Update app.py

app.py

@@ -13,6 +13,10 @@ with open("embeddings_1.pkl", "rb") as fIn:
     stored_data = pickle.load(fIn)
     stored_embeddings = stored_data["embeddings"]
 
+with open("embeddings_2.pkl", "rb") as fIn:
+    stored_data_cpt = pickle.load(fIn)
+    stored_embeddings_cpt = stored_data_cpt["embeddings"]
+
 def validate_input(input_string):
     # Regular expression pattern to match letters and numbers, or letters only
     pattern = r'^[a-zA-Z0-9]+$|^[a-zA-Z]+$'
@@ -24,7 +28,17 @@ def validate_input(input_string):
         return False
 
 # Define the function for mapping code
-def mapping_code(user_input):
+def mapping_code(user_input, mode):
+    if mode == "CPT_to_SBS":
+        stored_embeddings = stored_embeddings_cpt
+        stored_data = stored_data_cpt
+        code_column = stored_data["CPT_CODE"]
+        description_column = stored_data["FULL_DESCRIPTION"]
+    elif mode == "SBS_to_CPT":
+        stored_embeddings = stored_embeddings_sbs
+        stored_data = stored_data_sbs
+        code_column = stored_data["SBS_code"]
+        description_column = stored_data["Description"]
 
     emb1 = model.encode(user_input.lower())
     similarities = []
@@ -33,7 +47,7 @@ def mapping_code(user_input):
         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)]
+    result = [(code, desc, sim) for (code, desc, sim) in zip(code_column, description_column, similarities)]
 
     # Sort results by similarity scores
     result.sort(key=lambda x: x[2], reverse=True)
@@ -55,29 +69,34 @@ def mapping_code(user_input):
 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 in correct spelling for best results.**</font>", unsafe_allow_html=True)
+    st.title("CPT-SBS Code Mapping")
 
-    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)
+    # Dropdown for user to choose mapping direction
+    mapping_mode = st.selectbox("Choose mapping direction:", ("CPT description to SBS code", "SBS description to CPT code"))
 
-    # 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')
+    if mapping_mode == "CPT description to SBS code":
+        user_input_label = "Enter CPT description:"
+        mode = "CPT_to_SBS"
+    else:
+        user_input_label = "Enter SBS description:"
+        mode = "SBS_to_CPT"
 
     # 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.")
+    user_input = st.text_input(user_input_label, placeholder="Enter description here...")
 
     # 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 .")
+            st.warning("Please input correct description.")
         else:
-            st.write("Please wait for a moment .... ")
+            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
+                mapping_results = mapping_code(user_input, mode)
                 # Display top 5 similar sentences
-                st.write("Top 5 similar sentences:")
+                st.write("Top 5 similar entries:")
                 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: