Update app.py

app.py

@@ -8,7 +8,8 @@ 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, num_return_sequences = 3):
+def paraphrase_text(input_text):
+    num_return_sequences = 3
     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,
@@ -16,7 +17,7 @@ def paraphrase_text(input_text, num_return_sequences = 3):
     paraphrased_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
     return paraphrased_text[0], paraphrased_text[1], paraphrased_text[2]
 
-examples = [["Uploading a video to YouTube can help exposure for your business.", "45"], ["Niagara Falls is viewed by thousands of tourists every year.", "30"]]
+examples = [["Uploading a video to YouTube can help exposure for your business."], ["Niagara Falls is viewed by thousands of tourists every year."]]
 
 demo = gr.Interface(fn=paraphrase_text, inputs=gr.Textbox(lines=3, placeholder="Enter sample text here", label="Original text"), outputs=[gr.Textbox(label="Paraphrasing 1"), gr.Textbox(label="Paraphrasing 2"), gr.Textbox(label="Paraphrasing 3")], examples=examples)
 demo.launch( debug = True )