app.py

import gradio as gr
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Set seed
def set_seed(seed):
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

set_seed(42)

# Load model and tokenizer
model_name = 'ramsrigouthamg/t5_paraphraser'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

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

# Paraphrasing function using top-k/top-p sampling
def paraphrase(text, num_return_sequences=5, top_k=120, top_p=0.98):
    sentence = text.strip()
    if not sentence:
        return "Please enter valid text."

    input_text = "paraphrase: " + sentence + " </s>"
    encoding = tokenizer.encode_plus(input_text, padding="longest", return_tensors="pt")
    input_ids = encoding["input_ids"].to(device)
    attention_masks = encoding["attention_mask"].to(device)

    outputs = model.generate(
        input_ids=input_ids,
        attention_mask=attention_masks,
        do_sample=True,
        max_length=256,
        top_k=top_k,
        top_p=top_p,
        early_stopping=True,
        num_return_sequences=num_return_sequences
    )

    final_outputs = []
    for beam_output in outputs:
        decoded = tokenizer.decode(beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
        if decoded.lower() != sentence.lower() and decoded not in final_outputs:
            final_outputs.append(decoded)

    return "\n\n".join(final_outputs) if final_outputs else "No paraphrases generated."

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 📝 T5 Paraphraser with Sampling (Top-k & Top-p)")
    
    input_text = gr.Textbox(label="Enter Text to Paraphrase", lines=3, placeholder="Type here...")
    num_return = gr.Slider(1, 500, step=10, value=5, label="Number of Paraphrases")
    top_k_slider = gr.Slider(10, 200, step=10, value=120, label="Top-k Sampling")
    top_p_slider = gr.Slider(0.5, 1.0, step=0.01, value=0.98, label="Top-p Nucleus Sampling")
    
    output = gr.Textbox(label="Paraphrased Outputs", lines=10)
    btn = gr.Button("Generate Paraphrases")

    btn.click(fn=paraphrase, inputs=[input_text, num_return, top_k_slider, top_p_slider], outputs=output)

demo.launch()