Update evaluation_queue.py

evaluation_queue.py

@@ -1,802 +1,8 @@
 """
-Model evaluation queue system for Dynamic Highscores.
-
-This module handles the evaluation queue, CPU-only processing,
-and enforces daily submission limits for users.
+Updated create_model_submission_ui function that properly displays benchmark names in dropdown.
+Replace this function in your evaluation_queue.py file.
 """
 
-import os
-import json
-import time
-import threading
-import queue as queue_module
-from datetime import datetime, timedelta
-import gradio as gr
-from huggingface_hub import HfApi, hf_hub_download, snapshot_download, model_info
-from datasets import load_dataset
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import sqlite3
-
-class EvaluationQueue:
-    """Manages the evaluation queue for model benchmarking."""
-    
-    def __init__(self, db_manager, auth_manager):
-        """Initialize the evaluation queue manager.
-        
-        Args:
-            db_manager: Database manager instance
-            auth_manager: Authentication manager instance
-        """
-        self.db_manager = db_manager
-        self.auth_manager = auth_manager
-        self.hf_api = HfApi()
-        self.queue = queue_module.Queue()
-        self.is_processing = False
-        self.worker_thread = None
-        self.model_tags = ["Merge", "Agent", "Reasoning", "Coding", "General", "Specialized", "Instruction", "Chat"]
-        self.current_evaluation = None
-        self.progress = 0
-        self.progress_lock = threading.Lock()
-        # Memory limit for models in GB (leave 2GB for system)
-        self.memory_limit_gb = 14.0
-    
-    def start_worker(self):
-        """Start the worker thread for processing the evaluation queue."""
-        if self.worker_thread is None or not self.worker_thread.is_alive():
-            self.is_processing = True
-            self.worker_thread = threading.Thread(target=self._process_queue)
-            self.worker_thread.daemon = True
-            self.worker_thread.start()
-    
-    def stop_worker(self):
-        """Stop the worker thread."""
-        self.is_processing = False
-        if self.worker_thread and self.worker_thread.is_alive():
-            self.worker_thread.join(timeout=1.0)
-    
-    def check_model_size(self, model_id):
-        """Check if a model will fit within RAM limitations.
-        
-        Args:
-            model_id: HuggingFace model ID
-            
-        Returns:
-            tuple: (will_fit, message)
-        """
-        try:
-            # Query model info from the HuggingFace API
-            model_info_obj = self.hf_api.model_info(model_id)
-            
-            # Check if model size information is available
-            if hasattr(model_info_obj, 'safetensors') and model_info_obj.safetensors:
-                # Calculate size in GB (divided by 1024^3)
-                total_size_gb = sum(
-                    file.size for file in model_info_obj.safetensors.values()
-                ) / (1024 * 1024 * 1024)
-            elif hasattr(model_info_obj, 'siblings'):
-                # Legacy method - calculate from file siblings
-                total_size_gb = sum(
-                    sibling.size for sibling in model_info_obj.siblings 
-                    if sibling.rfilename.endswith(('.bin', '.safetensors', '.pt'))
-                ) / (1024 * 1024 * 1024)
-            else:
-                # Can't determine size
-                return False, "Unable to determine model size. Please ensure model is under 14GB."
-            
-            # Account for memory overhead (tokenizer, processing, etc.)
-            estimated_ram_needed = total_size_gb * 1.3  # 30% overhead
-            
-            # Check against limit
-            if estimated_ram_needed > self.memory_limit_gb:
-                return False, f"Model is too large (approximately {total_size_gb:.1f}GB, needs {estimated_ram_needed:.1f}GB RAM). Maximum allowed is {self.memory_limit_gb}GB."
-            
-            return True, f"Model size check passed ({total_size_gb:.1f}GB, estimated {estimated_ram_needed:.1f}GB RAM usage)"
-            
-        except Exception as e:
-            print(f"Model size check error: {e}")
-            # If we can't check, be cautious
-            return False, f"Error checking model size: {str(e)}. Please ensure your model is under {self.memory_limit_gb}GB."
-    
-    def _process_queue(self):
-        """Process the evaluation queue in a separate thread."""
