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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): | |
| 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='red'>**⚠️ Please ensure the accuracy of your input spellings.**</font>", unsafe_allow_html=True) | |
| st.markdown("<font color='blue'>**💡 Note:** Please note that the similarity scores provided are not indicative of accuracy. Top 5 code descriptions provided should be verified with CPT descriptions 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 a full description for better search 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() | |