from ultralytics import YOLO
import torch, joblib
from huggingface_hub import hf_hub_download
from torchvision import transforms
import torchvision.models as models
import torch.nn as nn
import faiss, os, ast
import numpy as np
import pandas as pd

def load_db(csv_path="users/face_features.csv"):
    if not os.path.exists(csv_path):
        return None, [], []

    df = pd.read_csv(csv_path)

    df["features"] = df["features"].apply(ast.literal_eval)
    features = np.array(df["features"].tolist()).astype("float32")
    labels = df["label"].tolist()

    d = features.shape[1] 
    index = faiss.IndexFlatIP(d)  

    faiss.normalize_L2(features)  

    index.add(features)

    return index, labels, df

model_path = hf_hub_download(repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt")
face_detector = YOLO(model_path)

efficientnet_model = models.efficientnet_v2_s(weights=None)
efficientnet_model.classifier = nn.Identity()

state_dict = torch.load("faceVerificationModel/efficientnetv2_s_features.pth", map_location="cpu")
efficientnet_model.load_state_dict(state_dict)
efficientnet_model.eval()

pca_xgb = joblib.load("faceVerificationModel/pca_xgb_pipeline.pkl")

transform = transforms.Compose([
    transforms.Resize((224, 224)),   
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], 
                         [0.229, 0.224, 0.225])  
])