app.py

import streamlit as st
import torch
from sentence_transformers import SentenceTransformer, util

# Load the pre-trained SentenceTransformer model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Define the backend function
def mapping_code(user_input):
    emb1 = model.encode(user_input.lower())
    similarities = []
    for sentence_embed in sentences['embeds']:
        similarity = util.cos_sim(sentence_embed, emb1)
        similarities.append(similarity)

    # Combine similarity scores with 'code' and 'description'
    result = list(zip(sentences['SBS Code'], sentences['Long Description'], similarities))

    # Sort results by similarity scores
    result.sort(key=lambda x: x[2], reverse=True)

    # Return top 5 entries with 'code', 'description', and 'similarity_score'
    top_5_results = []
    for i in range(5):
        code, description, similarity_score = result[i]
        top_5_results.append({"Code": code, "Description": description, "Similarity Score": similarity_score})
    return top_5_results

# Streamlit frontend interface
def main():
    st.title("CPT Description Mapping")

    # Input text box for user input
    user_input = st.text_input("Enter CPT description:")

    # Button to trigger mapping
    if st.button("Map"):
        if user_input:
            st.write("Please wait for a moment .... ")

            # Call backend function to get mapping results
            mapping_results = mapping_code(user_input)

            # Display top 5 similar sentences
            st.write("Top 5 similar sentences:")
            for i, result in enumerate(mapping_results, 1):
                st.write(f"{i}. Code: {result['Code']}, Description: {result['Description']}, Similarity Score: {result['Similarity Score']:.4f}")

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