feat: Enhance customer analysis with transaction intelligence, refine NBO logic, and update UI styling and project metadata.

README.md

@@ -1,7 +1,7 @@
 ---
 title: Archon AI
 emoji: 🪙
-colorFrom: purple
+colorFrom: blue
 colorTo: gray
 sdk: gradio
 sdk_version: 6.4.0

app.py

@@ -4,225 +4,149 @@ import numpy as np
 import gradio as gr
 import plotly.graph_objects as go
 from google import genai
+from datetime import timedelta
 
-# --- CONFIG AI ---
+# --- CONFIG & AI ---
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-client_ai = None
-if GOOGLE_API_KEY:
-    try:
-        client_ai = genai.Client(api_key=GOOGLE_API_KEY)
-    except:
-        client_ai = None
-
-# --- UI STYLE: BANK NAGARI (LIGHT BLUE & GOLD) ---
+client_ai = genai.Client(api_key=GOOGLE_API_KEY) if GOOGLE_API_KEY else None
+
+# --- UI STYLING ---
 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: 35px; 
-    border-radius: 15px; 
-    border-bottom: 6px solid #F7BD87; 
-    margin-bottom: 25px; 
-    text-align: center;
-}
-
-/* Memastikan teks ARCHON-AI BOLD PUTIH */
-.nagari-header h1 { 
-    color: #FFFFFF !important; 
-    font-weight: 800 !important; 
-    margin: 0; 
-    text-shadow: 2px 2px 4px rgba(0,0,0,0.2);
-}
-
-.report-card { 
-    background: #FFFFFF; 
-    border-radius: 15px; 
-    padding: 25px; 
-    border: 1.5px solid #E0EDF4; 
-    box-shadow: 0 4px 15px rgba(5, 20, 222, 0.05); 
-}
-
-.status-card { 
-    background: #E0EDF4; 
-    padding: 20px; 
-    border-radius: 12px; 
-    border-left: 8px solid #0514DE; 
-    margin-bottom: 20px;
-}
-
-.nbo-box {
-    background: #fffdf0;
-    border: 2px solid #F7BD87;
-    padding: 20px;
-    border-radius: 10px;
-    margin-top: 15px;
-}
+.nagari-header { background: linear-gradient(135deg, #0514DE 0%, #82C3EB 100%); padding: 35px; border-radius: 15px; border-bottom: 6px solid #F7BD87; margin-bottom: 25px; text-align: center; }
+.nagari-header h1 { color: #FFFFFF !important; font-weight: 800 !important; margin: 0; }
+.card { background: #FFFFFF; border-radius: 12px; padding: 20px; border: 1px solid #E0EDF4; box-shadow: 0 4px 10px rgba(0,0,0,0.05); }
+.status-pill { padding: 5px 15px; border-radius: 20px; color: white; font-weight: bold; font-size: 0.9em; }
 """
 
-class ArchonMasterEngine:
+class ArchonBankNagari:
     def __init__(self):
         self.load_data()
 
     def load_data(self):
-        try:
-            # Proteksi ID error (C0014, dll) dengan sanitasi data awal
-            self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date']).fillna({"raw_description": "Transaksi Umum"})
-            self.df_cust = pd.read_csv('customers.csv').fillna(0)
-            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"File Error: {e}")
+        # FASE 1: DATA 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_off = pd.read_csv('offers.csv')
 
     def analyze(self, customer_id):
-        # Sanitasi ID: Hapus spasi dan jadikan huruf besar
         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]
-
-        if u_txn.empty or u_info_df.empty: return None
+        u_bal = self.df_bal[self.df_bal['customer_id'] == cid].copy()
+        u_rep = self.df_rep[self.df_rep['customer_id'] == cid].copy()
+        u_info = self.df_cust[self.df_cust['customer_id'] == cid].iloc[0] if cid in self.df_cust['customer_id'].values else None
 
-        u_info = u_info_df.iloc[0]
+        if u_txn.empty or u_info is None: return None
 
-        # --- FASE 4: DETERMINISTIC CALCULATION ---
-        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()
-        er = min(expense / ref_income, 1.0) if ref_income > 0 else 1.0
+        # --- FASE 2: TRANSACTION INTELLIGENCE ---
+        # Mapping 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')
         
-        # Penentuan Discretionary Ratio (Estimasi Fase 2)
-        disc_keywords = ['online', 'shopee', 'tokopedia', 'cafe', 'resto', 'cinema', 'travel', 'fashion', 'promo']
-        disc_spending = u_txn[(u_txn['transaction_type'] == 'debit') & 
-                             (u_txn['raw_description'].str.lower().str.contains('|'.join(disc_keywords)))]['amount'].sum()
-        disc_ratio = disc_spending / expense if expense > 0 else 0
+        # Risk Spending Flag (Rolling 30 days 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()
+
+        # Behavior Signal
+        q75 = u_txn['amount'].quantile(0.75)
+        def get_signal(row):
+            if row['expense_type'] == 'discretionary' and row['amount'] > q75: return 'impulsive'
+            return 'normal'
+        u_txn['behavior_signal'] = u_txn.apply(get_signal, axis=1)
+
+        # --- FASE 3 & 4: AGGREGATION & RISK ---
+        income = u_txn[u_txn['transaction_type'] == 'credit']['amount'].sum()
+        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)
         
