data_loader.py

"""

Dashboard Data Loader



Handles data retrieval from the backend database and transforms

data into chart-ready formats for dashboard visualization.



This layer abstracts database queries and provides clean interfaces

for the visualization components.

"""

import logging
import uuid
from typing import Any, Dict, List, Optional

from backend.scoring.aggregator import ScoreAggregator

import json
from pathlib import Path

from dashboard.schemas import (
    AttackBreakdown,
    AttackBreakdownList,
    BenchmarkComparisonData,
    BenchmarkInfo,
    BenchmarkStats,
    ComparisonData,
    DeltaRobustnessData,
    HeatmapData,
    MetricSummary,
    RadarData,
    RunMetadata,
    RunSummary,
)

logger = logging.getLogger(__name__)


# Sample data for demo mode
SAMPLE_RUNS = [
    {
        "id": "sample-run-001",
        "model_name": "gpt-4",
        "model_version": "v1.0",
        "dataset_version": "v1.0",
        "timestamp": "2024-01-15T10:30:00Z",
        "status": "completed",
        "composite_score": 0.75,
    },
    {
        "id": "sample-run-002", 
        "model_name": "claude-3-sonnet",
        "model_version": "v1.0",
        "dataset_version": "v1.0",
        "timestamp": "2024-01-16T14:20:00Z",
        "status": "completed",
        "composite_score": 0.82,
    },
    {
        "id": "sample-run-003",
        "model_name": "Mistral-7B-v0.1",
        "model_version": "v1.0",
        "dataset_version": "v1.0",
        "timestamp": "2024-01-17T09:15:00Z",
        "status": "completed",
        "composite_score": 0.68,
    },
    {
        "id": "sample-run-004",
        "model_name": "Llama-2-70b",
        "model_version": "v1.0",
        "dataset_version": "v1.0",
        "timestamp": "2024-01-18T11:30:00Z",
        "status": "completed",
        "composite_score": 0.71,
    },
    {
        "id": "sample-run-005",
        "model_name": "gpt-3.5-turbo",
        "model_version": "v1.0",
        "dataset_version": "v1.0",
        "timestamp": "2024-01-19T13:45:00Z",
        "status": "completed",
        "composite_score": 0.65,
    },
]


# Model-specific score ranges for demo mode (hallucination, toxicity, bias, confidence)
MODEL_SCORE_RANGES = {
    "gpt-4": {"hall": (0.08, 0.18), "tox": (0.02, 0.08), "bias": (0.03, 0.12), "conf": (0.75, 0.92)},
    "claude-3-sonnet": {"hall": (0.06, 0.15), "tox": (0.01, 0.06), "bias": (0.02, 0.10), "conf": (0.78, 0.95)},
    "mistral-7b-v0.1": {"hall": (0.12, 0.28), "tox": (0.04, 0.12), "bias": (0.06, 0.18), "conf": (0.65, 0.85)},
    "llama-2-70b": {"hall": (0.10, 0.22), "tox": (0.03, 0.10), "bias": (0.05, 0.15), "conf": (0.70, 0.88)},
    "gpt-3.5-turbo": {"hall": (0.15, 0.32), "tox": (0.05, 0.14), "bias": (0.07, 0.20), "conf": (0.60, 0.82)},
}

def _get_sample_results(run_id: str) -> List[Dict[str, Any]]:
    """Generate sample results for demo mode."""
    import random
    
    # Handle case where run_id might be a list (from Gradio dropdown)
    if isinstance(run_id, list):
        run_id = run_id[0] if run_id else "default"
    
    # Convert to string if not already
    run_id = str(run_id)
    
    random.seed(hash(run_id) % 10000)
    
    # Find the model name from the run_id to get appropriate score ranges
    model_name = None
    for run in SAMPLE_RUNS:
        if run["id"] == run_id:
            model_name = run["model_name"].lower()
            break
    
    # Get score ranges for this model, or use default ranges
    if model_name:
        # Try exact match first
        ranges = MODEL_SCORE_RANGES.get(model_name)
        # Try partial match
        if not ranges:
            for key in MODEL_SCORE_RANGES:
                if key in model_name or model_name in key:
                    ranges = MODEL_SCORE_RANGES[key]
                    break
    else:
        ranges = None
    
