| import torch |
| from transformers import PegasusForConditionalGeneration, PegasusTokenizer |
|
|
| model_name = 'tuner007/pegasus_paraphrase' |
| torch_device = 'cuda' if torch.cuda.is_available() else 'cpu' |
| tokenizer = PegasusTokenizer.from_pretrained(model_name) |
| model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device) |
|
|
| def get_response(input_text,num_return_sequences): |
| batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device) |
| translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5) |
| tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True) |
| return tgt_text |
|
|
| from sentence_splitter import SentenceSplitter, split_text_into_sentences |
|
|
| splitter = SentenceSplitter(language='en') |
|
|
| def paraphraze(text): |
| sentence_list = splitter.split(text) |
| paraphrase = [] |
|
|
| for i in sentence_list: |
| a = get_response(i,1) |
| paraphrase.append(a) |
| paraphrase2 = [' '.join(x) for x in paraphrase] |
| paraphrase3 = [' '.join(x for x in paraphrase2) ] |
| paraphrased_text = str(paraphrase3).strip('[]').strip("'") |
| return paraphrased_text |
|
|
| import gradio as gr |
| def summarize(text): |
|
|
| paraphrased_text = paraphraze(text) |
| return paraphrased_text |
| gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=7, placeholder="Enter text here"), outputs=[gr.outputs.Textbox(label="Paraphrased Text")],examples=[["This Api is the best quillbot api alternative with no words limit." |
| ]]).launch(inline=False) |
