"""
Vehicle Monitoring Use Case for Post-Processing

This module provides vehicle monitoring functionality with congestion detection,
zone analysis, and alert generation.

"""

from typing import Any, Dict, List, Optional
from dataclasses import asdict
import time
from datetime import datetime, timezone

from ..core.base import BaseProcessor, ProcessingContext, ProcessingResult, ConfigProtocol, ResultFormat
from ..utils import (
    filter_by_confidence,
    filter_by_categories,
    apply_category_mapping,
    count_objects_by_category,
    count_objects_in_zones,
    calculate_counting_summary,
    match_results_structure
)
from dataclasses import dataclass, field
from ..core.config import BaseConfig, AlertConfig, ZoneConfig

@dataclass
class VehicleMonitoringConfig(BaseConfig):
    """Configuration for license plate detection use case in vehicle monitoring."""

    # Zone configuration
    zone_config: Optional[ZoneConfig] = None

    # Detection settings
    confidence_threshold: float = 0.6

    vehicle_categories: List[str] = field(
        default_factory=lambda: ['army vehicle', 'car', 'bicycle', 'bus', 'auto rickshaw', 'garbagevan', 'truck', 'minibus', 'motorbike', 'pickup', 'policecar', 'rickshaw', 'scooter', 'suv', 'taxi', 'three wheelers -CNG-', 'human hauler', 'van', 'wheelbarrow']
    )

    target_vehicle_categories: List[str] = field(
        default_factory=lambda: ['car', 'bicycle', 'bus', 'garbagevan', 'truck', 'motorbike', 'van']
    )

    # Alert configuration
    alert_config: Optional[AlertConfig] = None

    # Time window configuration
    time_window_minutes: int = 60
    enable_unique_counting: bool = True

    # Category mapping
    index_to_category: Optional[Dict[int, str]] = field(
        default_factory=lambda: {
            1: "bicycle",
            2: "car",
            3: "motorbike",
            4: "auto rickshaw",
            5: "bus",
            6: "garbagevan",
            7: "truck",
            8: "minibus",
            10: "army vehicle",
            11: "pickup",
            12: "policecar",
            13: "rickshaw",
            14: "scooter",
            15: "suv",
            16: "taxi",
            17: "three wheelers -CNG-",
            18: "human hauler",
            19: "van",
            20: "wheelbarrow",
        }
    )

    def _post_init_(self):
        """Post-initialization validation."""
        if self.confidence_threshold < 0.0 or self.confidence_threshold > 1.0:
            raise ValueError("confidence_threshold must be between 0.0 and 1.0")

class VehicleMonitoringUseCase(BaseProcessor):
    """Vehicle monitoring use case with zone analysis and alerting."""
    
    def __init__(self):
        """Initialize vehicle monitoring use case."""
        super().__init__("vehicle_monitoring")
        self.category = "traffic"
    
