"""涨幅轮动矩阵 API。

供「概念分析 → 涨幅RPS轮动」对话框调用。返回最近 N 个交易日的概念涨幅
排名矩阵:每列(日期)各自把所有概念按当天涨幅从高到低排序。
"""
from __future__ import annotations

from fastapi import APIRouter, Query, Request
from fastapi.responses import StreamingResponse
from pydantic import BaseModel

from app.services import rps_rotation
from app.services.concept_rotation_analyzer import analyze_rotation_stream

router = APIRouter(prefix="/api/rps", tags=["rps"])


@router.get("/rotation")
def get_rotation(
    request: Request,
    days: int = Query(12, ge=7, le=30, description="最近 N 个交易日(7-30)"),
) -> dict:
    """概念涨幅轮动矩阵。

    Returns:
        dates: 日期字符串列表(最新在最前)
        columns: {日期: [[概念名, 涨幅小数], ...]} 每列各自降序
        concept_count: 去重概念总数
    """
    return rps_rotation.build_rps_rotation(request.app.state.repo, days)


class AnalyzeRequest(BaseModel):
    """AI 概念轮动分析请求。"""
    days: int = 12   # 分析最近 N 个交易日
    focus: str = ""  # 用户追加的关注点


@router.post("/rotation-analyze")
async def analyze_rotation(request: Request, req: AnalyzeRequest):
    """AI 概念轮动分析 — NDJSON 流式返回。

    装配轮动矩阵信号 + 大盘背景 → 分析提示词 → 流式调用 LLM →
    逐 chunk 以 NDJSON 推给前端(每行一个 JSON)。

    协议:
      {"type":"meta","days","summary"}
      {"type":"delta","content":"..."}
      {"type":"error","message":"..."}
      {"type":"done"}
    """
    repo = request.app.state.repo
    days = max(7, min(30, req.days))

    async def stream_gen():
        async for chunk in analyze_rotation_stream(
            repo, days, req.focus,
            username=request.state.username,
        ):
            yield chunk + "\n"

    return StreamingResponse(
        stream_gen(),
        media_type="application/x-ndjson",
        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
    )