import streamlit as st
from app_models.rubert_MODEL import classify_text
from app_models.bag_of_words_MODEL import predict
from app_models.lstm_MODEL import predict_review

class_prefix =  'This review is likely...'

def run():
    st.title("Movie Review Classification")
    st.write("This page will compare three models: Bag of Words/TF-IDF, LSTM, and BERT.")
    
    # Example placeholder for user input
    user_input = st.text_area("")


    if st.button('Classify with All Models'):
        # Bag of Words/TF-IDF prediction
        bow_tfidf_result = predict(user_input)
        st.write(f'{class_prefix} {bow_tfidf_result} according to Bag of Words/TF-IDF.')
        
        # LSTM prediction
        lstm_result = predict_review(user_input)
        st.write(f'{class_prefix} {lstm_result} according to LSTM.')
        
        # ruBERT prediction
        rubert_result = classify_text(user_input)
        st.write(f'{class_prefix} {rubert_result} according to ruBERT.')

    # Placeholder buttons for model selection
    # if st.button('Classify with BoW/TF-IDF'):
    #     st.write(f'{class_prefix}{predict(user_input)}')
    # if st.button('Classify with LSTM'):
    #     st.write(f'{class_prefix}{predict_review(user_input)}')
    # if st.button('Classify with ruBERT'):
    #     st.write(f'{class_prefix}{classify_text(user_input)}')