"""
agent/chat.py — Oil Risk Analyst Agent (混合架构: 本地模板 + LLM 增强)
========================================================================
- 常见问题: 直接从平台数据生成专业回答 (即时响应)
- 复杂分析: 调用 SiliconFlow Qwen2.5-7B-Instruct (带重试)
"""
import json, os, re, time
import pandas as pd
import numpy as np
from config import (
    OUTPUT_DIR, SILICONFLOW_API_KEY, SILICONFLOW_BASE_URL, SILICONFLOW_MODEL
)

# ═══════════════════════════════════════════════════════════
# 数据层
# ═══════════════════════════════════════════════════════════

_cache = {}

def _results():
    if 'results' not in _cache:
        fp = os.path.join(OUTPUT_DIR, 'v2_championship_results.csv')
        _cache['results'] = pd.read_csv(fp) if os.path.exists(fp) else None
    return _cache['results']

def _reports():
    if 'reports' not in _cache:
        fp = os.path.join(OUTPUT_DIR, 'v2_nlg_reports.json')
        if os.path.exists(fp):
            with open(fp, 'r', encoding='utf-8') as f:
                _cache['reports'] = json.load(f)
        else:
            _cache['reports'] = {}
    return _cache['reports']

def _hedge():
    if 'hedge' not in _cache:
        fp = os.path.join(OUTPUT_DIR, 'v2_hedge_backtest.json')
        if os.path.exists(fp):
            with open(fp, 'r', encoding='utf-8') as f:
                _cache['hedge'] = json.load(f)
        else:
            _cache['hedge'] = {}
    return _cache['hedge']

def _events():
    if 'events' not in _cache:
        fp = os.path.join(OUTPUT_DIR, 'event_timeline.json')
        if os.path.exists(fp):
            with open(fp, 'r', encoding='utf-8') as f:
                evts = json.load(f)
            evts.sort(key=lambda e: e.get('date', ''), reverse=True)
            _cache['events'] = evts
        else:
            _cache['events'] = []
    return _cache['events']

def _latest():
    """获取最新预测行。"""
    r = _results()
    if r is None:
        return None
    return r.iloc[-1]

def _latest_report(benchmark='WTI'):
    """获取最新NLG报告。"""
    rp = _reports()
    if not rp:
        return None
    # Try specific benchmark first, then any
    keys = sorted(rp.keys())
    bm_keys = [k for k in keys if benchmark in k]
    key = bm_keys[-1] if bm_keys else keys[-1]
    entry = rp[key]
    return entry if isinstance(entry, str) else entry.get('report', str(entry)[:500])

# ═══════════════════════════════════════════════════════════
# 本地回答引擎 — 常见问题秒回
# ═══════════════════════════════════════════════════════════

IND_MAP = {
    '航空': 'aviation', 'aviation': 'aviation',
    '物流': 'logistics', 'logistics': 'logistics',
    '化工': 'chemical', 'chemical': 'chemical', 'chemicals': 'chemical',
    '制造': 'manufacturing', 'manufacturing': 'manufacturing',
    '上游': 'upstream', '油气': 'upstream', 'upstream': 'upstream',
}

IND_ZH = {'aviation': '航空', 'logistics': '物流', 'chemical': '化工',
           'manufacturing': '制造', 'upstream': '上游油气'}

IND_PROFILE = {
    'aviation': '航空燃油占运营成本30-40%，油价波动10%影响利润5-8%，敏感度最高',
    'logistics': '柴油占物流成本25-35%，可通过燃油附加费部分传导，但存在时滞',
    'chemical': '原油作为石化原料占成本40-60%，裂解价差直接影响利润率',
    'manufacturing': '能源成本占制造成本10-20%，主要通过电价和天然气间接传导',
    'upstream': '油价上涨是收入利好，但需防范暴跌风险保护资本开支',
}


def _try_local_answer(msg):
    """尝试本地回答，返回 (reply, confidence)。"""
    m = msg.lower()
    last = _latest()
    if last is None:
        return None, 0

    # ── 1. 风险等级/研判 ──
    if any(w in m for w in ['风险等级', '风险研判', '当前风险', '油价风险']):
        report = _latest_report()
        if report:
            return report, 0.95

    # ── 2. 完整报告/月度报告 ──
    if any(w in m for w in ['完整报告', '月度报告', '详细分析', '报告']):
        report = _latest_report()
        if report:
            return report, 0.9

