app.py

from pathlib import Path

import streamlit as st

from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
from transformers import TextClassificationPipeline


@st.cache_data()
def get_pipe():
    model = AutoModelForSequenceClassification.from_pretrained(
        "issai/rembert-sentiment-analysis-polarity-classification-kazakh")
    tokenizer = AutoTokenizer.from_pretrained("issai/rembert-sentiment-analysis-polarity-classification-kazakh")
    return TextClassificationPipeline(model=model, tokenizer=tokenizer)


st.title('KazSandra')
static_folder = Path(__file__).parent / 'static'
assert static_folder.exists()

st.write((static_folder / 'description.txt').read_text())
st.image(str(static_folder / 'kazsandra.jpg'))

input_text = st.text_area('Input text', placeholder='Provide your text', value='Осы кітап қызық сияқты.')
# reviews = ["Бұл бейнефильм маған түк ұнамады.", "Осы кітап қызық сияқты."]
pipe = get_pipe()
# for review in reviews:
if input_text:
    out = pipe(input_text)[0]
    st.text("Label: {label}\nScore: {score}".format(**out))