import gradio as gr
from transformers import PegasusForConditionalGeneration
from transformers import PegasusTokenizer
from transformers import pipeline

model_name = "google/pegasus-xsum"
pegasus_tokenizer = PegasusTokenizer.from_pretrained(model_name)
def summarize(input_text):
  nwords=len(input_text.split(" "))
  # Define summarization pipeline 
  summarizer = pipeline("summarization", model=model_name, tokenizer=pegasus_tokenizer,min_length=int(nwords/10)+10, max_length=int(nwords/5+10), framework="pt")
  summary=summarizer(input_text)[0]['summary_text']
  return(summary)
gr.Interface(fn=summarize,inputs=gr.inputs.Textbox(placeholder="Paste the text to be summarized here..."),outputs="textbox").launch();