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import streamlit as st
import torch
from sentence_transformers import SentenceTransformer, util
#from spellchecker import SpellChecker
import pickle
import re
# Load the pre-trained SentenceTransformer model
model = SentenceTransformer('neuml/pubmedbert-base-embeddings')
# Load stored data
with open("embeddings_1.pkl", "rb") as fIn:
stored_data = pickle.load(fIn)
stored_embeddings = stored_data["embeddings"]
def validate_input(input_string):
# Regular expression pattern to match letters and numbers, or letters only
pattern = r'^[a-zA-Z0-9]+$|^[a-zA-Z]+$'
# Check if input contains at least one non-numeric character
if re.match(pattern, input_string) or input_string.isdigit():
return True
else:
return False
# Define the function for mapping code
def mapping_code(user_input):
emb1 = model.encode(user_input.lower())
similarities = []
for sentence in stored_embeddings:
similarity = util.cos_sim(sentence, emb1)
similarities.append(similarity)
# Filter results with similarity scores above 0.70
result = [(code, desc, sim) for (code, desc, sim) in zip(stored_data["SBS_code"], stored_data["Description"], similarities)]
# Sort results by similarity scores
result.sort(key=lambda x: x[2], reverse=True)
num_results = min(5, len(result))
# Return top 5 entries with 'code', 'description', and 'similarity_score'
top_5_results = []
if num_results > 0:
for i in range(num_results):
code, description, similarity_score = result[i]
top_5_results.append({"Code": code, "Description": description, "Similarity Score": similarity_score})
else:
top_5_results.append({"Code": "", "Description": "No match", "Similarity Score": 0.0})
return top_5_results
# Streamlit frontend interface
import streamlit as st
def main():
st.title("CPT Description Mapping")
st.markdown("<font color='blue'>**💡 Please enter the input CPT description with specific available details for best results.**</font>", unsafe_allow_html=True)
st.markdown("<font color='blue'>**💡 Note:** Please note that the similarity scores of each code are the calculated based on language module matching and the top 5 codes descriptions results should be verified with CPT description by the user.</font>", unsafe_allow_html=True)
# user_slider_input_number = st.sidebar.slider('Select similarity threshold', 0.0, 1.0, 0.7, 0.01, key='slider1', help='Adjust the similarity threshold')
# Input text box for user input
user_input = st.text_input("Enter CPT description:", placeholder="Please enter the input CPT description with specific available details for best results.")
# Button to trigger mapping
if st.button("Map"):
if not user_input.strip(): # Check if input is empty or contains only whitespace
st.error("Input box cannot be empty.")
elif validate_input(user_input):
st.warning("Please input correct description containing only letters and numbers, or letters only.")
else:
st.write("Please wait for a moment .... ")
# Call backend function to get mapping results
try:
mapping_results = mapping_code(user_input) # user_slider_input_number
# 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: {float(result['Similarity Score']):.4f}")
except ValueError as e:
st.error(str(e))
if __name__ == "__main__":
main()
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