app.py

import gradio as gr

from transformers import BartForConditionalGeneration, BartTokenizer, pipeline

# Load the model and tokenizer using the authentication token
model = BartForConditionalGeneration.from_pretrained("sshleifer/distilbart-cnn-12-6")
tokenizer = BartTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")

# Create the summarization pipeline with the loaded model and tokenizer
get_completion = pipeline("summarization", model=model, tokenizer=tokenizer)

def summarize(input):
    output = get_completion(input)
    return output[0]['summary_text']

demo = gr.Interface(fn=summarize,
                    inputs=[gr.Textbox(label="Text to summarize", lines=6)],
                    outputs=[gr.Textbox(label="Result", lines=3)],
                    title="Text summarization with distilbart-cnn",
                    description="Summarize any text using the `shleifer/distilbart-cnn-12-6` model under the hood!"
                   )

demo.launch(inline=False)