import shutil
import subprocess
import threading
import time

from fastapi import FastAPI, File, HTTPException, Request, UploadFile

from . import config
from .detector import MockDetector, ONNXDetector

app = FastAPI(title="Silk AI Service", version="0.1.0")
START_TIME = time.time()
_lock = threading.Lock()
_requests = 0
_latency_total = 0.0


@app.middleware("http")
async def count_requests(request: Request, call_next):
    global _requests, _latency_total
    start = time.time()
    response = await call_next(request)
    latency = (time.time() - start) * 1000
    with _lock:
        _requests += 1
        _latency_total += latency
    return response

if config.MODEL_MODE == "onnx":
    detector = ONNXDetector(config.MODEL_PATH, tuple(config.MODEL_LABELS))
else:
    detector = MockDetector(class_name=config.MOCK_CLASS, confidence=config.MOCK_CONFIDENCE)


@app.get("/health")
def health():
    return {"status": "ok", "model": config.MODEL_MODE}


@app.post("/detect")
async def detect(file: UploadFile = File(...)):
    data = await file.read()
    if not data:
        raise HTTPException(status_code=400, detail="图片为空")
    try:
        detections = detector.detect(data)
    except ValueError as exc:
        raise HTTPException(status_code=400, detail=str(exc)) from exc
    return {
        "model": config.MODEL_MODE,
        "detections": [
            {
                "bbox": d["bbox"],
                "class": d["class_name"],
                "confidence": d["confidence"],
            }
            for d in detections
        ],
    }


try:
    import cv2  # noqa: E402
except ImportError:
    cv2 = None


@app.post("/stream-detect")
async def stream_detect(body: dict):
    """摄像头流 AI 巡检骨架（#23）：拉流抽帧 → 检测。

    二期功能：正式接入前需补摄像头视角数据与巡检任务编排。
    """
    url = (body or {}).get("url") or ""
    if not url:
        raise HTTPException(status_code=400, detail="缺少 url（rtsp/http-flv 流地址）")
    if cv2 is None:
        raise HTTPException(status_code=503, detail="OpenCV 未安装，无法拉流")
    capture = cv2.VideoCapture(url)
    if not capture.isOpened():
        raise HTTPException(status_code=502, detail="无法连接视频流")
    try:
        frames = 0
        detections = []
        while frames < 3:
            ok, frame = capture.read()
            if not ok:
                break
            ok_encode, buf = cv2.imencode(".jpg", frame)
            if ok_encode:
                detections.extend(detector.detect(buf.tobytes()))
            frames += 1
    finally:
        capture.release()
    return {"model": config.MODEL_MODE, "frames": frames, "detections": detections[:10]}


@app.get("/metrics")
def metrics():
    """AI 服务监控（#26 骨架）：请求量/延迟/GPU 信息（nvidia-smi 可选）。"""
    with _lock:
        reqs = _requests
        avg = _latency_total / _requests if _requests else 0.0
    gpu = None
    if shutil.which("nvidia-smi"):
        try:
            out = subprocess.run(
                ["nvidia-smi", "--query-gpu=name,temperature.gpu,memory.used,memory.total", "--format=csv,noheader,nounits"],
                capture_output=True, text=True, timeout=10,
            )
            if out.returncode == 0 and out.stdout.strip():
                parts = [p.strip() for p in out.stdout.strip().split(",")]
                gpu = {"name": parts[0], "tempC": parts[1], "memUsedMB": parts[2], "memTotalMB": parts[3]}
        except Exception:
            gpu = None
    return {
        "model": config.MODEL_MODE,
        "uptimeSeconds": int(time.time() - START_TIME),
        "requests": reqs,
        "avgLatencyMs": round(avg, 2),
        "gpu": gpu,
    }