main.py

import gradio as gr
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
import re
import os

def load_model():
    """Load the model from local storage"""
    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
    print(f"Using device: {torch_device}")

    # Load tokenizer and model from local directory
    tokenizer = PegasusTokenizer.from_pretrained('./models')
    model = PegasusForConditionalGeneration.from_pretrained('./models').to(torch_device)
    return tokenizer, model, torch_device

def split_into_paragraphs(text):
    """Split text into paragraphs while preserving empty lines."""
    paragraphs = text.split('\n\n')
    return [p.strip() for p in paragraphs if p.strip()]

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

def get_response(input_text, num_return_sequences, tokenizer, model, torch_device):
    batch = tokenizer.prepare_seq2seq_batch(
        [input_text],
        truncation=True,
        padding='longest',
        max_length=80,
        return_tensors="pt"
    ).to(torch_device)

    translated = model.generate(
        **batch,
        num_beams=10,
        num_return_sequences=num_return_sequences,
        temperature=1.0,
        repetition_penalty=2.8,
        length_penalty=1.2,
        max_length=80,
        min_length=5,
        no_repeat_ngram_size=3
    )

    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
    return tgt_text[0]

def get_response_from_text(context, tokenizer, model, torch_device):
    """Process entire text while preserving paragraph structure."""
    paragraphs = split_into_paragraphs(context)
    paraphrased_paragraphs = []

    for paragraph in paragraphs:
        sentences = split_into_sentences(paragraph)
        paraphrased_sentences = []

        for sentence in sentences:
            if len(sentence.split()) < 3:
                paraphrased_sentences.append(sentence)
                continue

            try:
                paraphrased = get_response(sentence, 1, tokenizer, model, torch_device)
                if not any(phrase in paraphrased.lower() for phrase in ['it\'s like', 'in other words']):
                    paraphrased_sentences.append(paraphrased)
                else:
                    paraphrased_sentences.append(sentence)
            except Exception as e:
                print(f"Error processing sentence: {e}")
                paraphrased_sentences.append(sentence)

        paraphrased_paragraphs.append(' '.join(paraphrased_sentences))

    return '\n\n'.join(paraphrased_paragraphs)

def create_interface():
    """Create and configure the Gradio interface"""
    # Load model and tokenizer
    tokenizer, model, torch_device = load_model()

    def greet(context):
        return get_response_from_text(context, tokenizer, model, torch_device)

    # Create interface with improved styling
    iface = gr.Interface(
        fn=greet,
        inputs=gr.Textbox(
            lines=15,
            label="Input Text",
            placeholder="Enter your text here...",
            elem_classes="input-text"
        ),
        outputs=gr.Textbox(
            lines=15,
            label="Paraphrased Text",
            elem_classes="output-text"
        ),
        title="Advanced Text Paraphraser",
        description="Enter text to generate a high-quality paraphrased version while maintaining paragraph structure.",
        theme="default",
        css="""
            .input-text, .output-text {
                font-size: 16px !important;
                font-family: Arial, sans-serif !important;
                min-height: 300px !important;
            }
        """
    )
    return iface

if __name__ == "__main__":
    # Create and launch the interface
    interface = create_interface()
    interface.launch()