app.py

import streamlit as st
import transformers

# use @st.cache decorator to cache model — because it is too large, we do not want to reload it every time
# use allow_output_mutation = True to tell streamlit that model should be treated as immutable object — singleton
# @st.cache(allow_output_mutation = True)

# load model and tokenizer
tokenizer = transformers.AutoTokenizer.from_pretrained("yeshpanovrustem/xlm-roberta-large-ner-kazakh")
model = transformers.AutoModelForTokenClassification.from_pretrained("yeshpanovrustem/xlm-roberta-large-ner-kazakh")

# # define function for ner


# st.markdown("# Hello")
# # st.set_page_config(page_title = "Kazakh Named Entity Recognition", page_icon = "🔍")
# # st.title("🔍 Kazakh Named Entity Recognition")

# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x)