perbaiikan UI

app.py

@@ -6,112 +6,132 @@ import plotly.graph_objects as go
 from transformers import pipeline
 from huggingface_hub import InferenceClient
 
-# --- KONFIGURASI ENGINE ---
+# --- CONFIG & STYLING ---
 HF_TOKEN = os.getenv("HF_TOKEN")
 client = InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.3", token=HF_TOKEN)
 
-class ArchonIndustrialEngine:
+# CSS Kustom untuk font dan tampilan mewah
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap');
+body { font-family: 'Inter', sans-serif; background-color: #f8fafc; }
+.gradio-container { max-width: 1200px !important; }
+.risk-card { padding: 20px; border-radius: 12px; border: 1px solid #e2e8f0; background: white; }
+.high-risk { border-left: 8px solid #ef4444; }
+.medium-risk { border-left: 8px solid #f59e0b; }
+.low-risk { border-left: 8px solid #10b981; }
+"""
+
+class ArchonExecutive:
     def __init__(self):
-        # Fase 2: AI Classifier (Automasi Kategori)
+        self.load_data()
         try:
-            self.classifier = pipeline("text-classification", model="archon_v1", tokenizer="archon_v1")
+            self.classifier = pipeline("text-classification", model="archon_v1")
         except:
             self.classifier = None
-        self.load_data()
 
     def load_data(self):
-        # Fase 1: Data Foundation (Single Source of Truth)
         self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date'])
-        self.df_cust = pd.read_csv('customers.csv')
         self.df_bal = pd.read_csv('balances_revised.csv', parse_dates=['month'])
         self.df_rep = pd.read_csv('repayments_revised.csv', parse_dates=['due_date'])
 
-    def analyze(self, customer_id):
-        # 1. Validation ID
+    def calculate_logic(self, customer_id):
+        # 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]
         
-        if u_txn.empty or u_bal.empty:
-            return None, "ID Nasabah Tidak Ditemukan", None, None
+        if u_txn.empty or u_bal.empty: return None
 
-        # 2. Fase 4: Risk Scoring (Logika Bobot 30/20/20/20/10)
+        # --- FASE 4: DETERMINISTIC SCORING (WEIGHTED) ---
         income = u_txn[u_txn['transaction_type'] == 'credit']['amount'].sum()
         expense = u_txn[u_txn['transaction_type'] == 'debit']['amount'].sum()
-        er = expense / income if income > 0 else 1.1
+        er = expense / income if income > 0 else 1.0
         
-        er_score = 1.0 if er > 0.8 else (0.5 if er > 0.5 else 0.0)
-        bt_score = 1.0 if len(u_bal) >= 2 and u_bal.iloc[-1]['avg_balance'] < u_bal.iloc[-2]['avg_balance'] else 0.0
-        od_score = 1.0 if (u_bal['min_balance'] <= 0).any() else 0.0
-        mp_score = 1.0 if (u_rep['status'] == 'late').any() else 0.0
+        # Scoring per variabel (0, 0.5, 1)
+        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_score) + (0.2 * bt_score) + (0.2 * od_score) + (0.2 * mp_score) + 0.1
-        risk_lv = "HIGH" if final_score >= 0.7 else ("MEDIUM" if final_score >= 0.4 else "LOW")
-
-        # 3. Fase 6: Explainable Summary (Ringkasan Laporan)
-        flags = []
-        if er_score == 1: flags.append("⚠️ Pengeluaran Kritis (>80%)")
-        if bt_score == 1: flags.append("📉 Tren Saldo Menurun")
-        if od_score == 1: flags.append("🚫 Saldo Pernah Minus")
-        if mp_score == 1: flags.append("❌ Riwayat Telat Bayar")
+        # Rumus Bobot Manajemen (Fase 4)
+        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")
         