    def get_config_schema(self) -> Dict[str, Any]:
        """Get configuration schema for vehicle monitoring."""
        return {
            "type": "object",
            "properties": {
                "confidence_threshold": {
                    "type": "number",
                    "minimum": 0.0,
                    "maximum": 1.0,
                    "default": 0.5,
                    "description": "Minimum confidence threshold for vehicle detections"
                },
                "enable_tracking": {
                    "type": "boolean",
                    "default": False,
                    "description": "Enable tracking for unique vehicle counting"
                },
                "zone_config": {
                    "type": "object",
                    "properties": {
                        "zones": {
                            "type": "object",
                            "additionalProperties": {
                                "type": "array",
                                "items": {
                                    "type": "array",
                                    "items": {"type": "number"},
                                    "minItems": 2,
                                    "maxItems": 2
                                },
                                "minItems": 3
                            },
                            "description": "Zone definitions as polygons for congestion monitoring"
                        },
                        "zone_confidence_thresholds": {
                            "type": "object",
                            "additionalProperties": {"type": "number", "minimum": 0.0, "maximum": 1.0},
                            "description": "Per-zone confidence thresholds"
                        }
                    }
                },
                "vehicle_categories": {
                    "type": "array",
                    "items": {"type": "string"},
                    "default": ['army vehicle', 'car', 'bicycle', 'bus', 'auto rickshaw', 'garbagevan', 'truck', 'minibus', 'motorbike', 'pickup', 'policecar', 'rickshaw', 'scooter', 'suv', 'taxi', 'three wheelers -CNG-', 'human hauler', 'van', 'wheelbarrow'],
                    "description": "Category names that represent vehicles"
                },
                "enable_unique_counting": {
                    "type": "boolean",
                    "default": True,
                    "description": "Enable unique vehicle counting using tracking"
                },
                "time_window_minutes": {
                    "type": "integer",
                    "minimum": 1,
                    "default": 60,
                    "description": "Time window for vehicle counting analysis in minutes"
                },
                "alert_config": {
                    "type": "object",
                    "properties": {
                        "count_thresholds": {
                            "type": "object",
                            "additionalProperties": {"type": "integer", "minimum": 1},
                            "description": "Count thresholds for vehicle alerts"
                        },
                        "occupancy_thresholds": {
                            "type": "object", 
                            "additionalProperties": {"type": "integer", "minimum": 1},
                            "description": "Zone occupancy thresholds for vehicle alerts"
                        }
                    }
                }
            },
            "required": ["confidence_threshold"],
            "additionalProperties": False
        }
    
    def create_default_config(self, **overrides) -> VehicleMonitoringConfig:
        """Create default configuration with optional overrides."""
        defaults = {
            "category": self.category,
            "usecase": self.name,
            "confidence_threshold": 0.5,
            "enable_tracking": False,
            "enable_analytics": True,
            "enable_unique_counting": True,
            "time_window_minutes": 60,
            "vehicle_categories":['army vehicle', 'car', 'bicycle', 'bus', 'auto rickshaw', 'garbagevan', 'truck', 'minibus', 'motorbike', 'pickup', 'policecar', 'rickshaw', 'scooter', 'suv', 'taxi', 'three wheelers -CNG-', 'human hauler', 'van', 'wheelbarrow'],
        }
        defaults.update(overrides)
        return VehicleMonitoringConfig(**defaults)
    
    def process(self, data: Any, config: ConfigProtocol, context: Optional[ProcessingContext] = None, stream_info: Optional[Dict[str, Any]] = None) -> ProcessingResult:
        """
        Process vehicle monitoring use case.
        
        Args:
            data: Raw model output (detection or tracking format)
            config: Vehicle monitoring configuration
            context: Processing context
            stream_info: Stream information containing frame details
            
        Returns:
            ProcessingResult: Processing result with vehicle monitoring analytics
        """
        start_time = time.time()
        
        try:
            # Ensure we have the right config type
            if not isinstance(config, VehicleMonitoringConfig):
                return self.create_error_result(
                    "Invalid configuration type for vehicle monitoring",
                    usecase=self.name,
                    category=self.category,
                    context=context
                )
            
            # Initialize processing context if not provided
            if context is None:
                context = ProcessingContext()
            
            # Detect input format
            input_format = match_results_structure(data)
            context.input_format = input_format
            context.confidence_threshold = config.confidence_threshold
            
            self.logger.info(f"Processing vehicle monitoring with format: {input_format.value}")
            
            # Step 1: Apply confidence filtering
            processed_data = data
            if config.confidence_threshold is not None:
                processed_data = filter_by_confidence(processed_data, config.confidence_threshold)
                self.logger.debug(f"Applied confidence filtering with threshold {config.confidence_threshold}")
            
            # Step 2: Apply category mapping if provided
            if config.index_to_category:
                processed_data = apply_category_mapping(processed_data, config.index_to_category)
                self.logger.debug("Applied category mapping")
            
            # Step 2.5: Filter to only include vehicle categories
            vehicle_processed_data = processed_data
            if config.target_vehicle_categories:
                vehicle_processed_data = filter_by_categories(processed_data.copy(), config.target_vehicle_categories)
                self.logger.debug(f"Applied vehicle category filtering for: {config.target_vehicle_categories}")
            
