"""
Video Content Analyzer for GAIA Agent - Phase 5
Provides comprehensive video content analysis including scene segmentation, temporal patterns, and content summarization.

Features:
- Scene segmentation and analysis
- Temporal pattern recognition
- Object interaction analysis
- Content summarization and reporting
- Key frame identification and extraction
- Video metadata analysis
"""

import os
import logging
import cv2
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
import json
from datetime import datetime, timedelta
from pathlib import Path
import tempfile

# Configure logging
logger = logging.getLogger(__name__)

class VideoContentAnalyzer:
    """Advanced video content analyzer for scene understanding and temporal analysis."""
    
    def __init__(self):
        """Initialize the video content analyzer."""
        self.available = True
        self.temp_dir = tempfile.mkdtemp()
        
        # Analysis parameters
        self.scene_change_threshold = 0.3
        self.keyframe_interval = 30  # Extract keyframe every 30 frames
        self.min_scene_duration = 2.0  # Minimum scene duration in seconds
        self.max_scenes = 50  # Maximum number of scenes to analyze
        
        # Initialize analysis components
        self._init_scene_analyzer()
        self._init_temporal_analyzer()
        
        logger.info(f"📹 Video Content Analyzer initialized - Available: {self.available}")
    
    def _init_scene_analyzer(self):
        """Initialize scene analysis components."""
        try:
            # Scene change detection parameters
            self.scene_detector_params = {
                'histogram_bins': 32,
                'color_spaces': ['HSV', 'RGB'],
                'comparison_methods': [cv2.HISTCMP_CORREL, cv2.HISTCMP_CHISQR],
                'motion_threshold': 0.1
            }
            logger.info("✅ Scene analyzer initialized")
        except Exception as e:
            logger.warning(f"⚠️ Scene analyzer initialization failed: {e}")
    
    def _init_temporal_analyzer(self):
        """Initialize temporal analysis components."""
        try:
            # Temporal pattern analysis parameters
            self.temporal_params = {
                'pattern_window': 10,  # Analyze patterns over 10 frame windows
                'smoothing_factor': 0.3,
                'trend_threshold': 0.1,
                'periodicity_detection': True
            }
            logger.info("✅ Temporal analyzer initialized")
        except Exception as e:
            logger.warning(f"⚠️ Temporal analyzer initialization failed: {e}")
    
    def analyze_video_content(self, video_path: str, 
                            object_detections: List[List[Dict[str, Any]]] = None,
                            question: str = None) -> Dict[str, Any]:
        """
        Perform comprehensive video content analysis.
        
        Args:
            video_path: Path to video file
            object_detections: Optional pre-computed object detections per frame
            question: Optional question to guide analysis
            
        Returns:
            Comprehensive content analysis results
        """
        try:
            logger.info(f"📹 Starting video content analysis for: {video_path}")
            
            # Extract video metadata
            metadata = self._extract_video_metadata(video_path)
            
            # Perform scene segmentation
            scenes = self._segment_scenes(video_path)
            
            # Extract key frames
            keyframes = self._extract_keyframes(video_path, scenes)
            
            # Analyze temporal patterns
            temporal_analysis = self._analyze_temporal_patterns(
                video_path, object_detections, scenes
            )
            
            # Perform content summarization
            content_summary = self._summarize_content(
                scenes, keyframes, temporal_analysis, object_detections
            )
            
            # Generate interaction analysis
            interaction_analysis = self._analyze_object_interactions(
                object_detections, scenes
            )
            
            # Create comprehensive report
            analysis_report = self._create_content_report(
                metadata, scenes, keyframes, temporal_analysis,
                content_summary, interaction_analysis, question
            )
            
            return analysis_report
            
        except Exception as e:
            logger.error(f"❌ Video content analysis failed: {e}")
            return {
                'success': False,
                'error': f'Content analysis failed: {str(e)}'
            }
    
    def _extract_video_metadata(self, video_path: str) -> Dict[str, Any]:
        """Extract comprehensive video metadata."""
        try:
            cap = cv2.VideoCapture(video_path)
            if not cap.isOpened():
                raise Exception("Failed to open video file")
            
            # Basic properties
            fps = cap.get(cv2.CAP_PROP_FPS)
            frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            duration = frame_count / fps if fps > 0 else 0
            
