perbaiki logic error

app.py

@@ -4,63 +4,109 @@ import gradio as gr
 import os
 from transformers import pipeline
 
-# --- KONFIGURASI SISTEM ---
-MODEL_PATH = "archon_v1" # Folder model BERT hasil training Anda
-CATEGORIES = ["groceries", "utilities", "transport", "healthcare", "education", "restaurant", "entertainment"]
+# --- KONFIGURASI PATH & MODEL ---
+MODEL_PATH = "archon_v1"
+# Kategori wajib sesuai instruksi Fase 2
+ESSENTIAL_CATS = {'groceries', 'utilities', 'transport', 'healthcare', 'education'}
+DISCRETIONARY_CATS = {'restaurant', 'cafe', 'entertainment', 'fashion', 'online_shopping', 'travel'}
 
 class ArchonBankEngine:
     def __init__(self):
-        # AI Layer: Automasi Klasifikasi Narasi Transaksi (Tahap 2)
+        # Pilar 1: AI Classifier (Fase 2)
         self.classifier = pipeline("text-classification", model=MODEL_PATH, tokenizer=MODEL_PATH)
-        self.load_data()
+        self.load_all_data()
 
-    def load_data(self):
-        # Tahap 1: Data Foundation (Merge Dataset)
+    def load_all_data(self):
+        # FASE 1: DATA FOUNDATION
         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'])
+        self.df_off = pd.read_csv('offers.csv')
 
-    def run_pipeline(self, customer_id):
-        # 1. Filter Data Nasabah
-        cust_txn = self.df_txn[self.df_txn['customer_id'] == customer_id].copy()
-        
-        # 2. Tahap 2: AI Intelligence (Automasi Labeling)
-        # BERT mendeteksi merchant_category dari narasi yang ambigu
-        cust_txn['category'] = cust_txn['raw_description'].apply(lambda x: CATEGORIES[int(self.classifier(x)[0]['label'].split('_')[-1])])
-        
-        # 3. Tahap 4: Risk Labeling (Early Warning)
-        # Bobot: Expense 30%, Trend 20%, Overdraft 20%, Missed Payment 20%
-        # (Logika kalkulasi skor sesuai rumus di dokumen Anda)
-        risk_score = 0.75 # Simulasi hasil perhitungan
-        risk_level = "HIGH" if risk_score >= 0.7 else "LOW"
-        
-        # 4. Tahap 5: NBO Engine (Action Layer)
-        action = "restructuring_suggestion" if risk_level == "HIGH" else "promote_saving"
+    def analyze_customer(self, customer_id):
+        # --- PRE-PROCESSING DATA NASABAH ---
+        user_txn = self.df_txn[self.df_txn['customer_id'] == customer_id].copy()
+        user_bal = self.df_bal[self.df_bal['customer_id'] == customer_id].sort_values('month')
+        user_rep = self.df_rep[self.df_rep['customer_id'] == customer_id]
+        user_info = self.df_cust[self.df_cust['customer_id'] == customer_id].iloc[0]
+
+        if user_txn.empty: return {"Error": "Data Nasabah Tidak Ditemukan"}
+
+        # --- FASE 2: TRANSACTION INTELLIGENCE (Automasi AI) ---
+        # AI menentukan kategori dari deskripsi transaksi yang ambigu
+        user_txn['merchant_category'] = user_txn['raw_description'].apply(lambda x: self.classifier(x)[0]['label'])
         
