Create app.py

app.py

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+# https://huggingface.co/tuner007/pegasus_paraphrase
+
+import nltk
+from nltk import sent_tokenize
+nltk.download('punkt')
+
+import gradio as gr
+
+import torch
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+
+import warnings
+warnings.filterwarnings('ignore')
+
+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 paraphraser(input_text,num_return_sequences=1):
+  sentence_list = sent_tokenize(input_text)
+
+  output = []
+  for sentence in sentence_list:
+    batch = tokenizer.prepare_seq2seq_batch([sentence],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)
+    output.extend(tgt_text)
+
+  paraphrase = [' '.join(x for x in output)]
+  paraphrased_text = str(paraphrase).strip('[]').strip("'")
+
+  return paraphrased_text
+  
+paraphraseUI = gr.Interface(fn=paraphraser, inputs='textbox', outputs='text', title="ParaPhraser", theme='dark')
+paraphraseUI.launch(inbrowser=True)