import logging
import time
import re
import asyncio
from concurrent.futures import ThreadPoolExecutor

import service.pyav as pyav
import service.pygpu as pygpu
import service.ai_text_ollama as ai_text
import service.ai_asr as pyasr
from setting import OUTPUT_DIR

logger = logging.getLogger(__name__)
executor = ThreadPoolExecutor(max_workers=1)
task_queue = asyncio.Queue()

async def start_worker():
    asyncio.create_task(gpu_worker())  # 开启 Worker


# --- 后台工作进程 (Consumer) ---
async def gpu_worker():
    logger.info("🏃 Worker 开始监听任务队列")
    while True:
        if not pyasr.check_ready():
            logger.info("休眠 10s 等待模型加载完成...")
            await asyncio.sleep(10)
            continue

        # 获取任务
        task = await task_queue.get()
        task_id, audio_path, srt_path, video_path = task
        logger.info(f"⚡ 开始处理任务 [{task_id}]: {audio_path}")
        try:
            start_t = time.time()
            # 2. 推理 (在线程池中运行同步识别，防止阻塞事件循环)
            # 使用 run_in_executor 将同步函数包装成异步，不再阻塞 Event Loop
            # 这样在处理视频的同时，FastAPI 依然可以接收新请求并 put 到队列中
            loop = asyncio.get_event_loop()
            result = await loop.run_in_executor(
                executor,
                pyasr.get_text,
                audio_path
            )

            text = result['text']
            timestamp_list = result['timestamps']
            text_list = re.split(r"([。！？；，])", text)
            srt_list = pyav.get_precise_srt(text_list, timestamp_list)
            pyav.save_srt_file(srt_list, srt_path)

            with open(f"{OUTPUT_DIR}/{task_id}.txt", "w", encoding="utf-8") as f:
                f.write(text)

            await loop.run_in_executor(
                executor,
                pyav.generate_video,
                audio_path,
                srt_path,
                video_path
            )
            logger.info(f"🎉 任务 [{task_id}] 完成，耗时: {time.time() - start_t:.2f}s")
        except Exception as e:
            logger.error(f"❌ 任务 [{task_id}] 失败: {str(e)}")
        finally:
            # 4. 定时/按需清理 GPU 显存
            task_queue.task_done()
            pygpu.clear_gpu_memory()


async def put_task(task_id, save_path, srt_path, video_path):
    await task_queue.put((task_id, save_path, srt_path, video_path))

def get_tasks():
    return task_queue.qsize()

def translate_to_zh(text):
    result = ai_text.translate2zh(text)
    return result