evaluation_queue.py

"""
Model evaluation queue system for Dynamic Highscores.

This module handles the evaluation queue, CPU-only processing,
and enforces daily submission limits for users.
"""

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
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)
            
            # Initialize total size
            total_size_gb = 0
            
            # Try different approaches to get model size based on API response structure
            if hasattr(model_info_obj, 'safetensors') and model_info_obj.safetensors:
                # New API format with safetensors dict
                for file_info in model_info_obj.safetensors.values():
                    if hasattr(file_info, 'size'):
                        total_size_gb += file_info.size / (1024 * 1024 * 1024)
                    elif isinstance(file_info, dict) and 'size' in file_info:
                        total_size_gb += file_info['size'] / (1024 * 1024 * 1024)
            
            # Fallback to siblings method
            if total_size_gb == 0 and hasattr(model_info_obj, 'siblings'):
                for sibling in model_info_obj.siblings:
                    if hasattr(sibling, 'size'):
                        if sibling.rfilename.endswith(('.bin', '.safetensors', '.pt')):
                            total_size_gb += sibling.size / (1024 * 1024 * 1024)
                    elif isinstance(sibling, dict) and 'size' in sibling:
                        if sibling.get('rfilename', '').endswith(('.bin', '.safetensors', '.pt')):
                            total_size_gb += sibling['size'] / (1024 * 1024 * 1024)
            
            # If we still couldn't determine size, try a reasonable guess based on model name
            if total_size_gb == 0:
                # Try to guess from model name (e.g., if it has "7b" in the name)
                model_name = model_id.lower()
                size_indicators = {
                    "1b": 1, "2b": 2, "3b": 3, "5b": 5, "7b": 7, "8b": 8,
                    "10b": 10, "13b": 13, "20b": 20, "30b": 30, "65b": 65, "70b": 70
                }
                
                for indicator, size in size_indicators.items():
                    if indicator in model_name.replace("-", "").replace("_", ""):
                        total_size_gb = size * 2  # Rough estimate: param count × 2 for size in GB
                        break
            
            # If we still couldn't determine size, use a default
            if total_size_gb == 0:
                # Try direct API method
                try:
                    print(f"Checking model size with direct method for {model_id}")
                    # Print out the entire structure for debugging
                    print(f"Model info: {model_info_obj.__dict__}")
                    
                    # Default to a conservative estimate
                    total_size_gb = 5  # Assume a 5GB model as default
                except Exception as e:
                    print(f"Direct size check failed: {e}")
                    return True, "Unable to determine model size accurately, but allowing submission with caution"
            
            # Account for memory overhead
            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}")
            # Log more details for debugging
            import traceback
            traceback.print_exc()
            
            # Allow submission with warning
            return True, f"Warning: Could not verify 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.
    
    Args:
        evaluation_queue: Evaluation queue instance
        auth_manager: Authentication manager instance
        db_manager: Database manager instance
        
    Returns:
        gr.Blocks: Gradio Blocks component with model submission UI
    """
    with gr.Blocks() as submission_ui:
        # Store user authentication state
        user_state = gr.State(None)
        
        # Check authentication on load
        def check_auth_on_load(request: gr.Request):
            if request:
                # Special handling for HF Spaces OAuth
                if 'SPACE_ID' in os.environ:
                    username = request.headers.get("HF-User")
                    if username:
                        user = db_manager.get_user_by_username(username)
                        if user:
                            print(f"User authenticated via HF Spaces OAuth: {username}")
                            return user
                else:
                    # Standard token-based auth
                    user = auth_manager.check_login(request)
                    if user:
                        return user
            return None
        
        with gr.Tab("Submit Model"):
            gr.Markdown(f"""
            ### Model Size Restrictions
            
            Models must fit within {evaluation_queue.memory_limit_gb}GB of RAM for evaluation.
            Large models will be rejected to ensure all evaluations can complete successfully.
            """, elem_classes=["info-text"])
            
            with gr.Row():
                with gr.Column(scale=2):
                    model_id_input = gr.Textbox(
                        placeholder="HuggingFace model ID (e.g., 'gpt2', 'facebook/opt-350m')",
                        label="Model ID"
                    )
                    
                    check_size_button = gr.Button("Check Model Size")
                    size_check_result = gr.Markdown("")
                    model_name_input = gr.Textbox(
                        placeholder="Display name for your model",
                        label="Model Name"
                    )
                    
                    model_description_input = gr.Textbox(
                        placeholder="Brief description of your model",
                        label="Description",
                        lines=3
                    )
                    
                    model_parameters_input = gr.Number(
                        label="Number of Parameters (billions)",
                        precision=2
                    )
                
                with gr.Column(scale=1):
                    model_tag_input = gr.Dropdown(
                        choices=evaluation_queue.model_tags,
                        label="Model Tag",
                        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",
                        choices=[("none", "Loading benchmarks...")],
                        value=None
                    )
                    
                    refresh_benchmarks_button = gr.Button("Refresh Benchmarks")
            
            submit_model_button = gr.Button("Submit for Evaluation")
            submission_status = gr.Markdown("")
            auth_message = gr.Markdown("")
        
