app.py

import gradio as gr
from transformers import pipeline
import torch

# Lightweight fallback model
def load_model():
    try:
        return pipeline("text2text-generation", 
                      model="prithivida/parrot_paraphraser_on_T5",
                      device="cuda" if torch.cuda.is_available() else "cpu")
    except:
        return None

parrot_pipeline = load_model()

def paraphrase_text(text):
    if not text.strip():
        return "⚠️ Please enter text"
        
    if parrot_pipeline is None:
        return "❌ Model unavailable. Try again later."
    
    try:
        outputs = parrot_pipeline(
            text,
            num_return_sequences=3,
            num_beams=5,
            max_length=128
        )
        return "\n\n".join([f"{i+1}. {res['generated_text']}" 
                          for i, res in enumerate(outputs)])
    except Exception as e:
        return f"⚠️ Error: {str(e)}"