            # Additional properties
            fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))
            codec = "".join([chr((fourcc >> 8 * i) & 0xFF) for i in range(4)])
            
            cap.release()
            
            metadata = {
                'filename': os.path.basename(video_path),
                'duration_seconds': duration,
                'fps': fps,
                'frame_count': frame_count,
                'resolution': {'width': width, 'height': height},
                'aspect_ratio': width / height if height > 0 else 1.0,
                'codec': codec,
                'file_size': os.path.getsize(video_path) if os.path.exists(video_path) else 0,
                'analysis_timestamp': datetime.now().isoformat()
            }
            
            logger.info(f"📊 Video metadata extracted: {duration:.1f}s, {width}x{height}, {fps:.1f} FPS")
            return metadata
            
        except Exception as e:
            logger.error(f"❌ Failed to extract video metadata: {e}")
            return {}
    
    def _segment_scenes(self, video_path: str) -> List[Dict[str, Any]]:
        """Segment video into distinct scenes based on visual changes."""
        try:
            cap = cv2.VideoCapture(video_path)
            if not cap.isOpened():
                raise Exception("Failed to open video file")
            
            scenes = []
            prev_hist = None
            scene_start = 0
            frame_count = 0
            fps = cap.get(cv2.CAP_PROP_FPS)
            
            scene_id = 0
            
            while cap.isOpened():
                ret, frame = cap.read()
                if not ret:
                    break
                
                # Calculate histogram for scene change detection
                hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
                hist = cv2.calcHist([hsv], [0, 1, 2], None, 
                                  [self.scene_detector_params['histogram_bins']] * 3, 
                                  [0, 180, 0, 256, 0, 256])
                
                # Detect scene change
                if prev_hist is not None:
                    correlation = cv2.compareHist(hist, prev_hist, cv2.HISTCMP_CORREL)
                    
                    if correlation < self.scene_change_threshold:
                        # Scene change detected
                        scene_end = frame_count
                        scene_duration = (scene_end - scene_start) / fps
                        
                        if scene_duration >= self.min_scene_duration:
                            scene = {
                                'id': scene_id,
                                'start_frame': scene_start,
                                'end_frame': scene_end,
                                'start_time': scene_start / fps,
                                'end_time': scene_end / fps,
                                'duration': scene_duration,
                                'frame_count': scene_end - scene_start
                            }
                            scenes.append(scene)
                            scene_id += 1
                            
                            if len(scenes) >= self.max_scenes:
                                break
                        
                        scene_start = frame_count
                
                prev_hist = hist
                frame_count += 1
            
            # Add final scene
            if scene_start < frame_count:
                scene_duration = (frame_count - scene_start) / fps
                if scene_duration >= self.min_scene_duration:
                    scene = {
                        'id': scene_id,
                        'start_frame': scene_start,
                        'end_frame': frame_count,
                        'start_time': scene_start / fps,
                        'end_time': frame_count / fps,
                        'duration': scene_duration,
                        'frame_count': frame_count - scene_start
                    }
                    scenes.append(scene)
            
            cap.release()
            
            logger.info(f"🎬 Scene segmentation complete: {len(scenes)} scenes detected")
            return scenes
            
        except Exception as e:
            logger.error(f"❌ Scene segmentation failed: {e}")
            return []
    
    def _extract_keyframes(self, video_path: str, scenes: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Extract representative keyframes from video scenes."""
        try:
            cap = cv2.VideoCapture(video_path)
            if not cap.isOpened():
                raise Exception("Failed to open video file")
            
            keyframes = []
            fps = cap.get(cv2.CAP_PROP_FPS)
            
            for scene in scenes:
                # Extract keyframes from each scene
                scene_keyframes = []
                
                # Extract keyframe from middle of scene
                mid_frame = (scene['start_frame'] + scene['end_frame']) // 2
                cap.set(cv2.CAP_PROP_POS_FRAMES, mid_frame)
                ret, frame = cap.read()
                
                if ret:
                    keyframe = {
                        'scene_id': scene['id'],
                        'frame_number': mid_frame,
                        'timestamp': mid_frame / fps,
                        'type': 'scene_representative',
                        'frame_data': frame,
                        'visual_features': self._extract_visual_features(frame)
                    }
                    scene_keyframes.append(keyframe)
                