        with gr.Tab("Evaluation Queue"):
            refresh_queue_button = gr.Button("Refresh Queue")
            
            with gr.Row():
                with gr.Column(scale=1):
                    queue_stats = gr.JSON(
                        label="Queue Statistics"
                    )
                
                with gr.Column(scale=2):
                    queue_status = gr.Dataframe(
                        headers=["ID", "Model", "Benchmark", "Status", "Submitted"],
                        label="Recent Evaluations"
                    )
            
            with gr.Row(visible=True) as progress_container:
                with gr.Column():
                    current_eval_info = gr.Markdown("No evaluation currently running")
                    # Use a simple text display for progress instead of Progress component
                    progress_display = gr.Markdown("Progress: 0%")
        
        # Event handlers
        def check_model_size_handler(model_id):
            if not model_id:
                return "Please enter a HuggingFace model ID."
            
            try:
                will_fit, message = evaluation_queue.check_model_size(model_id)
                
                if will_fit:
                    return f"✅ {message}"
                else:
                    return f"❌ {message}"
            except Exception as e:
                print(f"Model size check error: {e}")
                import traceback
                traceback.print_exc()
                return f"Error checking model size: {str(e)}"
            
        def refresh_benchmarks_handler():
            benchmarks = db_manager.get_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)
        
        def submit_model_handler(model_id, model_name, model_description, model_parameters, model_tag, benchmark_id, user):
            # Check if user is logged in
            if not user:
                return "Please log in to submit a model."
            
            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)
                
                if not will_fit:
                    return f"❌ {size_message}"
                
                # Add model to database
                model_db_id = db_manager.add_model(
                    name=model_name,
                    hf_model_id=model_id,
                    user_id=user["id"],
                    tag=model_tag,
                    parameters=str(model_parameters) if model_parameters else None,
                    description=model_description
                )
                
                if not model_db_id:
                    return "Failed to add model to database."
                
                # Submit for evaluation
                eval_id, message = evaluation_queue.submit_evaluation(
                    model_id=model_db_id,
                    benchmark_id=benchmark_id,
                    user_id=user["id"]
                )
                
                if eval_id:
                    return f"✅ Model submitted successfully. {size_message}\nEvaluation ID: {eval_id}"
                else:
                    return message
            except Exception as e:
                print(f"Error submitting model: {str(e)}")
                import traceback
                traceback.print_exc()
                return f"Error submitting model: {str(e)}"
        
        def refresh_queue_handler():
            # Get queue statistics
            stats = evaluation_queue.get_queue_status()
            
            # Get recent evaluations (all statuses, limited to 20)
            evals = db_manager.get_evaluation_results(limit=20)
            
            # Format for dataframe
            eval_data = []
            for eval in evals:
                eval_data.append([
                    eval["id"],
                    eval["model_name"],
                    eval["benchmark_name"],
                    eval["status"],
                    eval["submitted_at"]
                ])
            
            # Also update progress display
            current_eval, progress = evaluation_queue.get_current_progress()
            if current_eval:
                model_info = db_manager.get_model(current_eval['model_id'])
                benchmark_info = db_manager.get_benchmark(current_eval['benchmark_id'])
                
                if model_info and benchmark_info:
                    eval_info = f"**Currently Evaluating:** {model_info['name']} on {benchmark_info['name']}"
                    progress_text = f"Progress: {progress}%"
                    return stats, eval_data, eval_info, progress_text
            
            return stats, eval_data, "No evaluation currently running", "Progress: 0%"
        
        # Update authentication status
        def update_auth_message(user):
            if user:
                return f"Logged in as {user['username']}"
            else:
                return "Please log in to submit a model."
        
        # Connect event handlers
        check_size_button.click(
            fn=check_model_size_handler,
            inputs=[model_id_input],
            outputs=[size_check_result]
        )
        
        refresh_benchmarks_button.click(
            fn=refresh_benchmarks_handler,
            inputs=[],
            outputs=[benchmark_dropdown]
        )
        
        submit_model_button.click(
            fn=submit_model_handler,
            inputs=[
                model_id_input,
                model_name_input,
                model_description_input,
                model_parameters_input,
                model_tag_input,
                benchmark_dropdown,
                user_state
            ],
            outputs=[submission_status]
        )
        
        refresh_queue_button.click(
            fn=refresh_queue_handler,
            inputs=[],
            outputs=[queue_stats, queue_status, current_eval_info, progress_display]
        )
        
        # Initialize on load
        submission_ui.load(
            fn=check_auth_on_load,
            inputs=[],
            outputs=[user_state]
        )
        
        submission_ui.load(
            fn=lambda user: update_auth_message(user),
            inputs=[user_state],
            outputs=[auth_message]
        )
        
        submission_ui.load(
            fn=refresh_benchmarks_handler,
            inputs=[],
            outputs=[benchmark_dropdown]
        )
        
        submission_ui.load(
            fn=refresh_queue_handler,
            inputs=[],
            outputs=[queue_stats, queue_status, current_eval_info, progress_display]
        )
    
    return submission_ui