from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
from modelscope import snapshot_download

import io
import os
import tempfile
import json
from typing import Optional

import torch
import gradio as gr  # 添加Gradio库

from config import model_config

device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_dir = snapshot_download(model_config['model_dir'])

# 初始化模型
model = AutoModel(
    model=model_dir,
    trust_remote_code=False,
    remote_code="./model.py",
    vad_model="fsmn-vad",
    vad_kwargs={"max_single_segment_time": 30000},
    ncpu=4,
    batch_size=1,
    hub="ms",
    device=device,
)

def transcribe_audio(file, vad_model="fsmn-vad", vad_kwargs='{"max_single_segment_time": 30000}', 
                     ncpu=4, batch_size=1, language="auto", use_itn=True, batch_size_s=60, 
                     merge_vad=True, merge_length_s=15, batch_size_threshold_s=50, 
                     hotword=" ", spk_model="cam++", ban_emo_unk=False):
    try:
        # 将字符串转换为字典
        vad_kwargs = json.loads(vad_kwargs)
        
        # 创建临时文件并保存上传的音频文件
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
            temp_file_path = temp_file.name
            temp_file.write(file.read())

        try:
            # 生成结果
            res = model.generate(
                input=temp_file_path,  # 使用临时文件路径作为输入
                cache={},
                language=language,
                use_itn=use_itn,
                batch_size_s=batch_size_s,
                merge_vad=merge_vad,
                merge_length_s=merge_length_s,
                batch_size_threshold_s=batch_size_threshold_s,
                hotword=hotword,
                spk_model=spk_model,
                ban_emo_unk=ban_emo_unk
            )

            # 处理结果
            text = rich_transcription_postprocess(res[0]["text"])
            
            return text
        
        finally:
            # 确保在处理完毕后删除临时文件
            if os.path.exists(temp_file_path):
                os.remove(temp_file_path)
    
    except Exception as e:
        return str(e)

# 创建Gradio界面
inputs = [
    gr.Audio(type="file"),  # 上传音频，移除了source参数
    gr.Textbox(value="fsmn-vad", label="VAD Model"),
    gr.Tex