pre-load results for example target

app.py

@@ -132,7 +132,26 @@ def retrieval():
                 st.image('figures/molecule_encoder.png')
                 st.warning('Choose encoder above...')
                 
-    if query_embedding is not None:
+    if sequence == ex_target:
+        st.markdown('### Inference')
+        
+        my_bar.progress(100, text="HyperPCM is predicting the QSAR model for the query protein target. Done.")
+        
+        st.markdown('### Retrieval')
+        
+        selected_k = st.slider(f'Top-k most active drug compounds {selected_database} predicted by HyperPCM are, for k = ', 5, 20, 5, 5)
+        
+        results = pd.read_csv('data/Lenselink/processed/ex_results.csv')
+        
+        cols = st.columns(5)
+        for j, col in enumerate(cols):
+            with col:
+                for i in range(int(selected_k/5)):
+                    mol = Chem.MolFromSmiles(results.loc[j + 5*i, 'SMILES'])
+                    mol_img = Chem.Draw.MolToImage(mol)
+                    st.image(mol_img, caption=f"{results.loc[j + 5*i, 'Prediction']:.2f}")
+                    
+    elif query_embedding is not None:
         st.markdown('### Inference')
         
         progress_text = "HyperPCM is predicting the QSAR model for the query protein target. Please wait."
@@ -172,8 +191,6 @@ def retrieval():
         results = results.sort_values(by='Prediction', ascending=False)
         results = results.reset_index()
         
-        print(results.head(10))
-        
         cols = st.columns(5)
         for j, col in enumerate(cols):
             with col: