fix runtime error

app.py

@@ -3,68 +3,50 @@ import pandas as pd
 import numpy as np
 import gradio as gr
 import plotly.graph_objects as go
-import google.generativeai as genai
+from google import genai
 from transformers import pipeline
 
-# --- KONFIGURASI AI ---
-# Pastikan GOOGLE_API_KEY sudah ada di Secrets Hugging Face
+# --- KONFIGURASI AI (SDK 2026) ---
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-genai.configure(api_key=GOOGLE_API_KEY)
-gemini = genai.GenerativeModel('gemini-1.5-flash')
+client = genai.Client(api_key=GOOGLE_API_KEY)
 
-# --- STYLE CSS BANK NAGARI (Marun & Emas) ---
+# --- STYLE CSS BANK NAGARI ---
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;700&display=swap');
-body { font-family: 'Inter', sans-serif; background-color: #f0f2f5; }
-.gradio-container { max-width: 1200px !important; }
-.nagari-card { 
-    background: white; 
-    border-radius: 15px; 
-    padding: 25px; 
-    box-shadow: 0 10px 25px rgba(0,0,0,0.05); 
-    border-top: 6px solid #800000;
-}
-.header-box { 
-    background: linear-gradient(135deg, #800000 0%, #a52a2a 100%); 
-    color: white; 
-    padding: 30px; 
-    border-radius: 15px; 
-    margin-bottom: 20px;
-    border-bottom: 5px solid #FFD700;
-}
+.gradio-container { font-family: 'Inter', sans-serif !important; background-color: #f8fafc !important; }
+.nagari-header { background: linear-gradient(135deg, #800000 0%, #a52a2a 100%); color: white; padding: 25px; border-radius: 15px; border-bottom: 5px solid #FFD700; margin-bottom: 20px; }
+.risk-card { background: white; border-radius: 12px; padding: 20px; box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1); border-top: 6px solid #800000; }
+.advice-box { background: #fffdf0; border-left: 5px solid #FFD700; padding: 15px; border-radius: 8px; }
 """
 
-class ArchonProductionEngine:
+class ArchonNagariEngine:
     def __init__(self):
         self.load_data()
-        # Pilar 1: BERT Classifier (Fase 2)
         try:
             self.classifier = pipeline("text-classification", model="archon_v1", tokenizer="archon_v1")
         except:
             self.classifier = None
 
     def load_data(self):
-        # Fase 1: Data Foundation (Fixing C0014 error with fillna)
-        self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date']).fillna("")
+        # Fix Error C0014 dengan penanganan data kosong yang lebih agresif
+        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")
 
-    def analyze_customer(self, customer_id):
-        # Filter & Validate
+    def run_analysis(self, customer_id):
         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
+            return None
 
-        # --- FASE 4: RISK SCORING (WEIGHTED) ---
+        # --- FASE 4: RISK SCORING ---
         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 1.2
         
-        # Scoring Logic
         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
@@ -72,68 +54,79 @@ class ArchonProductionEngine:
         
         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
 
-        # --- FASE 6: RINGKASAN LAPORAN ---
-        summary = f"### 📊 ANALISIS RISIKO: {risk_lv}\n"
-        summary += f"- **Rasio Pengeluaran**: {er:.1%}\n"
-        summary += f"- **Kesehatan Saldo**: {'⚠️ Tren Menurun' if bt_s == 1 else '✅ Tren Stabil'}\n"
-        summary += f"- **Status Kredit**: {'⚠️ Ada Keterlambatan' if mp_s == 1 else '✅ Lancar'}"
-
-        # --- FASE 5: NBO (DYNAMIC GEMINI) ---
-        recent_desc = ", ".join(u_txn.tail(3)['raw_description'].tolist())
-        prompt = f"""
-        Bertindaklah sebagai Senior Advisor Bank Nagari. Nasabah {customer_id} memiliki risiko {risk_lv}.
-        Rasio belanja terhadap gaji: {er:.2%}. Transaksi terakhir: {recent_desc}.
-        Berikan saran finansial yang SANGAT spesifik (bukan template), hangat, dan gunakan sapaan 'Bapak/Ibu'.
-        Berikan 1 solusi produk bank (tabungan/investasi/kredit) yang relevan. Maksimal 3 kalimat.
-        """
+    def get_gemini_advice(self, risk_lv, er, cust_id, u_txn):
+        # FASE 5: NBO (NEW SDK 2026)
+        last_txn = u_txn.tail(3)['raw_description'].tolist()
+        prompt = f"Berikan saran finansial hangat khas perbankan (sapa Bapak/Ibu) untuk nasabah {cust_id} dengan risiko {risk_lv} dan rasio pengeluaran {er:.1%}. Transaksi terakhir: {last_txn}. Maks 3 kalimat."
+        
         try:
-            advice = gemini.generate_content(prompt).text
+            response = client.models.generate_content(model="gemini-1.5-flash", contents=prompt)
+            return response.text
         except:
-            advice = "Mohon maaf, layanan konsultasi AI sedang sibuk. Silakan hubungi Relationship Manager Anda."
+            return "Kami menyarankan peninjauan berkala pada pengeluaran bulanan Bapak/Ibu untuk menjaga stabilitas saldo."
 
