import torch
import gradio as gr
import yt_dlp as youtube_dl
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read

import tempfile
import os
import time

# Available model sizes
MODEL_CHOICES = ["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"]

current_choice = "tiny"
DEFAULT_MODEL_NAME = f"openai/whisper-{current_choice}"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000
YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files

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

# Initialize the pipeline with the default model
pipe = pipeline(
    task="automatic-speech-recognition",
    model=DEFAULT_MODEL_NAME,
    chunk_length_s=30,
    device=device,
)


def transcribe(model_size, inputs, task):
    if inputs is None:
        raise gr.Error(
            "No audio file submitted! Please upload or record an audio file before submitting your request."
        )

    global current_choice
    global pipe

    current_choice = model_size

    MODEL_NAME = f"openai/whisper-{model_size}"
    if (
        pipe.model.name_or_path != MODEL_NAME
    ):  # Reload the pipeline if model has changed
        pipe = pipeline(
            task="automatic-speech-recognition",
            model=MODEL_NAME,
            chunk_length_s=30,
            device=device,
        )

    text = pipe(
        inputs,
        batch_size=BATCH_SIZE,
        generate_kwargs={"task": task},
        return_timestamps=True,
    )["text"]
    return 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 download_yt_audio(yt_url, filename):
    info_loader = youtube_dl.YoutubeDL()

    try:
        info = info_loader.extract_info(yt_url, download=False)
    except youtube_dl.utils.DownloadError as err:
        raise gr.Error(str(err))

    file_length = info["duration_string"]
    file_h_m_s = file_length.split(":")
    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]

    if len(file_h_m_s) == 1:
        file_h_m_s.insert(0, 0)
    if len(file_h_m_s) == 2:
        file_h_m_s.insert(0, 0)
    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]

    if file_length_s > YT_LENGTH_LIMIT_S:
        yt_length_limit_hms = time.strftime(
            "%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S)
        )
        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
        raise gr.Error(
            f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video."
        )

    ydl_opts = {
        "outtmpl": filename,
        "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best",
    }

    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
        try:
            ydl.download([yt_url])
        except youtube_dl.utils.ExtractorError as err:
            raise gr.Error(str(err))


def yt_transcribe(yt_url, task, max_filesize=75.0):
    html_embed_str = _return_yt_html_embed(yt_url)

    with tempfile.TemporaryDirectory() as tmpdirname:
        filepath = os.path.join(tmpdirname, "video.mp4")
        download_yt_audio(yt_url, filepath)
        with open(filepath, "rb") as f:
            inputs = f.read()

    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}

    text = pipe(
        inputs,
        batch_size=BATCH_SIZE,
        generate_kwargs={"task": task},
        return_timestamps=True,
    )["text"]

    return html_embed_str, text


demo = gr.Blocks()

mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Dropdown(MODEL_CHOICES, label="Model Size", value=current_choice),
        gr.Audio(sources=["microphone"], type="filepath"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs="text",
    theme="default",
    title="Whisper: Transcribe Audio",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo allows selection of any of the"
        f" [OpenAI Whisper model sizes](https://huggingface.co/openai/whisper-large-v3) and  Transformers to transcribe audio files"
        " of arbitrary length. Large and above are multilingual."
        " Based on https://huggingface.co/spaces/openai/whisper"
    ),
    allow_flagging="never",
)

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Dropdown(MODEL_CHOICES, label="Model Size", value=current_choice),
        gr.Audio(sources=["upload"], type="filepath", label="Audio file"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs="text",
    theme="default",
    title="Whisper: Transcribe Audio",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
        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.Dropdown(MODEL_CHOICES, label="Model Size", value=current_choice),
        gr.Textbox(
            lines=1,
            placeholder="Paste the URL to a YouTube video here",
            label="YouTube URL",
        ),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs=["html", "text"],
    theme="default",
    title="Whisper: Transcribe Audio",
    description=(
        "Transcribe long-form YouTube videos with the click of a button! Demo uses the OpenAI Whisper checkpoint"
        f" [{DEFAULT_MODEL_NAME}](https://huggingface.co/{DEFAULT_MODEL_NAME}) and  Transformers to transcribe video files of"
        " arbitrary length."
    ),
    allow_flagging="never",
)

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

demo.launch()