from PIL import Image
import faiss
import numpy as np
import torch
from torchvision import transforms

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.Normalize(
        mean=(0.485, 0.456, 0.406), 
        std=(0.229, 0.224, 0.225)
    )
])

def get_ft(
    extractor: torch.nn.Module,
    img: Image.Image
) -> np.ndarray:
    img = transform(img)
    ft = extractor(img.unsqueeze(0).to(device))
    return ft.detach().cpu().numpy()

def get_topk(
    index: faiss.Index,
    ft: np.ndarray,
    topk: int = 10
) -> tuple[np.ndarray, np.ndarray]:
    """
    Get top-k nearest neighbors from the index
    Args:
        index: Faiss index
        ft: Input feature
        topk: Number of nearest neighbors to return
    Returns:
        Tuple of (distances, indices) for top-k matches
    """
    # Search index for nearest neighbors
    distances, indices = index.search(ft, topk)
    return distances, indices
 

# EXAMPLE:

# image = Image.open('path/to/your/image.jpg')
# image = transform(image)

# extractor = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
# extractor.eval()
# extractor.to(device)

# ft = get_ft(...)
# indices, distances = ...