-        # --- FASE 6: VISUALISASI ---
-        # Plot 1: Income vs Expense
+    def create_plots(self, u_bal, u_txn):
+        # Plot 1: Cashflow
         u_txn['m'] = u_txn['date'].dt.to_period('M').dt.to_timestamp()
-        monthly_cf = u_txn.groupby(['m', 'transaction_type'])['amount'].sum().unstack().fillna(0)
-        
+        cf = u_txn.groupby(['m', 'transaction_type'])['amount'].sum().unstack().fillna(0)
         fig1 = go.Figure()
-        fig1.add_trace(go.Bar(x=monthly_cf.index, y=monthly_cf.get('credit', 0), name='Income', marker_color='#2e7d32'))
-        fig1.add_trace(go.Bar(x=monthly_cf.index, y=monthly_cf.get('debit', 0), name='Expense', marker_color='#800000'))
+        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='#800000'))
         fig1.update_layout(title="Inflow vs Outflow", barmode='group', template='plotly_white')
 
-        # Plot 2: Balance Trend
+        # Plot 2: Balance
         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='#800000', width=4)))
-        fig2.update_layout(title="Tren Saldo Rata-rata", template='plotly_white')
-
-        return summary, advice, fig1, fig2
-
-# --- UI DASHBOARD ---
-engine = ArchonProductionEngine()
-
-with gr.Blocks(css=custom_css) as demo:
-    with gr.Div(elem_id="header", elem_classes="header-box"):
-        gr.Markdown("# 🛡️ ARCHON-AI EXECUTIVE DASHBOARD")
-        gr.Markdown("Financial Resilience Engine | Bank Nagari Edition")
-
+        fig2.add_trace(go.Scatter(x=u_bal['month'], y=u_bal['avg_balance'], name='Saldo Rata-rata', line=dict(color='#800000', width=4)))
+        fig2.update_layout(title="Tren Saldo", template='plotly_white')
+        
+        return fig1, fig2
+
+# --- UI INTERFACE ---
+engine = ArchonNagariEngine()
+
+def process(cust_id):
+    res = engine.run_analysis(cust_id)
+    if not res: return "### ⚠️ Nasabah Tidak Ditemukan", "Data tidak tersedia.", None, None
+    
+    risk_lv, score, er, u_bal, u_txn = res
+    advice = engine.get_gemini_advice(risk_lv, er, cust_id, u_txn)
+    f1, f2 = engine.create_plots(u_bal, u_txn)
+    
+    report_html = f"""
+    <div style='padding: 20px; border-radius: 10px; background: white; border-left: 10px solid {"#d32f2f" if risk_lv=="HIGH" else "#f9a825"};'>
+        <h2 style='color: #800000; margin: 0;'>Status: {risk_lv}</h2>
+        <p><b>Risk Score:</b> {score:.2f} | <b>Expense Ratio:</b> {er:.1%}</p>
+    </div>
+    """
+    return report_html, advice, f1, f2
+
+with gr.Blocks() as demo:
+    gr.HTML("""
+    <div class="nagari-header">
+        <h1 style="margin:0;">🛡️ ARCHON-AI: BANK NAGARI EDITION</h1>
+        <p style="margin:0; opacity: 0.8;">Automated Financial Resilience & Risk Intelligence</p>
+    </div>
+    """)
+    
     with gr.Row():
         with gr.Column(scale=1):
-            with gr.Div(elem_classes="nagari-card"):
-                id_in = gr.Textbox(label="Customer ID", placeholder="C0001 - C0120")
-                btn = gr.Button("PROSES ANALISIS", variant="primary")
-                out_sum = gr.Markdown()
+            id_input = gr.Textbox(label="Customer ID", placeholder="C0001 - C0120")
+            btn = gr.Button("ANALYZE NOW", variant="primary")
+            out_report = gr.HTML()
         
         with gr.Column(scale=2):
             with gr.Tabs():
-                with gr.TabItem("Analisis Arus Kas"):
+                with gr.TabItem("Arus Kas"):
                     plot_cf = gr.Plot()
                 with gr.TabItem("Tren Saldo"):
                     plot_bal = gr.Plot()
                     
-    with gr.Div(elem_classes="nagari-card", style="margin-top: 20px;"):
-        out_adv = gr.Textbox(label="Virtual Advisor (Gemini Generative AI)", lines=4)
+    out_advice = gr.Textbox(label="Personalized NBO (Powered by Gemini 1.5)", lines=4, elem_classes="advice-box")
 
-    btn.click(fn=engine.analyze_customer, inputs=id_in, outputs=[out_sum, out_adv, plot_cf, plot_bal])
+    btn.click(fn=process, inputs=id_input, outputs=[out_report, out_advice, plot_cf, plot_bal])
 
-demo.launch()
+# Jalankan dengan CSS di launch
+demo.launch(css=custom_css)

requirements.txt

@@ -1,4 +1,4 @@
-google-generativeai
+google-genai
 gradio
 pandas
 numpy