add logs

inference_app.py

@@ -1,4 +1,5 @@
 import time
+import traceback
 
 import gradio as gr
 
@@ -10,16 +11,17 @@ from env_consts import RUN_CONFIG_PATH, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH, MODEL
 def predict(input_sequence, input_ligand, input_protein, model_variation):
     print("Strating inference!!!!!!!!!!!!!!!!!", input_sequence, input_ligand, input_protein)
     start_time = time.time()
-    # Do inference here
-    # return an output pdb file with the protein and ligand with resname LIG or UNK.
-    # also return any metrics you want to log, metrics will not be used for evaluation but might be useful for users
-    ckpt_path = MODEL_NAME_TO_CKPT[model_variation]
-    metrics = run_on_sample_seqs(input_sequence, input_protein, input_ligand, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH,
-                                 RUN_CONFIG_PATH, ckpt_path)
-    end_time = time.time()
-    run_time = end_time - start_time
-
-    return [OUTPUT_PROT_PATH, OUTPUT_LIG_PATH], metrics, run_time
+    try:
+        ckpt_path = MODEL_NAME_TO_CKPT[model_variation]
+        metrics = run_on_sample_seqs(input_sequence, input_protein, input_ligand, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH,
+                                     RUN_CONFIG_PATH, ckpt_path)
+        end_time = time.time()
+        run_time = end_time - start_time
+        return [OUTPUT_PROT_PATH, OUTPUT_LIG_PATH], metrics, run_time
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        traceback.print_exc()  # Print the full traceback
+        return None, {"error": str(e)}, "Error occurred"  # return error message to the output.
 
 
 with gr.Blocks() as app: