"""技能差距分析 AI 引擎

差距分析 + 定制简历优化 + Agent 规划/执行。
依赖：LLM 枚举、skill_gap_analyzer/prompts
"""

import asyncio
import time

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

from app.ai.models import LLM
from app.ai.skill_gap_analyzer.prompts import (
    SKILL_GAP_PROMPT, SUMMARY_OPTIMIZE_PROMPT, EXPERIENCE_OPTIMIZE_PROMPT,
    AGENT_PLAN_PROMPT, AGENT_MODULE_EDIT_PROMPT, AGENT_MODULE_ADD_PROMPT, MODULE_SCHEMAS,
)
from app.core.logger import log
from app.tool.json_helper import parse_llm_json


# ===== 差距分析 =====

_skill_gap_chain = (
    ChatPromptTemplate.from_messages([("system", SKILL_GAP_PROMPT), ("human", "请开始分析。")])
    | LLM.DOUBAO_LITE_32K.create(temperature=0)
    | StrOutputParser()
)


async def analyze_skill_gap(skill_tags: list[str], resume_json: str) -> list[str]:
    """分析技能差距，返回缺失技能列表"""
    try:
        raw = await _skill_gap_chain.ainvoke({"skill_tags": str(skill_tags), "resume_json": resume_json})
        result = parse_llm_json(raw)
        if isinstance(result, list):
            return [s for s in result if isinstance(s, str) and s in skill_tags]
        return skill_tags  # 解析异常降级：全部标记缺失
    except Exception as e:
        log.warning(f"AI技能差距分析失败: {e}")
        return skill_tags  # 降级：全部标记缺失


# ===== 定制简历 - summary 优化 =====

_summary_optimize_chain = (
    ChatPromptTemplate.from_messages([("system", SUMMARY_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
    | LLM.DOUBAO_LITE_32K.create(temperature=0.3)
    | StrOutputParser()
)


async def optimize_summary(job_title: str, add_skills: list[str], original_summary: str) -> str:
    """优化个人概述，融入技能关键词"""
    t0 = time.monotonic()
    try:
        result = await _summary_optimize_chain.ainvoke({
            "job_title": job_title, "add_skills": "、".join(add_skills) if add_skills else "无",
            "original_summary": original_summary or "暂无",
        })
        log.info(f"AI优化summary完成 ({round(time.monotonic() - t0, 2)}s)")
        return result
    except Exception as e:
        log.warning(f"AI优化summary失败: {e} ({round(time.monotonic() - t0, 2)}s)")
        return original_summary


# ===== 定制简历 - experience 优化 =====

_experience_optimize_chain = (
    ChatPromptTemplate.from_messages([("system", EXPERIENCE_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
    | LLM.DOUBAO_LITE_32K.create(temperature=0.3)
    | StrOutputParser()
)


async def optimize_module(job_title: str, job_description: str, module_data: str) -> list | dict | None:
    """优化单条经历描述，返回修改后的记录数据"""
    t0 = time.monotonic()
    try:
        raw = await _experience_optimize_chain.ainvoke({
            "job_title": job_title, "job_description": job_description or "",
            "original_module_data": module_data,
        })
        result = parse_llm_json(raw)
        log.info(f"AI优化经历模块完成 ({round(time.monotonic() - t0, 2)}s)")
        return result
    except Exception as e:
        log.warning(f"AI优化经历模块失败: {e} ({round(time.monotonic() - t0, 2)}s)")
        return None


# ===== Agent - 规划 =====

_plan_chain = (
    ChatPromptTemplate.from_messages([("system", AGENT_PLAN_PROMPT), ("human", "请分析用户指令。")])
    | LLM.ZM_GPT_5_4.create(temperature=0)
    | StrOutputParser()
)


async def plan_edit(job_title: str, job_description: str, resume_json: str,
                    chat_history: str, instruction: str) -> dict | None:
    """Agent 规划：分析用户指令，返回原子操作列表或对话回复"""
    try:
        raw = await _plan_chain.ainvoke({
            "job_title": job_title, "job_description": job_description,
            "resume_json": resume_json,
            "chat_history": chat_history, "instruction": instruction,
        })
        result = parse_llm_json(raw)
        return result if isinstance(result, dict) else None
    except Exception as e:
        log.warning(f"AI规划失败: {e}")
        return None


# ===== Agent - 单条记录修改 =====

_record_edit_chain = (
    ChatPromptTemplate.from_messages([("system", AGENT_MODULE_EDIT_PROMPT), ("human", "请执行修改。")])
    | LLM.ZM_GPT_5_4.create(temperature=0.3)
    | StrOutputParser()
)


async def execute_record_edit(job_title: str, job_description: str, instruction: str,
                              chat_history: str, module_schema: str,
                              record_data: str) -> dict | list | None:
    """修改单条记录：根据指令修改指定记录数据"""
    try:
        raw = await _record_edit_chain.ainvoke({
            "job_title": job_title, "job_description": job_description,
            "instruction": instruction, "chat_history": chat_history,
            "module_schema": module_schema, "record_data": record_data,
        })
        return parse_llm_json(raw)
    except Exception as e:
        log.warning(f"AI单条记录修改失败: {e}")
        return None


# ===== Agent - 新增记录 =====

_record_add_chain = (
    ChatPromptTemplate.from_messages([("system", AGENT_MODULE_ADD_PROMPT), ("human", "请生成新记录。")])
    | LLM.ZM_GPT_5_4.create(temperature=0.3)
    | StrOutputParser()
)


async def execute_record_add(job_title: str, job_description: str, instruction: str,
                             chat_history: str, module_schema: str) -> dict | None:
    """新增一条记录：根据指令生成新记录"""
    try:
        raw = await _record_add_chain.ainvoke({
            "job_title": job_title, "job_description": job_description,
            "instruction": instruction, "chat_history": chat_history,
            "module_schema": module_schema,
        })
        result = parse_llm_json(raw)
        return result if isinstance(result, dict) else None
    except Exception as e:
        log.warning(f"AI新增记录失败: {e}")
        return None