import json
import os

from setting import OUTPUT_DIR

os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"

import logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(name)s] %(levelname)s: %(message)s'
)

import torch
import service.pyav as pyav
import service.pygpu as pygpu
import service.ai_asr as ai_asr
import service.ai_image_ollama as ai_image

logger = logging.getLogger(__name__)

def process(audio_path, srt_path, video_path):
    try:
        # 执行识别
        ai_asr.generate_srt(audio_path, srt_path)
        pyav.generate_video(audio_path, srt_path, video_path)
    except torch.OutOfMemoryError:
        logger.error(f"❌ 显存溢出！跳过文件: {audio_path}")
        pygpu.clear_gpu_memory()  # 发生 OOM 后强制清理一次
    except Exception as e:
        logger.error(f"💥 处理 {audio_path} 时发生未知错误: {e}")
    finally:
        # 每一个文件处理完都主动清理一次，确保下一个文件有足够的初始空间
        pygpu.clear_gpu_memory()


if __name__ == "__main__":
    audio = 'abc.mp3'
    srt = 'abc.mp3.srt'
    video = 'abc.mp3.mp4'
    ai_asr.generate_srt(audio, srt)
    pyav.generate_video(audio, srt, video)

    video = 'abc.mp4'
    video_dict = pyav.get_video_info(video)
    start_scene_times = pyav.get_scene_times(video, 0.3)
    # 按场景分割视频
    # pyav.split_video_by_scenes(video, start_scene_times, OUTPUT_DIR)
    # 计算的每个场景的抽帧时间点
    target_times = pyav.calculate_mid_points(video, start_scene_times)
    # 使用 ffmpeg 抽帧
    scenes = pyav.extract_frames(video, target_times, OUTPUT_DIR)
    # 使用 ai 对抽帧的画面进行识别
    scenes_result = ai_image.describe_frame(scenes)
    video_dict['scenes'] = scenes_result
    print(json.dumps(video_dict, indent=8, ensure_ascii=False))