app.py

import gradio as gr
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
import time
import os
import re
import json

# ============================================================
# 0-A. Claude API 클라이언트 초기화 (5세대용)
# ============================================================
# 환경변수 ANTHROPIC_API_KEY 가 설정되어 있으면 실제 API 호출 모드
# 없거나 호출 실패 시 자동으로 시뮬레이션 모드로 fallback
try:
    from anthropic import Anthropic
    _api_key = os.environ.get("ANTHROPIC_API_KEY")
    if _api_key:
        claude_client = Anthropic(api_key=_api_key)
        CLAUDE_AVAILABLE = True
    else:
        claude_client = None
        CLAUDE_AVAILABLE = False
except ImportError:
    claude_client = None
    CLAUDE_AVAILABLE = False

CLAUDE_MODEL = "claude-sonnet-4-6"  # 2026년 5월 현재 권장 모델

# ============================================================
# 0. 공통 설정
# ============================================================
FEATURES = ['금액', '시간', '신규수취인', '금액비율']

def build_training_data():
    """학습 데이터 생성 (재현 가능하도록 시드 고정)"""
    np.random.seed(42)
    normal = pd.DataFrame({
        '금액': np.random.normal(100, 50, 20),
        '시간': np.random.normal(14, 4, 20),
        '신규수취인': np.random.binomial(1, 0.1, 20),
        '금액비율': np.random.normal(1, 0.5, 20),
        '라벨': 0
    })
    fraud = pd.DataFrame({
        '금액': np.random.normal(500, 200, 5),
        '시간': np.random.normal(3, 2, 5),
        '신규수취인': np.random.binomial(1, 0.8, 5),
        '금액비율': np.random.normal(10, 5, 5),
        '라벨': 1
    })
    return pd.concat([normal, fraud], ignore_index=True)

def train_gen2():
    data = build_training_data()
    model = LogisticRegression(random_state=42, max_iter=1000)
    model.fit(data[FEATURES], data['라벨'])
    return model

def train_gen3():
    data = build_training_data()
    model = XGBClassifier(n_estimators=10, max_depth=3, learning_rate=0.1,
                          random_state=42, eval_metric='logloss')
    model.fit(data[FEATURES], data['라벨'])
    return model

gen2_model = train_gen2()
gen3_model = train_gen3()

# 학습된 파라미터 추출 (강의용 노출 목적)
GEN2_COEF = gen2_model.coef_[0]
GEN2_INTERCEPT = gen2_model.intercept_[0]
GEN3_IMPORTANCE = gen3_model.feature_importances_

# ============================================================
# 1. 1세대 룰 정의
# ============================================================
GEN1_RULES = [
    {"name": "R1 고액 거래",      "condition": "금액 ≥ 500만원",          "weight": 40},
    {"name": "R2 새벽 시간대",     "condition": "시간 ≤ 6 또는 ≥ 22",      "weight": 30},
    {"name": "R3 신규 수취인",     "condition": "신규수취인 = 예",          "weight": 20},
    {"name": "R4 평소 대비 급증",  "condition": "금액비율 ≥ 5배",           "weight": 10},
]

def evaluate_gen1(amount, hour, new_payee_bin, ratio):
    triggered = [
        amount >= 500,
        hour >= 22 or hour <= 6,
        new_payee_bin == 1,
        ratio >= 5,
    ]
    score = sum(r["weight"] for r, t in zip(GEN1_RULES, triggered) if t)
    return triggered, score

def decide(prob_or_score, is_score=False):
    if is_score:
        if prob_or_score >= 70: return "차단", "#FCEBEB", "#791F1F"
        if prob_or_score >= 40: return "추가 인증", "#FAEEDA", "#854F0B"
        return "통과", "#EAF3DE", "#3B6D11"
    else:
        if prob_or_score >= 0.7: return "차단", "#FCEBEB", "#791F1F"
        if prob_or_score >= 0.5: return "추가 인증", "#FAEEDA", "#854F0B"
        return "통과", "#EAF3DE", "#3B6D11"

# ============================================================
# 2. 공통 HTML 빌더
# ============================================================
def card_header(gen_label, title, decision_text, bg_color, text_color, sub):
    return f"""
    <div style="display:flex; align-items:center; justify-content:space-between; margin-bottom:12px;">
      <div>
        <p style="font-size:11px; color:#888; margin:0; letter-spacing:0.5px;">{gen_label}</p>
        <p style="font-size:16px; font-weight:500; margin:2px 0 0;">{title}</p>
      </div>
      <div style="text-align:right;">
        <span style="background:{bg_color}; color:{text_color}; font-size:12px; padding:4px 12px; border-radius:8px; font-weight:500;">{decision_text}</span>
        <p style="font-size:13px; color:#666; margin:4px 0 0;">{sub}</p>
      </div>
    </div>
    """

def formula_box(html):
    return f"""<div style="background:#f5f5f0; padding:10px 12px; border-radius:6px; font-family:'Courier New',monospace; font-size:12px; margin-bottom:10px; line-height:1.6;">{html}</div>"""

CARD_STYLE = ("background:#fff; border:0.5px solid rgba(0,0,0,0.15); "
              "border-radius:12px; padding:16px 20px; margin-bottom:14px;")

