import torch
import gradio as gr
from transformers import pipeline
import pytube as pt

MODEL_NAME = "openai/whisper-small"

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

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


def transcribe(microphone, file_upload):
    warn_output = ""
    if (microphone is not None) and (file_upload is not None):
        warn_output = (
            "WARNING: You've uploaded an audio file and used the microphone. "
            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        )
        file = microphone

    elif (microphone is None) and (file_upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    file = microphone if microphone is not None else file_upload

    text = pipe(file)["text"]

    return warn_output + text


def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    HTML_str = (
        '<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="Whisper Audio Transcribe",
    description="Transcribe long audio/ microphone input (powered by 🤗transformers) with a click of a button!",
    allow_flagging="never",
)

yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[
        gr.inputs.Textbox(
            lines=1, placeholder="Paste a URL to YT video here", label="yt_url"
        )
    ],
    outputs=["html", "text"],
    layout="horizontal",
    theme="huggingface",
    title="Whisper YT Transcribe",
    description="Transcribe long YouTube videos (powered by 🤗transformers) with a click of a button!",
    allow_flagging="never",
)

with demo:
    gr.TabbedInterface(
        [mf_transcribe, yt_transcribe], ["Audio Transcribe", "YouTube Transcribe"]
    )

demo.launch(enable_queue=True)