo", template='plotly_white')
         return fig1, fig2
 
 # --- UI LOGIC ---
 engine = ArchonNagariEngine()
 
-def run_app(cust_id):
+def process(cust_id):
     res = engine.analyze(cust_id)
-    if not res: return "## ❌ ID Tidak Terdaftar", "Gunakan ID C0001-C0120", None, None, ""
+    if not res: return "## ❌ ID Tidak Valid", "Data tidak ditemukan.", None, None
     
     risk_lv, score, er, u_bal, u_txn, inc, exp = res
-    advice = engine.get_generative_advice(risk_lv, er, cust_id, u_txn)
+    advice = engine.get_adaptive_advice(risk_lv, er, score, u_txn, inc, exp)
     f1, f2 = engine.create_plots(u_bal, u_txn)
     
-    # INTERPRETASI DATA YANG ELEGAN (Fase 6)
-    interp = f"""
-    ### 🛡️ 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.
-    """
+    color = "#ef4444" if risk_lv == "HIGH" else ("#f59e0b" if risk_lv == "MEDIUM" else "#10b981")
     
-    report_html = f"""
-    <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>
+    status_html = f"""
+    <div class='card-style'>
+        <h2 style='color: #0514DE; margin:0;'>Hasil Analisis AI</h2>
+        <p style='margin:10px 0;'>Level Risiko: <span class='status-badge' style='background:{color}'>{risk_lv}</span></p>
+        <p><b>Risk Score:</b> {score:.2f} / 1.00</p>
+        <p><b>Expense Ratio:</b> {er:.1%}</p>
     </div>
     """
-    return report_html, advice, f1, f2, interp
+    return status_html, advice, f1, f2
 
 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>")
+    gr.HTML("<div class='nagari-header'><h1>🛡�� ARCHON-AI: FINANCIAL ADVISOR</h1><p>Inteligensi Manajemen Risiko & Resiliensi Perbankan</p></div>")
     
     with gr.Row():
         with gr.Column(scale=1):
-            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()
+            id_in = gr.Textbox(label="Customer ID", placeholder="C0001 - C0120")
+            btn = gr.Button("RUN ANALYSIS", variant="primary")
+            out_status = gr.HTML()
             gr.Markdown("---")
-            out_interp = gr.Markdown(elem_classes="interpretation-panel")
+            gr.Markdown("**Interpretasi Metrik:**\n* **Risk Score**: Evaluasi gabungan saldo, cicilan, dan belanja.\n* **Expense Ratio**: Persentase gaji yang terpakai bulan ini.")
         
         with gr.Column(scale=2):
             with gr.Tabs():
-                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")
+                with gr.TabItem("Inflow vs Outflow"): plot_1 = gr.Plot()
+                with gr.TabItem("Tren Pertumbuhan Saldo"): plot_2 = gr.Plot()
+            out_advice = gr.Markdown(elem_classes="advice-box")
 
-    btn.click(fn=run_app, inputs=id_in, outputs=[out_status, out_advice, plot_1, plot_2, out_interp])
+    btn.click(fn=process, inputs=id_in, outputs=[out_status, out_advice, plot_1, plot_2])
 
 demo.launch()