change layout of about page

app.py

@@ -78,63 +78,48 @@ def retrieval():
     
     with col2:
         selected_encoder = st.selectbox(
-                'Select target encoder',('SeqVec', 'None')
+                'Select target encoder',('SeqVec')
             )
         if sequence:
-            if selected_encoder == 'SeqVec':
-                st.image('figures/protein_encoder_done.png')
-                with st.spinner('Encoding in progress...'):
-
-                    with open(os.path.join(data_path, f'Lenselink/processed/SeqVec_encoding_test.pickle'), 'rb') as handle:
-                        test_set = pickle.load(handle)
-
-                    if sequence in list(test_set.keys()):
-                        query_embedding = test_set[sequence]
-                    else: 
-                        from bio_embeddings.embed import SeqVecEmbedder
-                        encoder = SeqVecEmbedder()
-                        embeddings = encoder.embed_batch([sequence])
-                        for emb in embeddings:
-                            query_embedding = encoder.reduce_per_protein(emb)
-                            break
-                    
-                st.success('Encoding complete.')
-            else: 
-                query_embedding = None
-                st.image('figures/protein_encoder.png')
-                st.warning('Choose encoder above...')
+            st.image('figures/protein_encoder_done.png')
+            with st.spinner('Encoding in progress...'):
+
+                with open(os.path.join(data_path, f'Lenselink/processed/SeqVec_encoding_test.pickle'), 'rb') as handle:
+                    test_set = pickle.load(handle)
+
+                if sequence in list(test_set.keys()):
+                    query_embedding = test_set[sequence]
+                else: 
+                    from bio_embeddings.embed import SeqVecEmbedder
+                    encoder = SeqVecEmbedder()
+                    embeddings = encoder.embed_batch([sequence])
+                    for emb in embeddings:
+                        query_embedding = encoder.reduce_per_protein(emb)
+                        break
+                
+            st.success('Encoding complete.')
     
     with col3:
         selected_database = st.selectbox(
-                'Select database',('Lenselink', 'None')
+                'Select database',('Lenselink', 'Davis', 'DUD-E')
             )
-        if selected_database == 'Lenselink':
-            c1, c2 = st.columns(2)
-            with c2:
-                st.image('figures/multi_molecules.png', use_column_width='always') #, width=125)
-            with st.spinner('Loading data...'):
-                batch_size = 2048
-                dataset = DrugRetrieval(os.path.join(data_path, selected_database), sequence, query_embedding)
-                dataloader = DataLoader(dataset, num_workers=2, batch_size=batch_size, shuffle=False, collate_fn=collate_target)
-            st.success('Data loaded.')
-        else: 
-            dataset = None
-            dataloader = None
-            st.warning('Choose database above...')
+        c1, c2 = st.columns(2)
+        with c2:
+            st.image('figures/multi_molecules.png', use_column_width='always') #, width=125)
+        with st.spinner('Loading data...'):
+            batch_size = 2048
+            dataset = DrugRetrieval(os.path.join(data_path, selected_database), sequence, query_embedding)
+            dataloader = DataLoader(dataset, num_workers=2, batch_size=batch_size, shuffle=False, collate_fn=collate_target)
+        st.success('Data loaded.')
     
     with col4:
         selected_encoder = st.selectbox(
-                'Select drug encoder',('CDDD', 'None')
+                'Select drug encoder',('CDDD')
             )
-        if selected_database:
-            if selected_encoder == 'CDDD':
-                st.image('figures/molecule_encoder_done.png')
-                st.success('Encoding complete.')
-            else: 
-                st.image('figures/molecule_encoder.png')
-                st.warning('Choose encoder above...')
+        st.image('figures/molecule_encoder_done.png')
+        st.success('Encoding complete.')
                 
-    if sequence == ex_target:
+    if sequence == ex_target and selected_database == 'Lenselink':
         st.markdown('### Inference')
 
         progress_text = "HyperPCM is predicting the QSAR model for the query protein target. Please wait."
@@ -144,7 +129,7 @@ def retrieval():
         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)
@@ -155,7 +140,7 @@ def retrieval():
                     mol_img = Chem.Draw.MolToImage(mol)
                     st.image(mol_img, caption=f"{results.loc[j + 5*i, 'Prediction']:.2f}")
 
-        st.download_button('Download retrieved drug compounds.', results.head(selected_k).to_csv(index=False).encode('utf-8'), file_name='retrieved_drugs.csv')
+        st.download_button(f'Download retrieved drug compounds from the {selected_database} database.', results.head(selected_k).to_csv(index=False).encode('utf-8'), file_name='retrieved_drugs.csv')
                     
     elif query_embedding is not None:
         st.markdown('### Inference')
@@ -205,14 +190,23 @@ def retrieval():
                     mol_img = Chem.Draw.MolToImage(mol)
                     st.image(mol_img, caption=f"{results.loc[j + 5*i, 'Prediction']:.2f}")
 
-        st.download_button('Download retrieved drug compounds.', results.head(selected_k).to_csv(index=False).encode('utf-8'), file_name='retrieved_drugs.csv')
-    
+        st.download_button(f'Download retrieved drug compounds from the {selected_database} database.', results.head(selected_k).to_csv(index=False).encode('utf-8'), file_name='retrieved_drugs.csv')
+
+
+
 page_names_to_func = {
     'Retrieval': retrieval,
     'About': about_page
 }
 
-selected_page = st.sidebar.selectbox('Choose function', page_names_to_func.keys())
-st.sidebar.markdown('')
-page_names_to_func[selected_page]()
+#selected_page = st.sidebar.selectbox('Choose function', page_names_to_func.keys())
+#st.sidebar.markdown('')
+#page_names_to_func[selected_page]()
+
+tab1, tab2 = st.tabs(page_names_to_func.keys())
+
+with tab1:
+    page_names_to_func['Retrieval']()
 
+with tab2: 
+    page_names_to_func['About']()