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Add error handling for model loading and improve analysis feedback
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import streamlit as st
from utils.prediction import load_model_for_prediction
#import transformers
#from transformers import pipeline
st.title('My first app')
x = st.slider('Select a value')
st.write(x, 'squared is', x * x)
# load the model and cache it
model, label_encoder, tokenizer = load_model_for_prediction()
if model is None or label_encoder is None or tokenizer is None:
st.error("Failed to load model components")
st.stop()
st.session_state['model'] = model
st.session_state['label_encoder'] = label_encoder
st.session_state['tokenizer'] = tokenizer
st.success('Model loaded successfully')