-        while self.is_processing:
-            try:
-                # Get the next evaluation from the database
-                pending_evals = self.db_manager.get_evaluation_results(status="pending")
-                
-                if pending_evals:
-                    # Sort by priority and added_at
-                    next_eval = pending_evals[0]
-                    
-                    # Update status to running
-                    self.db_manager.update_evaluation_status(next_eval['id'], 'running')
-                    
-                    # Set current evaluation and reset progress
-                    with self.progress_lock:
-                        self.current_evaluation = next_eval
-                        self.progress = 0
-                    
-                    try:
-                        # Get model and benchmark details
-                        model_info = self.db_manager.get_model(next_eval['model_id'])
-                        benchmark_info = self.db_manager.get_benchmark(next_eval['benchmark_id'])
-                        
-                        if model_info and benchmark_info:
-                            # Check if model will fit in memory
-                            will_fit, message = self.check_model_size(model_info['hf_model_id'])
-                            
-                            if not will_fit:
-                                raise Exception(f"Model too large for evaluation: {message}")
-                                
-                            # Run the evaluation
-                            results = self._run_evaluation(
-                                model_info['hf_model_id'],
-                                benchmark_info['dataset_id']
-                            )
-                            
-                            # Calculate overall score
-                            score = self._calculate_overall_score(results)
-                            
-                            # Update status to completed with results
-                            self.db_manager.update_evaluation_status(
-                                next_eval['id'],
-                                'completed',
-                                results=results,
-                                score=score
-                            )
-                        else:
-                            raise Exception("Model or benchmark not found")
-                    except Exception as e:
-                        print(f"Evaluation error: {e}")
-                        # Update status to failed with error message
-                        error_results = {"error": str(e)}
-                        self.db_manager.update_evaluation_status(
-                            next_eval['id'], 
-                            'failed',
-                            results=error_results
-                        )
-                    
-                    # Clear current evaluation
-                    with self.progress_lock:
-                        self.current_evaluation = None
-                        self.progress = 0
-                else:
-                    # No evaluations in queue, sleep for a bit
-                    time.sleep(5)
-            except Exception as e:
-                print(f"Queue processing error: {e}")
-                time.sleep(5)
-    
-    def _run_evaluation(self, model_id, dataset_id):
-        """Run an evaluation for a model on a benchmark.
-        
-        Args:
-            model_id: HuggingFace model ID
-            dataset_id: HuggingFace dataset ID (with optional config)
-            
-        Returns:
-            dict: Evaluation results
-        """
-        # Update progress
-        with self.progress_lock:
-            self.progress = 5  # Starting evaluation
-        
-        # Parse dataset ID and config
-        if ":" in dataset_id:
-            dataset_id, config = dataset_id.split(":", 1)
-        else:
-            config = None
-        
-        # Update progress
-        with self.progress_lock:
-            self.progress = 10  # Loading dataset
-        
-        # Load the dataset
-        try:
-            if config:
-                dataset = load_dataset(dataset_id, config, split="test")
-            else:
-                dataset = load_dataset(dataset_id, split="test")
-        except Exception as e:
-            return {"error": f"Failed to load dataset: {str(e)}"}
-        
-        # Update progress
-        with self.progress_lock:
-            self.progress = 20  # Loading model
-        
-        try:
-            # Load the model with memory optimization settings
-            device = "cpu"
-            model = AutoModelForCausalLM.from_pretrained(
-                model_id,
-                device_map=device,
-                torch_dtype=torch.float32,  # Use float32 for CPU
-                low_cpu_mem_usage=True,     # Enable memory optimization
-                offload_folder="offload",   # Enable offloading if needed
-                offload_state_dict=True,    # Offload state dict for memory saving
-                max_memory={0: f"{self.memory_limit_gb}GB"}  # Limit memory usage
-            )
-            tokenizer = AutoTokenizer.from_pretrained(model_id)
-        except Exception as e:
-            print(f"Model loading error: {e}")
-            return {"error": f"Failed to load model: {str(e)}"}
-        
-        # Update progress
-        with self.progress_lock:
-            self.progress = 30  # Determining task type
-        
-        # Determine task type based on dataset features
-        task_type = self._determine_task_type(dataset)
-        
-        # Update progress
-        with self.progress_lock:
-            self.progress = 40  # Starting evaluation
-        
-        try:
-            # Run appropriate evaluation based on task type
-            if task_type == "text-generation":
-                results = self._evaluate_text_generation(model, tokenizer, dataset)
-            elif task_type == "question-answering":
-                results = self._evaluate_question_answering(model, tokenizer, dataset)
-            elif task_type == "classification":
-                results = self._evaluate_classification(model, tokenizer, dataset)
-            elif task_type == "code-generation":
-                results = self._evaluate_code_generation(model, tokenizer, dataset)
-            else:
-                # Default to general evaluation
-                results = self._evaluate_general(model, tokenizer, dataset)
-        except Exception as e:
-            print(f"Evaluation task error: {e}")
-            return {"error": f"Evaluation failed: {str(e)}"}
-        
-        # Update progress
-        with self.progress_lock:
-            self.progress = 95  # Cleaning up
-        
-        # Clean up to free memory
-        del model
-        del tokenizer
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        
-        # Update progress
-        with self.progress_lock:
-            self.progress = 100  # Completed
-        
-        return results
-    
-    def get_current_progress(self):
-        """Get the current evaluation progress.
