repeat dummy smiles in retrieval

app.py

@@ -193,17 +193,12 @@ def retrieval():
             )
     
     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'], ['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'], ['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'],
-                    ['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']]
+    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[i,j])
+                mol = Chem.MolFromSmiles(dummy_smiles[j])
                 mol_img = Chem.Draw.MolToImage(mol)
                 st.image(mol_img)