app.py

import gradio as gr
from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("yuewu/T5_abstract2title")
model = T5ForConditionalGeneration.from_pretrained("yuewu/T5_abstract2title")

def title2abstract(text):

    input_ids = tokenizer(
        text, 
        padding='max_length',
        max_length=512,
        return_tensors="pt").input_ids

    generated_ids = model.generate(
        input_ids, 
        max_length=128, 
        # num_beams=3,
        # no_repeat_ngram_size=2,
        num_return_sequences=3,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        early_stopping=True)

    generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

    output = f'1. {generated_text[0]}\n\n2. {generated_text[1]}\n\n3. {generated_text[2]}'

    # output = generated_text

    return output

demo = gr.Interface(fn=title2abstract, inputs="text", outputs="text",
                    title="Abstract to title generator",
                    description="Give a chemistry paper abstract and the model will suggest 3 titles.")
demo.launch()