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app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import os
+import torch
+import torch.nn as nn
+from transformers.activations import get_activation
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModel
+from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
+import math
+
+
+st.title('GPT2:')
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+@st.cache(allow_output_mutation=True)
+def get_model():
+    tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln85Paraphrase")
+    model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln85Paraphrase")
+    return model, tokenizer
+    
+model, tokenizer = get_model()
+
+g = """***
+
+original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. 
+infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. 
+
+***
+
+original:"""
+
+def prefix_format(sentence):
+  words = sentence.split()
+  if "[MASK]" in sentence:
+    words2 = words.index("[MASK]")
+    #print(words2)
+    output = ("<|SUF|> " + ' '.join(words[words2+1:]) + " <|PRE|> " + ' '.join(words[:words2]) + " <|MID|>")
+    st.write(output)
+  else:
+    st.write("Add [MASK] to sentence")
+    
+with st.form(key='my_form'):
+    prompt = st.text_area(label='Enter sentence', value=g)
+    submit_button = st.form_submit_button(label='Submit')
+    if submit_button:
+      with torch.no_grad():
+        outputs = model(sequence, labels=input_ids)
+        loss, logits = outputs[:2]
+        perplex = math.exp(loss)
+        st.write(perplex)