    # ── 3. 预测区间 ──
    if any(w in m for w in ['预测区间', '分位数', 'q10', 'q50', 'q90', '下个月']):
        q10 = last['pred_q10_1m']
        q50 = last['pred_q50_1m']
        q90 = last['pred_q90_1m']
        vol = last['pred_vol']
        date = str(last['test_date'])[:7]
        reply = (f"**{date} 油价预测区间：**\n\n"
                 f"- **Q10 (悲观):** {q10:.1%}  ← 有10%概率跌幅超此\n"
                 f"- **Q50 (中枢):** {q50:.1%}  ← 最可能的变动\n"
                 f"- **Q90 (乐观):** {q90:.1%}  ← 有10%概率涨幅超此\n"
                 f"- **波动率:** {vol:.1%}\n\n"
                 f"**解读:** 区间跨度{q90-q10:.1%}，"
                 f"{'偏上行' if q50 > 0 else '偏下行'}，"
                 f"波动率{vol:.1%}{'较高，建议增加对冲' if vol > 0.05 else '可控'}。")
        return reply, 0.9

    # ── 4. 风险趋势 ──
    if any(w in m for w in ['趋势', '走势', '变化', '最近', '历史', '几个月']):
        r = _results()
        tail = r.tail(6)
        lines = ["**近6个月风险趋势:**\n"]
        for _, row in tail.iterrows():
            date = str(row['test_date'])[:7]
            lvl = row['risk_level']
            bias = row['risk_bias']
            top = row['top_factor']
            q50 = row['pred_q50_1m']
            emoji = {'High': '🔴', 'Medium-High': '🟠', 'Medium': '🟡',
                     'Low-Medium': '🔵', 'Low': '🟢'}.get(lvl, '⚪')
            lines.append(f"  {emoji} **{date}**: {lvl} | {bias} | 中枢{q50:+.1%} | 主导: {top}")

        # 趋势判断
        levels = tail['risk_level'].tolist()
        level_map = {'Low': 0, 'Low-Medium': 1, 'Medium': 2, 'Medium-High': 3, 'High': 4}
        nums = [level_map.get(l, 2) for l in levels]
        if nums[-1] > nums[0]:
            trend = "📈 总体趋势：风险**上升**"
        elif nums[-1] < nums[0]:
            trend = "📉 总体趋势：风险**下降**"
        else:
            trend = "➡️ 总体趋势：风险**持平**"
        lines.append(f"\n{trend}")

        return '\n'.join(lines), 0.9

    # ── 5. 行业分析/对冲建议 ──
    detected_ind = None
    for kw, ind in IND_MAP.items():
        if kw in m:
            detected_ind = ind
            break

    if detected_ind or any(w in m for w in ['对冲', '套保', 'cfo', '行业']):
        ind = detected_ind or 'aviation'
        hedge = _hedge()
        h = hedge.get(ind, {})
        zh = IND_ZH.get(ind, ind)
        profile = IND_PROFILE.get(ind, '')

        risk_level = last.get(f"risk_level", "Medium")
        q50 = last['pred_q50_1m']
        vol = last['pred_vol']

        ratio = h.get('recommended_ratio_pct', '50%')
        tool = {'futures': '期货锁价', 'put': '看跌期权', 'collar': '零成本领口'}.get(
            h.get('recommended_tool', 'futures'), '期货锁价')
        rationale = h.get('rationale', '')
        saving = h.get('total_saving', 0)
        vol_red = h.get('vol_reduction', 0)

        reply = (f"**{zh}行业专项分析报告**\n\n"
                 f"**一、行业画像**\n{profile}\n\n"
                 f"**二、当前油价环境**\n"
                 f"- 风险等级: **{risk_level}**\n"
                 f"- 1M预测中枢: **{q50:+.1%}**，波动率: **{vol:.1%}**\n"
                 f"- 主导因子: **{last.get('top_factor', 'N/A')}**\n\n"
                 f"**三、对冲建议**\n"
                 f"- 推荐对冲比例: **{ratio}**\n"
                 f"- 推荐工具: **{tool}**\n"
                 f"- 理由: {rationale}\n\n"
                 f"**四、历史回测**\n"
                 f"- 按推荐比例累计节省: **${saving:.1f}M**\n"
                 f"- 波动率降低: **{vol_red}%**\n\n"
                 f"**五、银行行动建议**\n")

        if risk_level in ('High', 'Medium-High'):
            reply += (f"1. 立即联络{zh}客户，提示油价上行风险\n"
                      f"2. 推荐对冲方案: {ratio} {tool}，锁定未来3-6个月成本\n"
                      f"3. 建议预留流动性缓冲以应对波动\n")
        else:
            reply += (f"1. 常规跟进{zh}客户，当前风险可控\n"
                      f"2. 建议维持基础对冲({ratio})，无需过度套保\n"
                      f"3. 关注下一轮OPEC+会议可能的政策变化\n")

        return reply, 0.95

    # ── 6. 压力测试 ──
    if any(w in m for w in ['压力', '如果', '假设', '中东', '冲突', '崩塌', '减产', '战争']):
        vol = last['pred_vol']
        q50 = last['pred_q50_1m']