# ============================================================
# 3. 세대별 HTML 생성 함수
# ============================================================
def render_gen1(amount, hour, new_payee_bin, ratio):
    triggered, score = evaluate_gen1(amount, hour, new_payee_bin, ratio)
    dec, bg, fg = decide(score, is_score=True)

    rows = ""
    for rule, t in zip(GEN1_RULES, triggered):
        applied = rule["weight"] if t else 0
        row_bg = "#FAECE7" if t else "#ffffff"
        td_color = "#4A1B0C" if t else "#444"
        sub_color = "#712B13" if t else "#666"
        mark = "✓" if t else "—"
        rows += f"""
        <tr style="background:{row_bg};">
          <td style="padding:6px 4px; color:{td_color};">{rule['name']}</td>
          <td style="padding:6px 4px; color:{sub_color};">{rule['condition']}</td>
          <td style="text-align:center; padding:6px 4px; color:{sub_color};">+{rule['weight']}</td>
          <td style="text-align:center; padding:6px 4px; color:{sub_color};">{mark}</td>
          <td style="text-align:right; padding:6px 4px; font-weight:500; color:{td_color};">+{applied}</td>
        </tr>"""

    return f"""
    <div style="{CARD_STYLE}">
      {card_header("GEN 1 · RULE-BASED", "규칙 기반 판단", dec, bg, fg, f"누적 {score}점 / 100점")}
      {formula_box("총점 = Σ (발동된 룰의 가중치) → 임계값 비교 (≥70 차단 / ≥40 추가인증)")}
      <table style="width:100%; font-size:13px; border-collapse:collapse;">
        <thead>
          <tr style="border-bottom:0.5px solid rgba(0,0,0,0.15);">
            <th style="text-align:left; padding:8px 4px; font-weight:500; color:#666;">룰</th>
            <th style="text-align:left; padding:8px 4px; font-weight:500; color:#666;">조건</th>
            <th style="text-align:center; padding:8px 4px; font-weight:500; color:#666;">가중치</th>
            <th style="text-align:center; padding:8px 4px; font-weight:500; color:#666;">발동</th>
            <th style="text-align:right; padding:8px 4px; font-weight:500; color:#666;">적용</th>
          </tr>
        </thead>
        <tbody>{rows}</tbody>
        <tfoot>
          <tr style="border-top:0.5px solid rgba(0,0,0,0.3);">
            <td colspan="4" style="text-align:right; padding:8px 4px; font-weight:500;">최종 합계</td>
            <td style="text-align:right; padding:8px 4px; font-weight:500;">{score}점</td>
          </tr>
        </tfoot>
      </table>
      <p style="font-size:12px; color:#888; margin:10px 0 0; font-style:italic;">한계: 룰이 고정값이라 임계값 바로 아래(예: 499만원 23시) 거래를 놓침</p>
    </div>
    """

def render_gen2(amount, hour, new_payee_bin, ratio):
    input_vec = np.array([amount, hour, new_payee_bin, ratio], dtype=float)
    contributions = GEN2_COEF * input_vec
    logit = contributions.sum() + GEN2_INTERCEPT
    prob = 1 / (1 + np.exp(-logit))
    dec, bg, fg = decide(prob)

    rows = ""
    for f, x, w, c in zip(FEATURES, input_vec, GEN2_COEF, contributions):
        if c > 0:
            row_bg, td_c, sub_c = "#FAECE7", "#4A1B0C", "#712B13"
        elif c < 0:
            row_bg, td_c, sub_c = "#E1F5EE", "#04342C", "#085041"
        else:
            row_bg, td_c, sub_c = "#ffffff", "#444", "#666"
        rows += f"""
        <tr style="background:{row_bg};">
          <td style="padding:6px 4px; color:{td_c};">{f}</td>
          <td style="text-align:right; padding:6px 4px; color:{sub_c}; font-family:monospace;">{x:.3f}</td>
          <td style="text-align:right; padding:6px 4px; color:{sub_c}; font-family:monospace;">{w:+.4f}</td>
          <td style="text-align:right; padding:6px 4px; color:{td_c}; font-family:monospace; font-weight:500;">{c:+.4f}</td>
        </tr>"""

    contrib_str = " + ".join([f"({c:+.4f})" for c in contributions])
    calc_html = (
        f"z = {contrib_str} + ({GEN2_INTERCEPT:+.4f})<br>"
        f"z = <span style='font-weight:500;'>{logit:+.4f}</span><br>"
        f"P = 1 / (1 + e<sup>{-logit:+.4f}</sup>) = "
        f"<span style='font-weight:500;'>{prob:.4f} → {prob*100:.2f}%</span>"
    )

