app.py

import gradio as gr
from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("MahmoudH/t5-v1_1-base-finetuned-sci_summ")
model = TFAutoModelForSeq2SeqLM.from_pretrained("MahmoudH/t5-v1_1-base-finetuned-sci_summ")

def predict(text):
    text = "summarize: " + text
    tokenized_inputs = tokenizer([text])
    output = model.generate(
        input_ids=tokenized_inputs["input_ids"],
        attention_mask=tokenized_inputs["attention_mask"],
        max_new_tokens=256,
        length_penalty=0.5,
        num_beams=4,
        do_sample=True
    )
    summary = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    return summary

input_box = gr.Textbox(label="Input")
output_box = gr.Textbox(label="Summary")

gr.Interface(fn=predict, inputs=input_box, outputs=output_box).launch()