omit protein display temporarily

app.py

@@ -43,6 +43,7 @@ def about_page():
         #st.image('hyper-dti.png') todo 
     )
 
+
 def display_dti():
     st.markdown('##')
     smiles = st.text_input("Enter the SMILES of the query drug compound", value="CC(=O)OC1=CC=CC=C1C(=O)O", placeholder="CC(=O)OC1=CC=CC=C1C(=O)O")
@@ -59,12 +60,70 @@ def display_dti():
             st.write("")
         st.markdown('##')
 
+
+def display_protein():
+    """
     sequence = st.text_input("Enter the amino-acid sequence of the query protein target", value="HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA", placeholder="HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA")
     
     if sequence:
-        st.markdown(f'TODO plot {sequence}')
+        def esm_search(model, sequnce, batch_converter,top_k=5):
+        batch_labels, batch_strs, batch_tokens = batch_converter([("protein1", sequnce),])
+        
+        # Extract per-residue representations (on CPU)
+        with torch.no_grad():
+            results = model(batch_tokens, repr_layers=[12], return_contacts=True)
+        token_representations = results["representations"][12]
     
-# example proteins ["HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA"], ["AHKLFIGGLPNYLNDDQVKELLTSFGPLKAFNLVKDSATGLSKGYAFCEYVDINVTDQAIAGLNGMQLGDKKLLVQRASVGAKNA"]
+        token_list = token_representations.tolist()[0][0][0]
+        
+        client = Client(
+            url=st.secrets["DB_URL"], user=st.secrets["USER"], password=st.secrets["PASSWD"])
+        
+        result = client.fetch("SELECT seq, distance('topK=500')(representations, " + str(token_list) + ')'+ "as dist FROM default.esm_protein_indexer_768")
+        
+        result_temp_seq = []
+        
+        for i in result:
+            # result_temp_coords = i['seq']
+            result_temp_seq.append(i['seq'])
+        
+        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]):
+            print(result_temp_seq[1])
+        elif st.button(result_temp_seq[2]):
+            print(result_temp_seq[2])
+        elif st.button(result_temp_seq[3]):
+            print(result_temp_seq[3])
+        elif st.button(result_temp_seq[4]):
+            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
+    
+    # example proteins ["HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA"], ["AHKLFIGGLPNYLNDDQVKELLTSFGPLKAFNLVKDSATGLSKGYAFCEYVDINVTDQAIAGLNGMQLGDKKLLVQRASVGAKNA"]
+    """
 
 page_names_to_func = {
     'About': about_page,