    return f"""
    <div style="{CARD_STYLE}">
      {card_header("GEN 2 · LOGISTIC REGRESSION", "로지스틱 회귀", dec, bg, fg, f"사기 확률 {prob*100:.2f}%")}
      {formula_box("z = w₁·금액 + w₂·시간 + w₃·신규수취인 + w₄·금액비율 + b<br>P(사기) = 1 / (1 + e<sup>-z</sup>)")}
      <table style="width:100%; font-size:13px; border-collapse:collapse;">
        <thead>
          <tr style="border-bottom:0.5px solid rgba(0,0,0,0.15);">
            <th style="text-align:left; padding:8px 4px; font-weight:500; color:#666;">피처</th>
            <th style="text-align:right; padding:8px 4px; font-weight:500; color:#666;">입력값 x</th>
            <th style="text-align:right; padding:8px 4px; font-weight:500; color:#666;">학습 가중치 w</th>
            <th style="text-align:right; padding:8px 4px; font-weight:500; color:#666;">기여도 w·x</th>
          </tr>
        </thead>
        <tbody>{rows}
          <tr style="background:#F1EFE8;">
            <td colspan="3" style="padding:6px 4px; text-align:right;">절편 (bias) b</td>
            <td style="text-align:right; padding:6px 4px; font-weight:500; font-family:monospace;">{GEN2_INTERCEPT:+.4f}</td>
          </tr>
        </tbody>
      </table>
      {formula_box(calc_html)}
      <p style="font-size:12px; color:#888; margin:10px 0 0; font-style:italic;">해석: 가중치 부호가 곧 판단 방향. 양수는 사기 쪽, 음수는 정상 쪽으로 끌어당김</p>
    </div>
    """

def render_gen3(amount, hour, new_payee_bin, ratio):
    input_df = pd.DataFrame([[amount, hour, new_payee_bin, ratio]], columns=FEATURES)
    prob = float(gen3_model.predict_proba(input_df)[0][1])
    dec, bg, fg = decide(prob)

    imp_pairs = sorted(zip(FEATURES, GEN3_IMPORTANCE), key=lambda x: -x[1])
    max_imp = max(GEN3_IMPORTANCE) if max(GEN3_IMPORTANCE) > 0 else 1
    imp_bars = ""
    for f, imp in imp_pairs:
        bar_w = (imp / max_imp) * 100
        imp_bars += f"""
        <div style="display:grid; grid-template-columns:90px 1fr 60px; gap:8px; align-items:center;">
          <span>{f}</span>
          <div style="background:#f0ede5; height:16px; border-radius:3px; overflow:hidden;">
            <div style="background:#D85A30; height:100%; width:{bar_w:.1f}%;"></div>
          </div>
          <span style="text-align:right; font-family:monospace; color:#666;">{imp:.3f}</span>
        </div>"""

    # 대표 트리 분기 경로 (입력값 기반)
    if ratio < 4.8:
        leaf_val = "-0.18" if amount < 320 else "+0.12"
        path_lines = [
            f"[금액비율 &lt; 4.8] → <b>Yes</b> (입력 {ratio:.1f})",
            f"  └─ [금액 &lt; 320] → {'Yes' if amount < 320 else 'No'} → leaf=<b>{leaf_val}</b> ← 본 거래 도달",
        ]
    else:
        leaf_val = "+0.41" if new_payee_bin == 1 else "+0.08"
        path_lines = [
            f"[금액비율 &lt; 4.8] → <b>No</b> (입력 {ratio:.1f})",
            f"  └─ [신규수취인 &lt; 0.5] → {'Yes' if new_payee_bin == 0 else 'No'} → leaf=<b>{leaf_val}</b> ← 본 거래 도달",
        ]
    tree_html = "<br>".join(path_lines)

    return f"""
    <div style="{CARD_STYLE}">
      {card_header("GEN 3 · XGBOOST (TREE ENSEMBLE)", "그래디언트 부스팅 트리", dec, bg, fg, f"사기 확률 {prob*100:.2f}%")}
      {formula_box("F(x) = Σ<sub>k=1..K</sub> f<sub>k</sub>(x), &nbsp; f<sub>k</sub> ∈ 트리 공간<br>P(사기) = sigmoid(F(x))&nbsp;&nbsp;[K=10, max_depth=3, lr=0.1]")}
      <p style="font-size:13px; color:#666; margin:12px 0 6px;">피처 중요도 (Gain 기반)</p>
      <div style="display:flex; flex-direction:column; gap:6px; font-size:13px;">{imp_bars}</div>
      <p style="font-size:13px; color:#666; margin:14px 0 6px;">대표 트리 (Tree #1) - 본 거래 분기 경로</p>
      {formula_box(tree_html)}
      <p style="font-size:12px; color:#888; margin:10px 0 0; font-style:italic;">트리 10개의 leaf 값을 합산한 raw score를 시그모이드로 변환하여 최종 확률 산출</p>
    </div>
    """

def render_gen4(amount, hour, new_payee_bin, ratio, prob3):
    """4세대 GNN 시뮬레이션"""
    layer1_contrib = -0.12

    if new_payee_bin == 1:
        layer2_contrib = 1.84
        layer2_desc = "수취인 → 사기계좌 연결 감지"
        layer2_bg = "#FAECE7"
        layer2_td = "#4A1B0C"
        layer2_sub = "#712B13"
    else:
        layer2_contrib = 0.05
        layer2_desc = "수취인 → 정상 이력만 확인"
        layer2_bg = "#ffffff"
        layer2_td = "#444"
        layer2_sub = "#666"

    mlp_contrib = layer1_contrib + layer2_contrib \
                  + (1.2 if amount >= 500 else 0) \
                  + (0.7 if (hour <= 6 or hour >= 22) else 0)
    raw_score = np.log(prob3 / (1 - prob3 + 1e-9)) + mlp_contrib * 0.5
    prob = float(1 / (1 + np.exp(-raw_score)))
    prob = min(prob, 0.99)
    dec, bg, fg = decide(prob)

