new attempt at displaying protein

app.py

@@ -51,7 +51,7 @@ def about_page():
 def predict_dti():
     st.markdown('## Predict drug-target interaction')
 
-    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.')
+    st.write('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)
     
@@ -173,7 +173,7 @@ def predict_dti():
         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.')
+        st.write('HyperPCM predicts an activity of xxx pChEMBL.')
     
 
 def retrieval():
@@ -242,11 +242,11 @@ def display_protein():
     sequence = st.text_input('Enter the amino-acid sequence of the query protein target', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
     
     if sequence:
-        model = esm.pretrained.esmfold_v1()
-        model = model.eval().cuda()
+        #model = esm.pretrained.esmfold_v1()
+        #model = model.eval().cuda()
         
-        with torch.no_grad():
-            output = model.infer_pdb(sequence)
+        #with torch.no_grad():
+        #    output = model.infer_pdb(sequence)
         
         #with open("result.pdb", "w") as f:
         #    f.write(output)
@@ -255,14 +255,11 @@ def display_protein():
         #struct = bsio.load_structure("result.pdb", extra_fields=["b_factor"])
         #print(struct.b_factor.mean())
         
-        st.write(output)
-    
-    """
-    sequence = st.text_input("Enter the amino-acid sequence of the query protein target", value="HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA", placeholder="HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA")
-    
-    if sequence:
-        def esm_search(model, sequnce, batch_converter,top_k=5):
-        batch_labels, batch_strs, batch_tokens = batch_converter([("protein1", sequnce),])
+        #st.write(output)
+        
+        model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
+        batch_converter = alphabet.get_batch_converter()
+        batch_labels, batch_strs, batch_tokens = batch_converter([("protein1", sequence),])
         
         # Extract per-residue representations (on CPU)
         with torch.no_grad():
@@ -284,9 +281,6 @@ def display_protein():
         
         result_temp_seq = list(set(result_temp_seq))
     
-        result_temp_seq = esm_search(model, sequence, esm_search,top_k=5)
-        st.text('search result: ')
-        # tab1, tab2, tab3, tab4, = st.tabs(["Cat", "Dog", "Owl"])
         if st.button(result_temp_seq[0]):
             print(result_temp_seq[0])
         elif st.button(result_temp_seq[1]):
@@ -299,26 +293,25 @@ def display_protein():
             print(result_temp_seq[4])
     
         start[2] = st.pyplot(visualize_3D_Coordinates(result_temp_coords).figure)
-        def show_protein_structure(sequence):
-    headers = {
-        'Content-Type': 'application/x-www-form-urlencoded',
-        }
-    response = requests.post('https://api.esmatlas.com/foldSequence/v1/pdb/', headers=headers, data=sequence)
-    name = sequence[:3] + sequence[-3:]
-    pdb_string = response.content.decode('utf-8')
-    with open('predicted.pdb', 'w') as f:
-        f.write(pdb_string)
-    struct = bsio.load_structure('predicted.pdb', extra_fields=["b_factor"])
-    b_value = round(struct.b_factor.mean(), 4)
-    render_mol(pdb_string)
-        if residues_marker:
-            start[3] = showmol(render_pdb_resn(viewer = render_pdb(id = id_PDB),resn_lst = [residues_marker]))
-        else:
-            start[3] = showmol(render_pdb(id = id_PDB))
-        st.session_state['xq'] = st.session_state.model
+        
+        headers = {
+            'Content-Type': 'application/x-www-form-urlencoded',
+            }
+        response = requests.post('https://api.esmatlas.com/foldSequence/v1/pdb/', headers=headers, data=sequence)
+        name = sequence[:3] + sequence[-3:]
+        pdb_string = response.content.decode('utf-8')
+        with open('predicted.pdb', 'w') as f:
+            f.write(pdb_string)
+        struct = bsio.load_structure('predicted.pdb', extra_fields=["b_factor"])
+        b_value = round(struct.b_factor.mean(), 4)
+        render_mol(pdb_string)
+            if residues_marker:
+                start[3] = showmol(render_pdb_resn(viewer = render_pdb(id = id_PDB),resn_lst = [residues_marker]))
+            else:
+                start[3] = showmol(render_pdb(id = id_PDB))
+            st.session_state['xq'] = st.session_state.model
     
     # example proteins ["HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA"], ["AHKLFIGGLPNYLNDDQVKELLTSFGPLKAFNLVKDSATGLSKGYAFCEYVDINVTDQAIAGLNGMQLGDKKLLVQRASVGAKNA"]
-    """
 
 def display_context():
     st.markdown('## Display context')