Upload app.py

app.py

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+import streamlit as st
+import torch
+from sentence_transformers import SentenceTransformer,util
+#from transformers import pipeline
+import pandas as pd 
+import numpy as np 
+import pickle
+
+
+# Load the pre-trained SentenceTransformer model
+#pipeline = pipeline(task="Sentence Similarity", model="all-MiniLM-L6-v2")
+model = SentenceTransformer('neuml/pubmedbert-base-embeddings')
+#sentence_embed = pd.read_csv('Reference_file.csv')
+with open("embeddings_1.pkl", "rb") as fIn:
+    stored_data = pickle.load(fIn)
+    stored_code = stored_data["SBS_code"]
+    stored_sentences = stored_data["Description"]
+    stored_embeddings = stored_data["embeddings"]
+
+import streamlit as st
+
+# 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)
+
+    # Combine similarity scores with 'code' and 'description'
+    result = list(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 similar sentences found", "Similarity Score": 0.0})
+
+    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: {float(result['Similarity Score']):.4f}")
+
+
+if __name__ == "__main__":
+    main()