Update app.py

app.py

@@ -1,6 +1,6 @@
 import os
 import sys
-import torch
+#import torch
 import numpy as np 
 import pandas as pd
 import streamlit as st
@@ -186,7 +186,18 @@ def retrieval():
     st.write('In the furute this page will retrieve the top-k drug compounds that are predicted to have the highest activity toward the given protein target from either the Lenselink or Davis datasets.')
 
     st.markdown('### Target')
-    
+
+    st.write(f'The top-{selected_k} most active drug coupounds from {selected_dataset} predicted by HyperPCM are: ')
+        dummy_smiles = ['CC(=O)OC1=CC=CC=C1C(=O)O', 'COc1cc(C=O)ccc1O', 'CC(=O)Nc1ccc(O)cc1', 'CC(=O)Nc1ccc(OS(=O)(=O)O)cc1', 'CC(=O)Nc1ccc(O[C@@H]2O[C@H](C(=O)O)[C@@H](O)[C@H](O)[C@H]2O)cc1']
+        cols = st.columns(5)
+        for j, col in enumerate(cols):
+            with col:
+                for i in range(int(selected_k/5)):
+                    mol = Chem.MolFromSmiles(dummy_smiles[j])
+                    mol_img = Chem.Draw.MolToImage(mol)
+                    st.image(mol_img) 
+
+    '''
     col1, col2, col3, col4 = st.columns(4)
     with col2:
         sequence = st.text_input('Enter query amino-acid sequence', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
@@ -247,6 +258,7 @@ def retrieval():
                     mol = Chem.MolFromSmiles(dummy_smiles[j])
                     mol_img = Chem.Draw.MolToImage(mol)
                     st.image(mol_img) 
+    '''
 
 '''
 def display_protein():