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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')
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() |