GradioApp.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(model, text, max_length=128, num_return_sequences=5, num_beams=25, temperature=0.7):
    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=1.5,
        num_return_sequences=num_return_sequences, no_repeat_ngram_size=5, num_beams=num_beams, max_length=max_length
    )

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

    return res

def fn(text, results_num=5, beams_num=25, temperature=0.7):
    return '\n'.join(paraphrase(model, text, num_return_sequences=results_num, num_beams=beams_num, temperature=temperature))

demo = gr.Interface(
    fn=fn,
    inputs=[gr.Textbox(lines=3, placeholder='Enter Text To Paraphrase'), gr.Slider(minimum=1, maximum=10, step=1, value=5), gr.Slider(minimum=1, maximum=50, step=1, value=25), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.7)],
    outputs=['text'],
)

demo.launch(share=True)