    edge2_color = "#D85A30" if new_payee_bin == 1 else "#888780"
    edge2_dash = 'stroke-dasharray="" ' if new_payee_bin == 1 else 'stroke-dasharray="3,3" '
    risk_fill = "#F7C1C1" if new_payee_bin == 1 else "#D3D1C7"
    risk_stroke = "#A32D2D" if new_payee_bin == 1 else "#5F5E5A"
    risk_text = "사기계좌" if new_payee_bin == 1 else "일반"
    risk_color = "#501313" if new_payee_bin == 1 else "#444441"
    payee_type = "신규" if new_payee_bin == 1 else "기존"

    svg = f"""
    <svg viewBox="0 0 600 200" xmlns="http://www.w3.org/2000/svg" style="width:100%; height:auto; max-height:200px;">
      <defs>
        <marker id="arr" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="5" markerHeight="5" orient="auto">
          <path d="M 0 0 L 10 5 L 0 10 z" fill="#888780"/>
        </marker>
      </defs>
      <line x1="300" y1="100" x2="130" y2="50" stroke="#888780" stroke-width="1" marker-end="url(#arr)"/>
      <line x1="300" y1="100" x2="130" y2="150" stroke="#888780" stroke-width="1" marker-end="url(#arr)"/>
      <line x1="300" y1="100" x2="470" y2="100" stroke="{edge2_color}" stroke-width="2" marker-end="url(#arr)"/>
      <line x1="470" y1="100" x2="560" y2="50" stroke="{edge2_color}" stroke-width="1.5" {edge2_dash}marker-end="url(#arr)"/>
      <line x1="470" y1="100" x2="560" y2="150" stroke="#888780" stroke-width="1" stroke-dasharray="3,3" marker-end="url(#arr)"/>
      <circle cx="130" cy="50" r="26" fill="#B5D4F4" stroke="#185FA5" stroke-width="1"/>
      <text x="130" y="54" text-anchor="middle" font-size="11" font-weight="500" fill="#0C447C">송금인</text>
      <text x="130" y="22" text-anchor="middle" font-size="10" fill="#185FA5">정상이력 95%</text>
      <circle cx="130" cy="150" r="22" fill="#D3D1C7" stroke="#5F5E5A" stroke-width="1"/>
      <text x="130" y="154" text-anchor="middle" font-size="11" fill="#444441">단말기</text>
      <text x="130" y="183" text-anchor="middle" font-size="10" fill="#5F5E5A">신규 IP</text>
      <rect x="260" y="78" width="80" height="44" rx="6" fill="#F0997B" stroke="#993C1D" stroke-width="1.5"/>
      <text x="300" y="96" text-anchor="middle" font-size="11" font-weight="500" fill="#4A1B0C">본 거래</text>
      <text x="300" y="112" text-anchor="middle" font-size="10" fill="#712B13">{amount:.0f}만 / {int(hour):02d}시</text>
      <circle cx="470" cy="100" r="26" fill="#F0997B" stroke="#993C1D" stroke-width="1.5"/>
      <text x="470" y="100" text-anchor="middle" font-size="11" font-weight="500" fill="#4A1B0C">수취인</text>
      <text x="470" y="138" text-anchor="middle" font-size="10" fill="#712B13">{payee_type} 1-hop</text>
      <circle cx="560" cy="50" r="18" fill="{risk_fill}" stroke="{risk_stroke}" stroke-width="1"/>
      <text x="560" y="54" text-anchor="middle" font-size="10" fill="{risk_color}">{risk_text}</text>
      <circle cx="560" cy="150" r="18" fill="#D3D1C7" stroke="#5F5E5A" stroke-width="1"/>
      <text x="560" y="154" text-anchor="middle" font-size="10" fill="#444441">일반</text>
      <text x="300" y="20" text-anchor="middle" font-size="11" fill="#5F5E5A">1-hop 이웃</text>
      <text x="560" y="20" text-anchor="middle" font-size="11" fill="#5F5E5A">2-hop 이웃</text>
    </svg>
    """

    table_html = f"""
    <table style="width:100%; font-size:13px; border-collapse:collapse;">
      <thead>
        <tr style="border-bottom:0.5px solid rgba(0,0,0,0.15);">
          <th style="text-align:left; padding:6px 4px; font-weight:500; color:#666;">계층</th>
          <th style="text-align:left; padding:6px 4px; font-weight:500; color:#666;">집계 내용</th>
          <th style="text-align:right; padding:6px 4px; font-weight:500; color:#666;">기여</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td style="padding:6px 4px;">Layer 1</td>
          <td style="padding:6px 4px; color:#666;">송금인 + 단말기 임베딩 평균</td>
          <td style="text-align:right; padding:6px 4px; font-family:monospace; color:#04342C;">{layer1_contrib:+.2f}</td>
        </tr>
        <tr style="background:{layer2_bg};">
          <td style="padding:6px 4px; color:{layer2_td};">Layer 2</td>
          <td style="padding:6px 4px; color:{layer2_sub};">{layer2_desc}</td>
          <td style="text-align:right; padding:6px 4px; font-family:monospace; font-weight:500; color:{layer2_td};">{layer2_contrib:+.2f}</td>
        </tr>
        <tr>
          <td style="padding:6px 4px;">MLP</td>
          <td style="padding:6px 4px; color:#666;">거래 임베딩 → 분류기 (raw score)</td>
          <td style="text-align:right; padding:6px 4px; font-family:monospace;">{mlp_contrib:+.2f}</td>
        </tr>
      </tbody>
    </table>
    """