    # Default ranges if no match
    if not ranges:
        ranges = {"hall": (0.05, 0.35), "tox": (0.02, 0.15), "bias": (0.05, 0.25), "conf": (0.60, 0.90)}
    
    attack_types = ["injection", "jailbreak", "bias_trigger", "context_poison", "role_confusion"]
    results = []
    
    for i in range(20):
        results.append({
            "id": f"{run_id}-result-{i}",
            "sample_id": f"sample-{i}",
            "attack_type": random.choice(attack_types) if i % 2 == 0 else None,
            "mutation_type": "paraphrase" if i % 3 == 0 else None,
            "hallucination": random.uniform(*ranges["hall"]),
            "toxicity": random.uniform(*ranges["tox"]),
            "bias": random.uniform(*ranges["bias"]),
            "confidence": random.uniform(*ranges["conf"]),
            "robustness": random.uniform(0.50, 0.85),
        })
    
    return results


class DashboardDataLoader:
    """

    Data loader for dashboard visualization.

    

    Responsibilities:

    - Fetch evaluation runs

    - Fetch evaluation results

    - Fetch benchmark artifacts

    - Transform data into chart-ready format

    

    Note: Communicates with backend via internal function calls (same container).

    No direct DB exposure to frontend.

    """

    def __init__(self, demo_mode: bool = True, tenant_id: Optional[str] = None):
        """

        Initialize data loader.

        

        Args:

            demo_mode: If True, return sample data without database

            tenant_id: Optional tenant ID for multi-tenant filtering

        """
        self._demo_mode = demo_mode
        self._tenant_id = tenant_id
        self._aggregator = ScoreAggregator()
    
    def _get_tenant_filter(self) -> Dict[str, Any]:
        """Get tenant filter for database queries."""
        if self._tenant_id is None:
            return {}
        return {"tenant_id": self._tenant_id}

    # =========================================================================
    # Run Retrieval - SYNCHRONOUS
    # =========================================================================

    def get_all_runs(self) -> List[Dict[str, Any]]:
        """

        Get all evaluation runs.

        

        Returns:

            List of run dictionaries with id, model_name, timestamp, status

        """
        if self._demo_mode:
            return SAMPLE_RUNS
        
        # Try to read from benchmark files
        runs = []
        runs_dir = Path("experiments/runs")
        
        if runs_dir.exists():
            for run_file in runs_dir.glob("*.json"):
                try:
                    with open(run_file, "r") as f:
                        run_data = json.load(f)
                        runs.append({
                            "id": run_data.get("run_id", run_file.stem),
                            "model_name": run_data.get("model_name", "unknown"),
                            "model_version": run_data.get("model_version", "v1.0"),
                            "dataset_version": run_data.get("dataset_version", "v1.0"),
                            "timestamp": run_data.get("timestamp", ""),
                            "status": run_data.get("status", "completed"),
                            "composite_score": run_data.get("composite_score"),
                        })
                except Exception as e:
                    logger.error(f"Error loading run {run_file}: {e}")
        
        return runs if runs else SAMPLE_RUNS

    def get_run_by_id(self, run_id: str) -> Optional[Dict[str, Any]]:
        """Get a specific run by ID."""
        if self._demo_mode:
            for run in SAMPLE_RUNS:
                if run["id"] == run_id:
                    return run
            return SAMPLE_RUNS[0] if SAMPLE_RUNS else None
        
        return None

    def get_run_results(self, run_id: str, limit: Optional[int] = None) -> List[Dict[str, Any]]:
        """Get results for a run."""
        if self._demo_mode:
            results = _get_sample_results(run_id)
            return results[:limit] if limit else results
        return []

    # =========================================================================
    # Run Summary - SYNCHRONOUS
    # =========================================================================

    def get_run_summary(self, run_id: str) -> Optional[RunSummary]:
        """Get complete summary for a run."""
        run_data = self.get_run_by_id(run_id)
        if run_data is None:
            return None
        
        results = self.get_run_results(run_id)
        
        if not results:
            return None
        
        # Calculate metrics
        hallucinations = [r["hallucination"] for r in results if r["hallucination"] is not None]
        toxicities = [r["toxicity"] for r in results if r["toxicity"] is not None]
        biases = [r["bias"] for r in results if r["bias"] is not None]
        confidences = [r["confidence"] for r in results if r["confidence"] is not None]
        
