# import gradio as gr

# gr.Interface.load("models/rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset").launch()


import gradio as gr
import os
import transformers
from transformers import pipeline
import time

auth_token = os.getenv("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")

# p = pipeline('automatic-speech-recognition', model='rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset')

# def transcribe(audio, state=""):
#     time.sleep(3)
#     text = p(audio)["text"]
#     state = text + " "
#     return state, state



# gr.Interface(
#     fn=transcribe, 
#     inputs=[
#         gr.inputs.Audio(source="microphone", type="filepath"),
#         'state'
#     ],
#     outputs=[
#         "textbox",
#         "state"
#     ],
#     live=False,
#     api_key=auth_token).launch()


demo = gr.load(
    "huggingface/rohitp1/kkkh_whisper_small_distillation_att_loss_libri360_epochs_100_batch_4_concat_dataset",
    title="Speech-to-text",
    inputs="mic",
    description="Let me try to guess what you're saying!",
    api_key=auth_token
)

demo.launch()