import streamlit as st
from sentence_transformers import SentenceTransformer, util

# Load the model
model = SentenceTransformer('sentence-transformers/msmarco-distilbert-dot-v5')

# Define the Streamlit app
def main():
    st.title("Text Embedding Generator")

    # Get user input
    text_input = st.text_area("Enter text to generate embeddings:", "")

    if st.button("Generate Embedding"):
        if text_input:
            # Call the function to get the embedding
            embedding = get_emb(text_input)

            # Display the embedding
            st.success("Embedding generated successfully:")
            st.write(embedding)
        else:
            st.warning("Please enter text to generate embeddings.")

# Function to get the embedding
def get_emb(text):
    return model.encode(text)

# Run the Streamlit app
if __name__ == "__main__":
    main()