    return prob, f"""
    <div style="{CARD_STYLE}">
      {card_header("GEN 4 · GNN (GRAPH NEURAL NETWORK)", "그래프 신경망 (시뮬레이션)", dec, bg, fg, f"사기 확률 {prob*100:.2f}%")}
      {formula_box("h<sub>v</sub><sup>(l+1)</sup> = σ(W · AGG({{h<sub>u</sub><sup>(l)</sup> : u ∈ N(v)}}) + B · h<sub>v</sub><sup>(l)</sup>)<br>P(사기) = MLP(h<sub>거래</sub><sup>(L)</sup>) &nbsp; [2-hop 메시지 패싱]")}
      <p style="font-size:13px; color:#666; margin:12px 0 6px;">2-hop 이웃 그래프</p>
      {svg}
      <p style="font-size:13px; color:#666; margin:14px 0 6px;">이웃 집계 결과 (Message Passing)</p>
      {table_html}
      <p style="font-size:12px; color:#888; margin:10px 0 0; font-style:italic;">강점: 1-3세대가 보지 못하는 '관계망'을 학습. 수취인이 2-hop 내 사기계좌와 연결된 사실을 자동 포착</p>
    </div>
    """

# ============================================================
# 5세대 — Claude API 실제 호출
# ============================================================
def build_claude_prompt(amount, hour, new_payee_bin, ratio, prior_avg, prior_dec):
    """Claude에게 보낼 시스템 프롬프트와 사용자 메시지 구성"""
    system_prompt = (
        "당신은 한국 은행의 FDS(이상금융거래탐지시스템) 분석 전문가입니다. "
        "주어진 거래 정보와 1-4세대 모델의 사전 판단을 바탕으로 사기 여부를 종합 판정하세요. "
        "특히 부동산 잔금 송금, 사업자 대금 결제 같은 정상 거래 패턴과 보이스피싱·대포통장 패턴을 구분해야 합니다. "
        "반드시 다음 JSON 형식으로만 응답하세요(다른 텍스트 금지):\n"
        '{\n'
        '  "risk_score": <0.0~1.0 사이 사기 의심도>,\n'
        '  "decision": "차단" | "추가 인증" | "통과",\n'
        '  "reasoning_steps": [\n'
        '    {"step": "<추론 단계>", "attention": <0.0~1.0>},\n'
        '    ... (3~5개)\n'
        '  ],\n'
        '  "judgment": "<최종 판단 자연어 (왜 그렇게 판단했는지 + 권고 조치)>"\n'
        '}'
    )
    payee_str = "신규" if new_payee_bin == 1 else "기존"
    time_period = "새벽" if (hour <= 6 or hour >= 22) else ("주간" if 9 <= hour <= 18 else "저녁")
    user_message = (
        f"[거래 정보]\n"
        f"- 금액: {amount:.0f}만원\n"
        f"- 거래 시간: {int(hour):02d}시 ({time_period})\n"
        f"- 수취인: {payee_str} 수취인\n"
        f"- 과거 대비 배율: {ratio:.1f}배 (송금인의 평균 거래액 대비)\n\n"
        f"[1-4세대 모델 사전 판단]\n"
        f"- 평균 사기 확률: {prior_avg*100:.1f}%\n"
        f"- 종합 판정: {prior_dec}\n\n"
        f"위 거래에 대해 FDS 전문가 관점에서 종합 판정해주세요."
    )
    return system_prompt, user_message

def call_claude_api(amount, hour, new_payee_bin, ratio, prior_avg, prior_dec):
    """실제 Claude API 호출. 성공 시 (parsed_dict, raw_meta) 반환, 실패 시 (None, error_msg)"""
    if not CLAUDE_AVAILABLE:
        return None, "API 키 미설정 (ANTHROPIC_API_KEY 환경변수 없음)"

    system_prompt, user_message = build_claude_prompt(
        amount, hour, new_payee_bin, ratio, prior_avg, prior_dec
    )
    try:
        t0 = time.time()
        response = claude_client.messages.create(
            model=CLAUDE_MODEL,
            max_tokens=1024,
            temperature=0.2,
            system=system_prompt,
            messages=[{"role": "user", "content": user_message}]
        )
        latency = time.time() - t0

        raw_text = response.content[0].text.strip()