-        # Scoring logic (Fase 4 - Bobot 30/20/20/20/10)
+        # 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
         
-        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, disc_ratio, u_bal, u_txn, expense, ref_income, mp_s
+        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")
 
-    def build_expert_nbo(self, risk_lv, score, er, disc_ratio, u_bal, expense, income, mp_s, cid, u_txn):
-        # --- FASE 5: NBO DECISION ENGINE (LOGIKA ADAPTIF) ---
-        msg = f"### 📊 LAPORAN INTELIJEN ARCHON\n"
-        msg += f"Berdasarkan profil mutasi, tingkat resiliensi Bapak/Ibu berada di level **{risk_lv}** (Skor: {score:.2f}).\n\n"
-        
-        # 1. PENJELASAN METRIK (FASE 6)
-        msg += f"**Analisis Arus Kas:**\n"
-        msg += f"- **Rasio Pengeluaran ({er:.1%})**: Anda menghabiskan Rp{expense:,.0f} dari pendapatan Rp{income:,.0f}. "
-        if er > 0.8: msg += "⚠️ Kondisi ini kritis bagi stabilitas jangka panjang."
-        
-        if not u_bal.empty:
-            last_bal = u_bal.iloc[-1]['avg_balance']
-            msg += f"\n- **Stabilitas Saldo**: Saldo rata-rata terakhir Rp{last_bal:,.0f}. "
-            if len(u_bal) > 1 and last_bal < u_bal.iloc[-2]['avg_balance']: msg += "📉 Tren menurun terpantau."
-        
-        # 2. PENENTUAN AKSI NBO (FASE 5)
-        action = ""
-        reason = ""
-        
+        # --- 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 = "Restructuring Suggestion"
-            reason = "Terdeteksi tekanan pada pembayaran cicilan atau saldo kritis. Fokus pada penyelamatan likuiditas."
-        elif disc_ratio > 0.6:
-            action = "Spending Control"
-            reason = "Pengeluaran gaya hidup (discretionary) melebihi 60%. Perlu pembatasan transaksi non-esensial segera."
-        elif risk_lv == "MEDIUM" and disc_ratio > 0.4:
-            action = "Budgeting Alert"
-            reason = "Pola belanja mulai tidak stabil. Disarankan mengaktifkan fitur notifikasi budget."
-        elif risk_lv == "LOW" and disc_ratio <= 0.3:
-            action = "Promote Investment / Saving Boost"
-            reason = "Kondisi finansial sangat prima dengan surplus dana. Waktunya memaksimalkan pertumbuhan aset."
+            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 = "Financial Education"
-            reason = "Stabilitas terjaga, namun diperlukan literasi untuk pengelolaan cashflow yang lebih optimal."
-
-        msg += f"\n\n<div class='nbo-box'>**🎯 REKOMENDASI AKSI (NBO):**\n"
-        msg += f"**Tindakan**: {action}\n\n"
-        msg += f"**Alasan**: {reason}</div>"
-
-        # 3. GENERATIVE NBO (PARALLEL EXECUTION)
-        if client_ai:
-            merchants = u_txn.tail(2)['raw_description'].tolist()
-            prompt = f"Advisor Bank Nagari: Nasabah {cid} risiko {risk_lv}, expense {er:.1%}, aksi {action}. Terakhir belanja di {merchants}. Beri saran sangat hangat & personal (panggil Bapak/Ibu), maks 2 kalimat."
-            try:
-                resp = client_ai.models.generate_content(model="gemini-1.5-flash", contents=prompt)
-                msg += f"\n\n---\n**💡 SARAN VIRTUAL ADVISOR (AI):**\n{resp.text}"
-            except: pass
-            
-        return msg
-
-    def create_viz(self, u_bal, u_txn):
-        # Grafik Inflow vs Outflow
-        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')
-
-        # Grafik Tren 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="Grafik Tren Kesehatan Saldo", template='plotly_white')
-        return fig1, fig2
-
-# --- UI EXECUTION ---
-engine = ArchonMasterEngine()
+            action, reason = "Financial Education", "stable_cashflow"
+
+        return risk_lv, final_score, er, u_bal, u_txn, action, reason
+
+    def get_ai_narrative(self, risk_lv, er, cid, u_txn):
+        if not client_ai: return "Koneksi Advisor AI tidak tersedia."
+        tx = u_txn.tail(2)['raw_description'].tolist()
+        prompt = f"Advisor Bank Nagari: Nasabah {cid} ({risk_lv} risk, expense {er:.1%}). Terakhir belanja di {tx}. Beri 1 saran hangat personal (Bapak/Ibu) maks 3 kalimat."
+        try:
+            return client_ai.models.generate_content(model="gemini-1.5-flash", contents=prompt).text
+        except: return "Kami menyarankan Bapak/Ibu untuk menjaga rasio pengeluaran agar tetap stabil bulan ini."
+
+# --- UI ---
+engine = ArchonBankNagari()
 
