import torch

import gradio as gr
import pytube as pt
from transformers import pipeline
from huggingface_hub import model_info
#from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

MODEL_NAME = "ihanif/w2v-bert2-pashto-augmented"
lang = "ps"

#load pre-trained model and tokenizer
#processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
#model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)

device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    #chunk_length_s=30,
    device=device,
)

#pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")

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"
    #     )

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

    if (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"]
    #transcription = wav2vec_model(audio)["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()

examples=[["example-1.wav", "example-1.wav"],["example-2.wav", "example-2.wav"]]
# examples=["example-1.wav"]

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="(Pashto ASR) د پښتو اتوماتیک وینا پیژندنه",
    description=(
        "</p> تاسو کولی شئ یو آډیو فایل اپلوډ کړئ یا په خپل وسیله مایکروفون وکاروئ. مهرباني وکړئ ډاډ ترلاسه کړئ چې تاسو اجازه ورکړې ده<p>"
    ),
    #allow_flagging="never",
    flagging_options=["Transcription is not in Pashto", "Transcription is wrong"],
    examples=examples,
)
mf_transcribe.launch()

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

#demo.launch(enable_queue=False)