#---------------------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()