app.py

import gradio as gr
from transformers import pipeline

# Load the paraphrase model
model_name = "AventIQ-AI/t5-paraphrase-generation"
paraphrase_pipeline = pipeline("text2text-generation", model=model_name)

def generate_paraphrase(text, temperature):
    """Generate a paraphrased version of the input text."""
    if not text.strip():
        return "⚠️ Please enter some text to paraphrase."
    
    # Limit input to 700 words
    words = text.split()
    if len(words) > 700:
        return "⚠️ Input too long! Please enter a maximum of 700 words."
    
    try:
        result = paraphrase_pipeline(
            text,
            temperature=temperature,
            top_k=50,
            do_sample=True,
            max_new_tokens=1000,  # 🚀 Extended output length
            repetition_penalty=1.2,  # 🔄 Reduces repetition
            early_stopping=False  # ⛔ Prevents early cutoff
        )

        # Ensure correct output formatting
        return result[0].get("generated_text", "⚠️ Paraphrasing failed. Please try again.")
    
    except Exception as e:
        return f"⚠️ An error occurred: {str(e)}"

# Define Gradio Interface
description = """
## ✨ AI Paraphrasing Tool
Enter text and let AI generate a paraphrased version!
- **Creativity (Temperature)** controls how varied the output is.
- **Input is limited to 700 words.**
- **Now supports longer paraphrased outputs!**
"""

demo = gr.Interface(
    fn=generate_paraphrase,
    inputs=[
        gr.Textbox(label="Enter text", placeholder="Type your text to paraphrase...", lines=7),  # Bigger input box
        gr.Slider(0.5, 1.5, value=1.0, step=0.1, label="Creativity (Temperature)"),
    ],
    outputs=gr.Textbox(label="Paraphrased Text", lines=15),  # 🔥 Expands output display
    title="📝 AI Paraphraser",
    description=description,
    theme="huggingface",
    live=True,
)

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