                # Extract additional keyframes for longer scenes
                if scene['duration'] > 10:  # For scenes longer than 10 seconds
                    # Extract keyframes at 1/4 and 3/4 points
                    for fraction in [0.25, 0.75]:
                        frame_pos = int(scene['start_frame'] + 
                                      fraction * (scene['end_frame'] - scene['start_frame']))
                        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_pos)
                        ret, frame = cap.read()
                        
                        if ret:
                            keyframe = {
                                'scene_id': scene['id'],
                                'frame_number': frame_pos,
                                'timestamp': frame_pos / fps,
                                'type': 'temporal_sample',
                                'frame_data': frame,
                                'visual_features': self._extract_visual_features(frame)
                            }
                            scene_keyframes.append(keyframe)
                
                keyframes.extend(scene_keyframes)
            
            cap.release()
            
            logger.info(f"🖼️ Keyframe extraction complete: {len(keyframes)} keyframes extracted")
            return keyframes
            
        except Exception as e:
            logger.error(f"❌ Keyframe extraction failed: {e}")
            return []
    
    def _extract_visual_features(self, frame: np.ndarray) -> Dict[str, Any]:
        """Extract visual features from a frame."""
        try:
            features = {}
            
            # Color histogram
            hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
            hist_h = cv2.calcHist([hsv], [0], None, [32], [0, 180])
            hist_s = cv2.calcHist([hsv], [1], None, [32], [0, 256])
            hist_v = cv2.calcHist([hsv], [2], None, [32], [0, 256])
            
            features['color_histogram'] = {
                'hue': hist_h.flatten().tolist(),
                'saturation': hist_s.flatten().tolist(),
                'value': hist_v.flatten().tolist()
            }
            
            # Edge density
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            edges = cv2.Canny(gray, 50, 150)
            edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1])
            features['edge_density'] = float(edge_density)
            
            # Brightness and contrast
            features['brightness'] = float(np.mean(gray))
            features['contrast'] = float(np.std(gray))
            
            # Dominant colors
            features['dominant_colors'] = self._get_dominant_colors(frame)
            
            return features
            
        except Exception as e:
            logger.error(f"❌ Visual feature extraction failed: {e}")
            return {}
    
    def _get_dominant_colors(self, frame: np.ndarray, k: int = 3) -> List[List[int]]:
        """Extract dominant colors from frame using k-means clustering."""
        try:
            # Reshape frame to list of pixels
            pixels = frame.reshape(-1, 3)
            
            # Use k-means to find dominant colors
            from sklearn.cluster import KMeans
            kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
            kmeans.fit(pixels)
            
            # Get dominant colors
            colors = kmeans.cluster_centers_.astype(int)
            return colors.tolist()
            
        except ImportError:
            # Fallback without sklearn
            return [[128, 128, 128]]  # Gray as default
        except Exception as e:
            logger.error(f"❌ Dominant color extraction failed: {e}")
            return [[128, 128, 128]]
    
    def _analyze_temporal_patterns(self, video_path: str, 
                                 object_detections: List[List[Dict[str, Any]]] = None,
                                 scenes: List[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Analyze temporal patterns in video content."""
        try:
            temporal_analysis = {
                'motion_patterns': [],
                'object_appearance_patterns': [],
                'scene_transition_patterns': [],
                'activity_levels': [],
                'periodicity': {}
            }
            
            if not object_detections:
                return temporal_analysis
            
            # Analyze motion patterns
            motion_levels = []
            for frame_detections in object_detections:
                # Calculate motion level based on number and size of objects
                motion_level = len(frame_detections)
                if frame_detections:
                    avg_area = np.mean([det.get('area', 0) for det in frame_detections])
                    motion_level += avg_area / 10000  # Normalize area contribution
                
                motion_levels.append(motion_level)
            
            temporal_analysis['motion_patterns'] = motion_levels
            
            # Analyze object appearance patterns
            object_counts_over_time = []
            bird_counts_over_time = []
            animal_counts_over_time = []
            
            for frame_detections in object_detections:
                object_count = len(frame_detections)
                bird_count = sum(1 for det in frame_detections 
                               if det.get('species_type') == 'bird')
                animal_count = sum(1 for det in frame_detections 
                                 if det.get('species_type') == 'animal')
                
                object_counts_over_time.append(object_count)
                bird_counts_over_time.append(bird_count)
                animal_counts_over_time.append(animal_count)
            
            temporal_analysis['object_appearance_patterns'] = {
                'total_objects': object_counts_over_time,
                'birds': bird_counts_over_time,
                'animals': animal_counts_over_time
            }
            
