import gradio as gr

from pytube import YouTube
import random
import requests,json
import subprocess,os


def del_down_file():
    command = f'rm -rf *.mp4'
    subprocess.call(command, shell=True)

def get_video(url):


    # 下载视频
    url = url
    output_dir = '.'
    command = f'you-get -f mp4 -o {output_dir} {url}'
    print(command)
    subprocess.call(command, shell=True)
    
    mp4_files = []  # 用于存储所有找到的 mp4 文件名

    # 遍历指定目录中的所有文件
    for filename in os.listdir('.'):
        # 检查文件是否以 '.mp4' 结尾
        if filename.endswith('.mp4'):
            # 如果是，将文件名添加到 mp4_files 列表中
            mp4_files.append(filename)
    print(mp4_files)
    mp4_file = mp4_files[0]
    os.rename(mp4_file, 'my_mp4.mp4')
    return 'my_mp4.mp4'
    


def create(prompt,openai_key):
  
    headers = {
        'Content-Type': 'application/json',
        'Authorization': f'Bearer {openai_key}',
       
    }
    data =   {
        "model": "text-davinci-003",
        "prompt": prompt,
        "temperature": 0.7,
        "max_tokens": 1024,
        "top_p": 1.0,
        "frequency_penalty": 0.0,
        "presence_penalty": 0.0
    }
    print(headers ,prompt,openai_key)
    url = 'https://api.openai.com/v1/completions'
    r = requests.post(url,headers=headers,
            data=json.dumps(data))
    print(r.text)
    return r.json()

def split_list(l, n):
    for i in range(0, len(l), n):
        yield l[i:i+n]
        

def convert(res,openai_key):


    data = res.json()
    prediction = data['prediction']
    content = []
    for x in prediction:
        content.append(x['transcription'])
    auido_txt = '\n'.join(content)
    answer = ''

    try:
        answer = ''
        for txt_line in split_list(content,10):
            txt_line_content = '\n'.join(txt_line)
            prompt = f"\n\n将下面的内容使用简体中文总结5条要点出来：\n\n{txt_line_content}"
            open_ai_res = create(prompt,openai_key)
            answer += prompt + '\n GPT3:\n' + open_ai_res['choices'][0]['text']
    except Exception as e:
        print('open ai api error',e)

    res_content = f'{answer}'

    return res_content

    
        

def get_audio(url):
    
    # yt = YouTube(url)
    # audio_file = f'{random.randint(10000,100000)}.mp4'
    # print(f'{url} {audio_file} start get audio ...')
    # yt.streams.filter(only_audio=True)[0].download(filename=audio_file)
    # print('aodio over ..')
    audio_file = get_video(url)
    return audio_file

def get_transcript(url,openai_key):
    headers = {
        'accept': 'application/json',
        'x-gladia-key': '89b0adf5-fb2c-48ba-8a66-76b02827fd14',
        # requests won't add a boundary if this header is set when you pass files=
        # 'Content-Type': 'multipart/form-data',
    }
    audio_file = get_audio(url)

    print(audio_file)
    
    files = {
        'audio': (f"{audio_file}", open(f'{audio_file}', 'rb'), 'video/mp4'),
        'language': (None, 'english'),
        'language_behaviour': (None, 'automatic single language'),
    }
    print('get transcription from  api.gladia.io ...')
    response = requests.post('https://api.gladia.io/audio/text/audio-transcription/', headers=headers, files=files)
    print(response.text)
    del_down_file()
    return convert(response,openai_key)
    




with gr.Blocks() as demo:

    with gr.Row():

        with gr.Column():

            with gr.Row():
                url = gr.Textbox(placeholder='Youtube video URL', label='URL')
                openai_key = gr.Textbox(placeholder='Your openai key', label='OPENAI KEY')
               

            with gr.Row():
                gr.Markdown("自动从youtube视频中，获取音频内容，并使用GPT总结其要点")
                transcribe_btn = gr.Button('Transcribe')

        with gr.Column():
            outputs = gr.Textbox(placeholder='Transcription of the video', label='Transcription')

    transcribe_btn.click(get_transcript, inputs=[url,openai_key], outputs=outputs)

demo.launch(debug=True)