        # Get attack coverage
        attack_types = set()
        for r in results:
            if r.get("attack_type"):
                attack_types.add(r["attack_type"])
        
        # Calculate metric summaries
        metric_summaries = []
        
        if hallucinations:
            metric_summaries.append(MetricSummary.from_values("hallucination", hallucinations))
        if toxicities:
            metric_summaries.append(MetricSummary.from_values("toxicity", toxicities))
        if biases:
            metric_summaries.append(MetricSummary.from_values("bias", biases))
        if confidences:
            metric_summaries.append(MetricSummary.from_values("confidence", confidences))
        
        # Calculate composite score from means
        composite_score = None
        if hallucinations and toxicities and biases and confidences:
            mean_h = sum(hallucinations) / len(hallucinations)
            mean_t = sum(toxicities) / len(toxicities)
            mean_b = sum(biases) / len(biases)
            mean_c = sum(confidences) / len(confidences)
            composite_score = self._aggregator.calculate_composite(
                mean_h, mean_t, mean_b, mean_c
            )
        
        # Calculate vulnerability index
        vulnerability_index = RunSummary.calculate_vulnerability_index(
            mean_h if hallucinations else 0.0,
            mean_t if toxicities else 0.0,
            mean_b if biases else 0.0,
        )
        
        # Build metadata
        from datetime import datetime
        
        metadata = RunMetadata(
            run_id=run_data["id"],
            timestamp=datetime.fromisoformat(run_data["timestamp"].replace("Z", "+00:00")) if run_data.get("timestamp") else datetime.utcnow(),
            model_name=run_data["model_name"],
            model_version=run_data["model_version"],
            dataset_version=run_data["dataset_version"],
            config_hash="demo_hash",
            status=run_data["status"],
        )
        
        return RunSummary(
            metadata=metadata,
            metric_summary=metric_summaries,
            composite_score=composite_score,
            total_samples=len(results),
            attack_coverage=sorted(list(attack_types)),
            vulnerability_index=vulnerability_index,
        )

    # =========================================================================
    # Radar Chart Data - SYNCHRONOUS
    # =========================================================================

    def get_radar_data(self, run_id: str) -> Optional[RadarData]:
        """Get radar chart data for a run."""
        run_data = self.get_run_by_id(run_id)
        if run_data is None:
            return None
        
        results = self.get_run_results(run_id)
        
        if not results:
            return None
        
        # Calculate means
        hallucinations = [r["hallucination"] for r in results if r["hallucination"] is not None]
        toxicities = [r["toxicity"] for r in results if r["toxicity"] is not None]
        biases = [r["bias"] for r in results if r["bias"] is not None]
        confidences = [r["confidence"] for r in results if r["confidence"] is not None]
        
        if not all([hallucinations, toxicities, biases, confidences]):
            return None
        
        mean_h = sum(hallucinations) / len(hallucinations)
        mean_t = sum(toxicities) / len(toxicities)
        mean_b = sum(biases) / len(biases)
        mean_c = sum(confidences) / len(confidences)
        
        return RadarData.from_metrics(
            mean_hallucination=mean_h,
            mean_toxicity=mean_t,
            mean_bias=mean_b,
            mean_confidence=mean_c,
            model_name=run_data["model_name"],
            run_id=run_id,
        )

    # =========================================================================
    # Heatmap Data - SYNCHRONOUS
    # =========================================================================

    def get_attack_heatmap(self, run_id: str) -> Optional[HeatmapData]:
        """Get attack vulnerability heatmap data."""
        results = self.get_run_results(run_id)
        
        if not results:
            return None
        
        # Convert to dict format for from_results
        heatmap_data = HeatmapData.from_results(results)
        heatmap_data.run_id = run_id
        return heatmap_data

