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| import gradio as gr | |
| from transformers import BartTokenizer, BartForConditionalGeneration | |
| if __name__ == "__main__": | |
| # Load finetuned model and tokenizer | |
| tokenizer = BartTokenizer.from_pretrained("NielsV/distilbart-cnn-6-6-reddit") | |
| model = BartForConditionalGeneration.from_pretrained("NielsV/distilbart-cnn-6-6-reddit") | |
| # Function to write a TLDR | |
| def generate_tldr(input_txt): | |
| inputs = tokenizer(input_txt, max_length=1024, return_tensors="pt") | |
| summary_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=60) | |
| return tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] | |
| demo = gr.Interface( | |
| fn=generate_tldr, | |
| inputs=gr.Textbox(lines=5, placeholder="...", label="Post to summarize..."), | |
| outputs=gr.Textbox(lines=2, label="Too long, didn't read:"), | |
| title="A tldr-bot trained on reddit posts.", | |
| description="For more details check the following repository: https://github.com/VerleysenNiels/arxiv-summarizer" | |
| ) | |
| demo.launch() |