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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() |