        # JSON 추출: 응답 중 첫 번째 { 부터 마지막 } 까지
        json_match = re.search(r'\{.*\}', raw_text, re.DOTALL)
        if not json_match:
            return None, f"JSON 파싱 실패: {raw_text[:200]}"

        parsed = json.loads(json_match.group(0))

        meta = {
            "input_tokens": response.usage.input_tokens,
            "output_tokens": response.usage.output_tokens,
            "latency": latency,
            "model": CLAUDE_MODEL,
            "raw_text": raw_text,
        }
        return parsed, meta
    except Exception as e:
        return None, f"API 호출 실패: {type(e).__name__}: {str(e)[:200]}"

def render_gen5_card(amount, hour, new_payee_bin, ratio, prior_avg, prior_dec,
                     prob, decision_text, reasoning_steps, judgment_text,
                     meta_html, source_label):
    """5세대 카드 HTML 렌더링 (API/시뮬레이션 공용)"""
    dec, bg, fg = decide(prob)

    context_html = (
        f"[SYS] 당신은 한국 은행의 FDS 분석 전문가입니다. 거래 정보와 1-4세대 사전 판단을 토대로 종합 판정하세요.<br>"
        f"[INPUT] amount={amount:.0f}만, hour={int(hour):02d}, "
        f"new_payee={'true' if new_payee_bin==1 else 'false'}, ratio={ratio:.1f}×<br>"
        f"[PRIOR] 1-4세대 평균: {prior_avg*100:.1f}% / 종합: {prior_dec}<br>"
        f"[TASK] JSON 형식으로 risk_score, decision, reasoning_steps, judgment 출력"
    )

    cot_rows = ""
    for i, step in enumerate(reasoning_steps, 1):
        step_text = step.get("step", "") if isinstance(step, dict) else str(step)
        attn = step.get("attention", 0.0) if isinstance(step, dict) else 0.0
        try:
            attn = float(attn)
        except (ValueError, TypeError):
            attn = 0.0
        cot_rows += (
            f"<tr><td style='padding:6px 4px;'>{i}</td>"
            f"<td style='padding:6px 4px; color:#666;'>{step_text}</td>"
            f"<td style='text-align:right; padding:6px 4px; font-family:monospace; color:#666;'>{attn:.2f}</td></tr>"
        )

    judg_color = "#3B6D11" if prob < 0.5 else "#633806"
    judgment_html = (
        f"<b style='color:{judg_color};'>{decision_text} 권고 (의심도 {prob*100:.0f}%)</b><br><br>"
        f"{judgment_text}"
    )

    return f"""
    <div style="{CARD_STYLE}">
      {card_header("GEN 5 · FOUNDATION MODEL (LLM)", f"초거대 추론 모델 ({source_label})", dec, bg, fg, f"의심도 {prob*100:.0f}%")}
      <p style="font-size:13px; color:#666; margin:4px 0 6px;">컨텍스트 토큰화</p>
      <div style="background:#f5f5f0; padding:10px 12px; border-radius:6px; font-family:monospace; font-size:11px; line-height:1.7; margin-bottom:12px;">{context_html}</div>
      <p style="font-size:13px; color:#666; margin:4px 0 6px;">추론 체인 (Chain-of-Thought)</p>
      <table style="width:100%; font-size:13px; border-collapse:collapse; margin-bottom:12px;">
        <thead>
          <tr style="border-bottom:0.5px solid rgba(0,0,0,0.15);">
            <th style="text-align:left; padding:6px 4px; font-weight:500; color:#666; width:8%;">단계</th>
            <th style="text-align:left; padding:6px 4px; font-weight:500; color:#666; width:72%;">추론 내용</th>
            <th style="text-align:right; padding:6px 4px; font-weight:500; color:#666; width:20%;">Attention</th>
          </tr>
        </thead>
        <tbody>{cot_rows}</tbody>
      </table>
      <p style="font-size:13px; color:#666; margin:4px 0 6px;">생성된 자연어 판단</p>
      <div style="background:#FAEEDA; padding:12px 14px; border-radius:6px; font-size:13px; line-height:1.7; color:#412402;">{judgment_html}</div>
      <details style="margin-top:10px;">
        <summary style="font-size:12px; color:#666; cursor:pointer;">생성 파라미터 보기</summary>
        <div style="background:#f5f5f0; padding:8px 12px; border-radius:6px; font-family:monospace; font-size:11px; margin-top:6px; line-height:1.6;">{meta_html}</div>
      </details>
      <p style="font-size:12px; color:#888; margin:10px 0 0; font-style:italic;">강점: 사전 학습된 도메인 지식으로 '왜 사기인지' 자연어 설명 + 권고 조치까지 자동 생성</p>
    </div>
    """

def render_gen5_simulation(amount, hour, new_payee_bin, ratio, prior_avg, prior_dec):
    """API 호출 실패/미설정 시 fallback용 시뮬레이션"""
    is_high_risk = (new_payee_bin == 1 and (hour <= 6 or hour >= 22) and amount >= 500)
    prob = 0.95 if is_high_risk else min(prior_avg + 0.02, 0.98)
    payee_str = "신규 수취인" if new_payee_bin == 1 else "기존 수취인"
    time_str = "새벽" if (hour <= 6 or hour >= 22) else "일반"

    reasoning_steps = [
        {"step": f"시간 {int(hour):02d}시 + {payee_str} → 보이스피싱 정형 패턴 매칭", "attention": 0.42},
        {"step": f"평소 {ratio:.1f}배 금액 → 송금인 평소 행동 이탈도 측정", "attention": 0.28},
        {"step": f"4세대 GNN 신호 + {amount:.0f}만원 고액 → 위험 가중", "attention": 0.21},
        {"step": "최근 보이스피싱 트렌드 사전 지식 활용", "attention": 0.09},
    ]