    # =========================================================================
    # Attack Breakdown - SYNCHRONOUS
    # =========================================================================

    def get_attack_breakdown(self, run_id: str) -> Optional[AttackBreakdownList]:
        """Get per-attack metric breakdown data."""
        results = self.get_run_results(run_id)
        
        if not results:
            return None
        
        # Create breakdown list
        breakdown_list = AttackBreakdownList.from_results(results, run_id=run_id)
        return breakdown_list

    def get_attack_types_for_run(self, run_id: str) -> List[str]:
        """Get list of attack types for a run."""
        results = self.get_run_results(run_id)
        
        if not results:
            return []
        
        attack_types = set()
        for result in results:
            attack_type = result.get("attack_type") or "none"
            attack_types.add(attack_type)
        
        return sorted(list(attack_types))

    # =========================================================================
    # Model Comparison - SYNCHRONOUS
    # =========================================================================

    def get_model_comparison(self, run_ids: List[str]) -> Optional[ComparisonData]:
        """Get comparison data for multiple runs."""
        if not run_ids or len(run_ids) < 2:
            return None
        
        models = []
        hallucination_scores = []
        toxicity_scores = []
        bias_scores = []
        confidence_scores = []
        composite_scores = []
        sample_counts = []
        
        for run_id in run_ids:
            run_data = self.get_run_by_id(run_id)
            if run_data is None:
                continue
            
            results = self.get_run_results(run_id)
            if not results:
                continue
            
            models.append(run_data["model_name"])
            
            # Calculate means
            hallucinations = [r["hallucination"] for r in results if r["hallucination"] is not None]
            toxicities = [r["toxicity"] for r in results if r["toxicity"] is not None]
            biases = [r["bias"] for r in results if r["bias"] is not None]
            confidences = [r["confidence"] for r in results if r["confidence"] is not None]
            
            mean_h = sum(hallucinations) / len(hallucinations) if hallucinations else 0.0
            mean_t = sum(toxicities) / len(toxicities) if toxicities else 0.0
            mean_b = sum(biases) / len(biases) if biases else 0.0
            mean_c = sum(confidences) / len(confidences) if confidences else 0.0
            
            hallucination_scores.append(mean_h)
            toxicity_scores.append(mean_t)
            bias_scores.append(mean_b)
            confidence_scores.append(mean_c)
            
            # Calculate composite
            composite = self._aggregator.calculate_composite(mean_h, mean_t, mean_b, mean_c)
            composite_scores.append(composite)
            
            sample_counts.append(len(results))
        
        if len(models) < 2:
            return None
        
        return ComparisonData(
            models=models,
            hallucination=hallucination_scores,
            toxicity=toxicity_scores,
            bias=bias_scores,
            confidence=confidence_scores,
            composite_score=composite_scores,
            sample_count=sample_counts,
        )

    def get_delta_robustness(self, run_ids: List[str]) -> List[DeltaRobustnessData]:
        """Get delta robustness comparison for multiple runs."""
        comparison = self.get_model_comparison(run_ids)
        
        if comparison is None:
            return []
        
        # Find baseline (first model or lowest composite)
        baseline_score = min(comparison.composite_score)
        
        deltas = []
        for i, model in enumerate(comparison.models):
            delta = comparison.composite_score[i] - baseline_score
            deltas.append(
                DeltaRobustnessData(
                    model_name=model,
                    delta_robustness=delta,
                    composite_score=comparison.composite_score[i],
                    rank=i + 1,
                )
            )
        
        # Sort by composite score descending
        deltas.sort(key=lambda x: x.composite_score, reverse=True)
        
        # Update ranks
        for i, delta in enumerate(deltas):
            delta.rank = i + 1
        
        return deltas

    # =========================================================================
    # Benchmark Artifacts - SYNCHRONOUS
    # =========================================================================

    def _get_benchmark_path(self, benchmark_id: str) -> Path:
        """Get the file path for a benchmark artifact."""
        # Use absolute path relative to the data_loader.py file location
        # This works in both local development and HuggingFace Spaces
        base_dir = Path(__file__).parent.parent / "experiments" / "benchmarks"
        return base_dir / f"{benchmark_id}.json"

    def list_benchmarks(self) -> List[BenchmarkInfo]:
        """List all available benchmarks."""
        benchmarks = []
        
        # Use absolute path based on the location of this file
        # This works in both local development and HuggingFace Spaces/Docker
        base_dir = Path(__file__).parent.parent / "experiments" / "benchmarks"
        
        if not base_dir.exists():
            logger.warning(f"Benchmarks directory does not exist: {base_dir}")
            return benchmarks
        
        # Find all JSON files in the benchmarks directory
        for json_file in base_dir.glob("*.json"):
            benchmark_id = json_file.stem
            try:
                with open(json_file, "r") as f:
                    data = json.load(f)
                
                info = BenchmarkInfo.from_json(benchmark_id, data)
                benchmarks.append(info)
            except Exception as e:
                logger.error(f"Error loading benchmark {benchmark_id}: {e}")
                continue
        
        # Sort by timestamp descending (most recent first)
        benchmarks.sort(key=lambda x: x.timestamp, reverse=True)
        
        return benchmarks

    def get_benchmark_comparison(self, benchmark_id: str) -> Optional[BenchmarkComparisonData]:
        """Get benchmark comparison data for multiple models."""
        benchmark_path = self._get_benchmark_path(benchmark_id)
        
        if not benchmark_path.exists():
            logger.warning(f"Benchmark not found: {benchmark_path}")
            return None
        
        try:
            with open(benchmark_path, "r") as f:
                data = json.load(f)
            
            comparison = BenchmarkComparisonData.from_json(benchmark_id, data)
            
            # Log benchmark view
            logger.info(
                f"DASHBOARD_VIEW_BENCHMARK benchmark_id={benchmark_id} "
                f"model_count={comparison.total_models}"
            )
            
            return comparison
        except Exception as e:
            logger.error(f"Error loading benchmark {benchmark_id}: {e}")
            return None

    def get_benchmark_stats(self, benchmark_id: str) -> Optional[BenchmarkStats]:
        """Get statistical summary for a benchmark."""
        comparison = self.get_benchmark_comparison(benchmark_id)
        
        if comparison is None:
            return None
        
        stats = BenchmarkStats.from_comparison_data(benchmark_id, comparison)
        
        logger.info(
            f"DASHBOARD_COMPARE_MODELS benchmark_id={benchmark_id} "
            f"model_count={stats.total_models}"
        )
        
        return stats

    # =========================================================================
    # Monitoring Data - SYNCHRONOUS
    # =========================================================================

    def get_monitoring_trends(

        self,

        model_version: Optional[str] = None,

        window_size: int = 50,

    ) -> Dict[str, Any]:
        """

        Get monitoring trend data for dashboard visualization.

        

        Args:

            model_version: Optional model version to filter by

            window_size: Number of data points to return

            

        Returns:

            Dictionary with trend data for all metrics

        """
        # In demo mode, return sample data
        if self._demo_mode:
            return self._get_sample_monitoring_trends(window_size)
        
        # In production, try to get from monitoring pipeline
        try:
            from backend.monitoring.pipeline import get_monitoring_pipeline
            
            pipeline = get_monitoring_pipeline()
            dashboard_data = pipeline.get_dashboard_data(trend_length=window_size)
            
            return {
                "timestamps": [ts.isoformat() for ts in dashboard_data.timestamps],
                "robustness": dashboard_data.robustness_trend,
                "hallucination": dashboard_data.hallucination_trend,
                "toxicity": dashboard_data.toxicity_trend,
                "bias": dashboard_data.bias_trend,
                "confidence": dashboard_data.confidence_trend,
                "rolling_robustness": dashboard_data.rolling_robustness,
                "rolling_hallucination": dashboard_data.rolling_hallucination,
                "rolling_toxicity": dashboard_data.rolling_toxicity,
                "rolling_confidence": dashboard_data.rolling_confidence,
            }
        except Exception as e:
            logger.error(f"Error getting monitoring trends: {e}")
            return self._get_sample_monitoring_trends(window_size)

    def get_active_alerts(

        self,

        model_version: Optional[str] = None,

    ) -> Dict[str, Any]:
        """

        Get active alerts for dashboard display.