        # 识别冲击场景
        supply_shock = -15 if any(w in m for w in ['供给', '减产', '中断', '中东', '冲突']) else 0
        demand_shock = -20 if any(w in m for w in ['需求', '崩塌', '衰退']) else 0
        geo_spike = 3 if any(w in m for w in ['地缘', '冲突', '中东', '战争']) else 1

        shock = abs(supply_shock)/100 + abs(demand_shock)/100
        stressed_vol = vol * (1 + shock) * (max(1, geo_spike) ** 0.5)
        stress_level = 'High' if stressed_vol > 0.12 else ('Medium' if stressed_vol > 0.06 else 'Low')

        scenario_name = []
        if supply_shock: scenario_name.append(f'供给冲击{supply_shock}%')
        if demand_shock: scenario_name.append(f'需求冲击{demand_shock}%')
        if geo_spike > 1: scenario_name.append(f'地缘风险×{geo_spike}')
        scenario = '、'.join(scenario_name) or '基准情景'

        reply = (f"**压力测试结果 — {scenario}**\n\n"
                 f"- 基准波动率: **{vol:.1%}**\n"
                 f"- 冲击后波动率: **{stressed_vol:.1%}** ({stressed_vol/vol:.0%})\n"
                 f"- 压力风险等级: **{stress_level}**\n\n")
        if stress_level == 'High':
            reply += ("**⚠️ 高风险预警:**\n"
                      "1. 立即提升对冲比例至 **50%以上**\n"
                      "2. 启动紧急风控预案，增加保证金缓冲\n"
                      "3. 重点关注航空、化工等高敏感行业客户\n")
        elif stress_level == 'Medium':
            reply += ("**⚡ 中等风险:**\n"
                      "1. 建议维持 **30%** 对冲并密切关注\n"
                      "2. 做好应急方案预案\n"
                      "3. 适度增加库存\n")
        else:
            reply += ("**✅ 风险可控:**\n"
                      "1. 当前策略无需调整\n"
                      "2. 维持常规对冲即可\n")
        return reply, 0.9

    # ── 7. 模型验证 ──
    if any(w in m for w in ['准确', '验证', '可靠', '覆盖率', 'wis', '模型']):
        r = _results()
        # Drop rows with NaN
        valid = r.dropna(subset=['actual_ret_1m', 'pred_q10_1m', 'pred_q90_1m', 'pred_vol', 'actual_vol'])
        ar = valid['actual_ret_1m'].values
        q10 = valid['pred_q10_1m'].values
        q90 = valid['pred_q90_1m'].values
        pv = valid['pred_vol'].values
        av = valid['actual_vol'].values
        n = len(valid)

        cov = ((ar >= q10) & (ar <= q90)).mean()
        wis_val = ((q90-q10)+(2/0.2)*np.maximum(q10-ar,0)+(2/0.2)*np.maximum(ar-q90,0)).mean()
        nq10 = np.quantile(ar, 0.10); nq90 = np.quantile(ar, 0.90)
        naive_wis = ((nq90-nq10)+(2/0.2)*np.maximum(nq10-ar,0)+(2/0.2)*np.maximum(ar-nq90,0)).mean()
        corr = np.corrcoef(av, pv)[0,1] if len(av) > 1 else 0
        wis_pct = (1-wis_val/naive_wis)*100 if naive_wis != 0 else 0

        reply = (f"**模型验证报告 (共 {n} 个月)**\n\n"
                 f"**核心指标:**\n"
                 f"- 80%区间覆盖率: **{cov:.1%}** (目标≥80%)\n"
                 f"- WIS得分: **{wis_val:.4f}** (优于基准 {wis_pct:+.1f}%)\n"
                 f"- 波动率相关性: **{corr:.3f}**\n\n"
                 f"**评估:** "
                 f"{'✅ 模型表现优异' if cov >= 0.75 and wis_pct > 0 else '⚠️ 模型有改进空间'}。"
                 f"覆盖率{cov:.1%}{'达标' if cov >= 0.75 else '偏低'}，"
                 f"WIS{'优于' if wis_pct > 0 else '劣于'}朴素基准{abs(wis_pct):.1f}%。")
        return reply, 0.9

    # ── 无法本地回答 ──
    return None, 0


# ═══════════════════════════════════════════════════════════
# LLM 增强 — 仅用于复杂/自定义分析
# ═══════════════════════════════════════════════════════════

