final touches to dummy functionalities

app.py

@@ -51,7 +51,7 @@ def about_page():
 def predict_dti():
     st.markdown('## Predict drug-target interaction')
 
-    st.warning('In the future this page will display the predicted interaction betweek the given drug compounds and protein target by the HyperPCM mdoel.')
+    st.text('In the future this page can be used to predict interactions betweek a query drug compound and a query protein target by the HyperPCM mdoel.')
     
     col1, col2 = st.columns(2)
     
@@ -162,23 +162,37 @@ def predict_dti():
     if drug_embedding is None or prot_embedding is None:
         st.warning('Waiting for both drug and target embeddings to be computed...')
     else:
-        st.warning('In the future inference will be run with HyperPCM on the given drug compound and protein target...')
+        st.markdown('### Inference')
+    
+        import time
+        progress_text = "HyperPCM predicts the interaction between the query drug compound toward the query protein target. Please wait."
+        my_bar = st.progress(0, text=progress_text)
+        for i in range(100):
+            time.sleep(0.1)
+            my_bar.progress(i + 1, text=progress_text)
+        my_bar.progress(100, text="HyperPCM predicts the interaction between the query drug compound toward the query protein target. Done.")
+            
+        st.markdown('### Interaction')
+        st.text('HyperPCM predicts an activity of xxx pChEMBL.')
     
 
 def retrieval():
-    st.markdown('## Retrieve top-k')
+    st.markdown('## Retrieve top-k most active drug compounds')
 
     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 query amino-acid sequence', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
     
-    if sequence:
-        col1, col2 = st.columns(2)
-        with col1:
+    col1, col2, col3, col4 = st.columns(4)
+    with col2:
+        sequence = st.text_input('Enter query amino-acid sequence', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
+        if sequence:
             st.error('Visualization coming soon...')
-        
-        with col2:
+    
+    with col3:
+        if sequence:
+            st.image('protein_encoder_done.png')
+            
             with st.spinner('Encoding in progress...'):
                 from bio_embeddings.embed import SeqVecEmbedder
                 encoder = SeqVecEmbedder()
@@ -186,7 +200,6 @@ def retrieval():
                 for emb in embeddings:
                     embedding = encoder.reduce_per_protein(emb)
                     break
-            st.image('protein_encoder_done.png')
             st.success('Encoding complete.')
 
     st.markdown('### Inference')
@@ -222,7 +235,7 @@ def retrieval():
                 st.image(mol_img) 
 
 def display_protein():
-    st.markdown('## Display protein')
+    st.markdown('## Display protein structure')
     st.write('In the future this page will display the ESM predicted sequence of a protein target.')
 
     st.markdown('### Target')