        

        Args:

            model_version: Optional model version to filter by

            

        Returns:

            Dictionary with alert data

        """
        # In demo mode, return sample data
        if self._demo_mode:
            return self._get_sample_alerts()
        
        # In production, try to get from monitoring pipeline
        try:
            from backend.monitoring.pipeline import get_monitoring_pipeline
            
            pipeline = get_monitoring_pipeline()
            alerts = pipeline.get_active_alerts()
            
            # Convert alerts to dict format
            alert_list = []
            for alert in alerts:
                alert_list.append({
                    "id": alert.id,
                    "alert_type": alert.alert_type.value if hasattr(alert.alert_type, 'value') else str(alert.alert_type),
                    "severity": alert.severity.value if hasattr(alert.severity, 'value') else str(alert.severity),
                    "model_version": alert.model_version,
                    "metric_name": alert.metric_name,
                    "baseline_value": alert.baseline_value,
                    "current_value": alert.current_value,
                    "drift_magnitude": alert.drift_magnitude,
                    "threshold": alert.threshold,
                    "timestamp": alert.timestamp.isoformat() if hasattr(alert.timestamp, 'isoformat') else str(alert.timestamp),
                    "is_resolved": alert.is_resolved,
                })
            
            return {
                "alerts": alert_list,
                "total": len(alert_list),
            }
        except Exception as e:
            logger.error(f"Error getting active alerts: {e}")
            return self._get_sample_alerts()

    def get_drift_status(

        self,

        model_version: Optional[str] = None,

    ) -> Dict[str, Any]:
        """

        Get current drift detection status.

        

        Args:

            model_version: Optional model version to filter by

            

        Returns:

            Dictionary with drift status for each metric

        """
        # In demo mode, return sample data
        if self._demo_mode:
            return {
                "hallucination": {"is_drift": False, "magnitude": 0.0},
                "toxicity": {"is_drift": False, "magnitude": 0.0},
                "bias": {"is_drift": False, "magnitude": 0.0},
                "confidence": {"is_drift": False, "magnitude": 0.0},
                "robustness": {"is_drift": False, "magnitude": 0.0},
            }
        
        # In production, try to get from monitoring pipeline
        try:
            from backend.monitoring.pipeline import get_monitoring_pipeline
            
            pipeline = get_monitoring_pipeline()
            dashboard_data = pipeline.get_dashboard_data()
            
            drift_status = {}
            for metric_name, drift_result in dashboard_data.drift_status.items():
                drift_status[metric_name] = {
                    "is_drift": drift_result.is_drift_detected,
                    "magnitude": drift_result.drift_magnitude,
                    "baseline": drift_result.baseline_value,
                    "current": drift_result.live_value,
                    "threshold": drift_result.threshold,
                    "severity": drift_result.severity.value if hasattr(drift_result.severity, 'value') else str(drift_result.severity),
                }
            
            return drift_status
        except Exception as e:
            logger.error(f"Error getting drift status: {e}")
            return {
                "hallucination": {"is_drift": False, "magnitude": 0.0},
                "toxicity": {"is_drift": False, "magnitude": 0.0},
                "bias": {"is_drift": False, "magnitude": 0.0},
                "confidence": {"is_drift": False, "magnitude": 0.0},
                "robustness": {"is_drift": False, "magnitude": 0.0},
            }

    def get_monitoring_config(self) -> Dict[str, Any]:
        """

        Get monitoring configuration.

        

        Returns:

            Dictionary with monitoring config

        """
        # In demo mode, return default config
        if self._demo_mode:
            return {
                "window_size": 100,
                "sampling_rate": 1.0,
                "lightweight_hallucination": True,
                "hallucination_threshold": 0.08,
                "toxicity_threshold": 0.05,
                "bias_threshold": 0.05,
                "confidence_threshold": 0.15,
                "robustness_threshold": 0.10,
            }
        