SYSTEM_PROMPT = """你是「油刃有余 OilVerse」平台的AI助手「Oil Risk Agent」。

你拥有实时的平台预测数据和事件时间线，你的回答必须：
1. 先给结论(一句话加粗)，再给支撑(3-5条要点)，最后给行动建议
2. 用 **加粗** 标记关键数字和结论
3. 每次回答控制在 200 字以内
4. 绝对不要输出工具名、函数名、JSON等技术内容
5. 如果是闲聊，简短回答身份即可
6. 引用最近事件作为分析支撑，说明「事件→因子异动→风险信号→对冲建议」的因果链"""


def _build_data_context(msg):
    """为LLM构建精炼的数据上下文。"""
    last = _latest()
    if last is None:
        return ""

    ctx = [f"分析日期: {str(last['test_date'])[:7]}",
           f"风险等级: {last['risk_level']}",
           f"方向偏置: {last['risk_bias']}",
           f"1M区间: [{last['pred_q10_1m']:.1%}, {last['pred_q90_1m']:.1%}]",
           f"波动率: {last['pred_vol']:.1%}",
           f"主导因子: {last['top_factor']}",
           f"Regime匹配: {last.get('regime_match', 'N/A')} ({last.get('regime_similarity', 0):.0%})"]

    # Add recent events as causal context
    evts = _events()
    if evts:
        ctx.append('\n[近期关键事件]')
        for ev in evts[:3]:
            impact_zh = {'bullish': '利多', 'bearish': '利空', 'neutral': '中性'}.get(ev.get('impact', ''), '')
            ctx.append(f"- {ev['date']} {ev['title']} ({impact_zh}): {ev.get('risk_signal', '')}")

    return '\n'.join(ctx)


def _call_llm_enhanced(user_message, history):
    """调用 LLM，带精炼上下文。"""
    import requests

    data_ctx = _build_data_context(user_message)
    enriched = f"{user_message}\n\n[平台数据]\n{data_ctx}" if data_ctx else user_message

    messages = [{'role': 'system', 'content': SYSTEM_PROMPT}]
    for h in history[-4:]:  # 只保留最近2轮对话
        messages.append(h)
    messages.append({'role': 'user', 'content': enriched})

    headers = {
        'Authorization': f'Bearer {SILICONFLOW_API_KEY}',
        'Content-Type': 'application/json',
    }
    payload = {
        'model': SILICONFLOW_MODEL,
        'messages': messages,
        'temperature': 0.3,
        'max_tokens': 500,
        'stream': False,
    }

    last_err = None
    for attempt in range(2):
        try:
            resp = requests.post(
                f'{SILICONFLOW_BASE_URL}/chat/completions',
                headers=headers, json=payload, timeout=45
            )
            resp.raise_for_status()
            data = resp.json()
            reply = data['choices'][0]['message']['content']
            # 清理残留
            reply = re.sub(r'</?tool_call>', '', reply)
            reply = re.sub(r'\b(query_\w+|run_\w+)\(.*?\)', '', reply)
            return reply.strip()
        except requests.exceptions.Timeout:
            last_err = "LLM响应超时"
            time.sleep(2)
        except requests.exceptions.ConnectionError:
            last_err = "无法连接LLM服务"
            time.sleep(2)
        except Exception as e:
            return f"LLM调用失败: {e}"

    return f"⚠️ {last_err}，请稍后重试。\n\n💡 你可以尝试更具体的问题，如「航空行业对冲建议」「当前风险等级」等，这些可以即时响应。"


# ═══════════════════════════════════════════════════════════
# 主入口
# ═══════════════════════════════════════════════════════════

def chat_with_agent(user_message, history=None):
    """
    混合架构对话入口:
    1. 先尝试本地回答(即时)
    2. 无法本地回答时调用 LLM
    """
    if history is None:
        history = []

    # 闲聊快速回复
    greets = ['你好', '你是谁', 'hello', 'hi', '嗨', '在吗']
    if any(user_message.strip().lower() == g for g in greets):
        reply = "👋 你好！我是油价风险分析 Agent，基于平台实时数据为你提供专业分析。\n\n你可以问我：\n• 当前风险等级和预测区间\n• 行业专项分析（航空/物流/化工/制造/上游）\n• 对冲策略和工具推荐\n• 压力测试模拟\n• 模型验证指标"
        history.append({'role': 'user', 'content': user_message})
        history.append({'role': 'assistant', 'content': reply})
        return reply, history

    # 尝试本地回答
    local_reply, confidence = _try_local_answer(user_message)
    if local_reply and confidence >= 0.85:
        history.append({'role': 'user', 'content': user_message})
        history.append({'role': 'assistant', 'content': local_reply})
        return local_reply, history

    # LLM 增强回答
    reply = _call_llm_enhanced(user_message, history)
    history.append({'role': 'user', 'content': user_message})
    history.append({'role': 'assistant', 'content': reply})
    return reply, history


if __name__ == '__main__':
    print("油价风险分析 Agent（输入 quit 退出）")
    print("=" * 50)
    h = []
    while True:
        q = input("\n你: ").strip()
        if q.lower() in ('quit', 'exit', 'q'):
            break
        reply, h = chat_with_agent(q, h)
        print(f"\nAgent: {reply}")