app.py

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

# --- Load Model ---
model_name = "Vamsi/T5_Paraphrase_Paws"  # switched to Vamsi model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()

# --- Helpers ---
def split_paragraphs(text):
    """Split text into paragraphs based on line breaks."""
    paragraphs = [p.strip() for p in text.split("\n") if p.strip()]
    return paragraphs

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

def clean_sentence(sent):
    """Clean and ensure sentence ends with punctuation."""
    sent = re.sub(r'\s+', ' ', sent).strip()
    if not sent.endswith(('.', '!', '?')):
        sent += "."
    return sent

# --- Main function ---
def paraphrase_fn(text, num_return_sequences=1, temperature=1.2, top_p=0.92):
    if not text.strip():
        return "Enter some text"

    num_return_sequences = int(num_return_sequences)
    paragraphs = split_paragraphs(text)
    paraphrased_paragraphs = []

    for para in paragraphs:
        sentences = split_sentences(para)
        paraphrased_sentences = []

        for sent in sentences:
            input_text = "paraphrase: " + sent + " </s>"
            inputs = tokenizer([input_text], return_tensors="pt", truncation=True, padding=True).to(device)

            outputs = model.generate(
                **inputs,
                max_new_tokens=128,
                num_return_sequences=num_return_sequences,
                do_sample=True,
                top_p=float(top_p),
                temperature=float(temperature),
            )
            decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)

            seen, unique = set(), []
            for d in decoded:
                d = clean_sentence(d)
                if d not in seen:
                    unique.append(d)
                    seen.add(d)

            paraphrased_sentences.append(unique[0])

        # Join sentences for this paragraph
        paraphrased_paragraphs.append(" ".join(paraphrased_sentences))

    # Join paragraphs with double line breaks to preserve paragraphing
    return "\n\n".join(paraphrased_paragraphs)

# --- Gradio Interface ---
iface = gr.Interface(
    fn=paraphrase_fn,
    inputs=[
        gr.Textbox(lines=12, placeholder="Paste text here..."),
        gr.Slider(1, 3, step=1, value=1, label="Variants"),
        gr.Slider(0.5, 2.0, step=0.1, value=1.2, label="Temperature"),
        gr.Slider(0.6, 1.0, step=0.01, value=0.92, label="Top-p"),
    ],
    outputs=gr.Textbox(label="Output"),
    title="📝 Writenix API",
    description="This Space provides a UI *and* an API for paraphrasing text while preserving paragraphs."
)

iface.launch()