"""简历诊断 AI 引擎：并行诊断 + 汇总评价"""

import asyncio
import time

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

from app.ai.model_config import DiagnoserModel
from app.ai.resume_diagnoser.prompts import DIAGNOSE_MODULE_PROMPT, SUMMARY_PROMPT
from app.core.logger import log
from app.tool.json_helper import parse_llm_json


# 诊断链（StrOutputParser 拿原始文本，再手动解析 JSON，避免 markdown 代码块导致解析失败）
_diagnose_chain = (
    ChatPromptTemplate.from_messages([("system", DIAGNOSE_MODULE_PROMPT), ("human", "请开始诊断。")])
    | DiagnoserModel.MODULE
    | StrOutputParser()
)

# 汇总评价链（纯文本输出）
_summary_chain = (
    ChatPromptTemplate.from_messages([("system", SUMMARY_PROMPT), ("human", "请生成整体评价。")])
    | DiagnoserModel.SUMMARY
    | StrOutputParser()
)


async def diagnose_all(tasks: list[dict]) -> list[dict]:
    """并行诊断所有模块记录

    tasks: [{"module_type": ..., "target_position": ..., "context": ..., "description_text": ...}, ...]
    返回: 与 tasks 一一对应的诊断结果列表
    """
    log.info(f"开始{len(tasks)}路并行AI诊断")
    results = await asyncio.gather(*[_safe_invoke(task) for task in tasks])
    log.info("并行AI诊断完成")
    return results


async def generate_summary(grade: str, urgent_total: int, important_total: int,
                           expression_total: int, target_position: str, all_findings: str) -> str:
    """AI 生成整体评价文本"""
    inp = {
        "grade": grade, "urgent_total": str(urgent_total),
        "important_total": str(important_total), "expression_total": str(expression_total),
        "target_position": target_position or "未指定", "all_findings": all_findings,
    }
    try:
        return await _summary_chain.ainvoke(inp)
    except Exception as e:
        log.warning(f"AI生成整体评价失败: {e}")
        return "简历诊断已完成，请查看各模块的详细诊断结果。"


async def _safe_invoke(task: dict) -> dict:
    """单条记录诊断，失败返回空结果"""
    module_type = task.get("module_type", "unknown")
    log.info(f"AI诊断[{module_type}]开始")
    start = time.time()
    raw = ""
    try:
        raw = await _diagnose_chain.ainvoke(task)
        elapsed = time.time() - start
        log.info(f"AI诊断[{module_type}]完成, 耗时{elapsed:.2f}s")
        return parse_llm_json(raw)
    except Exception as e:
        elapsed = time.time() - start
        log.warning(f"AI诊断[{module_type}]失败, 耗时{elapsed:.2f}s: {e}\n原始输出: {raw[:500]}")
        return _empty_result()


def _empty_result() -> dict:
    return {
        "finding": "", "importance": "", "suggestion": "",
        "urgent_issues": {"typo": 0},
        "important_issues": {"no_result": 0, "no_quantify": 0, "weak_relevance": 0},
        "expression_issues": {"not_concise": 0, "format_inconsistent": 0},
        "optimized_content": None,
    }