import os
import urllib.request
import cv2
import numpy as np
from backend.database import get_all_embeddings

# Model configuration paths
MODELS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models")
os.makedirs(MODELS_DIR, exist_ok=True)

YUNET_PATH = os.path.join(MODELS_DIR, "face_detection_yunet_2023mar.onnx")
SFACE_PATH = os.path.join(MODELS_DIR, "face_recognition_sface_2021dec.onnx")

# Hugging Face Direct Download URLs
YUNET_URL = "https://huggingface.co/opencv/face_detection_yunet/resolve/main/face_detection_yunet_2023mar.onnx"
SFACE_URL = "https://huggingface.co/opencv/face_recognition_sface/resolve/main/face_recognition_sface_2021dec.onnx"

def download_model(url, save_path):
    if os.path.exists(save_path) and os.path.getsize(save_path) > 10000:
        return True
    
    print(f"[*] Downloading AI model {os.path.basename(save_path)} from Hugging Face...")
    try:
        req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
        with urllib.request.urlopen(req) as response, open(save_path, 'wb') as out_file:
            out_file.write(response.read())
        print(f"[+] Download complete: {os.path.basename(save_path)} ({os.path.getsize(save_path)} bytes)!")
        return True
    except Exception as e:
        print(f"[-] Error downloading model {os.path.basename(save_path)}: {e}")
        if os.path.exists(save_path):
            os.remove(save_path)
        return False

class FaceEngine:
    def __init__(self, cosine_threshold: float = 0.363):
        # 1. Download models if missing
        download_model(YUNET_URL, YUNET_PATH)
        download_model(SFACE_URL, SFACE_PATH)
        
        # 2. Load models
        print("[*] Loading YuNet Face Detector...")
        # Set default input size (320x240), it will be updated dynamically based on frame dimensions
        self.detector = cv2.FaceDetectorYN.create(YUNET_PATH, "", (320, 240))
        
        print("[*] Loading SFace Face Recognizer...")
        self.recognizer = cv2.FaceRecognizerSF.create(SFACE_PATH, "")
        
        self.cosine_threshold = cosine_threshold
        self.known_faces = []
        
        # 3. Load known embeddings into memory
        self.reload_embeddings()
        print(f"[+] Face Engine ready! {len(self.known_faces)} face templates loaded in memory cache.")

    def reload_embeddings(self):
        """Fetches all employee face templates from the DB and caches them in memory."""
        try:
            self.known_faces = get_all_embeddings()
            print(f"[+] Reloaded face embeddings in cache. Total templates: {len(self.known_faces)}")
        except Exception as e:
            print(f"[-] Failed to reload face embeddings: {e}")
            self.known_faces = []

    def detect_faces(self, frame):
        """Detects faces in a frame and returns bounding boxes and 5-point landmarks."""
        h, w = frame.shape[:2]
        self.detector.setInputSize((w, h))
        retval, faces = self.detector.detect(frame)
        return retval, faces

    def extract_embedding(self, frame, face_landmarks):
        """Aligns, crops a face from landmarks, and extracts the 128-dimensional embedding vector."""
        try:
            aligned_face = self.recognizer.alignCrop(frame, face_landmarks)
            feature = self.recognizer.feature(aligned_face)
            return feature
        except Exception as e:
            print(f"[-] Embedding extraction failed: {e}")
            return None

    def match_face(self, query_embedding):
        """
        Compares query embedding with cached templates using Cosine Similarity.
        Returns the best matching employee or None.
        """
        if not self.known_faces:
            return None, "Unknown", 0.0

        best_score = -1.0
        best_emp_id = None
        best_emp_name = None
        
        # Store individual best scores per employee to aggregate multi-angle samples
        employee_best_matches = {}

        for face_template in self.known_faces:
            emp_id = face_template["employee_id"]
            emp_name = face_template["name"]
            known_emb = face_template["embedding"]
            
            try:
                # SFace cosine matching yields similarity score (higher is better, threshold is 0.363)
                score = self.recognizer.match(query_embedding, known_emb, cv2.FaceRecognizerSF_FR_COSINE)
                
                if emp_id not in employee_best_matches or score > employee_best_matches[emp_id]["score"]:
                    employee_best_matches[emp_id] = {
                        "name": emp_name,
                        "score": score
                    }
            except Exception as e:
                print(f"[-] Match calculation failed: {e}")
                continue

        # Find the absolute best match among all employees
        for emp_id, data in employee_best_matches.items():
            if data["score"] > best_score:
                best_score = data["score"]
                best_emp_id = emp_id
                best_emp_name = data["name"]

        # Evaluate threshold
        if best_score >= self.cosine_threshold:
            return best_emp_id, best_emp_name, float(best_score)
        else:
            return None, "Unknown", float(best_score) if best_score != -1.0 else 0.0