change figure path

app.py

@@ -45,7 +45,7 @@ def about_page():
         """
     )
 
-    st.image('hyper-dti.png', caption='Overview of HyperPCM architecture.')
+    st.image('figures/hyper-dti.png', caption='Overview of HyperPCM architecture.')
 
 
 def predict_dti():
@@ -95,11 +95,11 @@ def predict_dti():
                     drug_embedding = [0,1,2,3,4,5]
                 else: 
                     drug_embedding = None
-                    st.image('molecule_encoder.png')
+                    st.image('figures/molecule_encoder.png')
                     st.warning('Choose encoder above...')
                 
                 if drug_embedding is not None:
-                    st.image('molecule_encoder_done.png')
+                    st.image('figures/molecule_encoder_done.png')
                     st.success('Encoding complete.')
 
     with col2:
@@ -152,11 +152,11 @@ def predict_dti():
                             break
                 else: 
                     prot_embedding = None
-                    st.image('protein_encoder.png')
+                    st.image('figures/protein_encoder.png')
                     st.warning('Choose encoder above...')
                     
                 if prot_embedding is not None:
-                    st.image('protein_encoder_done.png')
+                    st.image('figures/protein_encoder_done.png')
                     st.success('Encoding complete.')
 
     if drug_embedding is None or prot_embedding is None:
@@ -191,7 +191,7 @@ def retrieval():
     
     with col3:
         if sequence:
-            st.image('protein_encoder_done.png')
+            st.image('figures/protein_encoder_done.png')
             
             with st.spinner('Encoding in progress...'):
                 from bio_embeddings.embed import SeqVecEmbedder
@@ -268,8 +268,7 @@ def display_protein():
     
         token_list = token_representations.tolist()[0][0][0]
         
-        client = Client(
-            url=st.secrets["DB_URL"], user=st.secrets["USER"], password=st.secrets["PASSWD"])
+        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")