-        
-        Returns:
-            tuple: (current_evaluation, progress_percentage)
-        """
-        with self.progress_lock:
-            return self.current_evaluation, self.progress
-    
-    def _determine_task_type(self, dataset):
-        """Determine the task type based on dataset features.
-        
-        Args:
-            dataset: HuggingFace dataset
-            
-        Returns:
-            str: Task type
-        """
-        features = dataset.features
-        
-        # Check for common feature patterns
-        if "question" in features and "answer" in features:
-            return "question-answering"
-        elif "code" in features or "solution" in features:
-            return "code-generation"
-        elif "label" in features or "class" in features:
-            return "classification"
-        elif "input" in features and "output" in features:
-            return "text-generation"
-        else:
-            return "general"
-    
-    def _evaluate_text_generation(self, model, tokenizer, dataset):
-        """Evaluate a model on text generation tasks.
-        
-        Args:
-            model: HuggingFace model
-            tokenizer: HuggingFace tokenizer
-            dataset: HuggingFace dataset
-            
-        Returns:
-            dict: Evaluation results
-        """
-        # Set up generation pipeline
-        generator = pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            device="cpu"
-        )
-        
-        # Sample a subset for evaluation (to keep runtime reasonable)
-        if len(dataset) > 100:
-            dataset = dataset.select(range(100))
-        
-        # Track metrics
-        correct = 0
-        total = 0
-        generated_texts = []
-        
-        # Process each example
-        for i, example in enumerate(dataset):
-            # Update progress based on completion percentage
-            with self.progress_lock:
-                self.progress = 40 + int((i / len(dataset)) * 50)
-            
-            input_text = example.get("input", example.get("prompt", ""))
-            expected_output = example.get("output", example.get("target", ""))
-            
-            if not input_text or not expected_output:
-                continue
-            
-            # Generate text
-            generated = generator(
-                input_text,
-                max_length=100,
-                num_return_sequences=1
-            )
-            
-            generated_text = generated[0]["generated_text"]
-            generated_texts.append(generated_text)
-            
-            # Simple exact match check
-            if expected_output.strip() in generated_text:
-                correct += 1
-            
-            total += 1
-        
-        # Calculate metrics
-        accuracy = correct / total if total > 0 else 0
-        
-        return {
-            "accuracy": accuracy,
-            "samples_evaluated": total,
-            "generated_samples": generated_texts[:5]  # Include a few samples
-        }
-    
-    def _evaluate_question_answering(self, model, tokenizer, dataset):
-        """Evaluate a model on question answering tasks.
