Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import torch
 from sentence_transformers import SentenceTransformer, util
-from spellchecker import SpellChecker
+#from spellchecker import SpellChecker
 import pickle
 
 # Load the pre-trained SentenceTransformer model
@@ -12,23 +12,12 @@ with open("embeddings_1.pkl", "rb") as fIn:
     stored_data = pickle.load(fIn)
     stored_embeddings = stored_data["embeddings"]
 
-spell = SpellChecker()
 
-# Define a function to check for misspelled words
-def check_misspelled_words(user_input):
-    # Tokenize the input into words
-    words = user_input.split()
-
-    # Get a list of misspelled words excluding words containing only numbers
-    misspelled = [word for word in words if word.isalpha() and not word.isdigit() and not spell.correction(word.lower()) == word.lower()]
-
-    return misspelled
 
 # Define the function for mapping code
 # Define the function for mapping code
 def mapping_code(user_input,user_slider_input_number):
-    if len(user_input.split()) < 1:  # Check if sentence has less than 5 words
-        raise ValueError("Input sentence should be at least 5 words long.")
+
     emb1 = model.encode(user_input.lower())
     similarities = []
     for sentence in stored_embeddings:
@@ -57,8 +46,10 @@ def mapping_code(user_input,user_slider_input_number):
 # Streamlit frontend interface
 def main():
     st.title("CPT Description Mapping")
+    st.markdown("<font color='red'>**⚠️ Ensure that you input the accurate spellings.**</font>", unsafe_allow_html=True)
 
-    st.markdown("**Note:** Similarity scores are not absolute and should be further confirmed manually for accuracy.")
+    st.markdown("<font color='blue'>**💡 Note:** Similarity scores are not absolute and should be further confirmed manually for accuracy.</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')
 
@@ -72,16 +63,11 @@ def main():
             st.write("Please wait for a moment .... ")
             # Call backend function to get mapping results
             try:
-                misspelled_words = check_misspelled_words(user_input)
-                if misspelled_words:
-                    st.write("Please enter a detailed correct full description")
-                    st.write(f"Kindly check if these words are spelt correctly :{misspelled_words}")
-                else:
-                    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}")
+                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))