import asyncio
import base64
import io
import json
import os
import tempfile
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Annotated, Optional

import av
import cv2
import numpy as np
import requests as http_requests
from fastapi import Body, FastAPI, UploadFile, File, Form, HTTPException, Request
from fastapi.responses import StreamingResponse
from pydantic import BaseModel

from loader_http_client import LoaderHttpClient, LoadResult

app = FastAPI(title="Azaion.Detections")
executor = ThreadPoolExecutor(max_workers=2)

LOADER_URL = os.environ.get("LOADER_URL", "http://loader:8080")
ANNOTATIONS_URL = os.environ.get("ANNOTATIONS_URL", "http://annotations:8080")

_MEDIA_STATUS_NEW = 1
_MEDIA_STATUS_AI_PROCESSING = 2
_MEDIA_STATUS_AI_PROCESSED = 3
_MEDIA_STATUS_ERROR = 6

_VIDEO_EXTENSIONS = frozenset({".mp4", ".mov", ".webm", ".mkv", ".avi", ".m4v"})
_IMAGE_EXTENSIONS = frozenset({".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tif", ".tiff"})

loader_client = LoaderHttpClient(LOADER_URL)
annotations_client = LoaderHttpClient(ANNOTATIONS_URL)
inference = None
_event_queues: list[asyncio.Queue] = []
_active_detections: dict[str, asyncio.Task] = {}


class TokenManager:
    def __init__(self, access_token: str, refresh_token: str):
        self.access_token = access_token
        self.refresh_token = refresh_token

    def get_valid_token(self) -> str:
        exp = self._decode_exp(self.access_token)
        if exp and exp - time.time() < 60:
            self._refresh()
        return self.access_token

    def _refresh(self):
        try:
            resp = http_requests.post(
                f"{ANNOTATIONS_URL}/auth/refresh",
                json={"refreshToken": self.refresh_token},
                timeout=10,
            )
            if resp.status_code == 200:
                self.access_token = resp.json()["token"]
        except Exception:
            pass

    @staticmethod
    def _decode_exp(token: str) -> Optional[float]:
        try:
            payload = token.split(".")[1]
            padding = 4 - len(payload) % 4
            if padding != 4:
                payload += "=" * padding
            data = json.loads(base64.urlsafe_b64decode(payload))
            return float(data.get("exp", 0))
        except Exception:
            return None

    @staticmethod
    def decode_user_id(token: str) -> Optional[str]:
        try:
            payload = token.split(".")[1]
            padding = 4 - len(payload) % 4
            if padding != 4:
                payload += "=" * padding
            data = json.loads(base64.urlsafe_b64decode(payload))
            uid = (
                data.get("sub")
                or data.get("userId")
                or data.get("user_id")
                or data.get("nameid")
                or data.get(
                    "http://schemas.xmlsoap.org/ws/2005/05/identity/claims/nameidentifier"
                )
            )
            if uid is None:
                return None
            return str(uid)
        except Exception:
            return None


def get_inference():
    global inference
    if inference is None:
        from inference import Inference
        inference = Inference(loader_client)
    return inference


class DetectionDto(BaseModel):
    centerX: float
    centerY: float
    width: float
    height: float
    classNum: int
    label: str
    confidence: float


class DetectionEvent(BaseModel):
    annotations: list[DetectionDto]
    mediaId: str
    mediaStatus: str
    mediaPercent: int


class HealthResponse(BaseModel):
    status: str
    aiAvailability: str
    engineType: Optional[str] = None
    errorMessage: Optional[str] = None


class AIConfigDto(BaseModel):
    frame_period_recognition: int = 4
    frame_recognition_seconds: int = 2
    probability_threshold: float = 0.25
    tracking_distance_confidence: float = 0.0
    tracking_probability_increase: float = 0.0
    tracking_intersection_threshold: float = 0.6
    model_batch_size: int = 8
    big_image_tile_overlap_percent: int = 20
    altitude: float = 400
    focal_length: float = 24
    sensor_width: float = 23.5


