"""
Mock Face Recognition Module for Testing (Windows-friendly)
This is a simplified version that doesn't require dlib/face_recognition
For production, install the full face_recognition library
"""

import numpy as np
import cv2
from config import Config
import logging
import hashlib

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

logger.warning("Using MOCK face recognition module - for testing only!")
logger.warning("Install face_recognition library for production use")


class FaceRecognitionModule:
    """Mock face recognition for testing without dlib dependencies"""
    
    def __init__(self, tolerance=None, model='hog'):
        self.tolerance = tolerance or Config.FACE_RECOGNITION_TOLERANCE
        self.model = model
        logger.info(f"Mock Face Recognition Module initialized")
    
    def detect_faces(self, image_array):
        """Mock face detection using OpenCV Haar Cascades"""
        try:
            # Convert to grayscale
            gray = cv2.cvtColor(image_array, cv2.COLOR_RGB2GRAY)
            
            # Load Haar cascade for face detection
            face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
            
            # Detect faces
            faces = face_cascade.detectMultiScale(gray, 1.3, 5)
            
            # Convert to format similar to face_recognition library
            face_locations = [(y, x+w, y+h, x) for (x, y, w, h) in faces]
            
            logger.info(f"Detected {len(face_locations)} face(s)")
            return face_locations
        except Exception as e:
            logger.error(f"Face detection error: {str(e)}")
            return []
    
    def generate_face_encoding(self, image_array):
        """
        Mock encoding generation using image hash
        In production, this would use deep learning-based face encodings
        """
        try:
            face_locations = self.detect_faces(image_array)
            
            if len(face_locations) == 0:
                return False, "No face detected in the image. Please ensure your face is clearly visible."
            
            if len(face_locations) > 1:
                return False, "Multiple faces detected. Please ensure only one person is in the frame."
            
            # Create a simple "encoding" using image hash (for testing only)
            # In production, this would be a 128-dimensional face encoding
            top, right, bottom, left = face_locations[0]
            face_region = image_array[top:bottom, left:right]
            
            # Resize to standard size
            face_resized = cv2.resize(face_region, (100, 100))
            
            # Create hash-based encoding (mock)
            face_bytes = face_resized.tobytes()
            hash_obj = hashlib.sha256(face_bytes)
            hash_digest = hash_obj.digest()  # Get bytes directly
            
            # Convert to numpy array (128 dimensions to match real encodings)
            # Use hash bytes to create 128-dimensional vector
            encoding = np.array([float(b) for b in hash_digest[:128]] + [0.0] * (128 - len(hash_digest[:128])))
            
            logger.info("Mock face encoding generated")
            return True, encoding
            
        except Exception as e:
            logger.error(f"Face encoding error: {str(e)}")
            return False, f"Face encoding failed: {str(e)}"
    
    def verify_face(self, captured_encoding, stored_encoding):
        """
        Mock face verification using encoding similarity
        In production, this would use Euclidean distance between face encodings
        """
        try:
            if captured_encoding is None or stored_encoding is None:
                return False, 0.0
            
            # Convert to numpy arrays
            if not isinstance(captured_encoding, np.ndarray):
                captured_encoding = np.array(captured_encoding)
            if not isinstance(stored_encoding, np.ndarray):
                stored_encoding = np.array(stored_encoding)
            
            # Calculate similarity (mock - using correlation)
            # In production, this would be face_recognition.face_distance()
            correlation = np.corrcoef(captured_encoding, stored_encoding)[0, 1]
            
            # Convert to distance (0 = identical, 1 = completely different)
            distance = 1 - abs(correlation)
            
            # Calculate confidence
            confidence = (1 - distance) * 100
            
            # Check if match
            is_match = distance <= self.tolerance
            
            logger.info(f"Mock verification: match={is_match}, confidence={confidence:.2f}%")
            
            return is_match, confidence
            
        except Exception as e:
            logger.error(f"Face verification error: {str(e)}")
            return False, 0.0
    
    def register_face(self, image_array):
        """Complete face registration"""
        try:
            face_locations = self.detect_faces(image_array)
            
            if len(face_locations) == 0:
                return False, "No face detected. Please ensure your face is clearly visible and well-lit.", None
            
            if len(face_locations) > 1:
                return False, "Multiple faces detected. Please ensure only one person is in the frame.", None
            
            success, result = self.generate_face_encoding(image_array)
            
            if success:
                return True, result, face_locations[0]
            else:
                return False, result, None
                
        except Exception as e:
            logger.error(f"Face registration error: {str(e)}")
            return False, f"Registration failed: {str(e)}", None
    
    def compare_faces_batch(self, known_encodings, face_encoding_to_check):
        """Compare against multiple encodings"""
        try:
            matches = []
            for known_encoding in known_encodings:
                is_match, _ = self.verify_face(face_encoding_to_check, known_encoding)
                matches.append(is_match)
            return matches
        except Exception as e:
            logger.error(f"Batch comparison error: {str(e)}")
            return []


# Singleton instance
face_recognition_module = FaceRecognitionModule()