Improve layout

app.py

@@ -247,6 +247,7 @@ def plot_3dmol(pdb_path, ppi_path, mut, attn, attn_mut_id=0):
         </script>
         </body></html>"""
     )
+    print(html)
 
     return f"""<iframe style="width: 100%; height: 600px" name="result" allow="midi; geolocation; microphone; camera; 
     display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
@@ -302,6 +303,8 @@ def predict(models, temp_dir, *inputs):
             raise gr.Error("Prediction failed. Please double check your inputs.")
         ddg = ddg.detach().numpy().tolist()
 
+        logging.info(f'Predictions made for {path}')
+
         # Update dataframe and attention tensor
         idx = df_sub.index
         df.loc[idx, 'ddG [kcal/mol]'] = ddg
@@ -313,8 +316,6 @@ def predict(models, temp_dir, *inputs):
             attn_pdb = attn
     df['Attn Id'] = df['Attn Id'].astype(int)
 
-    logging.info('Predictions made')
-
     # Round ddG values
     df['ddG [kcal/mol]'] = df['ddG [kcal/mol]'].round(3)
 
@@ -385,15 +386,14 @@ with app:
                 
         After making a prediction with the model, you will see binding free energy changes (ddG values) for each mutation and a 3D visualization of the PPI with mutated residues highlighted in red. The visualization additionally shows
         the attention coefficients of the model for the nearest neighboring residues, which quantifies the contribution of the residues to the predicted ddG value. The brighted and thicker a reisudes is, the more attention the model paid to it.
-        Currently, the web only visualizes the first mutation in the list.
     """)
 
-    with gr.Row():
+    with gr.Row(equal_height=True):
         with gr.Column():
             gr.Markdown("## PPI structure")
-            with gr.Row():
-                pdb_code = gr.Textbox(placeholder="1BUI", label="PDB code", info="Protein Data Bank code (https://www.rcsb.org/)")
-                partners = gr.Textbox(placeholder="A,B,C", label="Partners", info="Protein chains in the PDB file forming the PPI interface")
+            with gr.Row(equal_height=True):
+                pdb_code = gr.Textbox(placeholder="1BUI", label="PDB code", info="Protein Data Bank identifier for the structure (https://www.rcsb.org/)")
+                partners = gr.Textbox(placeholder="A,B,C", label="Partners", info="Protein chain identifiers in the PDB file forming the PPI interface (two or more)")
             pdb_path = gr.File(file_count="single", label="Or PDB file instead of PDB code")
 
         with gr.Column():
@@ -408,7 +408,7 @@ with app:
             ["1KNE", "A,P", ';'.join([f"TP6{a}" for a in AMINO_ACID_CODES_1])]
         ],
         inputs=[pdb_code, partners, muts],
-        label="Examples (click on a line to pre-fill inputs)"
+        label="Examples (click on a line to pre-fill the inputs)"
     )
 
     # Predict GUI