Delete app.py

app.py

@@ -1,70 +0,0 @@
-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()