import torch

import gradio as gr
import pytube as pt
from transformers import pipeline
from huggingface_hub import model_info

MODEL_NAME = "openai/whisper-medium"

device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

langs = model_info(MODEL_NAME).cardData["language"]

article = f"<details><summary>模型支持 {len(langs)} 语言! (单击展开)</summary>> {langs}</details>"

def transcribe(microphone, file_upload):
    warn_output = ""
    if (microphone is not None) and (file_upload is not None):
        warn_output = (
            "WARNING:上传一个音频文件或者使用麦克风录制. "
            "使用麦克风录制音频文件丢弃上传的音频文件.\n"
        )

    elif (microphone is None) and (file_upload is None):
        return "ERROR: 你必须使用麦克风录制或上传音频文件"

    file = microphone if microphone is not None else file_upload

    text = pipe(file,language='zh')["text"]

    return warn_output + text


def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    HTML_str = (
        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
        " </center>"
    )
    return HTML_str


def yt_transcribe(yt_url):
    yt = pt.YouTube(yt_url)
    html_embed_str = _return_yt_html_embed(yt_url)
    stream = yt.streams.filter(only_audio=True)[0]
    stream.download(filename="audio.mp3")

    text = pipe("audio.mp3")["text"]

    return html_embed_str, text


demo = gr.Blocks()

mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
        gr.inputs.Audio(source="upload", type="filepath", optional=True),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="口译示例: 音频转录",
    description=(
        "转录麦克风录制或上传的音频文件!"
    ),
    article=article,
    allow_flagging="never",
)

yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[gr.inputs.Textbox(lines=1, placeholder="请粘贴视频地址", label="视频地址URL")],
    outputs=["html", "text"],
    layout="horizontal",
    theme="huggingface",
    title="口译示例: 视频转录",
    description=(
        "转录上传的视频文件!"
    ),
    article=article,
    allow_flagging="never",
)

with demo:
    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["音频转录", "视频转录"])

demo.launch(enable_queue=True)