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import json | |
import streamlit as st | |
from transformers import AutoTokenizer, RobertaForSequenceClassification | |
with open("config.json") as f: | |
cfg = json.loads(f.read()) | |
# @st.cache(allow_output_mutation=True) | |
# def load_model(): | |
# tokenizer = AutoTokenizer.from_pretrained(cfg["model_name_or_path"]) | |
# model = RobertaForSequenceClassification.from_pretrained(cfg["model_name_or_path"]) | |
st.title("RoBERTa Marathi") | |
# with st.spinner("Loading model..."): | |
# generator, tokenizer = load_model() | |
input_text = st.text_input("Text:") | |
predict_button = st.button("Predict") | |
if predict_button: | |
with st.spinner("Generating prediction..."): | |
# Get prediction here | |
st.write("Predicted Label") | |