-        
-        Args:
-            model: HuggingFace model
-            tokenizer: HuggingFace tokenizer
-            dataset: HuggingFace dataset
-            
-        Returns:
-            dict: Evaluation results
-        """
-        # Set up QA pipeline
-        qa_pipeline = pipeline(
-            "question-answering",
-            model=model,
-            tokenizer=tokenizer,
-            device="cpu"
-        )
-        
-        # Sample a subset for evaluation
-        if len(dataset) > 100:
-            dataset = dataset.select(range(100))
-        
-        # Track metrics
-        exact_matches = 0
-        f1_scores = []
-        total = 0
-        
-        # Process each example
-        for i, example in enumerate(dataset):
-            # Update progress based on completion percentage
-            with self.progress_lock:
-                self.progress = 40 + int((i / len(dataset)) * 50)
-            
-            question = example.get("question", "")
-            context = example.get("context", "")
-            answer = example.get("answer", "")
-            
-            if not question or not answer:
-                continue
-            
-            # Get model prediction
-            if context:
-                result = qa_pipeline(question=question, context=context)
-            else:
-                # If no context provided, use the question as context
-                result = qa_pipeline(question=question, context=question)
-            
-            predicted_answer = result["answer"]
-            
-            # Calculate exact match
-            if predicted_answer.strip() == answer.strip():
-                exact_matches += 1
-            
-            # Calculate F1 score
-            f1 = self._calculate_f1(answer, predicted_answer)
-            f1_scores.append(f1)
-            
-            total += 1
-        
-        # Calculate metrics
-        exact_match_accuracy = exact_matches / total if total > 0 else 0
-        avg_f1 = sum(f1_scores) / len(f1_scores) if f1_scores else 0
-        
-        return {
-            "exact_match": exact_match_accuracy,
-            "f1": avg_f1,
-            "samples_evaluated": total
-        }
-    
-    def _evaluate_classification(self, model, tokenizer, dataset):
-        """Evaluate a model on classification tasks.
-        
-        Args:
-            model: HuggingFace model
-            tokenizer: HuggingFace tokenizer
-            dataset: HuggingFace dataset
-            
-        Returns:
-            dict: Evaluation results
-        """
-        # Set up classification pipeline
-        classifier = pipeline(
-            "text-classification",
-            model=model,
-            tokenizer=tokenizer,
-            device="cpu"
-        )
-        
-        # Sample a subset for evaluation
-        if len(dataset) > 100:
-            dataset = dataset.select(range(100))
-        
-        # Track metrics
-        correct = 0
-        total = 0
-        
-        # Process each example
-        for i, example in enumerate(dataset):
-            # Update progress based on completion percentage
-            with self.progress_lock:
-                self.progress = 40 + int((i / len(dataset)) * 50)
-            
-            text = example.get("text", example.get("sentence", ""))
-            label = str(example.get("label", example.get("class", "")))
-            
-            if not text or not label:
-                continue
-            
-            # Get model prediction
-            result = classifier(text)
-            predicted_label = result[0]["label"]
-            
-            # Check if correct
-            if str(predicted_label) == label:
-                correct += 1
-            
-            total += 1
-        
-        # Calculate metrics
-        accuracy = correct / total if total > 0 else 0
-        
-        return {
-            "accuracy": accuracy,
-            "samples_evaluated": total
-        }
-    
-    def _evaluate_code_generation(self, model, tokenizer, dataset):
-        """Evaluate a model on code generation tasks.
-        
-        Args:
-            model: HuggingFace model
-            tokenizer: HuggingFace tokenizer
-            dataset: HuggingFace dataset
-            
-        Returns:
-            dict: Evaluation results
-        """
-        # Set up generation pipeline
-        generator = pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            device="cpu"
-        )
-        
-        # Sample a subset for evaluation
-        if len(dataset) > 50:  # Smaller sample for code tasks
-            dataset = dataset.select(range(50))
-        
-        # Track metrics
-        exact_matches = 0
-        functional_matches = 0
-        total = 0
-        
-        # Process each example
-        for i, example in enumerate(dataset):
-            # Update progress based on completion percentage
-            with self.progress_lock:
-                self.progress = 40 + int((i / len(dataset)) * 50)
-            
-            prompt = example.get("prompt", example.get("input", ""))
-            solution = example.get("solution", example.get("output", ""))
-            
-            if not prompt or not solution:
-                continue
-            
-            # Generate code
-            generated = generator(
-                prompt,
-                max_length=200,
-                num_return_sequences=1
-            )
-            
-            generated_code = generated[0]["generated_text"]
-            
-            # Extract code from generated text (remove prompt)
-            if prompt in generated_code:
-                generated_code = generated_code[len(prompt):].strip()
-            
-            # Check exact match
-            if generated_code.strip() == solution.strip():
-                exact_matches += 1
-                functional_matches += 1
-            else:
-                # We would ideally check functional correctness here
-                # but that requires executing code which is complex and potentially unsafe
-                # For now, we'll use a simple heuristic
-                if len(generated_code) > 0 and any(keyword in generated_code for keyword in ["def ", "function", "return", "class"]):
-                    functional_matches += 0.5  # Partial credit
-            
-            total += 1
-        
-        # Calculate metrics
-        exact_match_rate = exact_matches / total if total > 0 else 0
-        functional_correctness = functional_matches / total if total > 0 else 0
-        
-        return {
-            "exact_match": exact_match_rate,
-            "functional_correctness": functional_correctness,
-            "samples_evaluated": total
-        }
-    
-    def _evaluate_general(self, model, tokenizer, dataset):
-        """General evaluation for any dataset type.