            # Step 3: Calculate comprehensive counting summary
            zones = config.zone_config.zones if config.zone_config else None
            vehicle_counting_summary = calculate_counting_summary(
                vehicle_processed_data,
                zones=zones
            )
            general_counting_summary = calculate_counting_summary(
                processed_data,
                zones=zones
            )
            
            # Step 4: Zone-based analysis if zones are configured
            zone_analysis = {}
            if config.zone_config and config.zone_config.zones:
                zone_analysis = count_objects_in_zones(
                    vehicle_processed_data, 
                    config.zone_config.zones
                )
                self.logger.debug(f"Analyzed {len(config.zone_config.zones)} zones")
            
            # Step 5: Generate insights and alerts
            insights = self._generate_insights(vehicle_counting_summary, zone_analysis, config)
            alerts = self._check_alerts(vehicle_counting_summary, zone_analysis, config)
            
            # Step 6: Calculate detailed metrics
            metrics = self._calculate_metrics(vehicle_counting_summary, zone_analysis, config, context)
            
            # Step 7: Extract predictions for API compatibility
            predictions = self._extract_predictions(processed_data)
            
            # Step 8: Generate human-readable summary
            summary = self._generate_summary(vehicle_counting_summary, zone_analysis, alerts)
            
            # Extract frame information from stream_info
            frame_number = None
            if stream_info:
                input_settings = stream_info.get("input_settings", {})
                start_frame = input_settings.get("start_frame")
                end_frame = input_settings.get("end_frame")
                # If start and end frame are the same, it's a single frame
                if start_frame is not None and end_frame is not None and start_frame == end_frame:
                    frame_number = start_frame
                elif start_frame is not None:
                    frame_number = start_frame
            
            # Step 9: Generate structured events and tracking stats with frame-based keys
            events_list = self._generate_events(vehicle_counting_summary, zone_analysis, alerts, config, frame_number)
            tracking_stats_list = self._generate_tracking_stats(vehicle_counting_summary, zone_analysis, insights, summary, config, frame_number)
            
            # Extract frame-based dictionaries from the lists
            events = events_list[0] if events_list else {}
            tracking_stats = tracking_stats_list[0] if tracking_stats_list else {}
            
            # Mark processing as completed
            context.mark_completed()
            
            # Create successful result
            result = self.create_result(
                data={
                    "general_counting_summary": general_counting_summary,
                    "counting_summary": vehicle_counting_summary,
                    "zone_analysis": zone_analysis,
                    "alerts": alerts,
                    "total_vehicles": vehicle_counting_summary.get("total_objects", 0),
                    "zones_count": len(config.zone_config.zones) if config.zone_config else 0,
                    "events": events,
                    "tracking_stats": tracking_stats
                },
                usecase=self.name,
                category=self.category,
                context=context
            )
            
            # Add human-readable information
            result.summary = summary
            result.insights = insights
            result.predictions = predictions
            result.metrics = metrics
            
            # Add warnings for low confidence detections
            if config.confidence_threshold and config.confidence_threshold < 0.3:
                result.add_warning(f"Low confidence threshold ({config.confidence_threshold}) may result in false positives")
            
            processing_time = context.processing_time or time.time() - start_time
            self.logger.info(f"Vehicle monitoring completed successfully in {processing_time:.2f}s")
            return result
            
        except Exception as e:
            self.logger.error(f"Vehicle monitoring failed: {str(e)}", exc_info=True)
            
            if context:
                context.mark_completed()
            
            return self.create_error_result(
                str(e), 
                type(e).__name__,
                usecase=self.name,
                category=self.category,
                context=context
            )
    
    def _generate_insights(self, counting_summary: Dict, zone_analysis: Dict, config: VehicleMonitoringConfig) -> List[str]:
        """Generate human-readable insights from vehicle counting results."""
        insights = []
        
        total_vehicles = counting_summary.get("total_objects", 0)
        
        if total_vehicles == 0:
            insights.append("No vehicles detected in the scene")
            return insights
        
