Add simple logging

app.py

@@ -1,7 +1,9 @@
+import sys
 import copy
 import random
 import tempfile
 import shutil
+import logging
 from pathlib import Path
 from functools import partial
 
@@ -26,6 +28,12 @@ from ppiformer.utils.torch import fill_diagonal
 from ppiformer.definitions import PPIFORMER_WEIGHTS_DIR
 
 
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[logging.StreamHandler(sys.stdout)]
+)
+
 random.seed(0)
 
 
@@ -248,6 +256,8 @@ def plot_3dmol(pdb_path, ppi_path, mut, attn, attn_mut_id=0):
 
 
 def predict(models, temp_dir, *inputs):
+    logging.info('Starting prediction')
+
     # Process input
     pdb_path, ppi_path, muts, muts_on_interface = process_inputs(inputs, temp_dir)
 
@@ -269,9 +279,12 @@ def predict(models, temp_dir, *inputs):
             muts_not_on_interface += f'... (and {n_muts_not_on_interface - n_muts_warn} more)'
         gr.Warning((
             f"{muts_not_on_interface} {'is' if n_muts_not_on_interface == 1 else 'are'} not on the interface. "
-            "The model will predict the effects of mutations on the whole complex. "
-            "This may lead to less accurate predictions."
+            f"The model will predict the effect{'s' if n_muts_not_on_interface > 1 else ''} of "
+            f"mutation{'s' if n_muts_not_on_interface > 1 else ''} on the whole complex. "
+            f"This may lead to less accurate predictions."
         ))
+
+    logging.info('Inputs processed')
     
     # Predict using interface for mutations on the interface and using the whole complex otherwise
     attn_ppi, attn_pdb = None, None
@@ -300,6 +313,7 @@ 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)
@@ -343,6 +357,8 @@ def predict(models, temp_dir, *inputs):
             col_count=(2, 'fixed'),
         )
 
+    logging.info('Prediction results prepared')
+
     return df, path, dropdown, dropdown_choices_to_plot_args