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import transformers |
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import gradio as gr |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base") |
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model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base") |
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def paraphrase( |
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Content_to_Rephrase, |
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num_beams=5, |
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num_beam_groups=5, |
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num_return_sequences=5, |
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repetition_penalty=10.0, |
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diversity_penalty=3.0, |
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no_repeat_ngram_size=2, |
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max_length=5000 |
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): |
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input_ids = tokenizer( |
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f'paraphrase: {Content_to_Rephrase}', |
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return_tensors="pt", padding="longest", |
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max_length=max_length, |
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truncation=True, |
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).input_ids |
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outputs = model.generate( |
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input_ids, repetition_penalty=repetition_penalty, |
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num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size, |
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num_beams=num_beams, num_beam_groups=num_beam_groups, |
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max_length=max_length, diversity_penalty=diversity_penalty |
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) |
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res = tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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res1 = res [0] |
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res2 = res [1] |
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res3 = res [3] |
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res4 = res [4] |
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return res1, res2, res3 |
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output1 = gr.Textbox(label="Rephrased: Option 1") |
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output2 = gr.Textbox(label="Rephrased: Option 2") |
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output3 = gr.Textbox(label="Rephrased: Option 3") |
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iface = gr.Interface(fn=paraphrase, |
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inputs=["text"], |
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outputs=[output1, output2, output3], |
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title="Free AI Sentence Rephraser", |
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description="<ul><li>Paste text in the input box and press 'Submit'.</li><li>Max length: ~35 words (larger content is summarized)</li><li>The rephrased sentences *may not* be better than the original input.</li><li>Model 'humarin' pre-trained by ChatGPT. Temp = 0.7</li></ul>", |
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examples=[ |
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["With the humble is wisdom."], |
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["Hatred stirs up strife."], |
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["The way of a fool is right in his own eyes."], |
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["Righteousness leads to life."], |
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], |
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cache_examples=True, |
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) |
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iface.launch() |