        # In production, try to get from monitoring pipeline
        try:
            from backend.monitoring.pipeline import get_monitoring_pipeline
            
            pipeline = get_monitoring_pipeline()
            config = pipeline.config
            
            return {
                "window_size": config.window_size,
                "sampling_rate": config.sampling_rate,
                "lightweight_hallucination": config.lightweight_hallucination,
                "hallucination_threshold": config.hallucination_threshold,
                "toxicity_threshold": config.toxicity_threshold,
                "bias_threshold": config.bias_threshold,
                "confidence_threshold": config.confidence_threshold,
                "robustness_threshold": config.robustness_threshold,
            }
        except Exception as e:
            logger.error(f"Error getting monitoring config: {e}")
            return {
                "window_size": 100,
                "sampling_rate": 1.0,
                "lightweight_hallucination": True,
                "hallucination_threshold": 0.08,
                "toxicity_threshold": 0.05,
                "bias_threshold": 0.05,
                "confidence_threshold": 0.15,
                "robustness_threshold": 0.10,
            }

    # =========================================================================
    # Sample Data Helpers
    # =========================================================================

    def _get_sample_monitoring_trends(self, window_size: int = 50) -> Dict[str, Any]:
        """Generate sample monitoring trends for demo mode."""
        import random
        from datetime import datetime, timedelta
        
        random.seed(42)
        
        # Generate timestamps
        base_time = datetime.utcnow()
        timestamps = [(base_time - timedelta(minutes=window_size - i)).isoformat() for i in range(window_size)]
        
        # Generate metrics with some variation
        robustness = [0.7 + random.uniform(-0.1, 0.1) for _ in range(window_size)]
        hallucination = [0.15 + random.uniform(-0.05, 0.05) for _ in range(window_size)]
        toxicity = [0.08 + random.uniform(-0.03, 0.03) for _ in range(window_size)]
        bias = [0.05 + random.uniform(-0.02, 0.02) for _ in range(window_size)]
        confidence = [0.75 + random.uniform(-0.1, 0.1) for _ in range(window_size)]
        
        return {
            "timestamps": timestamps,
            "robustness": robustness,
            "hallucination": hallucination,
            "toxicity": toxicity,
            "bias": bias,
            "confidence": confidence,
            "rolling_robustness": sum(robustness[-10:]) / 10,
            "rolling_hallucination": sum(hallucination[-10:]) / 10,
            "rolling_toxicity": sum(toxicity[-10:]) / 10,
            "rolling_confidence": sum(confidence[-10:]) / 10,
        }

    def _get_sample_alerts(self) -> Dict[str, Any]:
        """Generate sample alerts for demo mode."""
        from datetime import datetime, timedelta
        
        base_time = datetime.utcnow()
        
        sample_alerts = [
            {
                "id": "alert-001",
                "alert_type": "hallucination_drift",
                "severity": "high",
                "model_version": "gpt-4-v1",
                "metric_name": "hallucination",
                "baseline_value": 0.15,
                "current_value": 0.28,
                "drift_magnitude": 0.13,
                "threshold": 0.08,
                "timestamp": (base_time - timedelta(minutes=5)).isoformat(),
                "is_resolved": False,
            },
            {
                "id": "alert-002",
                "alert_type": "toxicity_drift",
                "severity": "medium",
                "model_version": "gpt-4-v1",
                "metric_name": "toxicity",
                "baseline_value": 0.05,
                "current_value": 0.12,
                "drift_magnitude": 0.07,
                "threshold": 0.05,
                "timestamp": (base_time - timedelta(minutes=15)).isoformat(),
                "is_resolved": False,
            },
            {
                "id": "alert-003",
                "alert_type": "confidence_collapse",
                "severity": "low",
                "model_version": "gpt-4-v1",
                "metric_name": "confidence",
                "baseline_value": 0.80,
                "current_value": 0.68,
                "drift_magnitude": 0.12,
                "threshold": 0.15,
                "timestamp": (base_time - timedelta(minutes=30)).isoformat(),
                "is_resolved": False,
            },
        ]
        
        return {
            "alerts": sample_alerts,
            "total": len(sample_alerts),
        }


# =============================================================================
# Factory Functions
# =============================================================================


def get_data_loader(demo_mode: bool = True) -> DashboardDataLoader:
    """

    Get a DashboardDataLoader instance.

    

    Args:

        demo_mode: If True, return sample data without database

        

    Returns:

        DashboardDataLoader instance

    """
    return DashboardDataLoader(demo_mode=demo_mode)