Create app.py

app.py

@@ -0,0 +1,38 @@
+import streamlit as st
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+	
+st.title(Paraphrase with Pegasus")
+	
+model_name = "tuner007/pegasus_paraphrase"
+torch_device = "cpu"
+tokenizer = PegasusTokenizer.from_pretrained(model_name)
+	
+	
+@st.cache(allow_output_mutation=True)
+def load_model():
+  model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
+  return model
+
+def get_response(
+  input_text, num_return_sequences, num_beams, max_length=60, temperature=1.5
+):
+
+  model = load_model()
+  batch = tokenizer([input_text], truncation=True, padding="longest", max_length=max_length, return_tensors="pt").to(torch_device)
+  translated = model.generate(**batch, max_length=max_length, num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=temperature)
+  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+  return tgt_text
+
+num_beams = 10
+num_return_sequences = st.slider("Number of paraphrases", 1, 10, 5, 1)
+context = st.text_area(label="Enter a sentence to paraphrase", max_chars=384)
+
+with st.expander("Advanced"):
+  temperature = st.slider("Temperature", 0.1, 5.0, 1.5, 0.1)
+  max_length = st.slider("Max length", 10, 100, 60, 10)
+
+if context:
+  response = get_response(context, num_return_sequences, num_beams, max_length, temperature)
+  for paraphrase in response:
+    st.write(paraphrase)
+