Add missing datetime import:

app.py

@@ -6,6 +6,7 @@ import pandas as pd
 import os
 import logging
 import json
+from datetime import datetime
 
 from src.core.evaluation import EvaluationManager, EvaluationRequest
 from src.core.queue_manager import QueueManager
@@ -31,13 +32,13 @@ from src.display.utils import (
     Precision
 )
 from src.envs import (
-    API, 
+    API,
     CACHE_PATH,
-    EVAL_REQUESTS_PATH, 
-    EVAL_RESULTS_PATH, 
-    QUEUE_REPO, 
-    REPO_ID, 
-    RESULTS_REPO, 
+    EVAL_REQUESTS_PATH,
+    EVAL_RESULTS_PATH,
+    QUEUE_REPO,
+    REPO_ID,
+    RESULTS_REPO,
     TOKEN
 )
 from src.populate import get_evaluation_queue_df, get_leaderboard_df
@@ -120,7 +121,7 @@ def process_evaluation_queue():
         if not request:
             logger.info("No more requests in the queue")
             break
-            
+
         try:
             logger.info(f"Processing request for model: {request.model_id}")
             # Convert queue request to evaluation request
@@ -131,20 +132,20 @@ def process_evaluation_queue():
                 weight_type="Safetensors",
                 submitted_time=request.timestamp
             )
-            
+
             # Run evaluation
             results = evaluation_manager.run_evaluation(eval_request)
             logger.info(f"Evaluation complete for {request.model_id}")
-            
+
             # Save results to stacklok/results
             save_results_to_repo(results, RESULTS_REPO)
-            
+
             # Mark request as complete
             queue_manager.mark_complete(request.request_id)
-            
+
             # Update leaderboard
             update_leaderboard()
-            
+
         except Exception as e:
             logger.error(f"Evaluation failed for {request.model_id}: {str(e)}", exc_info=True)
             # Move failed request back to pending queue
@@ -159,10 +160,10 @@ def save_results_to_repo(results, repo):
         api = HfApi()
         model_id = results['model_id'].replace('/', '_')
         filename = f"{model_id}_results.json"
-        
+
         # Convert results to JSON string
         json_results = json.dumps(results, indent=2)
-        
+
         # Save results to the repository
         api.upload_file(
             path_or_fileobj=json_results.encode(),
@@ -171,7 +172,7 @@ def save_results_to_repo(results, repo):
             repo_type="dataset",
             token=TOKEN
         )
-        
+
         logger.info(f"Saved results for {results['model_id']} to {repo}/{filename}")
     except Exception as e:
         logger.error(f"Failed to save results to {repo}: {str(e)}", exc_info=True)
@@ -211,7 +212,7 @@ def init_leaderboard(df):
 
     # Select only the columns we want to display
     df = df[COLS]
-    
+
     logger.info(f"Final leaderboard columns: {df.columns.tolist()}")
     logger.debug(f"Leaderboard data:\n{df}")
 
@@ -339,7 +340,7 @@ with demo:
                 """Handle new model submission."""
                 try:
                     logger.info(f"New submission received for {model}")
-                    
+
                     # Prepare request data
                     request_data = {
                         "model_id": model,
@@ -351,7 +352,7 @@ with demo:
                         "status": "PENDING",
                         "timestamp": datetime.now().isoformat()
                     }
-                    
+
                     # Add to queue in QUEUE_REPO
                     api = HfApi()
                     filename = f"{model.replace('/', '_')}_request.json"
@@ -362,15 +363,15 @@ with demo:
                         repo_type="dataset",
                         token=TOKEN
                     )
-                    
+
                     logger.info(f"Added request for {model} to {QUEUE_REPO}")
-                    
+
                     # Get updated pending evaluations
                     _, _, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
-                    
+
                     # Start processing queue in background
                     scheduler.add_job(process_evaluation_queue, id='process_queue_job', replace_existing=True)
-                    
+
                     return gr.Markdown("Submission successful! Your model has been added to the evaluation queue. Please check the 'Pending Evaluation Queue' for status updates."), pending_eval_queue_df
                 except Exception as e:
                     logger.error(f"Submission failed: {str(e)}", exc_info=True)