Create app.py

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+import torch
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import FSMTForConditionalGeneration, FSMTTokenizer
+
+st.title("Paraphraser Three -- Back Translation")
+
+user_input = st.text_area("Input sentence.")
+
+def load_en2de():
+  en2de = pipeline("translation_en_to_de", model="t5-base")
+  return en2de
+  
+def load_de2en():
+  model_name = "facebook/wmt19-de-en"
+  tokenizer = FSMTTokenizer.from_pretrained(model_name)
+  model_de_to_en = FSMTForConditionalGeneration.from_pretrained(model_name)
+  return tokenizer, model_de_to_en
+  
+en2de = load_en2de()
+tokenizer_de2en, de2en = load_de2en()
+
+en_to_de_output = en2de(user_input)
+translated_text = en_to_de_output[0]['translation_text']
+
+input_ids = tokenizer_de2en.encode(translated_text, return_tensors="pt")
+output_ids = de2en.generate(input_ids)[0]
+augmented_text = tokenizer_de2en.decode(output_ids, skip_special_tokens=True)
+
+st.write("Paraphrased text using back translation: ", augmented_text)
+  
+