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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
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/MASKGPT2")
#model = AutoModelForCausalLM.from_pretrained("BigSalmon/MASKGPT2")
#tokenizer = GPTNeoXTokenizerFast.from_pretrained("CarperAI/FIM-NeoX-1.3B")
#model = GPTNeoXForCausalLM.from_pretrained("BigSalmon/FormalInformalConcise-FIM-NeoX-1.3B")
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/FamilyFeud")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/FamilyFeud")
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')
submit_button6 = st.form_submit_button(label='Turn Into Infill Format. Just add [MASK] where you want it infilled')
if submit_button:
with torch.no_grad():
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput
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)
if submit_button6:
prefix_format(prompt) |