        # Basic count insight
        insights.append(f"EVENT: Detected {total_vehicles} vehicles in the scene")
        
        # Intensity calculation based on threshold percentage
        intensity_threshold = None
        if (config.alert_config and 
            config.alert_config.count_thresholds and 
            "all" in config.alert_config.count_thresholds):
            intensity_threshold = config.alert_config.count_thresholds["all"]
        
        if intensity_threshold is not None:
            # Calculate percentage relative to threshold
            percentage = (total_vehicles / intensity_threshold) * 100
            
            if percentage < 20:
                insights.append(f"INTENSITY: Low congestion in the scene ({percentage:.1f}% of capacity)")
            elif percentage <= 50:
                insights.append(f"INTENSITY: Moderate congestion in the scene ({percentage:.1f}% of capacity)")
            elif percentage <= 70:
                insights.append(f"INTENSITY:  Heavy congestion in the scene ({percentage:.1f}% of capacity)")
            else:
                insights.append(f"INTENSITY: Severe congestion in the scene ({percentage:.1f}% of capacity)")
        else:
            # Fallback to hardcoded thresholds if no alert config is set
            if total_vehicles > 15:
                insights.append(f"INTENSITY: Heavy congestion in the scene with {total_vehicles} vehicles")
            elif total_vehicles == 1:
                insights.append(f"INTENSITY: Low congestion in the scene")
        
        # Zone-specific insights
        if zone_analysis:
            for zone_name, zone_counts in zone_analysis.items():
                zone_total = sum(zone_counts.values()) if isinstance(zone_counts, dict) else zone_counts
                if zone_total > 0:
                    percentage = (zone_total / total_vehicles) * 100
                    insights.append(f"Zone '{zone_name}': {zone_total} vehicles ({percentage:.1f}% of total)")
                    
                    # Density insights
                    if zone_total > 10:
                        insights.append(f" High congestion density in zone '{zone_name}' with {zone_total} vehicles")
                    elif zone_total == 1:
                        insights.append(f"Low congestion in zone '{zone_name}'")
        
        # Category breakdown insights
        if "by_category" in counting_summary:
            category_counts = counting_summary["by_category"]
            for category, count in category_counts.items():
                if count > 0 and category in config.target_vehicle_categories:
                    percentage = (count / total_vehicles) * 100
                    insights.append(f"Category '{category}': {count} detections ({percentage:.1f}% of total)")
        
        # Unique counting insights
        if config.enable_unique_counting:
            unique_count = self._count_unique_tracks(counting_summary)
            if unique_count is not None:
                insights.append(f"Unique vehicle count: {unique_count}")
                if unique_count != total_vehicles:
                    insights.append(f"Detection efficiency: {unique_count}/{total_vehicles} unique tracks")
        
        return insights
    
    def _check_alerts(self, counting_summary: Dict, zone_analysis: Dict, config: VehicleMonitoringConfig) -> List[Dict]:
        """Check for alert conditions and generate vehicle alerts."""
        alerts = []
        
        if not config.alert_config:
            return alerts
        
        total_vehicles = counting_summary.get("total_objects", 0)
        
        # Count threshold alerts
        for category, threshold in config.alert_config.count_thresholds.items():
            if category == "all" and total_vehicles >= threshold:
                alerts.append({
                    "type": "count_threshold",
                    "severity": "warning",
                    "message": f"Total vehicle count ({total_vehicles}) exceeds threshold ({threshold})",
                    "category": category,
                    "current_count": total_vehicles,
                    "threshold": threshold
                })
            elif category in counting_summary.get("by_category", {}):
                count = counting_summary["by_category"][category]
                if count >= threshold:
                    alerts.append({
                        "type": "count_threshold",
                        "severity": "warning",
                        "message": f"{category} count ({count}) exceeds threshold ({threshold})",
                        "category": category,
                        "current_count": count,
                        "threshold": threshold
                    })
        