    if is_high_risk:
        decision_text = "차단"
        judgment_text = (
            f"{int(hour):02d}시 {time_str} 시간대에 평소보다 {ratio:.1f}배 급증한 {amount:.0f}만원이 "
            f"<u>{payee_str}</u>에게 이체되는 것은 전형적인 보이스피싱 패턴입니다. "
            f"수취인 계좌가 사기 의심 계좌와 2-hop 거리에 있다는 GNN 신호까지 더해져 위험도가 매우 높습니다.<br><br>"
            f"<b>권고 조치:</b> ① 즉시 거래 보류, ② 등록된 전화번호로 본인 직접 확인, ③ 확인 전 자금 동결 24시간 유지"
        )
    elif prob >= 0.5:
        decision_text = "추가 인증"
        judgment_text = (
            f"{int(hour):02d}시 거래에서 일부 이상 신호({ratio:.1f}배 금액, {payee_str})가 감지되었으나 "
            f"결정적 위험 패턴은 아닙니다. 차단보다는 추가 인증으로 본인 의사를 확인하는 것이 적절합니다.<br><br>"
            f"<b>권고 조치:</b> ① ARS 또는 OTP 추가 인증, ② 송금 의도 재확인 메시지 발송"
        )
    else:
        decision_text = "통과"
        judgment_text = (
            f"{int(hour):02d}시 거래의 패턴이 송금인의 평소 행동 범위 내에 있으며, "
            f"1-4세대 모델 모두 위험 신호를 강하게 보내지 않았습니다. 정상 거래로 판단됩니다.<br><br>"
            f"<b>권고 조치:</b> 별도 조치 없이 거래 진행"
        )

    meta_html = (
        "mode: <b>SIMULATION</b> (API 미연결)<br>"
        "이 결과는 if-else 하드코딩으로 생성된 시뮬레이션입니다.<br>"
        "실제 Claude 호출을 활성화하려면 ANTHROPIC_API_KEY 환경변수를 설정하세요."
    )

    return render_gen5_card(
        amount, hour, new_payee_bin, ratio, prior_avg, prior_dec,
        prob, decision_text, reasoning_steps, judgment_text,
        meta_html, source_label="시뮬레이션 모드"
    )

def render_gen5(amount, hour, new_payee_bin, ratio, prior_avg):
    """5세대 진입점: API 가능 시 Claude 호출, 실패 시 시뮬레이션 fallback"""
    prior_dec, _, _ = decide(prior_avg)

    if not CLAUDE_AVAILABLE:
        return render_gen5_simulation(amount, hour, new_payee_bin, ratio, prior_avg, prior_dec)

    parsed, meta_or_err = call_claude_api(amount, hour, new_payee_bin, ratio, prior_avg, prior_dec)

    if parsed is None:
        fallback_html = render_gen5_simulation(amount, hour, new_payee_bin, ratio, prior_avg, prior_dec)
        warning = (
            f'<div style="background:#FCEBEB; border-left:3px solid #A32D2D; padding:10px 14px; '
            f'border-radius:6px; margin-bottom:10px; font-size:13px; color:#4A1B0C;">'
            f'⚠️ Claude API 호출 실패. 시뮬레이션 모드로 대체합니다.<br>'
            f'<span style="font-family:monospace; font-size:11px; color:#712B13;">사유: {meta_or_err}</span>'
            f'</div>'
        )
        return warning + fallback_html

    try:
        prob = float(parsed.get("risk_score", 0.5))
        prob = max(0.0, min(1.0, prob))
        decision_text = parsed.get("decision", "추가 인증")
        reasoning_steps = parsed.get("reasoning_steps", [])
        judgment_text = parsed.get("judgment", "(판단 내용 누락)")

        meta = meta_or_err
        meta_html = (
            f"model: <b>{meta['model']}</b> / temperature: 0.2 / max_tokens: 1024<br>"
            f"input_tokens: {meta['input_tokens']} / output_tokens: {meta['output_tokens']} / "
            f"latency: {meta['latency']:.2f}s<br>"
            f"mode: <b style='color:#3B6D11;'>LIVE API CALL ✓</b>"
        )

        return render_gen5_card(
            amount, hour, new_payee_bin, ratio, prior_avg, prior_dec,
            prob, decision_text, reasoning_steps, judgment_text,
            meta_html, source_label=f"실제 {CLAUDE_MODEL}"
        )
    except (KeyError, ValueError, TypeError) as e:
        fallback_html = render_gen5_simulation(amount, hour, new_payee_bin, ratio, prior_avg, prior_dec)
        warning = (
            f'<div style="background:#FCEBEB; border-left:3px solid #A32D2D; padding:10px 14px; '
            f'border-radius:6px; margin-bottom:10px; font-size:13px; color:#4A1B0C;">'
            f'⚠️ Claude 응답 파싱 실패. 시뮬레이션 모드로 대체합니다.<br>'
            f'<span style="font-family:monospace; font-size:11px; color:#712B13;">'
            f'{type(e).__name__}: {str(e)[:150]}</span></div>'
        )
        return warning + fallback_html

