updates

hla_class.py

@@ -4,6 +4,7 @@ import numpy as np
 import joblib
 
 asset_home = './assets/hla/'
+CONFIDENCE_THRESHOLD = 0.1
 
 def tokenize_sequence(sequence):
     # configs
@@ -47,9 +48,10 @@ def bow_embedding(sequence):
 
     return bow_matrix
 
-def predict_class(encoding, conf_thresh=0.1):
+def predict_class(encoding, conf_thresh=CONFIDENCE_THRESHOLD):
     model_path = asset_home + 'model/rfm_hla.pkl'
     prediction = []
+    link_base = 'https://www.genecards.org/cgi-bin/carddisp.pl?gene='
     
     model = joblib.load(model_path)
 
@@ -70,7 +72,7 @@ def predict_class(encoding, conf_thresh=0.1):
             if prob > conf_thresh:  # Check if the probability is above the threshold
                 # Get the class name from the model's classes_
                 class_name = model.classes_[class_index]
-                prediction = prediction + [f'HLA-{class_name}: {prob:.4f}']  # Print class name and probability
+                prediction = prediction + [f'[HLA-{class_name}]({link_base}HLA-{class_name}) with confidence {prob:.2f}']  # Print class name and probability
                 any_class_above_threshold = True
     
         if not any_class_above_threshold:

pages/HLA_Type_Prediction.py

@@ -1,8 +1,8 @@
 import streamlit as st
 import numpy
-from hla_class import bow_embedding, predict_class
+from hla_class import bow_embedding, predict_class, CONFIDENCE_THRESHOLD
 
-min_seq_length = 100
+min_seq_length = 5
 max_seq_length = 10000
 
 st.set_page_config(page_title='HLA Gene Prediction')
@@ -14,17 +14,17 @@ st.markdown(
 )
 
 
-input_sequence = st.text_area('Enter your sequence to analyze',
-                              max_chars=max_seq_length,
+input_sequence = st.text_area('Enter your sequence to analyze',max_chars=max_seq_length,
                              help=f'enter between {min_seq_length} and {max_seq_length} characters without line breaks',
                              placeholder='aactaaaagactgacaaaatttttagtctctcgAATCGGGG...')
+conf_filt = st.slider('Confidence Cutoff', min_value=0.00, max_value=1.00, value=CONFIDENCE_THRESHOLD)
 
 if st.button('Predict', disabled=len(input_sequence)< min_seq_length):
     enc_seq = bow_embedding(input_sequence)
-    prediction = predict_class(enc_seq)
-    combined_string = '\n'.join(prediction)
-    st.markdown('## HLA Model Prediction')
-    st.markdown(combined_string)
+    prediction = predict_class(enc_seq, conf_filt)
+    st.markdown('### HLA Model Prediction')
+    for pred in prediction:
+        st.markdown(pred)
 
 
 footer='''<style>