"""Routes for the end-to-end pipeline API (document parse → build → eval)."""

from __future__ import annotations

import logging

from fastapi import APIRouter, HTTPException

from webapp.models import (
    PipelineJobRequest,
    PipelineJobResponse,
    PipelineJobStatus,
)
from webapp.services.pipeline_task_manager import pipeline_task_manager

router = APIRouter(prefix="/api/pipeline", tags=["pipeline"])
logger = logging.getLogger("webapp.api.pipeline")


@router.post(
    "/jobs",
    status_code=202,
    response_model=PipelineJobResponse,
    summary="提交全链路评估任务",
    responses={
        202: {
            "description": "任务已成功排队，立即返回 job_id。",
            "content": {
                "application/json": {
                    "example": {
                        "job_id": "a1b2c3d4e5f6",
                        "job_name": "siemens-ct-eval-2026",
                        "status": "queued",
                    }
                }
            },
        },
        422: {"description": "请求参数校验失败（docs_path 等必填字段缺失或格式错误）。"},
    },
)
def submit_pipeline_job(request: PipelineJobRequest) -> PipelineJobResponse:
    """提交一个「解析文档 → 生成题库 → RAGAS 评估 → 输出报告」全链路任务。

    任务在后台线程中异步执行，立即返回 `job_id`。
    通过 `GET /api/pipeline/jobs/{job_id}` 轮询 `status` / `phase` / `logs`。

    **Pipeline 执行阶段**：
    1. `parsing_documents` — 调用阿里云 DocMind 解析每份 PDF
    2. `generating_questions` — LLM 从文档片段生成草稿题库
    3. `evaluating` — RAGAS 在线评测打分（answer_model 答题 + judge_model 评分）
    4. `done` — 所有产物写入磁盘，`status` 变为 `completed`
    """
    logger.info(
        "[submit_pipeline] docs_path=%s  job_name=%r  gen_model=%s  judge=%s  max_docs=%s",
        request.docs_path, request.job_name, request.generation_model,
        request.judge_model, request.max_documents,
    )
    task = pipeline_task_manager.submit(request)
    logger.info("[submit_pipeline] queued  job_id=%s  job_name=%s", task.job_id, task.job_name)
    return PipelineJobResponse(
        job_id=task.job_id,
        job_name=task.job_name,
        status=task.status,
    )


@router.get(
    "/jobs/{job_id}",
    response_model=PipelineJobStatus,
    summary="查询任务状态",
    responses={
        200: {"description": "返回任务当前状态、执行阶段、日志及完成后的产物路径。"},
        404: {"description": "指定 job_id 的任务不存在。"},
    },
)
def get_pipeline_job(job_id: str) -> PipelineJobStatus:
    """查询一个 Pipeline 任务的当前状态、执行阶段、实时日志和结果。

    **轮询建议**：每 3–5 秒查询一次，直到 `status` 为 `completed` 或 `failed`。

    `result` 字段在任务完成后填充，包含：
    - `scores_csv` — 每道题目逐项评分
    - `summary_md` — 评估摘要 Markdown
    - `dataset_csv` — 生成的题库 CSV
    - `source_chunks_jsonl` — 文档片段索引
    """
    status = pipeline_task_manager.get(job_id)
    if status is None:
        logger.warning("[get_pipeline_job] not found  job_id=%s", job_id)
        raise HTTPException(status_code=404, detail=f"Pipeline job not found: {job_id}")
    logger.debug("[get_pipeline_job] job_id=%s  status=%s  phase=%s", job_id, status.status, status.phase)
    return status


@router.get(
    "/jobs",
    response_model=dict,
    summary="列出所有任务",
    responses={
        200: {
            "description": "按创建时间倒序返回本次服务器会话中所有的 Pipeline 任务。",
            "content": {
                "application/json": {
                    "example": {
                        "jobs": [
                            {
                                "job_id": "a1b2c3d4e5f6",
                                "job_name": "siemens-ct-eval",
                                "status": "completed",
                                "phase": "done",
                                "logs": ["[build] 17 documents parsed", "..."],
                                "result": {
                                    "total_questions": 19,
                                    "eval_run_id": "2026-06-18T...",
                                    "scores_csv": "outputs/pipeline/.../scores.csv",
                                    "summary_md": "outputs/pipeline/.../summary.md",
                                },
                                "error": None,
                            }
                        ]
                    }
                }
            },
        }
    },
)
def list_pipeline_jobs() -> dict:
    """返回本次服务器会话中所有已提交的 Pipeline 任务，按创建时间倒序排列。"""
    jobs = pipeline_task_manager.list_jobs()
    logger.info("[list_pipeline_jobs] count=%d", len(jobs))
    return {"jobs": [s.model_dump() for s in jobs]}