app.py

import gradio as gr
import random
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from datasets import load_dataset

tokenizer = AutoTokenizer.from_pretrained("liamvbetts/bart-large-cnn-v4")
model = AutoModelForSeq2SeqLM.from_pretrained("liamvbetts/bart-large-cnn-v4")

dataset = load_dataset("cnn_dailymail", "3.0.0")

def summarize(article):
    inputs = tokenizer(article, return_tensors="pt").input_ids
    outputs = model.generate(inputs, max_new_tokens=128, do_sample=False)
    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return summary

def get_random_article():
    random.seed()
    val_example = dataset["validation"].shuffle().select(range(1))
    val_article = val_example['article'][0][:1024]
    return val_article

def load_article():
    return get_random_article()

# Using Gradio Blocks
with gr.Blocks() as demo:
    gr.Markdown("## News Summary App")
    gr.Markdown("Enter a news text and get its summary, or load a random article from the validation set.")
    with gr.Row():
        input_text = gr.Textbox(lines=10, label="Input Text")
        output_text = gr.Textbox(label="Summary")
    load_article_button = gr.Button("Load Random Article")
    load_article_button.click(fn=load_article, inputs=[], outputs=input_text)
    summarize_button = gr.Button("Summarize")
    summarize_button.click(fn=summarize, inputs=input_text, outputs=output_text)

demo.launch()