added timestamps to infer.py and tested it

src/infer.py

@@ -1,4 +1,5 @@
 # infer.py
+from datetime import datetime
 import os
 import torch
 import time
@@ -19,6 +20,8 @@ from torch.utils.data import Dataset
 from dataset import InferenceDataset
 from model import Model, SingleTaskModel
 
+
+
 label_names = {
     'bug_report': ['No', 'Yes'],
     'feature_request': ['No', 'Yes'],
@@ -31,6 +34,8 @@ torch.manual_seed(SEED)
 np.random.seed(SEED)
 
 
+
+
 def parse_args():
     parser = argparse.ArgumentParser(description="RECLASS, Multitask learning for review classification.")
     parser.add_argument("--model_path", type=str, required=True, help=".pt file in outputs/")
@@ -113,6 +118,8 @@ def main():
     all_preds =       {task: [] for task in active_tasks}
     all_confidences = {task: [] for task in active_tasks}
     print(f"Running inference on {args.dataset} dataset")
+    start_time = time.time()
+    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
 
     with torch.no_grad():
         for batch in infer_loader:
@@ -129,6 +136,7 @@ def main():
                 all_preds[task].extend(preds.cpu().numpy())
                 all_confidences[task].extend(confidence.cpu().numpy())
 
+    end_time = time.time()
     df = pd.DataFrame({"text": infer_df[args.text_column]})
 
     for task in active_tasks:  # ensures ALL tasks included
@@ -137,10 +145,12 @@ def main():
 
     output_path = filename
     df.to_csv(output_path, index=False)
+    
     if not args.text:
         print(f"Inference finished. Predictions saved to {output_path}")
+        print(f"Time taken: {end_time - start_time:.2f} seconds")
     else:
-        print(f"Inference finished.\n")
+        print(f"Inference completed in {end_time - start_time:.2f} seconds.\n")
         print(df.to_string(index=False))
         again = input("Do you want to enter another text for inference? (y/n): ")
         if again.lower() == 'y':
@@ -148,6 +158,7 @@ def main():
         else:
             print("Exiting interactive inference.")
 
+    
 
 if __name__ == "__main__":
     main()