app.py

import torch
import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration

def generate_text(input_text):
    # Load pre-trained model and tokenizer
    model_name = 'kurry/t5_small_finetuned'
    model = T5ForConditionalGeneration.from_pretrained(model_name).to('cpu')
    tokenizer = T5Tokenizer.from_pretrained(model_name)

    # Generate summary
    inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", truncation=True).to('cpu')
    outputs = model.generate(inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

iface = gr.Interface(fn=generate_text, inputs='text', outputs='text')
iface.launch()