-        
-        Args:
-            model: HuggingFace model
-            tokenizer: HuggingFace tokenizer
-            dataset: HuggingFace dataset
-            
-        Returns:
-            dict: Evaluation results
-        """
-        # Set up generation pipeline
-        generator = pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            device="cpu"
-        )
-        
-        # Sample a subset for evaluation
-        if len(dataset) > 50:
-            dataset = dataset.select(range(50))
-        
-        # Find input and output fields
-        features = dataset.features
-        input_field = None
-        output_field = None
-        
-        for field in features:
-            if field.lower() in ["input", "prompt", "question", "text"]:
-                input_field = field
-            elif field.lower() in ["output", "target", "answer", "response"]:
-                output_field = field
-        
-        if not input_field:
-            # Just use the first string field as input
-            for field in features:
-                if isinstance(features[field], (str, list)):
-                    input_field = field
-                    break
-        
-        # Track metrics
-        total = 0
-        generated_texts = []
-        
-        # Process each example
-        for i, example in enumerate(dataset):
-            # Update progress based on completion percentage
-            with self.progress_lock:
-                self.progress = 40 + int((i / len(dataset)) * 50)
-            
-            if input_field and input_field in example:
-                input_text = str(example[input_field])
-                
-                # Generate text
-                generated = generator(
-                    input_text,
-                    max_length=100,
-                    num_return_sequences=1
-                )
-                
-                generated_text = generated[0]["generated_text"]
-                generated_texts.append({
-                    "input": input_text,
-                    "output": generated_text,
-                    "expected": str(example[output_field]) if output_field and output_field in example else "N/A"
-                })
-                
-                total += 1
-        
-        return {
-            "samples_evaluated": total,
-            "generated_samples": generated_texts[:5]  # Include a few samples
-        }
-    
-    def _calculate_f1(self, answer, prediction):
-        """Calculate F1 score between answer and prediction.
-        
-        Args:
-            answer: Ground truth answer
-            prediction: Model prediction
-            
-        Returns:
-            float: F1 score
-        """
-        # Tokenize
-        answer_tokens = answer.lower().split()
-        prediction_tokens = prediction.lower().split()
-        
-        # Calculate precision and recall
-        common_tokens = set(answer_tokens) & set(prediction_tokens)
-        
-        if not common_tokens:
-            return 0.0
-        
-        precision = len(common_tokens) / len(prediction_tokens)
-        recall = len(common_tokens) / len(answer_tokens)
-        
-        # Calculate F1
-        if precision + recall == 0:
-            return 0.0
-        
-        f1 = 2 * precision * recall / (precision + recall)
-        return f1
-    
-    def _calculate_overall_score(self, results):
-        """Calculate an overall score from evaluation results.
-        
-        Args:
-            results: Evaluation results dictionary
-            
-        Returns:
-            float: Overall score between 0 and 100
-        """
-        # If there was an error, return a low score
-        if "error" in results:
-            return 0.0
-            
-        score = 0.0
-        
-        # Check for common metrics and weight them
-        if "accuracy" in results:
-            score += results["accuracy"] * 100
-        
-        if "exact_match" in results:
-            score += results["exact_match"] * 100
-        
-        if "f1" in results:
-            score += results["f1"] * 100
-        
-        if "functional_correctness" in results:
-            score += results["functional_correctness"] * 100
-        
-        # If multiple metrics were found, average them
-        num_metrics = sum(1 for metric in ["accuracy", "exact_match", "f1", "functional_correctness"] if metric in results)
-        
-        if num_metrics > 0:
-            score /= num_metrics
-        else:
-            # Default score if no metrics available
-            score = 50.0
-        
-        return score
-    
-    def submit_evaluation(self, model_id, benchmark_id, user_id, priority=0):
-        """Submit a model for evaluation on a benchmark.
