import logging

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

DEFAULT_MODEL_NAME = "bhuang/whisper-medium-cv11-french-case-punctuation"
MODEL_NAMES = [
    "bhuang/whisper-small-cv11-french",
    "bhuang/whisper-small-cv11-french-case-punctuation",
    "bhuang/whisper-medium-cv11-french",
    "bhuang/whisper-medium-cv11-french-case-punctuation",
]
CHUNK_LENGTH_S = 30

logging.basicConfig(
    format="%(asctime)s [%(levelname)s] [%(name)s] %(message)s",
    datefmt="%Y-%m-%dT%H:%M:%SZ",
)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)

device = 0 if torch.cuda.is_available() else "cpu"

cached_models = {}

def maybe_load_cached_pipeline(model_name):
    pipe = cached_models.get(model_name)
    if pipe is None:
        # load pipeline
        pipe = pipeline(
            task="automatic-speech-recognition",
            model=model_name,
            chunk_length_s=CHUNK_LENGTH_S,
            device=device,
        )
        # set forced_decoder_ids
        pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="fr", task="transcribe")

        logger.info(f"`{model_name}` pipeline has been initialized")

        cached_models[model_name] = pipe
    return pipe


def transcribe(microphone, file_upload, model_name):
    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"
        )

    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

    pipe = maybe_load_cached_pipeline(model_name)
    text = pipe(file)["text"]

    logger.info(f"Transcription: {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, model_name):
    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")

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

    logger.info(f"Transcription: {text}")

    return html_embed_str, text


# load default model
maybe_load_cached_pipeline(DEFAULT_MODEL_NAME)

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),
        gr.inputs.Dropdown(choices=MODEL_NAMES, default=DEFAULT_MODEL_NAME, label="Whisper Model"),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="Whisper Demo: Transcribe Audio",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
        f" checkpoint [{DEFAULT_MODEL_NAME}](https://huggingface.co/{DEFAULT_MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
        " of arbitrary length."
    ),
    allow_flagging="never",
)

yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[
        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
        gr.inputs.Dropdown(choices=MODEL_NAMES, default=DEFAULT_MODEL_NAME, label="Whisper Model"),
    ],
    outputs=["html", "text"],
    layout="horizontal",
    theme="huggingface",
    title="Whisper Demo: Transcribe YouTube",
    description=(
        "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
        f" [{DEFAULT_MODEL_NAME}](https://huggingface.co/{DEFAULT_MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
        " arbitrary length."
    ),
    allow_flagging="never",
)

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

# demo.launch(server_name="0.0.0.0", debug=True, share=True)
demo.launch(enable_queue=True)