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app.py

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+import gradio as gr
+import torch
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+from sentence_splitter import SentenceSplitter, split_text_into_sentences
+
+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=10000,
+                                            return_tensors="pt").to(torch_device)
+    translated = model.generate(**batch, 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
+
+
+def get_response_from_text(
+        context="I am a student at the University of Washington. I am taking a course called Data Science."):
+    splitter = SentenceSplitter(language='en')
+    sentence_list = splitter.split(context)
+
+    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
+
+
+def greet(context):
+    return get_response_from_text(context)
+
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()