"""月报 AI 解析服务。

使用千问 LLM 从月报原始文本中提取：
1. 结构化指标数据（营收、现金流、团队等）
2. AI 摘要
3. 关注点列表
"""

import logging
from typing import Any

from app.services.llm_client import llm_client

logger = logging.getLogger(__name__)

SYSTEM_PROMPT = """你是投后管理领域的专业分析师。请分析以下企业月报内容，提取结构化信息。

输出 JSON 格式如下：
{
  "structured_data": {
    "revenue": {"value": "", "unit": "万元", "yoy_change": "", "note": ""},
    "cash_balance": {"value": "", "unit": "万元", "runway_months": 0, "note": ""},
    "burn_rate": {"value": "", "unit": "万元/月", "trend": "up/stable/down", "note": ""},
    "headcount": {"total": 0, "new_hires": 0, "departures": 0, "note": ""},
    "key_metrics": [{"name": "", "value": "", "change": "", "note": ""}]
  },
  "ai_summary": "一段 100-200 字的月报摘要，概括企业经营状况和关键变化",
  "ai_concerns": {
    "items": [
      {"category": "financial/operational/org/ai_specific", "severity": "low/medium/high", "description": ""}
    ],
    "highlights": ["本期亮点1", "本期亮点2"]
  }
}

注意：
- 如果月报内容不足以提取某项指标，对应字段留空或为 0
- severity 只能是 low/medium/high
- category 只能是 financial/operational/org/ai_specific
- 严格输出 JSON，不要包含其他文字"""

USER_PROMPT_TEMPLATE = """请分析以下 {period} 月报内容：

<<<USER_INPUT>>>
{content}
<<<END_USER_INPUT>>>
"""


async def parse_report(
    content: str,
    period_year: int,
    period_month: int,
) -> dict[str, Any]:
    """解析月报内容，返回结构化数据。

    Args:
        content: 月报原始文本
        period_year: 报告年份
        period_month: 报告月份

    Returns:
        包含 structured_data / ai_summary / ai_concerns 的字典

    Raises:
        RuntimeError: LLM 调用失败
    """
    if not content or not content.strip():
        return {
            "structured_data": {},
            "ai_summary": "月报内容为空",
            "ai_concerns": {"items": [], "highlights": []},
        }

    user_prompt = USER_PROMPT_TEMPLATE.format(
        period=f"{period_year}年{period_month}月",
        content=content[:4000],  # 限制输入长度
    )

    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": user_prompt},
    ]

    try:
        result = await llm_client.chat_json(messages, temperature=0.3, max_tokens=2000)
        logger.info("月报 AI 解析成功: period=%s-%s", period_year, period_month)
        return result
    except RuntimeError as e:
        logger.error("月报 AI 解析失败: %s", e)
        # 降级：返回空结构
        return {
            "structured_data": {},
            "ai_summary": f"AI 解析失败: {e}",
            "ai_concerns": {"items": [], "highlights": []},
            "fallback_used": True,
        }