# -*- coding: utf-8 -*-
"""
综合裁决节点
汇总法务/技术/财务三方意见，消解冲突，输出最终综合评级
"""
import json
from .base_agent import BaseAgent


JUDGE_SYSTEM_PROMPT = """你是一名资深的风险管理委员会主席，负责汇总法务、技术、财务三方专家的研判意见。

你的任务是：
1. 审阅三方专家的评估报告
2. 识别各方意见的冲突点
3. 基于优先级原则消解冲突（合规风险 > 技术风险 > 财务风险）
4. 输出 0-100 的综合风险评分
5. 给出最终核保建议

核保决策标准：
- 综合风险 ≥ 80分：建议【拒绝承保】
- 60 ≤ 综合风险 < 80分：建议【附条件承保】（高免赔额/限额）
- 40 ≤ 综合风险 < 60分：建议【标准承保】（标准费率上浮）
- 综合风险 < 40分：建议【优先承保】（可享费率优惠）

请输出JSON格式：
{
    "comprehensive_score": 0-100,
    "risk_level": "极高/高/中/低",
    "underwriting_decision": "拒绝承保/附条件承保/标准承保/优先承保",
    "six_dimension_scores": {
        "tech_disruption": 0-100,
        "talent_loss": 0-100,
        "algo_compliance": 0-100,
        "geopolitical": 0-100,
        "rd_capitalization": 0-100,
        "concentration": 0-100
    },
    "conflict_resolution": "...",
    "key_risks": ["..."],
    "underwriting_conditions": ["..."],
    "summary": "..."
}"""


class JudgeAgent(BaseAgent):
    """综合裁决节点"""

    def __init__(self):
        super().__init__(
            name="综合裁决节点",
            system_prompt=JUDGE_SYSTEM_PROMPT,
            role_icon="⚖️",
        )

    def evaluate(self, company_data: dict, law_result: dict,
                 tech_result: dict, finance_result: dict) -> dict:
        """汇总三方意见，输出综合裁决"""
        prompt = self._build_prompt(company_data, law_result, tech_result, finance_result)
        result = self.infer_json(prompt)

        if result.get("parse_error"):
            result = self._rule_based_evaluation(company_data, law_result, tech_result, finance_result)

        result["agent"] = "综合裁决节点"
        result["icon"] = self.role_icon
        return result

    def _build_prompt(self, company_data: dict, law_result: dict,
                      tech_result: dict, finance_result: dict) -> str:
        return f"""请对以下科创企业的三方评估结果进行综合裁决：

企业名称：{company_data.get('short_name', '未知')}
行业：{company_data.get('industry', '未知')}

=== 👩‍⚖️ 法务风控节点评估 ===
{json.dumps(law_result, ensure_ascii=False, indent=2)}

=== 👨‍🔬 技术风控节点评估 ===
{json.dumps(tech_result, ensure_ascii=False, indent=2)}

=== 👔 财务风控节点评估 ===
{json.dumps(finance_result, ensure_ascii=False, indent=2)}

请消解可能存在的判定冲突，输出综合裁决JSON。"""

    def _rule_based_evaluation(self, company_data: dict, law_result: dict,
                                tech_result: dict, finance_result: dict) -> dict:
        """规则引擎综合裁决"""

        # 提取各维度得分
        def safe_score(result: dict, key: str) -> int:
            item = result.get(key, {})
            if isinstance(item, dict):
                return item.get("score", 50)
            return 50

        # 六维评分
        scores = {
            "tech_disruption": safe_score(tech_result, "tech_disruption_risk"),
            "talent_loss": safe_score(tech_result, "talent_loss_risk"),
            "algo_compliance": safe_score(law_result, "algo_compliance_risk"),
            "geopolitical": safe_score(law_result, "geopolitical_risk"),
            "rd_capitalization": safe_score(finance_result, "rd_capitalization_risk"),
            "concentration": safe_score(finance_result, "concentration_risk"),
        }

        # 加权综合得分
        weights = {
            "tech_disruption": 0.20,
            "talent_loss": 0.15,
            "algo_compliance": 0.15,
            "geopolitical": 0.20,
            "rd_capitalization": 0.15,
            "concentration": 0.15,
        }

        comprehensive_score = int(
            sum(scores[k] * weights[k] for k in scores)
        )

        # 裁决
        if comprehensive_score >= 80:
            decision = "拒绝承保"
            risk_level = "极高"
        elif comprehensive_score >= 60:
            decision = "附条件承保"
            risk_level = "高"
        elif comprehensive_score >= 40:
            decision = "标准承保"
            risk_level = "中"
        else:
            decision = "优先承保"
            risk_level = "低"

        # 收集关键风险
        key_risks = []
        for dim, score in sorted(scores.items(), key=lambda x: x[1], reverse=True):
            if score >= 60:
                dim_names = {
                    "tech_disruption": "技术路线颠覆",
                    "talent_loss": "核心人员流失",
                    "algo_compliance": "算法/数据合规",
                    "geopolitical": "地缘政治/出口管制",
                    "rd_capitalization": "研发资本化操纵",
                    "concentration": "客户/供应商集中",
                }
                key_risks.append(f"{dim_names.get(dim, dim)}风险({score}分)")

        # 核保条件
        conditions = []
        if scores["geopolitical"] >= 70:
            conditions.append("要求提供出口管制合规声明及供应链替代方案")
        if scores["rd_capitalization"] >= 60:
            conditions.append("要求额外提供研发资本化会计政策说明及审计意见")
        if scores["concentration"] >= 60:
            conditions.append("要求提供客户分散化计划或前五大客户信用报告")
        if scores["talent_loss"] >= 60:
            conditions.append("要求核心技术人员签署竞业协议且公司有留任激励计划")

        company_name = company_data.get("short_name", "该企业")
        summary = (
            f"{company_name}综合风险评分{comprehensive_score}分(风险等级:{risk_level})。"
            f"核保建议:【{decision}】。"
            f"主要风险集中在{'、'.join(key_risks[:3]) if key_risks else '无突出风险'}。"
        )

        return {
            "comprehensive_score": comprehensive_score,
            "risk_level": risk_level,
            "underwriting_decision": decision,
            "six_dimension_scores": scores,
            "conflict_resolution": "基于优先级原则(合规>技术>财务)进行加权裁决",
            "key_risks": key_risks,
            "underwriting_conditions": conditions,
            "summary": summary,
        }