import streamlit as st
from transformers import pipeline
import json

# Load the text classification model from Hugging Face
@st.cache_resource
def load_model():
    model_name = "annyalvarez/MoralS-BERT"
    return pipeline("text-classification", model=model_name)

model = load_model()

# Streamlit app interface
st.title("Moral Values Text Classification")

# Text input from the user
user_input = st.text_area("Enter text for classification:", "")

if st.button("Classify"):
    if user_input:
        # Get model predictions
        predictions = model(user_input)

        # Sort predictions by score
        predictions = sorted(predictions, key=lambda x: x['score'], reverse=True)

        # Format the predictions as required
        output = [{
            "label": pred['label'],
            "score": pred['score']
        } for pred in predictions]

        # Display the output
        st.json(output)
    else:
        st.write("Please enter some text to classify.")