        # Zone occupancy threshold alerts
        for zone_name, threshold in config.alert_config.occupancy_thresholds.items():
            if zone_name in zone_analysis:
                zone_count = sum(zone_analysis[zone_name].values()) if isinstance(zone_analysis[zone_name], dict) else zone_analysis[zone_name]
                if zone_count >= threshold:
                    alerts.append({
                        "type": "occupancy_threshold",
                        "severity": "warning",
                        "message": f"Zone '{zone_name}' vehicle occupancy ({zone_count}) exceeds threshold ({threshold})",
                        "zone": zone_name,
                        "current_occupancy": zone_count,
                        "threshold": threshold
                    })
        
        return alerts
    
    def _calculate_metrics(self, counting_summary: Dict, zone_analysis: Dict, config: VehicleMonitoringConfig, context: ProcessingContext) -> Dict[str, Any]:
        """Calculate detailed metrics for vehicle analytics."""
        total_vehicles = counting_summary.get("total_objects", 0)
        
        metrics = {
            "total_vehicles": total_vehicles,
            "processing_time": context.processing_time or 0.0,
            "input_format": context.input_format.value,
            "confidence_threshold": config.confidence_threshold,
            "zones_analyzed": len(zone_analysis),
            "detection_rate": 0.0,
            "coverage_percentage": 0.0
        }
        
        # Calculate detection rate
        if config.time_window_minutes and config.time_window_minutes > 0:
            metrics["detection_rate"] = (total_vehicles / config.time_window_minutes) * 60
        
        # Calculate zone coverage
        if zone_analysis and total_vehicles > 0:
            vehicles_in_zones = sum(
                sum(zone_counts.values()) if isinstance(zone_counts, dict) else zone_counts
                for zone_counts in zone_analysis.values()
            )
            metrics["coverage_percentage"] = (vehicles_in_zones / total_vehicles) * 100
        
        # Unique tracking metrics
        if config.enable_unique_counting:
            unique_count = self._count_unique_tracks(counting_summary)
            if unique_count is not None:
                metrics["unique_vehicles"] = unique_count
                metrics["tracking_efficiency"] = (unique_count / total_vehicles) * 100 if total_vehicles > 0 else 0
        
        # Per-zone metrics
        if zone_analysis:
            zone_metrics = {}
            for zone_name, zone_counts in zone_analysis.items():
                zone_total = sum(zone_counts.values()) if isinstance(zone_counts, dict) else zone_counts
                zone_metrics[zone_name] = {
                    "count": zone_total,
                    "percentage": (zone_total / total_vehicles) * 100 if total_vehicles > 0 else 0
                }
            metrics["zone_metrics"] = zone_metrics
        
        return metrics
    
    def _extract_predictions(self, data: Any) -> List[Dict[str, Any]]:
        """Extract predictions from processed data for API compatibility."""
        predictions = []
        
        try:
            if isinstance(data, list):
                # Detection format
                for item in data:
                    prediction = self._normalize_prediction(item)
                    if prediction:
                        predictions.append(prediction)
            
            elif isinstance(data, dict):
                # Frame-based or tracking format
                for frame_id, items in data.items():
                    if isinstance(items, list):
                        for item in items:
                            prediction = self._normalize_prediction(item)
                            if prediction:
                                prediction["frame_id"] = frame_id
                                predictions.append(prediction)
        
        except Exception as e:
            self.logger.warning(f"Failed to extract predictions: {str(e)}")
        
        return predictions
    
    def _normalize_prediction(self, item: Dict[str, Any]) -> Dict[str, Any]:
        """Normalize a single prediction item."""
        if not isinstance(item, dict):
            return {}
        
        # Get the category from the item
        category = item.get("category", item.get("class", "unknown"))
        
        # Skip predictions with category 0 (human) or string 'human'
        if str(category) == "0" or category == "human":
            self.logger.debug(f"Skipping human detection (category: {category})")
            return {}
        
        return {
            "category": category,
            "confidence": item.get("confidence", item.get("score", 0.0)),
            "bounding_box": item.get("bounding_box", item.get("bbox", {})),
            "track_id": item.get("track_id")
        }
    
    def _get_detections_with_confidence(self, counting_summary: Dict) -> List[Dict]:
        """Extract detection items with confidence scores."""
        return counting_summary.get("detections", [])
    
    def _count_unique_tracks(self, counting_summary: Dict) -> Optional[int]:
        """Count unique tracks if tracking is enabled."""
        detections = self._get_detections_with_confidence(counting_summary)
        