_AI_SETTINGS_FIELD_KEYS = (
    (
        "frame_period_recognition",
        ("frame_period_recognition", "framePeriodRecognition", "FramePeriodRecognition"),
    ),
    (
        "frame_recognition_seconds",
        ("frame_recognition_seconds", "frameRecognitionSeconds", "FrameRecognitionSeconds"),
    ),
    (
        "probability_threshold",
        ("probability_threshold", "probabilityThreshold", "ProbabilityThreshold"),
    ),
    (
        "tracking_distance_confidence",
        (
            "tracking_distance_confidence",
            "trackingDistanceConfidence",
            "TrackingDistanceConfidence",
        ),
    ),
    (
        "tracking_probability_increase",
        (
            "tracking_probability_increase",
            "trackingProbabilityIncrease",
            "TrackingProbabilityIncrease",
        ),
    ),
    (
        "tracking_intersection_threshold",
        (
            "tracking_intersection_threshold",
            "trackingIntersectionThreshold",
            "TrackingIntersectionThreshold",
        ),
    ),
    (
        "model_batch_size",
        ("model_batch_size", "modelBatchSize", "ModelBatchSize"),
    ),
    (
        "big_image_tile_overlap_percent",
        (
            "big_image_tile_overlap_percent",
            "bigImageTileOverlapPercent",
            "BigImageTileOverlapPercent",
        ),
    ),
    (
        "altitude",
        ("altitude", "Altitude"),
    ),
    (
        "focal_length",
        ("focal_length", "focalLength", "FocalLength"),
    ),
    (
        "sensor_width",
        ("sensor_width", "sensorWidth", "SensorWidth"),
    ),
)


def _merged_annotation_settings_payload(raw: object) -> dict:
    if not raw or not isinstance(raw, dict):
        return {}
    merged = dict(raw)
    inner = raw.get("aiRecognitionSettings")
    if isinstance(inner, dict):
        merged.update(inner)
    cam = raw.get("cameraSettings")
    if isinstance(cam, dict):
        merged.update(cam)
    out = {}
    for snake, aliases in _AI_SETTINGS_FIELD_KEYS:
        for key in aliases:
            if key in merged and merged[key] is not None:
                out[snake] = merged[key]
                break
    return out


def _normalize_upload_ext(filename: str) -> str:
    s = Path(filename or "").suffix.lower()
    return s if s else ""


def _detect_upload_kind(filename: str, data: bytes) -> tuple[str, str]:
    ext = _normalize_upload_ext(filename)
    if ext in _VIDEO_EXTENSIONS:
        return "video", ext
    if ext in _IMAGE_EXTENSIONS:
        return "image", ext
    arr = np.frombuffer(data, dtype=np.uint8)
    if cv2.imdecode(arr, cv2.IMREAD_COLOR) is not None:
        return "image", ext if ext else ".jpg"
    try:
        bio = io.BytesIO(data)
        with av.open(bio):
            pass
        return "video", ext if ext else ".mp4"
    except Exception:
        raise HTTPException(status_code=400, detail="Invalid image or video data")


def _is_video_media_path(media_path: str) -> bool:
    return Path(media_path).suffix.lower() in _VIDEO_EXTENSIONS


def _post_media_record(payload: dict, bearer: str) -> bool:
    try:
        headers = {"Authorization": f"Bearer {bearer}"}
        r = http_requests.post(
            f"{ANNOTATIONS_URL}/api/media",
            json=payload,
            headers=headers,
            timeout=30,
        )
        return r.status_code in (200, 201)
    except Exception:
        return False


def _put_media_status(media_id: str, media_status: int, bearer: str) -> bool:
    try:
        headers = {"Authorization": f"Bearer {bearer}"}
        r = http_requests.put(
            f"{ANNOTATIONS_URL}/api/media/{media_id}/status",
            json={"mediaStatus": media_status},
            headers=headers,
            timeout=30,
        )
        return r.status_code in (200, 204)
    except Exception:
        return False


def _resolve_media_for_detect(
    media_id: str,
    token_mgr: Optional[TokenManager],
    override: Optional[AIConfigDto],
) -> tuple[dict, str]:
    cfg: dict = {}
    bearer = ""
    if token_mgr:
        bearer = token_mgr.get_valid_token()
        uid = TokenManager.decode_user_id(token_mgr.access_token)
        if uid:
            raw = annotations_client.fetch_user_ai_settings(uid, bearer)
            cfg.update(_merged_annotation_settings_payload(raw))
    if override is not None:
        for k, v in override.model_dump(exclude_defaults=True).items():
            cfg[k] = v
    media_path = annotations_client.fetch_media_path(media_id, bearer)
    if not media_path:
        raise HTTPException(
            status_code=503,
            detail="Could not resolve media path from annotations service",
        )
    return cfg, media_path


def detection_to_dto(det) -> DetectionDto:
    import constants_inf
    label = constants_inf.get_annotation_name(det.cls)
    return DetectionDto(
        centerX=det.x,
        centerY=det.y,
        width=det.w,
        height=det.h,
        classNum=det.cls,
        label=label,
        confidence=det.confidence,
    )


