import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from sentence_splitter import SentenceSplitter

device = "cuda"

tokenizer = AutoTokenizer.from_pretrained("NoaiGPT/777")
model = AutoModelForSeq2SeqLM.from_pretrained("NoaiGPT/777").to(device)

splitter = SentenceSplitter(language='en')

def process_and_generate(text):
    def generate_title(sentence):
        input_ids = tokenizer(f'paraphraser: {sentence}', return_tensors="pt", padding="longest", truncation=True, max_length=64).input_ids.to(device)
        outputs = model.generate(
            input_ids,
            num_beams=8,
            num_beam_groups=4,
            num_return_sequences=6,
            repetition_penalty=12.0,
            diversity_penalty=4.0,
            no_repeat_ngram_size=3,
            temperature=1.1,
            top_k=50,
            top_p=0.95,
            max_length=64
        )
        return tokenizer.batch_decode(outputs, skip_special_tokens=True)

    paragraphs = text.split('\n\n')
    results = []
    final_paragraphs = []

    for paragraph in paragraphs:
        sentences = splitter.split(paragraph)
        paragraph_results = []
        final_sentences = []

        for sentence in sentences:
            titles = generate_title(sentence)
            paragraph_results.append(f"Original: {sentence}\nParaphrases:\n" + "\n".join(titles))
            final_sentences.append(titles[0])  # Use the first paraphrase for the final paragraph

        results.append("\n\n".join(paragraph_results))
        final_paragraphs.append(" ".join(final_sentences))

    detailed_output = "\n\n---\n\n".join(results)
    final_text = "\n\n".join(final_paragraphs)

    return detailed_output, final_text

iface = gr.Interface(
    fn=process_and_generate,
    inputs=gr.Textbox(lines=10, label="Input Text"),
    outputs=[
        gr.Textbox(lines=20, label="Detailed Paraphrases"),
        gr.Textbox(lines=10, label="Final Paraphrased Text")
    ],
    title="Diverse Paraphrase Generator",
    description="Generate multiple diverse paraphrases for each sentence in the input text using NoaiGPT/777 model."
)

iface.launch()