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

MODEL_NAME = "kurianbenoy/whisper-small-ml-imasc" #this always needs to stay in line 8 :D sorry for the hackiness
lang = "ml"

device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
    batch_size=8,
)

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

def transcribe(microphone=None, file_upload=None):
    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

    text = pipe(file)["text"]

    return warn_output + text

def transcribe1(file):
    text = pipe(file)["text"]
    print(text)
    return text

#print(transcribe(None,"anil.wav"))

mf_transcribe = gr.Interface(
    fn=transcribe1,
    inputs=[
        gr.Audio(sources=["upload"], type="filepath")
    ],
    outputs="text",
    title="PALLAKKU - Whisper finetuned",
)

mf_transcribe.launch(debug=True,share=False)