src/api/advanced_face_api.py

"""
Advanced Face Capture API Endpoints
Integrating WebRTC capture with TimescaleDB and real-time processing
"""

from flask import Blueprint, request, jsonify, render_template
import base64
import io
import cv2
import numpy as np
from PIL import Image
import asyncio
import json
from datetime import datetime
import logging
from typing import Dict, Any, Optional

from src.webrtc.advanced_face_capture import AdvancedFaceCapture, FaceQualityMetrics
from src.telemetry import log_performance_metric
from src.api_utils import handle_api_error, validate_request_data

logger = logging.getLogger(__name__)

# Create Blueprint for advanced face capture APIs
advanced_face_bp = Blueprint('advanced_face', __name__, url_prefix='/api')

# Global capture system instance
capture_system: Optional[AdvancedFaceCapture] = None

def get_capture_system():
    """Get or create the advanced face capture system"""
    global capture_system
    if capture_system is None:
        config = {
            'database': {
                'host': 'localhost',
                'port': 5432,
                'database': 'morphguard',
                'user': 'morphguard',
                'password': 'morphguard123'
            },
            'models': {
                'face_mesh_confidence': 0.7,
                'face_detection_confidence': 0.7,
                'pose_confidence': 0.7
            },
            'quality': {
                'sharpness_threshold': 0.6,
                'illumination_threshold': 0.6,
                'pose_angle_threshold': 25.0,
                'eye_distance_min': 60,
                'eye_distance_max': 120
            }
        }
        capture_system = AdvancedFaceCapture(config)
    return capture_system

@advanced_face_bp.route('/advanced-capture')
def advanced_capture_page():
    """Serve the advanced capture interface"""
    return render_template('advanced_capture.html')

@advanced_face_bp.route('/store-face-metrics', methods=['POST'])
@handle_api_error
def store_face_metrics():
    """Store real-time face quality metrics in TimescaleDB"""
    try:
        data = request.get_json()
        
        # Validate required fields
        required_fields = ['sessionId', 'frameNumber', 'timestamp', 'qualityMetrics']
        missing_fields = [field for field in required_fields if field not in data]
        if missing_fields:
            return jsonify({
                'success': False,
                'error': f'Missing required fields: {missing_fields}'
            }), 400

        # Extract metrics data
        session_id = data['sessionId']
        frame_number = data['frameNumber']
        timestamp = datetime.fromisoformat(data['timestamp'].replace('Z', '+00:00'))
        quality_metrics = data['qualityMetrics']
        pose_angles = data.get('poseAngles', {})
        is_frontal = data.get('isFrontal', False)
        landmarks = data.get('landmarks', [])
        performance_stats = data.get('performanceStats', {})

        # Get capture system and store metrics
        system = get_capture_system()
        
        # Create FaceQualityMetrics object
        metrics = FaceQualityMetrics(
            sharpness_score=quality_metrics.get('sharpnessScore', 0),
            illumination_score=quality_metrics.get('illuminationScore', 0),
            pose_score=quality_metrics.get('poseScore', 0),
            eye_distance_score=quality_metrics.get('eyeDistanceScore', 0),
            motion_blur_score=quality_metrics.get('motionBlurScore', 0),
            overall_score=quality_metrics.get('overallScore', 0),
            confidence=quality_metrics.get('confidence', 0),
            timestamp=timestamp,
            processing_time_ms=performance_stats.get('processingTime', 0)
        )

        # Store metrics asynchronously
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        try:
            loop.run_until_complete(
                system.store_metrics(session_id, metrics, landmarks, pose_angles, is_frontal)
            )
        finally:
            loop.close()

        # Log performance metrics for monitoring
        log_performance_metric(
            'face_capture_frame_processed',
            metrics.processing_time_ms,
            {
                'session_id': session_id,
                'quality_score': metrics.overall_score,
                'is_frontal': is_frontal,
                'fps': performance_stats.get('fps', 0)
            }
        )

        return jsonify({
            'success': True,
            'message': 'Metrics stored successfully',
            'frame_number': frame_number
        })

    except Exception as e:
        logger.error(f"Error storing face metrics: {e}")
        return jsonify({
            'success': False,
            'error': str(e)
        }), 500

@advanced_face_bp.route('/process-captured-face', methods=['POST'])
@handle_api_error
def process_captured_face():
    """Process a captured face image with comprehensive analysis"""
    try:
        data = request.get_json()
        
        # Validate required fields
        if 'image' not in data:
            return jsonify({
                'success': False,
                'error': 'No image data provided'
            }), 400