# ============================================================
# 4. 메인 분석 함수
# ============================================================
def analyze_transaction(amount, hour, new_payee, ratio):
    start_time = time.time()
    new_payee_bin = 1 if new_payee == "예" else 0

    # 1세대
    g1 = render_gen1(amount, hour, new_payee_bin, ratio)
    _, score1 = evaluate_gen1(amount, hour, new_payee_bin, ratio)
    prob1 = min(score1 / 100, 0.99)

    # 2세대
    g2 = render_gen2(amount, hour, new_payee_bin, ratio)
    input_vec = np.array([amount, hour, new_payee_bin, ratio], dtype=float)
    logit2 = (GEN2_COEF * input_vec).sum() + GEN2_INTERCEPT
    prob2 = float(1 / (1 + np.exp(-logit2)))

    # 3세대
    g3 = render_gen3(amount, hour, new_payee_bin, ratio)
    input_df = pd.DataFrame([[amount, hour, new_payee_bin, ratio]], columns=FEATURES)
    prob3 = float(gen3_model.predict_proba(input_df)[0][1])

    # 4세대
    prob4, g4 = render_gen4(amount, hour, new_payee_bin, ratio, prob3)

    # 5세대 (1-4세대 평균을 prior로)
    prior_avg = (prob1 + prob2 + prob3 + prob4) / 4
    g5 = render_gen5(amount, hour, new_payee_bin, ratio, prior_avg)

    elapsed = time.time() - start_time

    summary = f"""
    <div style="background:#f5f5f0; border-radius:12px; padding:16px 20px; margin-bottom:14px;">
      <p style="font-size:13px; color:#666; margin:0 0 8px;">분석 대상 거래</p>
      <div style="display:grid; grid-template-columns:repeat(auto-fit, minmax(120px, 1fr)); gap:12px;">
        <div><p style="font-size:12px; color:#888; margin:0;">금액</p><p style="font-size:18px; font-weight:500; margin:2px 0 0;">{amount:.0f}만원</p></div>
        <div><p style="font-size:12px; color:#888; margin:0;">거래 시간</p><p style="font-size:18px; font-weight:500; margin:2px 0 0;">{int(hour):02d}시</p></div>
        <div><p style="font-size:12px; color:#888; margin:0;">신규 수취인</p><p style="font-size:18px; font-weight:500; margin:2px 0 0;">{new_payee}</p></div>
        <div><p style="font-size:12px; color:#888; margin:0;">과거 대비 배율</p><p style="font-size:18px; font-weight:500; margin:2px 0 0;">{ratio:.1f}×</p></div>
      </div>
      <p style="font-size:12px; color:#888; margin:10px 0 0;">분석 소요시간: {elapsed:.3f}초</p>
    </div>
    """

    return summary + g1 + g2 + g3 + g4 + g5

# ============================================================
# 5. Gradio UI
# ============================================================
with gr.Blocks(theme=gr.themes.Default(), title="FDS 1-5세대 비교 데모") as demo:
    _api_badge = (
        f'<span style="background:#EAF3DE; color:#3B6D11; padding:3px 10px; border-radius:8px; font-size:12px; font-weight:500;">'
        f'● Claude API 연결됨 ({CLAUDE_MODEL})</span>'
        if CLAUDE_AVAILABLE else
        '<span style="background:#FAEEDA; color:#854F0B; padding:3px 10px; border-radius:8px; font-size:12px; font-weight:500;">'
        '○ API 미연결 (5세대 시뮬레이션 모드)</span>'
    )
    gr.HTML(f"""
    <div style="text-align:center; padding:10px 0;">
      <h1 style="margin:0;">🛡️ FDS 1-5세대 비교 데모</h1>
      <p style="color:#666; margin:6px 0 0;">실무 운영 담당자 시연용 · 판단 수식·가중치·근거 전체 노출 모드</p>
      <div style="margin-top:8px;">{_api_badge}</div>
    </div>
    """)

    with gr.Row():
        with gr.Column(scale=1):
            amount_in = gr.Number(label="금액 (만원)", value=700)
            hour_in = gr.Slider(label="거래 시간 (0-23시)", minimum=0, maximum=23, value=3, step=1)
            payee_in = gr.Radio(label="신규 수취인", choices=["아니오", "예"], value="예")
            ratio_in = gr.Number(label="과거 대비 배율", value=14.0)
            submit_btn = gr.Button("🔍 분석 실행", variant="primary")

            gr.Examples(
                examples=[
                    [700, 3, "예", 14.0],
                    [800, 23, "예", 8.0],
                    [45, 2, "아니오", 1.2],
                    [1500, 14, "아니오", 2.0],
                    [499, 23, "예", 4.5],
                ],
                inputs=[amount_in, hour_in, payee_in, ratio_in],
                label="시연 예시 (마지막은 1세대 룰을 회피하는 케이스)"
            )

        with gr.Column(scale=2):
            output_html = gr.HTML(
                "<div style='padding:20px; color:#666;'>"
                "좌측에서 거래 조건을 설정하고 [분석 실행] 버튼을 누르세요."
                "</div>"
            )

    submit_btn.click(
        fn=analyze_transaction,
        inputs=[amount_in, hour_in, payee_in, ratio_in],
        outputs=output_html
    )

if __name__ == "__main__":
    demo.launch(ssr_mode=False)