app.py

import streamlit as st
from transformers import pipeline
from sentence_transformers import SentenceTransformer

# Load the Sentence Transformers model
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Load the sentiment analysis model
classifier = pipeline("sentiment-analysis", model="Suphawan/website_classification")

def main():
    st.title("Website classification")

    with st.form("text_field"):
        text = st.text_area('Enter some text:')
        # clicked will be True only when the button is clicked
        clicked = st.form_submit_button("Submit text")

        if clicked:
            # Perform sentiment analysis
            sentiment_results = classifier([text])
            sentiment = sentiment_results[0]['label']
            confidence = sentiment_results[0]['score']
            
            # Compute sentence embeddings
            sentence_embeddings = model.encode([text])

            st.write("Sentiment:", sentiment)
            st.write("Confidence:", confidence)
            st.write("Sentence Embeddings:", sentence_embeddings)

if __name__ == "__main__":
    main()