resources.py

from transformers import pipeline
import torch
import streamlit as st
from random import shuffle

@st.cache_data
def load_variables():
    return {
        "counter" : 0,
        "augment" : False
    }

@st.cache_data
def load_sentences():
    with open("sentences.pt", 'rb') as f:
        return shuffle(torch.load(f))

@st.cache_resource
def load_model(model):
    return pipeline("ner",
    model=model,
    aggregation_strategy="average"
    )