        if not detections:
            return None
        
        # Count unique track IDs
        unique_tracks = set()
        for detection in detections:
            track_id = detection.get("track_id")
            if track_id is not None:
                unique_tracks.add(track_id)
        
        return len(unique_tracks) if unique_tracks else None
    
    def _generate_summary(self, counting_summary: Dict, zone_analysis: Dict, alerts: List) -> str:
        """Generate human-readable summary."""
        total_vehicles = counting_summary.get("total_objects", 0)
        
        if total_vehicles == 0:
            return "No vehicles detected in the scene"
        
        summary_parts = [f"{total_vehicles} vehicles detected"]
        
        if zone_analysis:
            zones_with_vehicles = sum(1 for zone_counts in zone_analysis.values() 
                                    if (sum(zone_counts.values()) if isinstance(zone_counts, dict) else zone_counts) > 0)
            summary_parts.append(f"across {zones_with_vehicles}/{len(zone_analysis)} zones")
        
        if alerts:
            alert_count = len(alerts)
            summary_parts.append(f"with {alert_count} alert{'s' if alert_count != 1 else ''}")
        
        return ", ".join(summary_parts)
    
    def _generate_events(self, counting_summary: Dict, zone_analysis: Dict, alerts: List, config: VehicleMonitoringConfig, frame_number: Optional[int] = None) -> List[Dict]:
        """Generate structured events for the output format with frame-based keys."""
        from datetime import datetime, timezone
        
        # Use frame number as key, fallback to 'current_frame' if not available
        frame_key = str(frame_number) if frame_number is not None else "current_frame"
        
        events = [{frame_key: []}]
        frame_events = events[0][frame_key]
        total_vehicles = counting_summary.get("total_objects", 0)
        
        if total_vehicles > 0:
            # Determine event level based on thresholds
            level = "info"
            intensity = 5.0
            
            if config.alert_config and config.alert_config.count_thresholds:
                threshold = config.alert_config.count_thresholds.get("all", 15)
                intensity = min(10.0, (total_vehicles / threshold) * 10)
                
                if intensity >= 7:
                    level = "critical"
                elif intensity >= 5:
                    level = "warning"
                else:
                    level = "info"
            else:
                if total_vehicles > 25:
                    level = "critical"
                    intensity = 9.0
                elif total_vehicles > 15:
                    level = "warning" 
                    intensity = 7.0
                else:
                    level = "info"
                    intensity = min(10.0, total_vehicles / 3.0)
            
            # Main vehicle monitoring event
            event = {
                "type": "vehicle_monitoring",
                "stream_time": datetime.now(timezone.utc).strftime("%Y-%m-%d-%H:%M:%S UTC"),
                "level": level,
                "intensity": round(intensity, 1),
                "config": {
                    "min_value": 0,
                    "max_value": 10,
                    "level_settings": {"info": 2, "warning": 5, "critical": 7}
                },
                "application_name": "Vehicle Monitoring System",
                "application_version": "1.2",
                "location_info": None,
                "human_text": f"{total_vehicles} vehicles detected"
            }
            frame_events.append(event)
        
        # Add zone-specific events if applicable
        if zone_analysis:
            for zone_name, zone_count in zone_analysis.items():
                if isinstance(zone_count, dict):
                    zone_total = sum(zone_count.values())
                else:
                    zone_total = zone_count
                
                if zone_total > 0:
                    zone_intensity = min(10.0, zone_total / 8.0)  # Adjusted for vehicles
                    zone_level = "info"
                    if zone_intensity >= 7:
                        zone_level = "warning"
                    elif zone_intensity >= 5:
                        zone_level = "info"
                    
                    zone_event = {
                        "type": "congestion_zone_monitoring",
                        "stream_time": datetime.now(timezone.utc).strftime("%Y-%m-%d-%H:%M:%S UTC"),
                        "level": zone_level,
                        "intensity": round(zone_intensity, 1),
                        "config": {
                            "min_value": 0,
                            "max_value": 10,
                            "level_settings": {"info": 2, "warning": 5, "critical": 7}
                        },
                        "application_name": "Congestion Zone Monitoring System",
                        "application_version": "1.2",
                        "location_info": zone_name,
                        "human_text": f"Event: Congestion Zone Monitoring\nLevel: {zone_level.title()}\nTime: {datetime.now(timezone.utc).strftime('%Y-%m-%d-%H:%M:%S UTC')}\nZone: {zone_name}\nCount: {zone_total} vehicles"
                    }
                    frame_events.append(zone_event)
        
