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#---------------------AI Paraphraser - iFrame code -----------------------------
# With direct model load
#----------------------------------------------------------------
import transformers
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
def paraphrase(
question,
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: {question}',
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)
res1 = res [0]
res2 = res [1]
res3 = res [3]
res4 = res [4]
return res1, res2, res3
iface = gr.Interface(fn=paraphrase,
inputs=["text"],
outputs=["text","text","text"],
title="AI Paraphraser",
description="Paste text in the input box and press 'Submit'. The output need not be better than the original.",
examples=[
["Therefore, when we share we need to create real hype for users to want to be involved."],
["Ideas like this I am open to your suggestions, so we can really push through."],
["The main goal is for readers/users to feel the need to purchase the product."],
["The weather is getting more and more unpredictable these days."],
],
cache_examples=True,
)
iface.launch() |