            # Analyze activity levels
            window_size = self.temporal_params['pattern_window']
            activity_levels = []
            
            for i in range(0, len(motion_levels), window_size):
                window = motion_levels[i:i+window_size]
                if window:
                    activity_level = {
                        'start_frame': i,
                        'end_frame': min(i + window_size, len(motion_levels)),
                        'avg_motion': np.mean(window),
                        'max_motion': np.max(window),
                        'motion_variance': np.var(window)
                    }
                    activity_levels.append(activity_level)
            
            temporal_analysis['activity_levels'] = activity_levels
            
            # Detect periodicity in object appearances
            if len(bird_counts_over_time) > 20:  # Need sufficient data
                temporal_analysis['periodicity'] = self._detect_periodicity(
                    bird_counts_over_time, animal_counts_over_time
                )
            
            logger.info("📈 Temporal pattern analysis complete")
            return temporal_analysis
            
        except Exception as e:
            logger.error(f"❌ Temporal pattern analysis failed: {e}")
            return {}
    
    def _detect_periodicity(self, bird_counts: List[int], 
                          animal_counts: List[int]) -> Dict[str, Any]:
        """Detect periodic patterns in object appearances."""
        try:
            periodicity = {
                'bird_patterns': {},
                'animal_patterns': {},
                'combined_patterns': {}
            }
            
            # Simple autocorrelation-based periodicity detection
            def autocorrelation(signal, max_lag=50):
                signal = np.array(signal)
                n = len(signal)
                signal = signal - np.mean(signal)
                
                autocorr = []
                for lag in range(min(max_lag, n//2)):
                    if n - lag > 0:
                        corr = np.corrcoef(signal[:-lag], signal[lag:])[0, 1]
                        autocorr.append(corr if not np.isnan(corr) else 0)
                    else:
                        autocorr.append(0)
                
                return autocorr
            
            # Analyze bird count periodicity
            bird_autocorr = autocorrelation(bird_counts)
            if bird_autocorr:
                max_corr_idx = np.argmax(bird_autocorr[1:]) + 1  # Skip lag 0
                periodicity['bird_patterns'] = {
                    'dominant_period': max_corr_idx,
                    'correlation_strength': bird_autocorr[max_corr_idx],
                    'is_periodic': bird_autocorr[max_corr_idx] > 0.3
                }
            
            # Analyze animal count periodicity
            animal_autocorr = autocorrelation(animal_counts)
            if animal_autocorr:
                max_corr_idx = np.argmax(animal_autocorr[1:]) + 1
                periodicity['animal_patterns'] = {
                    'dominant_period': max_corr_idx,
                    'correlation_strength': animal_autocorr[max_corr_idx],
                    'is_periodic': animal_autocorr[max_corr_idx] > 0.3
                }
            
            return periodicity
            
        except Exception as e:
            logger.error(f"❌ Periodicity detection failed: {e}")
            return {}
    
    def _summarize_content(self, scenes: List[Dict[str, Any]], 
                         keyframes: List[Dict[str, Any]],
                         temporal_analysis: Dict[str, Any],
                         object_detections: List[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
        """Generate comprehensive content summary."""
        try:
            summary = {
                'overview': {},
                'scene_summary': [],
                'key_moments': [],
                'content_highlights': [],
                'statistical_summary': {}
            }
            
            # Overview
            total_duration = sum(scene.get('duration', 0) for scene in scenes)
            summary['overview'] = {
                'total_scenes': len(scenes),
                'total_duration': total_duration,
                'avg_scene_duration': total_duration / len(scenes) if scenes else 0,
                'keyframes_extracted': len(keyframes)
            }
            
            # Scene summary
            for scene in scenes:
                scene_summary = {
                    'scene_id': scene['id'],
                    'duration': scene['duration'],
                    'description': f"Scene {scene['id'] + 1}: {scene['duration']:.1f}s",
                    'activity_level': 'unknown'
                }
                
