import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

MODEL_ID = "daniB2112/bart-news-summarizer"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)

def summarize(text, max_length=150, min_length=40):
    inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
    summary_ids = model.generate(
        inputs["input_ids"],
        max_length=max_length,
        min_length=min_length,
        length_penalty=2.0,
        num_beams=4,
        early_stopping=True
    )
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

demo = gr.Interface(
    fn=summarize,
    inputs=[
        gr.Textbox(lines=10, placeholder="Paste news article here...", label="Article"),
        gr.Slider(50, 300, value=150, label="Max Summary Length"),
        gr.Slider(10, 100, value=40,  label="Min Summary Length"),
    ],
    outputs=gr.Textbox(label="Summary"),
    title="BART News Summarizer",
    description="Summarize news articles using daniB2112/bart-news-summarizer"
)

demo.launch()