app.py

import gradio as gr
from transformers import BartForConditionalGeneration
from transformers import BartTokenizer

tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')


def generate_summary(text, model, tokenizer):
    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
    summary_ids = model.generate(inputs, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary


def process(text):
    return generate_summary(text, model, tokenizer)


textbox = gr.Textbox(label="Pega el text aca:", placeholder="Texto...", lines=15)
demo = gr.Interface(fn=process, inputs=textbox, outputs="text")
demo.launch()