app.py

import gradio as gr
from transformers import pipeline
import torch

# Load summarizer
device = 0 if torch.cuda.is_available() else -1
summarizer = pipeline(
    "summarization",
    model="csebuetnlp/mT5_multilingual_XLSum",
    tokenizer="csebuetnlp/mT5_multilingual_XLSum",
    device=device
)

print("✅ Model loaded on:", "GPU" if device == 0 else "CPU")

# Function for API and UI
def summarize_text(text):
    if not text.strip():
        return "❌ Error: No text provided."
    
    max_len = 1000
    clean_text = text.strip()[:max_len]
    result = summarizer([clean_text], max_length=130, min_length=30, do_sample=False)
    return result[0]["summary_text"]

# Gradio Interface (UI + API)
iface = gr.Interface(
    fn=summarize_text,
    inputs=gr.Textbox(lines=10, placeholder="Paste your news article here..."),
    outputs="text",
    title="Multilingual News Summarizer",
    description="Summarizes news articles using mT5 multilingual XLSum model."
)

iface.launch()