app.py

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

# Load FLAN-T5 Large model
model_name = "google/flan-t5-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.float16)  # Use float16 for efficiency

# Move model to CPU (Change to "cuda" if using GPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Initialize paraphrase pipeline
paraphrase_pipeline = pipeline(
    "text2text-generation",
    model=model,
    tokenizer=tokenizer,
    truncation=True,
    device=0 if device == "cuda" else -1  # Use GPU if available
)

def split_sentences(text):
    """Split text into sentences using regex."""
    return re.split(r'(?<=[.!?])\s+', text.strip())

def paraphrase_text(text):
    """Paraphrases input text while maintaining sentence structure."""
    if not text.strip():
        return "⚠️ Please enter some text to paraphrase."

    sentences = split_sentences(text)

    # Apply FLAN-T5 paraphrasing to each sentence
    paraphrased_results = paraphrase_pipeline(
        [f"Rephrase this sentence: {sentence}" for sentence in sentences if sentence], 
        max_length=50, do_sample=True, batch_size=8, num_return_sequences=1
    )

    paraphrased_sentences = [result['generated_text'] for result in paraphrased_results]
    return " ".join(paraphrased_sentences)

# Define Gradio Interface
demo = gr.Interface(
    fn=paraphrase_text,
    inputs=gr.Textbox(label="Enter text", placeholder="Type your text to paraphrase...", lines=10),
    outputs=gr.Textbox(label="Paraphrased Text", lines=10),
    title="🚀 FLAN-T5 Paraphraser",
    description="Enter text and let AI generate a paraphrased version using FLAN-T5-Large!",
    theme="huggingface"
)

if __name__ == "__main__":
    demo.launch()