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app.py

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+import gradio as gr
+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 paraphrase_text(input_text, max_length):
+  batch = tokenizer([input_text],truncation=True,padding='longest',max_length=int(max_length), return_tensors="pt").to(torch_device)
+  translated = model.generate(**batch,max_length=int(max_length),num_beams=3, num_return_sequences=3, temperature=1.5)
+  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+  return tgt_text[0], tgt_text[1], tgt_text[2]
+
+examples = [["Begin your professional career by learning data science skills with Data science Dojo, a globally recognized e-learning platform where we teach students how to learn data science, data analytics, machine learning and more.", "45"], ["Hello, I am a paraphrasing tool. How can I help you?", "30"]]
+
+demo = gr.Interface(fn=paraphrase_text, inputs=["text", "text"], outputs=["text", "text", "text"], title="Paraphrase", examples=examples)
+demo.launch( debug = True )