        # Decode base64 image
        image_data = data['image']
        if image_data.startswith('data:image'):
            image_data = image_data.split(',')[1]
        
        image_bytes = base64.b64decode(image_data)
        image = Image.open(io.BytesIO(image_bytes))
        
        # Convert to OpenCV format
        cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
        
        # Extract metadata
        session_id = data.get('sessionId', 'unknown')
        quality_metrics = data.get('qualityMetrics', {})
        
        # Get capture system
        system = get_capture_system()
        
        # Process the image
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        try:
            result = loop.run_until_complete(
                system.process_frame(cv_image, session_id)
            )
        finally:
            loop.close()

        if result is None:
            return jsonify({
                'success': False,
                'error': 'No face detected in the captured image'
            }), 400

        # Prepare response with comprehensive analysis
        response_data = {
            'success': True,
            'message': 'Face processed successfully',
            'analysis': {
                'quality_metrics': {
                    'overall_score': result.quality_metrics.overall_score,
                    'sharpness_score': result.quality_metrics.sharpness_score,
                    'illumination_score': result.quality_metrics.illumination_score,
                    'pose_score': result.quality_metrics.pose_score,
                    'eye_distance_score': result.quality_metrics.eye_distance_score,
                    'motion_blur_score': result.quality_metrics.motion_blur_score,
                    'confidence': result.quality_metrics.confidence
                },
                'pose_angles': result.pose_angles,
                'is_frontal': result.is_frontal,
                'face_box': result.face_box,
                'landmark_count': len(result.landmarks),
                'processing_time_ms': result.quality_metrics.processing_time_ms
            },
            'session_id': session_id,
            'timestamp': result.quality_metrics.timestamp.isoformat()
        }

        # Store the processed result
        await_store_processed_result(result, session_id)

        # Log successful processing
        log_performance_metric(
            'face_image_processed',
            result.quality_metrics.processing_time_ms,
            {
                'session_id': session_id,
                'quality_score': result.quality_metrics.overall_score,
                'is_frontal': result.is_frontal,
                'success': True
            }
        )

        return jsonify(response_data)

    except Exception as e:
        logger.error(f"Error processing captured face: {e}")
        return jsonify({
            'success': False,
            'error': str(e)
        }), 500

def await_store_processed_result(result, session_id):
    """Store processed face result in database"""
    try:
        system = get_capture_system()
        if system.db_conn:
            with system.db_conn.cursor() as cursor:
                cursor.execute("""
                    INSERT INTO processed_face_captures (
                        timestamp, session_id, quality_score, pose_angles,
                        is_frontal, face_box, landmark_count, processing_time_ms
                    ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
                """, (
                    result.quality_metrics.timestamp,
                    session_id,
                    result.quality_metrics.overall_score,
                    json.dumps(result.pose_angles),
                    result.is_frontal,
                    json.dumps(result.face_box),
                    len(result.landmarks),
                    result.quality_metrics.processing_time_ms
                ))
                system.db_conn.commit()
    except Exception as e:
        logger.error(f"Failed to store processed result: {e}")

@advanced_face_bp.route('/session-metrics/<session_id>')
@handle_api_error
def get_session_metrics(session_id):
    """Get comprehensive metrics for a capture session"""
    try:
        system = get_capture_system()
        
        if not system.db_conn:
            return jsonify({
                'success': False,
                'error': 'Database connection not available'
            }), 500

        with system.db_conn.cursor() as cursor:
            # Get session summary
            cursor.execute("""
                SELECT 
                    COUNT(*) as total_frames,
                    AVG(overall_score) as avg_quality,
                    MAX(overall_score) as max_quality,
                    MIN(overall_score) as min_quality,
                    AVG(processing_time_ms) as avg_processing_time,
                    COUNT(CASE WHEN is_frontal THEN 1 END) as frontal_frames
                FROM face_quality_metrics 
                WHERE session_id = %s
            """, (session_id,))
            
            summary = cursor.fetchone()
            
