app.py

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, AutoModelWithLMHead


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/InformalToFormalLincoln21")
    model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln21")
    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:"""

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():
        text = tokenizer.encode(prompt)
        myinput, past_key_values = torch.tensor([text]), None
        myinput = myinput
        myinput= myinput.to(device)
        logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
        logits = logits[0,-1]
        probabilities = torch.nn.functional.softmax(logits)
        best_logits, best_indices = logits.topk(250)
        best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
        text.append(best_indices[0].item())
        best_probabilities = probabilities[best_indices].tolist()
        words = []              
        st.write(best_words)