Update backend

main.py

@@ -215,6 +215,8 @@ def load_dataset():
     
     if not dataset_path:
         print("Warning: Dataset file not found. Using hardcoded examples.")
+        print(f"Checked paths: {possible_paths}")
+        dataset_by_label = None
         return None
     
     try:
@@ -222,7 +224,7 @@ def load_dataset():
         with open(dataset_path, 'r') as f:
             data = json.load(f)
         
-        # Group examples by ground_truth label
+        # Group examples by ground_truth label - MUST declare global here!
         dataset_by_label = defaultdict(list)
         for item in data:
             label = item.get('ground_truth', 'Unknown')
@@ -230,9 +232,13 @@ def load_dataset():
                 dataset_by_label[label].append(item)
         
         print(f"Loaded {len(data)} examples. Examples per label: {dict((k, len(v)) for k, v in dataset_by_label.items())}")
+        print(f"Global dataset_by_label is now set: {dataset_by_label is not None}")
         return dataset_by_label
     except Exception as e:
         print(f"Error loading dataset: {e}")
+        import traceback
+        traceback.print_exc()
+        dataset_by_label = None
         return None
 
 
@@ -307,12 +313,16 @@ async def get_examples():
     
     # Load dataset if not already loaded
     if dataset_by_label is None:
-        load_dataset()
+        print("Dataset not loaded, attempting to load...")
+        result = load_dataset()
+        if result is None:
+            print("Failed to load dataset, using fallback examples")
     
     examples = []
     
     # If dataset is loaded, get random examples from each label
-    if dataset_by_label:
+    if dataset_by_label and len(dataset_by_label) > 0:
+        print(f"Using dataset with {len(dataset_by_label)} label categories")
         # Get one random example from each label
         for label in LABEL_MAP.values():
             if label in dataset_by_label and len(dataset_by_label[label]) > 0:
@@ -323,22 +333,28 @@ async def get_examples():
                 try:
                     api_format = convert_dataset_example_to_api_format(random_example)
                     
-                    # Create example entry
+                    # Create example entry with expected_output (ground_truth)
                     example_entry = {
                         "name": f"{label} Example",
                         "description": f"Example of {label.replace('_', ' ').title()}",
-                        "data": api_format
+                        "data": api_format,
+                        "expected_output": label  # Add ground truth label
                     }
                     examples.append(example_entry)
                 except Exception as e:
                     print(f"Error converting example for label {label}: {e}")
+                    import traceback
+                    traceback.print_exc()
                     continue
+        print(f"Generated {len(examples)} random examples from dataset")
     else:
         # Fallback to hardcoded examples if dataset not available
+        print("Using hardcoded fallback examples")
         examples = [
             {
                 "name": "Correct Example",
                 "description": "A properly formed tool call",
+                "expected_output": "Correct",
                 "data": {
                     "query": "What's the weather in New York?",
                     "enabled_tools": [
@@ -368,6 +384,7 @@ async def get_examples():
     
     # Shuffle examples to randomize order
     random.shuffle(examples)
+    print(f"Returning {len(examples)} examples (shuffled)")
     
     return {"examples": examples}