@app.get("/health")
def health() -> HealthResponse:
    if inference is None:
        return HealthResponse(status="healthy", aiAvailability="None")
    try:
        status = inference.ai_availability_status
        status_str = str(status).split()[0] if str(status).strip() else "None"
        error_msg = status.error_message if hasattr(status, 'error_message') else None
        engine_type = inference.engine_name
        return HealthResponse(
            status="healthy",
            aiAvailability=status_str,
            engineType=engine_type,
            errorMessage=error_msg,
        )
    except Exception as e:
        return HealthResponse(
            status="healthy",
            aiAvailability="None",
            errorMessage=str(e),
        )


@app.post("/detect")
async def detect_image(
    request: Request,
    file: UploadFile = File(...),
    config: Optional[str] = Form(None),
):
    from media_hash import compute_media_content_hash
    from inference import ai_config_from_dict

    image_bytes = await file.read()
    if not image_bytes:
        raise HTTPException(status_code=400, detail="Image is empty")

    orig_name = file.filename or "upload"
    kind, ext = _detect_upload_kind(orig_name, image_bytes)

    if kind == "image":
        arr = np.frombuffer(image_bytes, dtype=np.uint8)
        if cv2.imdecode(arr, cv2.IMREAD_COLOR) is None:
            raise HTTPException(status_code=400, detail="Invalid image data")

    config_dict = {}
    if config:
        config_dict = json.loads(config)

    auth_header = request.headers.get("authorization", "")
    access_token = auth_header.removeprefix("Bearer ").strip() if auth_header else ""
    refresh_token = request.headers.get("x-refresh-token", "")
    token_mgr = TokenManager(access_token, refresh_token) if access_token else None
    user_id = TokenManager.decode_user_id(access_token) if access_token else None

    videos_dir = os.environ.get(
        "VIDEOS_DIR", os.path.join(os.getcwd(), "data", "videos")
    )
    images_dir = os.environ.get(
        "IMAGES_DIR", os.path.join(os.getcwd(), "data", "images")
    )
    storage_path = None
    content_hash = None
    if token_mgr and user_id:
        content_hash = compute_media_content_hash(image_bytes)
        base = videos_dir if kind == "video" else images_dir
        os.makedirs(base, exist_ok=True)
        if not ext.startswith("."):
            ext = "." + ext
        storage_path = os.path.abspath(os.path.join(base, f"{content_hash}{ext}"))
        if kind == "image":
            with open(storage_path, "wb") as out:
                out.write(image_bytes)
        mt = "Video" if kind == "video" else "Image"
        payload = {
            "id": content_hash,
            "name": Path(orig_name).name,
            "path": storage_path,
            "mediaType": mt,
            "mediaStatus": _MEDIA_STATUS_NEW,
            "userId": user_id,
        }
        bearer = token_mgr.get_valid_token()
        _post_media_record(payload, bearer)
        _put_media_status(content_hash, _MEDIA_STATUS_AI_PROCESSING, bearer)

    media_name = Path(orig_name).stem.replace(" ", "")
    loop = asyncio.get_event_loop()
    inf = get_inference()
    results = []
    tmp_video_path = None

    def on_annotation(annotation, percent):
        results.extend(annotation.detections)

    ai_cfg = ai_config_from_dict(config_dict)

    def run_upload():
        nonlocal tmp_video_path
        if kind == "video":
            if storage_path:
                save = storage_path
            else:
                suf = ext if ext.startswith(".") else ".mp4"
                fd, tmp_video_path = tempfile.mkstemp(suffix=suf)
                os.close(fd)
                save = tmp_video_path
            inf.run_detect_video(image_bytes, ai_cfg, media_name, save, on_annotation)
        else:
            inf.run_detect_image(image_bytes, ai_cfg, media_name, on_annotation)

    try:
        await loop.run_in_executor(executor, run_upload)
        if token_mgr and user_id and content_hash:
            _put_media_status(
                content_hash, _MEDIA_STATUS_AI_PROCESSED, token_mgr.get_valid_token()
            )
        return [detection_to_dto(d) for d in results]
    except RuntimeError as e:
        if token_mgr and user_id and content_hash:
            _put_media_status(
                content_hash, _MEDIA_STATUS_ERROR, token_mgr.get_valid_token()
            )
        if "not available" in str(e):
            raise HTTPException(status_code=503, detail=str(e))
        raise HTTPException(status_code=422, detail=str(e))
    except ValueError as e:
        if token_mgr and user_id and content_hash:
            _put_media_status(
                content_hash, _MEDIA_STATUS_ERROR, token_mgr.get_valid_token()
            )
        raise HTTPException(status_code=400, detail=str(e))
    except Exception:
        if token_mgr and user_id and content_hash:
            _put_media_status(
                content_hash, _MEDIA_STATUS_ERROR, token_mgr.get_valid_token()
            )
        raise
    finally:
        if tmp_video_path and os.path.isfile(tmp_video_path):
            try:
                os.unlink(tmp_video_path)
            except OSError:
                pass


