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| 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) |