                # Determine activity level from temporal analysis
                if temporal_analysis.get('activity_levels'):
                    scene_start_frame = scene['start_frame']
                    scene_end_frame = scene['end_frame']
                    
                    relevant_activities = [
                        activity for activity in temporal_analysis['activity_levels']
                        if (activity['start_frame'] <= scene_end_frame and 
                            activity['end_frame'] >= scene_start_frame)
                    ]
                    
                    if relevant_activities:
                        avg_motion = np.mean([a['avg_motion'] for a in relevant_activities])
                        if avg_motion > 2:
                            scene_summary['activity_level'] = 'high'
                        elif avg_motion > 1:
                            scene_summary['activity_level'] = 'medium'
                        else:
                            scene_summary['activity_level'] = 'low'
                
                summary['scene_summary'].append(scene_summary)
            
            # Key moments (high activity periods)
            if temporal_analysis.get('activity_levels'):
                high_activity_moments = [
                    activity for activity in temporal_analysis['activity_levels']
                    if activity['avg_motion'] > 2
                ]
                
                summary['key_moments'] = [
                    {
                        'timestamp': moment['start_frame'] / 30,  # Assume 30 FPS
                        'duration': (moment['end_frame'] - moment['start_frame']) / 30,
                        'activity_level': moment['avg_motion'],
                        'description': f"High activity period: {moment['avg_motion']:.1f}"
                    }
                    for moment in high_activity_moments[:5]  # Top 5 moments
                ]
            
            # Statistical summary
            if object_detections:
                all_detections = [det for frame_dets in object_detections for det in frame_dets]
                
                species_counts = {}
                for detection in all_detections:
                    species = detection.get('species_type', 'unknown')
                    species_counts[species] = species_counts.get(species, 0) + 1
                
                summary['statistical_summary'] = {
                    'total_detections': len(all_detections),
                    'species_distribution': species_counts,
                    'avg_detections_per_frame': len(all_detections) / len(object_detections) if object_detections else 0
                }
            
            logger.info("📋 Content summarization complete")
            return summary
            
        except Exception as e:
            logger.error(f"❌ Content summarization failed: {e}")
            return {}
    
    def _analyze_object_interactions(self, object_detections: List[List[Dict[str, Any]]] = None,
                                   scenes: List[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Analyze interactions between detected objects."""
        try:
            interaction_analysis = {
                'proximity_interactions': [],
                'temporal_interactions': [],
                'species_interactions': {},
                'interaction_summary': {}
            }
            
            if not object_detections:
                return interaction_analysis
            
            # Analyze proximity interactions within frames
            for frame_idx, frame_detections in enumerate(object_detections):
                if len(frame_detections) > 1:
                    # Check all pairs of objects in the frame
                    for i, obj1 in enumerate(frame_detections):
                        for j, obj2 in enumerate(frame_detections[i+1:], i+1):
                            distance = self._calculate_object_distance(obj1, obj2)
                            
                            if distance < 100:  # Close proximity threshold
                                interaction = {
                                    'frame': frame_idx,
                                    'timestamp': frame_idx / 30,  # Assume 30 FPS
                                    'object1': obj1.get('class', 'unknown'),
                                    'object2': obj2.get('class', 'unknown'),
                                    'distance': distance,
                                    'interaction_type': 'proximity'
                                }
                                interaction_analysis['proximity_interactions'].append(interaction)
            
            # Analyze species interactions
            species_pairs = {}
            for interaction in interaction_analysis['proximity_interactions']:
                obj1_type = interaction['object1']
                obj2_type = interaction['object2']
                pair_key = tuple(sorted([obj1_type, obj2_type]))
                
                if pair_key not in species_pairs:
                    species_pairs[pair_key] = []
                species_pairs[pair_key].append(interaction)
            
            interaction_analysis['species_interactions'] = {
                f"{pair[0]}-{pair[1]}": {
                    'interaction_count': len(interactions),
                    'avg_distance': np.mean([i['distance'] for i in interactions]),
                    'duration': len(interactions) / 30  # Approximate duration
                }
                for pair, interactions in species_pairs.items()
            }
            