-        # 5. Tahap 6: Explainable Summary (Untuk Manajemen)
-        summary = f"Nasabah terdeteksi berisiko {risk_level} karena rasio pengeluaran tinggi."
+        # Penentuan expense_type sesuai aturan Fase 2
+        def set_expense_type(cat):
+            if any(k in cat.lower() for k in ESSENTIAL_CATS): return 'essential'
+            return 'discretionary'
+        user_txn['expense_type'] = user_txn['merchant_category'].apply(set_expense_type)
+
+        # --- FASE 3 & 4: RISK SCORING (Early Warning System) ---
+        # 1. Expense Ratio (Bulan terakhir)
+        income = user_txn[user_txn['transaction_type'] == 'credit']['amount'].sum()
+        expense = user_txn[user_txn['transaction_type'] == 'debit']['amount'].sum()
+        er = expense / income if income > 0 else 1.0
+        er_score = 1 if er > 0.8 else (0.5 if er > 0.5 else 0)
+
+        # 2. Balance Trend (Bulan ini vs bulan lalu)
+        if len(user_bal) >= 2:
+            bt = -1 if user_bal.iloc[-1]['avg_balance'] < user_bal.iloc[-2]['avg_balance'] else 1
+        else: bt = 0
+        bt_score = 1 if bt == -1 else 0
+
+        # 3. Overdraft & Missed Payment
+        od_score = 1 if (user_bal['min_balance'] <= 0).any() else 0
+        mp_score = 1 if (user_rep['status'] == 'late').any() else 0
+
+        # HITUNG FINAL RISK SCORE (Bobot Fix: 30%, 20%, 20%, 20%, 10%)
+        risk_score = (0.3 * er_score) + (0.2 * bt_score) + (0.2 * od_score) + (0.2 * mp_score) + 0.1
+        risk_level = "HIGH" if risk_score >= 0.7 else ("MEDIUM" if risk_score >= 0.4 else "LOW")
+
+        # --- FASE 5: NBO ENGINE ---
+        # Menentukan aksi sesuai kriteria Fase 5
+        disc_ratio = user_txn[user_txn['expense_type'] == 'discretionary']['amount'].sum() / expense if expense > 0 else 0
         
+        if risk_level == "HIGH":
+            action = "restructuring_suggestion"
+            reason = "REPAYMENT_RISK_DETECTED"
+        elif risk_level == "MEDIUM" and disc_ratio > 0.4:
+            action = "budgeting_alert"
+            reason = "HIGH_DISCRETIONARY_SPENDING"
+        elif risk_level == "LOW" and disc_ratio <= 0.4:
+            action = "promote_saving"
+            reason = "STABLE_CASHFLOW"
+        else:
+            action = "no_action"
+            reason = "COOLDOWN_ACTIVE"
+
+        # --- FASE 6: EXPLAINABLE SUMMARY ---
+        summary = f"Nasabah memiliki risiko {risk_level}. "
+        if er_score == 1: summary += "Pengeluaran sangat tinggi (>80% pendapatan). "
+        if bt_score == 1: summary += "Tren saldo menurun signifikan. "
+        if mp_score == 1: summary += "Terdeteksi riwayat telat bayar."
+
         return {
             "ID Nasabah": customer_id,
             "Level Risiko": risk_level,
             "Rekomendasi Aksi": action,
+            "Alasan": reason,
             "Penjelasan": summary
         }
 
+# --- INTEGRASI KE DASHBOARD ---
 engine = ArchonBankEngine()
 
-# --- DASHBOARD UNTUK MANAJEMEN ---
-def manager_dashboard(cust_id):
-    return engine.run_pipeline(cust_id)
+def run_app(cust_id):
+    return engine.analyze_customer(cust_id)
 
 demo = gr.Interface(
-    fn=manager_dashboard,
-    inputs=gr.Textbox(label="Masukkan Customer ID (Contoh: C0001)"),
+    fn=run_app,
+    inputs=gr.Textbox(label="Input Customer ID (C0001 - C0120)"),
     outputs="json",
-    title="🛡️ Archon-AI: Industrial Banking Automation",
-    description="Sistem automasi pengelolaan sumber daya informasi bank berdasarkan perilaku nasabah."
+    title="🛡️ Archon-AI Production Engine",
+    description="Sistem automasi perbankan sesuai instruksi Arahan Pembuatan AI Archon."
 )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()