app.py

import torch
from transformers import pipeline
import gradio as gr
import streamlit as st
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from gradio.mix import Parallel, Series


# model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
# processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
# inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt")
# generated_ids = model.generate(inputs["input_features"], attention_mask=inputs["attention_mask"])
# transcription = processor.batch_decode(generated_ids)


desc = "Is this working or what??"

def summarize(text):
  summ = gr.Interface.load('huggingface/google/pegasus-large')      
  summary = summ(text)
  return summary
iface = gr.Interface(fn=summarize,
                 theme='huggingface', 
                 title= 'sum_it', 
                 description= desc,
                 inputs= "text",
                 outputs= 'textbox')
iface.launch(inline = False)