            # Interaction summary
            interaction_analysis['interaction_summary'] = {
                'total_proximity_interactions': len(interaction_analysis['proximity_interactions']),
                'unique_species_pairs': len(species_pairs),
                'most_interactive_pair': max(species_pairs.keys(), 
                                           key=lambda x: len(species_pairs[x])) if species_pairs else None
            }
            
            logger.info("🤝 Object interaction analysis complete")
            return interaction_analysis
            
        except Exception as e:
            logger.error(f"❌ Object interaction analysis failed: {e}")
            return {}
    
    def _calculate_object_distance(self, obj1: Dict[str, Any], obj2: Dict[str, Any]) -> float:
        """Calculate distance between two objects based on their centers."""
        try:
            center1 = obj1.get('center', [0, 0])
            center2 = obj2.get('center', [0, 0])
            
            distance = np.sqrt((center1[0] - center2[0])**2 + (center1[1] - center2[1])**2)
            return float(distance)
            
        except Exception as e:
            logger.error(f"❌ Distance calculation failed: {e}")
            return float('inf')
    
    def _create_content_report(self, metadata: Dict[str, Any],
                             scenes: List[Dict[str, Any]],
                             keyframes: List[Dict[str, Any]],
                             temporal_analysis: Dict[str, Any],
                             content_summary: Dict[str, Any],
                             interaction_analysis: Dict[str, Any],
                             question: str = None) -> Dict[str, Any]:
        """Create comprehensive content analysis report."""
        try:
            report = {
                'success': True,
                'analysis_timestamp': datetime.now().isoformat(),
                'question': question,
                'metadata': metadata,
                'content_analysis': {
                    'scenes': scenes,
                    'keyframes': [
                        {k: v for k, v in kf.items() if k != 'frame_data'}  # Exclude frame data
                        for kf in keyframes
                    ],
                    'temporal_patterns': temporal_analysis,
                    'content_summary': content_summary,
                    'interactions': interaction_analysis
                },
                'insights': [],
                'recommendations': []
            }
            
            # Generate insights
            insights = []
            
            # Scene insights
            if scenes:
                avg_scene_duration = np.mean([s['duration'] for s in scenes])
                insights.append(f"Video contains {len(scenes)} distinct scenes with average duration of {avg_scene_duration:.1f}s")
            
            # Activity insights
            if temporal_analysis.get('activity_levels'):
                high_activity_count = sum(1 for a in temporal_analysis['activity_levels'] if a['avg_motion'] > 2)
                insights.append(f"Detected {high_activity_count} high-activity periods in the video")
            
            # Interaction insights
            if interaction_analysis.get('interaction_summary', {}).get('total_proximity_interactions', 0) > 0:
                total_interactions = interaction_analysis['interaction_summary']['total_proximity_interactions']
                insights.append(f"Found {total_interactions} object proximity interactions")
            
            report['insights'] = insights
            
            # Generate recommendations
            recommendations = []
            
            if question and 'bird' in question.lower():
                if temporal_analysis.get('object_appearance_patterns', {}).get('birds'):
                    max_birds = max(temporal_analysis['object_appearance_patterns']['birds'])
                    recommendations.append(f"Maximum simultaneous birds detected: {max_birds}")
            
            if len(scenes) > 10:
                recommendations.append("Video has many scene changes - consider analyzing key scenes only")
            
            report['recommendations'] = recommendations
            
            logger.info("📊 Content analysis report generated successfully")
            return report
            
        except Exception as e:
            logger.error(f"❌ Failed to create content report: {e}")
            return {
                'success': False,
                'error': f'Failed to create content report: {str(e)}'
            }
    
    def get_capabilities(self) -> Dict[str, Any]:
        """Get video content analyzer capabilities."""
        return {
            'available': self.available,
            'scene_change_threshold': self.scene_change_threshold,
            'keyframe_interval': self.keyframe_interval,
            'min_scene_duration': self.min_scene_duration,
            'max_scenes': self.max_scenes,
            'features': [
                'Scene segmentation',
                'Keyframe extraction',
                'Temporal pattern analysis',
                'Object interaction analysis',
                'Content summarization',
                'Visual feature extraction',
                'Activity level detection',
                'Periodicity detection'
            ]
        }


# Factory function for creating content analyzer
def create_video_content_analyzer() -> VideoContentAnalyzer:
    """Create and return a video content analyzer instance."""
    return VideoContentAnalyzer()


if __name__ == "__main__":
    # Test the content analyzer
    analyzer = VideoContentAnalyzer()
    print(f"Content analyzer available: {analyzer.available}")
    print(f"Capabilities: {json.dumps(analyzer.get_capabilities(), indent=2)}")