 def run_app(cust_id):
-    res = engine.analyze(cust_id)
-    if not res: return "## ❌ ID Tidak Valid", "Masukkan ID Customer yang terdaftar (C0001 - C0120).", None, None
+    data = engine.analyze(cust_id)
+    if not data: return "## ❌ ID Tidak Valid", "Gunakan C0001 - C0120", None, None
+    
+    risk_lv, score, er, u_bal, u_txn, action, reason = data
+    advice = engine.get_ai_narrative(risk_lv, er, cust_id, u_txn)
     
-    risk_lv, score, er, disc_ratio, u_bal, u_txn, exp, inc, mp_s = res
-    report = engine.build_expert_nbo(risk_lv, score, er, disc_ratio, u_bal, exp, inc, mp_s, cust_id, u_txn)
-    p1, p2 = engine.create_viz(u_bal, u_txn)
+    # Graphs
+    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="Trend Saldo (Fase 6)", 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='Inflow', marker_color='#82C3EB'))
+    f2.add_trace(go.Bar(x=cf.index, y=cf.get('debit', 0), name='Outflow', marker_color='#0514DE'))
+    f2.update_layout(title="Inflow vs Outflow", barmode='group', template='plotly_white')
+
     color = "#ef4444" if risk_lv == "HIGH" else ("#f59e0b" if risk_lv == "MEDIUM" else "#10b981")
-    status_html = f"""
-    <div class='metric-box'>
-        <h2 style='color: #0514DE; margin:0;'>Archon Insight Summary</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 LEVEL</div>
-        <p style='margin-top:15px; font-size:1.1em;'><b>Risk Score:</b> {score:.2f} | <b>Expense Ratio:</b> {er:.1%}</p>
-    </div>
+    report = f"""
+    ### 🛡️ HASIL AUDIT ARCHON-AI
+    - **Risk Score**: {score:.2f} ({risk_lv} RISK)
+    - **Expense Ratio**: {er:.1%}
+    
+    **🎯 REKOMENDASI NBO:**
+    **{action}** ({reason})
+    
+    **💡 SARAN VIRTUAL ADVISOR:**
+    {advice}
     """
-    return status_html, report, p1, p2
+    
+    status_html = f"<div style='background:{color}; color:white; padding:15px; border-radius:10px; text-align:center;'><h2>STATUS: {risk_lv}</h2></div>"
+    return status_html, report, f1, f2
 
 with gr.Blocks(css=custom_css) as demo:
-    # Header Bold Putih
-    gr.HTML("""
-    <div class='nagari-header'>
-        <h1>ARCHON-AI</h1>
-        <p style='color: white !important; opacity: 0.9; margin: 5px 0 0 0;'>Pusat Intelijen Risiko & Resiliensi Finansial Nasabah</p>
-    </div>
-    """)
-    
+    gr.HTML("<div class='nagari-header'><h1>ARCHON-AI</h1></div>")
     with gr.Row():
         with gr.Column(scale=1):
-            id_in = gr.Textbox(label="Input Customer ID", placeholder="C0001")
-            btn = gr.Button("ANALYZE CUSTOMER", variant="primary")
+            id_in = gr.Textbox(label="Customer ID")
+            btn = gr.Button("ANALYZE", variant="primary")
             out_status = gr.HTML()
-            gr.Markdown("---")
-            gr.Markdown("ℹ️ **Interpretasi**: Skor risiko menggabungkan data mutasi saldo harian, rasio belanja, dan ketepatan cicilan nasabah secara otomatis.")
-        
         with gr.Column(scale=2):
             with gr.Tabs():
-                with gr.TabItem("Analysis & NBO Recommendation"):
-                    out_report = gr.Markdown(elem_classes="report-card")
-                with gr.TabItem("Cashflow Insight"):
-                    plot_cf = gr.Plot()
-                with gr.TabItem("Saving Trend"):
-                    plot_bal = gr.Plot()
-
-    btn.click(fn=run_app, inputs=id_in, outputs=[out_status, out_report, plot_cf, plot_bal])
+                with gr.TabItem("Audit & Advice"): out_report = gr.Markdown()
+                with gr.TabItem("Visual Trends"):
+                    gr.Plot(label="Balance")
+                    gr.Plot(label="Cashflow")
+
+    btn.click(fn=run_app, inputs=id_in, outputs=[out_status, out_report, gr.Plot(), gr.Plot()])
 
 demo.launch()