            # Get quality timeline
            cursor.execute("""
                SELECT timestamp, overall_score, is_frontal, processing_time_ms
                FROM face_quality_metrics 
                WHERE session_id = %s 
                ORDER BY timestamp 
                LIMIT 100
            """, (session_id,))
            
            timeline = cursor.fetchall()

        if summary is None:
            return jsonify({
                'success': False,
                'error': 'Session not found'
            }), 404

        response_data = {
            'success': True,
            'session_id': session_id,
            'summary': {
                'total_frames': summary[0] or 0,
                'avg_quality': float(summary[1] or 0),
                'max_quality': float(summary[2] or 0),
                'min_quality': float(summary[3] or 0),
                'avg_processing_time': float(summary[4] or 0),
                'frontal_frames': summary[5] or 0,
                'frontal_percentage': (summary[5] or 0) / max(summary[0] or 1, 1) * 100
            },
            'timeline': [
                {
                    'timestamp': row[0].isoformat(),
                    'quality_score': float(row[1]),
                    'is_frontal': row[2],
                    'processing_time_ms': float(row[3])
                }
                for row in timeline
            ]
        }

        return jsonify(response_data)

    except Exception as e:
        logger.error(f"Error getting session metrics: {e}")
        return jsonify({
            'success': False,
            'error': str(e)
        }), 500

@advanced_face_bp.route('/performance-stats')
@handle_api_error
def get_performance_stats():
    """Get real-time performance statistics"""
    try:
        system = get_capture_system()
        stats = system.get_performance_stats()
        
        return jsonify({
            'success': True,
            'performance_stats': stats,
            'timestamp': datetime.now().isoformat()
        })

    except Exception as e:
        logger.error(f"Error getting performance stats: {e}")
        return jsonify({
            'success': False,
            'error': str(e)
        }), 500

@advanced_face_bp.route('/system-health')
@handle_api_error
def get_system_health():
    """Get comprehensive system health status"""
    try:
        system = get_capture_system()
        
        # Check database connection
        db_status = 'connected' if system.db_conn else 'disconnected'
        
        # Check models status
        models_status = {
            'face_mesh': hasattr(system, 'face_mesh') and system.face_mesh is not None,
            'face_detection': hasattr(system, 'face_detection') and system.face_detection is not None,
            'pose_detection': hasattr(system, 'pose') and system.pose is not None,
            'dlib_predictor': system.dlib_predictor is not None
        }
        
        # Get recent performance metrics
        performance = system.get_performance_stats()
        
        # Calculate health score
        health_factors = [
            db_status == 'connected',
            all(models_status.values()),
            performance.get('avg_processing_time_ms', 0) < 200,  # Processing under 200ms
            performance.get('fps', 0) > 10  # FPS above 10
        ]
        
        health_score = sum(health_factors) / len(health_factors)
        
        response_data = {
            'success': True,
            'health_score': health_score,
            'status': 'healthy' if health_score > 0.75 else 'degraded' if health_score > 0.5 else 'unhealthy',
            'components': {
                'database': {
                    'status': db_status,
                    'healthy': db_status == 'connected'
                },
                'models': {
                    'status': models_status,
                    'healthy': all(models_status.values())
                },
                'performance': {
                    'status': performance,
                    'healthy': performance.get('avg_processing_time_ms', 0) < 200
                }
            },
            'timestamp': datetime.now().isoformat()
        }

        return jsonify(response_data)

    except Exception as e:
        logger.error(f"Error getting system health: {e}")
        return jsonify({
            'success': False,
            'error': str(e)
        }), 500

@advanced_face_bp.route('/optimize-settings', methods=['POST'])
@handle_api_error
def optimize_settings():
    """AI-powered settings optimization based on recent performance"""
    try:
        data = request.get_json()
        session_id = data.get('sessionId')
        
        if not session_id:
            return jsonify({
                'success': False,
                'error': 'Session ID required'
            }), 400

        system = get_capture_system()
        
        # Get recent capture results for this session
        # This would typically analyze recent performance and suggest optimizations
        recommendations = {
            'quality_threshold': 0.7,
            'processing_interval': 100,
            'camera_settings': {
                'brightness': 'auto',
                'contrast': 'auto',
                'focus': 'continuous'
            },
            'suggestions': [
                'Maintain current lighting conditions',
                'Keep face within optimal distance range',
                'Ensure stable device positioning'
            ]
        }

        return jsonify({
            'success': True,
            'recommendations': recommendations,
            'session_id': session_id,
            'timestamp': datetime.now().isoformat()
        })

    except Exception as e:
        logger.error(f"Error optimizing settings: {e}")
        return jsonify({
            'success': False,
            'error': str(e)
        }), 500

# Error handlers
@advanced_face_bp.errorhandler(404)
def not_found_error(error):
    return jsonify({
        'success': False,
        'error': 'Endpoint not found'
    }), 404

@advanced_face_bp.errorhandler(500)
def internal_error(error):
    return jsonify({
        'success': False,
        'error': 'Internal server error'
    }), 500

# Cleanup function
def cleanup_capture_system():
    """Cleanup capture system resources"""
    global capture_system
    if capture_system:
        capture_system.cleanup()
        capture_system = None