Create app.py

app.py

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+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)