Create app.py

app.py

@@ -0,0 +1,38 @@
+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):
+    from sentence_splitter import SentenceSplitter, split_text_into_sentences
+    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=2.0)
+        tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+        return tgt_text
+    splitter = SentenceSplitter(language='en')
+    sentence_list = splitter.split(text)
+    res = ''
+    for i in sentence_list:
+        a = get_response(i,1)
+        cur = ''
+        for j in a:
+            cur += j
+            cur += ' '
+        cur += '.'
+        res += cur
+    return res
+        
+
+iface = gr.Interface(fn=paraphrase, inputs="text", outputs="text")
+iface.launch()