def _post_annotation_to_service(token_mgr: TokenManager, media_id: str,
                                 annotation, dtos: list[DetectionDto]):
    try:
        token = token_mgr.get_valid_token()
        image_b64 = base64.b64encode(annotation.image).decode() if annotation.image else None
        payload = {
            "mediaId": media_id,
            "source": 0,
            "videoTime": f"00:00:{annotation.time // 1000:02d}" if annotation.time else "00:00:00",
            "detections": [d.model_dump() for d in dtos],
        }
        if image_b64:
            payload["image"] = image_b64
        http_requests.post(
            f"{ANNOTATIONS_URL}/annotations",
            json=payload,
            headers={"Authorization": f"Bearer {token}"},
            timeout=30,
        )
    except Exception:
        pass


@app.post("/detect/{media_id}")
async def detect_media(
    media_id: str,
    request: Request,
    config: Annotated[Optional[AIConfigDto], Body()] = None,
):
    existing = _active_detections.get(media_id)
    if existing is not None and not existing.done():
        raise HTTPException(status_code=409, detail="Detection already in progress for this media")

    auth_header = request.headers.get("authorization", "")
    access_token = auth_header.removeprefix("Bearer ").strip() if auth_header else ""
    refresh_token = request.headers.get("x-refresh-token", "")
    token_mgr = TokenManager(access_token, refresh_token) if access_token else None

    config_dict, media_path = _resolve_media_for_detect(media_id, token_mgr, config)

    async def run_detection():
        loop = asyncio.get_event_loop()

        def _enqueue(event):
            for q in _event_queues:
                try:
                    q.put_nowait(event)
                except asyncio.QueueFull:
                    pass

        try:
            from inference import ai_config_from_dict

            if token_mgr:
                _put_media_status(
                    media_id,
                    _MEDIA_STATUS_AI_PROCESSING,
                    token_mgr.get_valid_token(),
                )

            with open(media_path, "rb") as mf:
                file_bytes = mf.read()

            video = _is_video_media_path(media_path)
            stem_name = Path(media_path).stem.replace(" ", "")
            ai_cfg = ai_config_from_dict(config_dict)

            inf = get_inference()
            if not inf.is_engine_ready:
                raise RuntimeError("Detection service unavailable")

            def on_annotation(annotation, percent):
                dtos = [detection_to_dto(d) for d in annotation.detections]
                event = DetectionEvent(
                    annotations=dtos,
                    mediaId=media_id,
                    mediaStatus="AIProcessing",
                    mediaPercent=percent,
                )
                loop.call_soon_threadsafe(_enqueue, event)
                if token_mgr and dtos:
                    _post_annotation_to_service(token_mgr, media_id, annotation, dtos)

            def on_status(media_name_cb, count):
                event = DetectionEvent(
                    annotations=[],
                    mediaId=media_id,
                    mediaStatus="AIProcessed",
                    mediaPercent=100,
                )
                loop.call_soon_threadsafe(_enqueue, event)
                if token_mgr:
                    _put_media_status(
                        media_id,
                        _MEDIA_STATUS_AI_PROCESSED,
                        token_mgr.get_valid_token(),
                    )

            def run_sync():
                if video:
                    inf.run_detect_video(
                        file_bytes,
                        ai_cfg,
                        stem_name,
                        media_path,
                        on_annotation,
                        on_status,
                    )
                else:
                    inf.run_detect_image(
                        file_bytes,
                        ai_cfg,
                        stem_name,
                        on_annotation,
                        on_status,
                    )

            await loop.run_in_executor(executor, run_sync)
        except Exception:
            if token_mgr:
                _put_media_status(
                    media_id, _MEDIA_STATUS_ERROR, token_mgr.get_valid_token()
                )
            error_event = DetectionEvent(
                annotations=[],
                mediaId=media_id,
                mediaStatus="Error",
                mediaPercent=0,
            )
            _enqueue(error_event)
        finally:
            _active_detections.pop(media_id, None)

    _active_detections[media_id] = asyncio.create_task(run_detection())
    return {"status": "started", "mediaId": media_id}


@app.get("/detect/stream")
async def detect_stream():
    queue: asyncio.Queue = asyncio.Queue(maxsize=100)
    _event_queues.append(queue)

    async def event_generator():
        try:
            while True:
                event = await queue.get()
                yield f"data: {event.model_dump_json()}\n\n"
        except asyncio.CancelledError:
            pass
        finally:
            _event_queues.remove(queue)

    return StreamingResponse(
        event_generator(),
        media_type="text/event-stream",
        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
    )