update comments for planned future functionalities

app.py

@@ -42,12 +42,16 @@ def about_page():
         a given protein target. We demonstrate state-of-the-art performance over previous methods on multiple 
         well-known benchmarks, particularly in zero-shot settings for unseen protein targets.
         """
-        #st.image('hyper-dti.png') todo 
     )
 
+    st.image('hyper-dti.png')
 
-def display_dti():
-    st.markdown('##')
+
+def predict_dti():
+    st.markdown('## Predict drug-target interaction')
+
+    st.write('In the future this page will display the predicted interaction betweek the given drug compounds and protein target by the HyperPCM mdoel.')
+    
     col1, col2 = st.columns(2)
     
     with col1:
@@ -105,12 +109,6 @@ def display_dti():
                 for emb in embeddings:
                     embedding = encoder.reduce_per_protein(emb)
                     break
-                #from huggingface_hub import hf_hub_download
-                #precomputed_embs = f'{selected_encoder}_encoding.csv'
-                #REPO_ID = "emmas96/Lenselink"
-                #embs_path = hf_hub_download(REPO_ID, precomputed_embs)
-                #embs = pd.read_csv(embs_path)
-                #embedding = embs[sequence]
             elif selected_encoder == 'UniRep':
                 from jax_unirep.utils import load_params
                 params = load_params() 
@@ -138,8 +136,13 @@ def display_dti():
                 st.write(f'{selected_encoder} embedding')
                 st.write(embedding)
 
+    st.write('TODO run inference with HyperPCM on the given drug compound and protein target.')
+    
+
 def retrieval():
-    st.markdown('##')
+    st.markdown('## Retrieve top-k')
+
+    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')
     sequence = st.text_input('Enter the amino-acid sequence of the query protein target', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
@@ -148,10 +151,28 @@ def retrieval():
         st.markdown('\n\n\n\n Plot of protein to be added soon. \n\n\n\n')
         
         selected_encoder = st.selectbox(
-            'Select encoder for protein target',('None', 'SeqVec', 'UniRep', 'ESM-1b', 'ProtT5')
+            'Select encoder for protein target',('SeqVec')
         )
+        from bio_embeddings.embed import SeqVecEmbedder
+        encoder = SeqVecEmbedder()
+        embeddings = encoder.embed_batch([sequence])
+        for emb in embeddings:
+            embedding = encoder.reduce_per_protein(emb)
+            break
+
+    st.write('TODO HyperPCM predicts the QSAR model for the given protein target.')
+
+    col1, col2 = st.columns(2)
+    with col1:
+        selected_dataset = st.selectbox(
+                'Select dataset from which the drug compounds should be retrieved',('Lenselink', 'Davis')
+            )
+    with col2:
+        selected_k = st.selectbox(
+                'Select the top-k number of drug compounds to retrieve',(5, 10, 15, 20)
+            )
     
-    st.markdown('### Retrieval of top-5 drug coupound from ChEMBL:')
+    st.write(f'The top-{selected_k} most active drug coupounds from {selected_dataset} predicted by HyperPCM are: ')
     col1, col2, col3, col4, col5 = st.columns(5)
     with col1:
         smiles = 'CC(=O)OC1=CC=CC=C1C(=O)O'
@@ -185,7 +206,8 @@ def retrieval():
         
 
 def display_protein():
-    st.markdown('##')
+    st.markdown('## Display protein')
+    st.write('In the future this page will display the ESM predicted sequence of a protein target.')
 
     st.markdown('### Target')
     sequence = st.text_input('Enter the amino-acid sequence of the query protein target', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
@@ -269,11 +291,26 @@ def display_protein():
     # example proteins ["HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA"], ["AHKLFIGGLPNYLNDDQVKELLTSFGPLKAFNLVKDSATGLSKGYAFCEYVDINVTDQAIAGLNGMQLGDKKLLVQRASVGAKNA"]
     """
 
+def display_context():
+    st.markdown('## Display context')
+    st.write('In the future this page will visualize the context module for a given protein, i.e., show important features and highly ranked / related proteins from the context.')
+
+def references():
+    st.markdown(
+        '''
+        ## References
+
+        This page will contain all references to related work.
+        '''
+    )
+
 page_names_to_func = {
     'About': about_page,
-    'Display DTI': display_dti,
+    'Predict DTI': predict_dti,
     'Retrieve Top-k': retrieval,
-    'Diplay Protein': display_protein
+    'Display Protein': display_protein,
+    'Display Context': display_context,
+    'References': references
 }
 
 selected_page = st.sidebar.selectbox('Choose function', page_names_to_func.keys())