app.py

import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM


tokenizer  = AutoTokenizer.from_pretrained('humarin/chatgpt_paraphraser_on_T5_base', cache_dir='./Models')
model = AutoModelForSeq2SeqLM.from_pretrained('humarin/chatgpt_paraphraser_on_T5_base', cache_dir='./Models')
# torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8, inplace=True)

def paraphrase(
    text,
    num_beams=5,
    num_beam_groups=5,
    num_return_sequences=5,
    repetition_penalty=10.0,
    diversity_penalty=3.0,
    no_repeat_ngram_size=2,
    temperature=0.7,
    max_length=128
):
    input_ids = tokenizer(
        f'paraphrase: {text}',
        return_tensors="pt", padding="longest",
        max_length=max_length,
        truncation=True,
    ).input_ids
    
    outputs = model.generate(
        input_ids, temperature=temperature, repetition_penalty=repetition_penalty,
        num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size,
        num_beams=num_beams, num_beam_groups=num_beam_groups,
        max_length=max_length, diversity_penalty=diversity_penalty
    )

    res = tokenizer.batch_decode(outputs, skip_special_tokens=True)

    return res

def fn(
    text,
    num_beams=5,
    num_beam_groups=5,
    num_return_sequences=5,
    repetition_penalty=10.0,
    diversity_penalty=3.0,
    no_repeat_ngram_size=2,
    temperature=0.7,
    max_length=128
):
    return '\n'.join(paraphrase(text, num_beams, num_beam_groups, num_return_sequences, repetition_penalty, diversity_penalty, no_repeat_ngram_size, temperature, max_length))

demo = gr.Interface(
    fn=fn,
    inputs=[
        gr.Textbox(lines=3, placeholder='Enter Text To Paraphrase'),
        gr.Slider(minimum=1, maximum=25, step=1, value=5),
        gr.Slider(minimum=1, maximum=25, step=1, value=5),
        gr.Slider(minimum=1, maximum=20, step=1, value=5),
        gr.Slider(minimum=0.6, maximum=20.1, step=0.5, value=10.1),
        gr.Slider(minimum=0.6, maximum=20.1, step=0.5, value=3.1),
        gr.Slider(minimum=1, maximum=10, step=1, value=2),
        gr.Slider(minimum=0.0, maximum=1000, step=0.1, value=0.7),
        gr.Slider(minimum=32, maximum=512, step=1, value=128),
    ],
    outputs=['text'],
)

demo.launch()