# -*- coding: utf-8 -*-
"""
技术风控节点
审查维度：技术路线竞争态势、核心人员稳定性、专利布局、技术替代风险
"""
import json
from .base_agent import BaseAgent


TECH_SYSTEM_PROMPT = """你是一名资深科技行业分析师和技术风控专家。
你精通半导体、人工智能、新能源、生物医疗等前沿科技领域的技术演进趋势。

你的任务是基于提供的企业数据，从技术角度评估以下风险：
1. 技术路线颠覆风险：企业押注的技术路线是否面临被替代的风险
2. 核心人员流失风险：关键技术人员的稳定性和不可替代性
3. 专利/技术壁垒：技术护城河的深度和可持续性
4. 技术迭代压力：行业技术迭代速度对企业的冲击

请以技术专家的视角进行深度评估，输出JSON格式：
{
    "agent": "技术风控节点",
    "tech_disruption_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
    "talent_loss_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
    "patent_moat": {"score": 0-100, "level": "强/中/弱", "detail": "..."},
    "tech_iteration_pressure": {"score": 0-100, "level": "高/中/低", "detail": "..."},
    "overall_tech_risk": {"score": 0-100, "level": "高/中/低"},
    "key_findings": ["..."],
    "recommendations": ["..."]
}"""


class TechAgent(BaseAgent):
    """技术风控节点"""

    def __init__(self):
        super().__init__(
            name="技术风控节点",
            system_prompt=TECH_SYSTEM_PROMPT,
            role_icon="👨‍🔬",
        )

    def evaluate(self, company_data: dict) -> dict:
        """执行技术风险评估"""
        prompt = self._build_prompt(company_data)
        result = self.infer_json(prompt)

        if result.get("parse_error"):
            result = self._rule_based_evaluation(company_data)

        result["agent"] = "技术风控节点"
        result["icon"] = self.role_icon
        return result

    def _build_prompt(self, company_data: dict) -> str:
        return f"""请对以下科创企业进行技术风险评估：

企业名称：{company_data.get('short_name', '未知')}
行业：{company_data.get('industry', '未知')}
领域：{company_data.get('sector', '未知')}
企业描述：{company_data.get('description', '')}

技术路线信息：
{json.dumps(company_data.get('tech_route', {}), ensure_ascii=False, indent=2)}

核心技术人员：
{json.dumps(company_data.get('core_tech_personnel', []), ensure_ascii=False, indent=2)}

财务中的研发指标：
- 研发费用: {company_data.get('financials', {}).get('rd_expense_2024', 0)}
- 研发营收比: {company_data.get('financials', {}).get('rd_revenue_ratio', 0)}

请输出严格的JSON评估结果。"""

    def _rule_based_evaluation(self, company_data: dict) -> dict:
        """基于规则的技术风险评估"""
        tech_route = company_data.get("tech_route", {})
        personnel = company_data.get("core_tech_personnel", [])
        financials = company_data.get("financials", {})

        # 技术路线颠覆风险
        competing_techs = tech_route.get("competing_techs", [])
        disruption_score = min(20 + len(competing_techs) * 15, 90)
        tech_moat = tech_route.get("tech_moat", "")
        if "差距" in tech_moat or "受制" in tech_moat:
            disruption_score = min(disruption_score + 20, 95)
        disruption_detail = f"面临 {len(competing_techs)} 条竞争技术路线: {', '.join(competing_techs[:3])}"

        # 核心人员流失风险
        talent_score = 20
        talent_detail = "核心团队稳定"
        departed = [p for p in personnel if "离职" in p.get("status", "")]
        high_importance = [p for p in personnel if p.get("importance") == "极高"]

        if departed:
            talent_score = 80
            talent_detail = f"已有核心人员离职: {', '.join(p['name'] for p in departed)}"
        elif len(high_importance) == 1:
            talent_score = 55
            talent_detail = f"高度依赖单一核心人员: {high_importance[0]['name']}"
        elif len(personnel) <= 2:
            talent_score = 45
            talent_detail = "核心技术团队规模偏小"

        # 专利壁垒
        patent_count = tech_route.get("patent_count", 0)
        if patent_count > 5000:
            patent_score = 20
            patent_detail = f"专利数量充足({patent_count}件)，技术壁垒较强"
        elif patent_count > 1000:
            patent_score = 35
            patent_detail = f"专利数量中等({patent_count}件)"
        else:
            patent_score = 60
            patent_detail = f"专利数量偏少({patent_count}件)，技术壁垒偏弱"

        # 技术迭代压力（基于研发投入比）
        rd_ratio = financials.get("rd_revenue_ratio", 0)
        if rd_ratio > 0.3:
            iter_score = 65
            iter_detail = f"研发营收比极高({rd_ratio:.1%})，说明行业技术迭代压力大"
        elif rd_ratio > 0.15:
            iter_score = 45
            iter_detail = f"研发投入较高({rd_ratio:.1%})，需持续技术投入"
        else:
            iter_score = 25
            iter_detail = f"研发投入适中({rd_ratio:.1%})"

        overall = int(disruption_score * 0.35 + talent_score * 0.25 +
                      patent_score * 0.15 + iter_score * 0.25)

        return {
            "tech_disruption_risk": {"score": disruption_score, "level": self._level(disruption_score), "detail": disruption_detail},
            "talent_loss_risk": {"score": talent_score, "level": self._level(talent_score), "detail": talent_detail},
            "patent_moat": {"score": patent_score, "level": "弱" if patent_score >= 50 else ("中" if patent_score >= 30 else "强"), "detail": patent_detail},
            "tech_iteration_pressure": {"score": iter_score, "level": self._level(iter_score), "detail": iter_detail},
            "overall_tech_risk": {"score": overall, "level": self._level(overall)},
            "key_findings": [disruption_detail, talent_detail, patent_detail],
            "recommendations": ["关注竞争技术路线发展", "加强核心人员留任激励"],
        }

    @staticmethod
    def _level(score: int) -> str:
        if score >= 70:
            return "高"
        elif score >= 40:
            return "中"
        return "低"