-        summary_report = f"""
-        ### 📋 LAPORAN RINGKAS NASABAH ({customer_id})
-        - **Status Risiko**: {risk_lv} (Skor: {final_score:.2f})
-        - **Cashflow**: Pemasukan Rp{income:,.0f} | Pengeluaran Rp{expense:,.0f}
-        - **Sinyal Perilaku**: {', '.join(flags) if flags else '✅ Keuangan Stabil'}
-        """
+        return risk_lv, score, er, u_bal, u_txn
 
-        # 4. Fase 5: Generative NBO (Dynamic Advice)
-        prompt = f"""[INST] Anda adalah penasihat keuangan bank. Nasabah {customer_id} memiliki risiko {risk_lv} dengan rasio pengeluaran {er:.2%}. 
-        Gunakan data ini untuk memberi 1 paragraf saran yang personal, tidak kaku, dan solutif dalam Bahasa Indonesia. [/INST]"""
+    def get_llm_advice(self, risk_lv, er, cust_id, u_txn):
+        # FASE 5: NBO ENGINE (LLM)
+        last_txn = u_txn.iloc[-1]['raw_description'] if not u_txn.empty else "N/A"
+        
+        prompt = f"<s>[INST] Anda adalah AI Financial Advisor Bank. Nasabah {cust_id} memiliki risiko {risk_lv} dengan pengeluaran {er:.1%}. Transaksi terakhirnya adalah '{last_txn}'. Berikan 1 saran finansial singkat yang SANGAT NATURAL (tidak kaku), empati, dan solutif dalam Bahasa Indonesia. Jangan gunakan kata 'Nasabah'. [/INST]</s>"
+        
         try:
-            advice = client.text_generation(prompt, max_new_tokens=150)
+            response = client.text_generation(prompt, max_new_tokens=150, temperature=0.7)
+            return response.strip()
         except:
-            advice = "Disarankan melakukan efisiensi pada kategori non-esensial."
-
-        # 5. Fase 6: Visualizations (Income vs Expense & Balance Trend)
-        monthly_data = u_txn.groupby(u_txn['date'].dt.to_period('M')).agg(
-            Inflow=('amount', lambda x: x[u_txn.loc[x.index, 'transaction_type'] == 'credit'].sum()),
-            Outflow=('amount', lambda x: x[u_txn.loc[x.index, 'transaction_type'] == 'debit'].sum())
-        ).reset_index()
-        monthly_data['date'] = monthly_data['date'].dt.to_timestamp()
+            return "Halo! Kami melihat rasio pengeluaran Anda cukup tinggi. Disarankan untuk mulai memantau pengeluaran discretionary bulan ini."
 
-        # Grafik 1: Inflow vs Outflow
-        fig1 = go.Figure()
-        fig1.add_trace(go.Bar(x=monthly_data['date'], y=monthly_data['Inflow'], name='Inflow', marker_color='#2ecc71'))
-        fig1.add_trace(go.Bar(x=monthly_data['date'], y=monthly_data['Outflow'], name='Outflow', marker_color='#e74c3c'))
-        fig1.update_layout(title="Inflow vs Outflow Bulanan", barmode='group', template='plotly_white')
+    def create_viz(self, u_bal, u_txn):
+        # Grafik Tren Saldo
+        fig_bal = go.Figure()
+        fig_bal.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], name='Avg Balance', line=dict(color='#10b981', width=4)))
+        fig_bal.add_trace(go.Bar(x=u_bal['month'], y=u_bal['min_balance'], name='Min Balance', marker_color='#94a3b8', opacity=0.4))
+        fig_bal.update_layout(title="Kesehatan Saldo (Fase 6)", template="plotly_white", margin=dict(t=40, b=0, l=0, r=0))
 
