apps/classifier.py

import json

import streamlit as st
from transformers import AutoTokenizer, RobertaForSequenceClassification, pipeline

with open("config.json") as f:
    cfg = json.loads(f.read())


@st.cache(allow_output_mutation=True, show_spinner=False)
def load_model(input_text, model_name_or_path):
    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
    model = RobertaForSequenceClassification.from_pretrained(model_name_or_path)

    nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
    result = nlp(input_text)
    return result


def app():
    st.title("RoBERTa Marathi")

    classifier = st.sidebar.selectbox("Select a Model", index=0, options=["Indic NLP", "iNLTK"])

    model_name_or_path = cfg["models"][classifier]
    input_text = st.text_input("Text:")

    predict_button = st.button("Predict")

    if predict_button:
        with st.spinner("Generating prediction..."):
            # Get prediction here
            result = load_model(input_text, model_name_or_path)

            st.markdown("**Predicted label:** " + result[0]["label"])