from __future__ import annotations

import json
import os
import sys
import tempfile
from datetime import datetime, timezone
from pathlib import Path
from typing import List, Tuple

from app.logging_config import format_json_pretty
from app.services.video_slice import get_video_duration, slice_video
from app.settings import Settings
from app.state import HealthSnapshot
from app.subprocess_run import run_subprocess_with_log
from loguru import logger

# 视频切片配置
DEFAULT_SLICE_DURATION = 10.0  # 每10秒切一个片段
DEFAULT_MIN_DURATION_FOR_SLICE = 15.0  # 视频超过15秒才切片

BEHAVIOR_EN_TO_ZH = {
    "feeding": "吃饵",
    "normal": "正常游行",
    "scared": "惊吓",
}


def _py_exe(settings: Settings) -> str:
    return settings.python_fish_action or sys.executable


def behavior_to_health(behavior_en: str) -> str:
    if behavior_en == "scared":
        return "不健康"
    return "健康"


def run_action_subprocess(mp4_path: Path, settings: Settings) -> str:
    script = settings.fish_action_root / "predict_video_x3d_3class.py"
    if not script.is_file():
        raise FileNotFoundError(f"Missing FishAction script: {script}")

    mp4_path = mp4_path.resolve()
    path_prefix = str(mp4_path.parent)
    rel_name = mp4_path.name

    with tempfile.NamedTemporaryFile(
        mode="w", suffix=".csv", delete=False, encoding="utf-8"
    ) as tmp:
        tmp.write(f"{rel_name} 0\n")
        csv_path = tmp.name

    out_fd, out_json = tempfile.mkstemp(suffix="_pred.json")
    os.close(out_fd)

    try:
        #: 使用脚本默认参数（clips_per_video=10, batch_size=8, num_workers=4）
        #: 如需调整，请修改 FishAction/predict_video_x3d_3class.py 中的默认值
        cmd = [
            _py_exe(settings),
            str(script),
            "--checkpoint",
            settings.action_checkpoint,
            "--csv",
            csv_path,
            "--path_prefix",
            path_prefix,
            "--output_json",
            out_json,
            "--log_interval",
            "0",
        ]
        proc = run_subprocess_with_log(
            cmd,
            cwd=str(settings.fish_action_root),
            env=os.environ.copy(),
            log_name="FishAction",
            stream_to_logger=False,
        )
        if proc.returncode != 0:
            err = proc.stdout or ""
            raise RuntimeError(
                f"predict_video_x3d_3class.py failed ({proc.returncode}): {err[-4000:]}"
            )
        if not Path(out_json).is_file():
            raise RuntimeError(f"No output_json written: {out_json}")
        with open(out_json, encoding="utf-8") as f:
            rows = json.load(f)
        if not rows:
            raise RuntimeError("Empty prediction JSON")
        pred_en = str(rows[0].get("pred_3class", "")).strip().lower()
        logger.debug(
            "[FishAction] prediction row:\n{}",
            format_json_pretty(rows[0]),
        )
        if pred_en not in BEHAVIOR_EN_TO_ZH:
            raise RuntimeError(f"Unexpected pred_3class: {pred_en!r}")
        return pred_en
    finally:
        Path(csv_path).unlink(missing_ok=True)
        Path(out_json).unlink(missing_ok=True)


def prepare_action_slices(
    mp4_path: Path, settings: Settings
) -> Tuple[List[Path], float]:
    """Check video duration and create slices if needed.

    Returns ``(slice_files, duration)``.  If the video is short enough,
    returns ``([mp4_path], duration)`` without slicing.
    """
    logger.info("[FishAction] start mp4={}", mp4_path.resolve())
    duration = get_video_duration(mp4_path)

    if duration > DEFAULT_MIN_DURATION_FOR_SLICE:
        logger.info(
            "[FishAction] video duration {}s > {}s, slicing into {}s segments",
            duration,
            DEFAULT_MIN_DURATION_FOR_SLICE,
            DEFAULT_SLICE_DURATION,
        )
        slice_files, _slice_dir = slice_video(mp4_path, DEFAULT_SLICE_DURATION)
        if len(slice_files) > 1:
            logger.info(
                "[FishAction] processing {} slices for {}",
                len(slice_files),
                mp4_path.name,
            )
            return slice_files, duration

    return [mp4_path], duration


def run_single_slice_inference(
    slice_file: Path,
    slice_index: int,
    total_slices: int,
    duration: float,
    mp4_name: str,
    settings: Settings,
) -> HealthSnapshot:
    """Run FishAction inference on a single video file / slice.

    Returns a ``HealthSnapshot`` (may contain error info if inference fails).
    """
    start_time = slice_index * DEFAULT_SLICE_DURATION
    end_time = min(start_time + DEFAULT_SLICE_DURATION, duration)

    try:
        pred_en = run_action_subprocess(slice_file, settings)
        zh = BEHAVIOR_EN_TO_ZH[pred_en]
        health = behavior_to_health(pred_en)
        snap = HealthSnapshot(
            behavior_result=zh,
            health_result=health,
            updated_at=datetime.now(timezone.utc),
            raw_class_en=pred_en,
        )
        if total_slices > 1:
            logger.info(
                "[FishAction] slice {} ({}s-{}s): pred={} behavior={} health={}",
                slice_index, start_time, end_time, pred_en, zh, health,
            )
        else:
            logger.info(
                "[FishAction] done mp4={} pred_3class={} behavior_zh={} health={}",
                mp4_name, pred_en, zh, health,
            )
        return snap
    except Exception as e:
        logger.error("[FishAction] failed to process slice {}: {}", slice_index, e)
        return HealthSnapshot(
            behavior_result="处理失败",
            health_result="未知",
            updated_at=datetime.now(timezone.utc),
            raw_class_en="error",
            error=str(e),
        )