Create app.py

app.py

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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()