-        # Grafik 2: Balance Trend
-        fig2 = go.Figure()
-        fig2.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], mode='lines+markers', name='Avg Balance', line=dict(color='#3498db', width=3)))
-        fig2.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['min_balance'], mode='lines', name='Min Balance', line=dict(dash='dot', color='#f1c40f')))
-        fig2.update_layout(title="Tren Saldo (Avg vs Min)", template='plotly_white')
+        # Grafik Income vs Expense
+        u_txn['month'] = u_txn['date'].dt.to_period('M').dt.to_timestamp()
+        monthly = u_txn.groupby(['month', 'transaction_type'])['amount'].sum().unstack().fillna(0)
+        
+        fig_cf = go.Figure()
+        fig_cf.add_trace(go.Bar(x=monthly.index, y=monthly.get('credit', 0), name='Income', marker_color='#3b82f6'))
+        fig_cf.add_trace(go.Bar(x=monthly.index, y=monthly.get('debit', 0), name='Expense', marker_color='#f43f5e'))
+        fig_cf.update_layout(title="Income vs Expense", barmode='group', template="plotly_white", margin=dict(t=40, b=0, l=0, r=0))
+        
+        return fig_bal, fig_cf
 
-        return summary_report, advice, fig1, fig2
+# --- INTERFACE ---
+engine = ArchonExecutive()
 
-# --- UI INTERFACE ---
-engine = ArchonIndustrialEngine()
+def analyze_and_show(cust_id):
+    data = engine.calculate_logic(cust_id)
+    if not data: return "## ❌ ID Tidak Ditemukan", "Mohon masukkan ID C0001 - C0120", None, None
+    
+    risk_lv, score, er, u_bal, u_txn = data
+    advice = engine.get_llm_advice(risk_lv, er, cust_id, u_txn)
+    v_bal, v_cf = engine.create_viz(u_bal, u_txn)
+    
+    color_class = "high-risk" if risk_lv == "HIGH" else ("medium-risk" if risk_lv == "MEDIUM" else "low-risk")
+    
+    report_md = f"""
+    <div class="risk-card {color_class}">
+        <h3>🛡️ Hasil Analisis: {risk_lv}</h3>
+        <p><b>Risk Score:</b> {score:.2f} | <b>Expense Ratio:</b> {er:.1%}</p>
+        <hr>
+        <p><i>Sistem mendeteksi aktivitas keuangan nasabah memerlukan perhatian khusus pada manajemen saldo harian.</i></p>
+    </div>
+    """
+    return report_md, advice, v_bal, v_cf
 
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
-    gr.Markdown("# 🪙 Archon-AI: Financial Resilience Dashboard")
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🏦 ARCHON: Financial Intelligence Dashboard")
+    gr.Markdown("Transformasi data transaksi menjadi insight prediktif dan aksi nyata.")
     
     with gr.Row():
         with gr.Column(scale=1):
-            input_id = gr.Textbox(label="Customer ID", placeholder="C0001 - C0120")
-            btn = gr.Button("Jalankan Analisis AI", variant="primary")
-            report_box = gr.Markdown(label="Summary Report")
+            input_id = gr.Textbox(label="Customer ID", placeholder="e.g. C0005")
+            btn = gr.Button("RUN ANALYSIS", variant="primary")
+            report_out = gr.HTML()
         
         with gr.Column(scale=2):
             with gr.Tabs():
-                with gr.TabItem("Cashflow Analysis"):
-                    plot_cash = gr.Plot()
-                with gr.TabItem("Balance History"):
+                with gr.TabItem("Balance Trend"):
                     plot_bal = gr.Plot()
+                with gr.TabItem("Cashflow Insight"):
+                    plot_cf = gr.Plot()
                     
-    advice_box = gr.Textbox(label="NBO (Generative AI)", lines=4)
+    with gr.Row():
+        advice_out = gr.Textbox(label="Archon AI Advice (Pilar 3 & 5)", lines=4)
 
-    btn.click(fn=engine.analyze, inputs=input_id, outputs=[report_box, advice_box, plot_cash, plot_bal])
+    btn.click(fn=analyze_and_show, inputs=input_id, outputs=[report_out, advice_out, plot_bal, plot_cf])
 
 demo.launch()