app.py

# Import libraries
import gradio as gr
import torch as torch
from transformers import pipeline

# Create a summarization pipeline
# summarizer = pipeline("summarization")
#text_summery = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
text_summery = pipeline("summarization", model="sshleifer/distilbart-cnn-6-6")
#text_summery = pipeline("summarization", model="facebook/bart-large-cnn")
#text_summery = pipeline("summarization", model="google/pegasus-xsum")

# Define a function that takes a text and returns a summary
def summarize(text):
  summary = text_summery(text, max_length=250, min_length=40, do_sample=False)[0]
  return summary["summary_text"]

# Create a Gradio interface
interface = gr.Interface(
  fn=summarize, # the function to wrap
  inputs=gr.Textbox(lines=20, label="Text to Summarize"), 
  outputs=gr.Textbox(label="Summary")
)

# Launch the interface
interface.launch()