load_model.py

import streamlit as st
import os
import git
#if(os.path.isdir("./TickerExtraction")==False):
git.Git("./").clone("https://huggingface.co/rajaatif786/TickerExtraction")
print(os.path.exists("./rajaatif786/TickerExtraction/entity_model2.pt"))
#st.write(os.listdir("./TickerExtraction/"))
#x = st.slider('Select a value')
#st.write(x, 'squared is', x * x)

import pandas as pd
import numpy as np
os.chdir("./TickerExtraction")
from EntityExtractor import EntityDataset, EntityBertNet,BertEntityExtractor, LABEL_MAP
import nltk
nltk.download('stopwords')
entity_extractor = BertEntityExtractor.load_trained_model()