-        
-        Args:
-            model_id: Model ID in the database
-            benchmark_id: Benchmark ID in the database
-            user_id: User ID submitting the evaluation
-            priority: Queue priority (higher = higher priority)
-            
-        Returns:
-            tuple: (evaluation_id, message)
-        """
-        # Check if user can submit today
-        if not self.auth_manager.can_submit_benchmark(user_id):
-            return None, "Daily submission limit reached. Try again tomorrow."
-        
-        try:
-            # Get model HuggingFace ID to check size
-            model_info = self.db_manager.get_model(model_id)
-            if not model_info:
-                return None, "Model not found in database."
-            
-            # Check if model will fit in memory
-            will_fit, message = self.check_model_size(model_info['hf_model_id'])
-            
-            if not will_fit:
-                return None, message
-                
-            # Add evaluation to database and queue
-            evaluation_id = self.db_manager.add_evaluation(
-                model_id=model_id,
-                benchmark_id=benchmark_id,
-                priority=priority
-            )
-            
-            # Update user's last submission date
-            self.auth_manager.update_submission_date(user_id)
-            
-            # Make sure worker is running
-            self.start_worker()
-            
-            return evaluation_id, f"Evaluation submitted successfully. {message}"
-        except Exception as e:
-            print(f"Submit evaluation error: {e}")
-            return None, f"Failed to submit evaluation: {str(e)}"
-    
-    def get_queue_status(self):
-        """Get the current status of the evaluation queue.
-        
-        Returns:
-            dict: Queue status information
-        """
-        try:
-            # Get evaluations from database
-            pending_evals = self.db_manager.get_evaluation_results(status="pending")
-            running_evals = self.db_manager.get_evaluation_results(status="running")
-            completed_evals = self.db_manager.get_evaluation_results(status="completed")
-            failed_evals = self.db_manager.get_evaluation_results(status="failed")
-            
-            # Get current evaluation progress
-            current_eval, progress = self.get_current_progress()
-            
-            return {
-                "pending": len(pending_evals),
-                "running": len(running_evals),
-                "completed": len(completed_evals),
-                "failed": len(failed_evals),
-                "is_processing": self.is_processing,
-                "current_evaluation": current_eval,
-                "progress": progress,
-                "memory_limit_gb": self.memory_limit_gb
-            }
-        except Exception as e:
-            print(f"Queue status error: {e}")
-            return {
-                "pending": 0,
-                "running": 0,
-                "completed": 0,
-                "failed": 0,
-                "is_processing": self.is_processing,
-                "current_evaluation": None,
-                "progress": 0,
-                "memory_limit_gb": self.memory_limit_gb,
-                "error": str(e)
-            }
-
-# Model submission UI components
 def create_model_submission_ui(evaluation_queue, auth_manager, db_manager):
     """Create the model submission UI components.
     
@@ -850,9 +56,12 @@ def create_model_submission_ui(evaluation_queue, auth_manager, db_manager):
                         info="Select one category that best describes your model"
                     )
                     
+                    # Fixed benchmark dropdown to properly show names
                     benchmark_dropdown = gr.Dropdown(
                         label="Benchmark",
-                        info="Select a benchmark to evaluate your model on"
+                        info="Select a benchmark to evaluate your model on",
+                        choices=[("none", "Loading benchmarks...")],
+                        value=None
                     )
                     
                     refresh_benchmarks_button = gr.Button("Refresh Benchmarks")
@@ -899,8 +108,14 @@ def create_model_submission_ui(evaluation_queue, auth_manager, db_manager):
         def refresh_benchmarks_handler():
             benchmarks = db_manager.get_benchmarks()
             
-            # Format for dropdown
-            choices = [(str(b["id"]), b["name"]) for b in benchmarks]
+            # Format for dropdown - properly formatted to display names
+            choices = []
+            for b in benchmarks:
+                # Add as tuple of (id, name) to ensure proper display
+                choices.append((str(b["id"]), b["name"]))
+            
+            if not choices:
+                choices = [("none", "No benchmarks available - add some first")]
             
             return gr.update(choices=choices)
         
@@ -914,6 +129,9 @@ def create_model_submission_ui(evaluation_queue, auth_manager, db_manager):
             if not model_id or not model_name or not model_tag or not benchmark_id:
                 return "Please fill in all required fields."
             
+            if benchmark_id == "none":
+                return "Please select a valid benchmark."
+            
             try:
                 # Check if model will fit in RAM
                 will_fit, size_message = evaluation_queue.check_model_size(model_id)