        # Add alert events with simplified human_text
        for alert in alerts:
            total_vehicles = counting_summary.get("total_objects", 0)
            intensity_message = "Low congestion in the scene"
            if config.alert_config and config.alert_config.count_thresholds:
                threshold = config.alert_config.count_thresholds.get("all", 15)
                percentage = (total_vehicles / threshold) * 100 if threshold > 0 else 0
                if percentage < 20:
                    intensity_message = "Low congestion in the scene"
                elif percentage <= 50:
                    intensity_message = "Moderate congestion in the scene"
                elif percentage <= 70:
                    intensity_message = "Heavy congestion in the scene"
                else:
                    intensity_message = "Severe congestion in the scene"
            else:
                if total_vehicles > 15:
                    intensity_message = "Heavy congestion in the scene"
                elif total_vehicles == 1:
                    intensity_message = "Low congestion in the scene"
                else:
                    intensity_message = "Moderate congestion in the scene"

            alert_event = {
                "type": alert.get("type", "congestion_alert"),
                "stream_time": datetime.now(timezone.utc).strftime("%Y-%m-%d-%H:%M:%S UTC"),
                "level": alert.get("severity", "warning"),
                "intensity": 8.0,
                "config": {
                    "min_value": 0,
                    "max_value": 10,
                    "level_settings": {"info": 2, "warning": 5, "critical": 7}
                },
                "application_name": "Congestion Alert System",
                "application_version": "1.2",
                "location_info": alert.get("zone"),
                "human_text": f"{datetime.now(timezone.utc).strftime('%Y-%m-%d-%H:%M:%S UTC')} : {intensity_message}"
            }
            frame_events.append(alert_event)
        
        return events
    
    def _generate_tracking_stats(self, counting_summary: Dict, zone_analysis: Dict, insights: List[str], summary: str, config: VehicleMonitoringConfig, frame_number: Optional[int] = None) -> List[Dict]:
        """Generate structured tracking stats for the output format with frame-based keys."""
        # Use frame number as key, fallback to 'current_frame' if not available
        frame_key = str(frame_number) if frame_number is not None else "current_frame"
        
        tracking_stats = [{frame_key: []}]
        frame_tracking_stats = tracking_stats[0][frame_key]
        total_vehicles = counting_summary.get("total_objects", 0)
        
        if total_vehicles > 0 or zone_analysis:
            # Create main tracking stats entry
            tracking_stat = {
                "all_results_for_tracking": {
                    "total_vehicles": total_vehicles,
                    "zone_analysis": zone_analysis,
                    "counting_summary": counting_summary,
                    "detection_rate": (total_vehicles / config.time_window_minutes * 60) if config.time_window_minutes else 0,
                    "zones_count": len(zone_analysis) if zone_analysis else 0,
                    "unique_count": self._count_unique_tracks(counting_summary),
                    "congestion_flow_rate": (total_vehicles / config.time_window_minutes) if config.time_window_minutes else 0
                },
                "human_text": self._generate_human_text_for_tracking(counting_summary, zone_analysis, insights, summary, config)
            }
            frame_tracking_stats.append(tracking_stat)
        
        return tracking_stats
    
    def _generate_human_text_for_tracking(self, counting_summary: Dict, zone_analysis: Dict, insights: List[str], summary: str, config: VehicleMonitoringConfig) -> str:
        """Generate human-readable text for tracking stats."""
        category_counts = counting_summary.get("by_category", {})
        if not category_counts:
            return "No vehicles detected"
        
        # Generate summary of vehicle counts by category
        parts = []
        for category, count in category_counts.items():
            if count > 0 and category in config.target_vehicle_categories:
                parts.append(f"{count} {category}{'s' if count != 1 else ''}")
        
        return " and ".join(parts) if parts else "No vehicles detected"