app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load T5-small model and tokenizer
model_name = "t5-small"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

def generate_paraphrase(text, max_length, temperature, num_outputs):
    """Generate paraphrased versions of the input text using T5-small."""
    if not text.strip():
        return ["⚠️ Please enter some text to paraphrase."]

    input_text = f"paraphrase: {text} </s>"
    input_ids = tokenizer.encode(input_text, return_tensors="pt")

    outputs = model.generate(
        input_ids,
        max_length=max_length,
        top_k=50,
        top_p=0.95,
        num_return_sequences=num_outputs,
        do_sample=True
    )

    paraphrased_texts = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
    return paraphrased_texts  # Returns a list of paraphrases

# Define Gradio Interface
description = """
## ✨ AI Paraphrasing Tool
Enter a sentence and let AI generate multiple paraphrased versions!
- Adjust **max length** for longer outputs.
- Tune **temperature** for more creative results.
- Choose **number of outputs** to generate multiple variations.
"""

demo = gr.Interface(
    fn=generate_paraphrase,
    inputs=[
        gr.Textbox(label="Enter text", placeholder="Type a sentence to paraphrase..."),
        gr.Slider(20, 100, value=50, step=5, label="Max Output Length"),
        gr.Slider(0.5, 1.5, value=1.0, step=0.1, label="Creativity (Temperature)"),
        gr.Dropdown(choices=[1, 2, 3, 4, 5], value=1, label="Number of Outputs")
    ],
    outputs=gr.Textbox(label="Paraphrased Text", lines=5),  # Allows multiple outputs
    title="📝 AI Paraphraser",
    description=description,
    theme="huggingface",
    live=True,
)

demo.launch()