from facenet_pytorch import MTCNN, InceptionResnetV1
import torch
import numpy as np
from PIL import Image
import os

# Load models once
mtcnn = MTCNN(image_size=160, margin=20)
model = InceptionResnetV1(pretrained='vggface2').eval()

def preprocess(image):
    face = mtcnn(Image.fromarray(image))
    if face is None:
        return None
    with torch.no_grad():
        embedding = model(face.unsqueeze(0)).squeeze().numpy()
    return embedding

def verify_identity(_, input_image, user_dir, threshold=0.7):
    input_embedding = preprocess(input_image)
    if input_embedding is None:
        return "❌ No face detected."

    best_match = None
    best_score = 0

    for user in os.listdir(user_dir):
        user_path = os.path.join(user_dir, user)
        for file in os.listdir(user_path):
            img_path = os.path.join(user_path, file)
            registered_img = np.array(Image.open(img_path))
            emb = preprocess(registered_img)
            if emb is None:
                continue
            score = np.dot(input_embedding, emb) / (np.linalg.norm(input_embedding) * np.linalg.norm(emb))
            if score > best_score:
                best_match, best_score = user, score

    if best_score >= threshold:
        return f"✅ Match: {best_match} (Similarity: {best_score:.2f})"
    else:
        return f"❌ Unknown (Best Similarity: {best_score:.2f})"