import torch

import spaces
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

MODEL_NAME = "openai/whisper-large-v3"
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"

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

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

    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))

# @spaces.GPU
# 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.Interface(
#     fn=transcribe,
#     inputs=[
#         gr.Audio(type="filepath"),
#         gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
#     ],
#     outputs=gr.Textbox(lines=3),
#     title="Whisper Large V3: Transcribe Audio",
#     description=(
#         "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper"
#         f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
#         " of arbitrary length."
#     ),
#     allow_flagging="never",
#)
import gradio as gr

demo = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(type="filepath"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs=gr.Textbox(), # Removed 'lines=3'
    title="Whisper Large V3: Transcribe Audio",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! "
        f"Demo uses the OpenAI Whisper checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) "
        "and 🤗 Transformers to transcribe audio files of arbitrary length."
    ),
    flagging_mode="never", # Replaced 'allow_flagging' with 'flagging_mode'
)


demo.launch()