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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() |