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import json | |
import streamlit as st | |
from transformers import AutoTokenizer, RobertaForSequenceClassification, pipeline | |
with open("config.json") as f: | |
cfg = json.loads(f.read()) | |
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"]) | |