embeddings.py

import datasets
from transformers import CLIPProcessor, CLIPModel
import torch
from PIL import Image

# Load the dataset
dataset = datasets.load_dataset("metmuseum/openaccess")

# Initialize the CLIP model and processor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# Set the model to use the CPU or Apple's MPS if available
# Change this if you have a fancier computer (:
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
model.to(device)

# Map to store embeddings with Object ID as key
embeddings_map = {}

# Function to create embeddings for a PIL Jpeg image
def create_embedding(image_pil):
    try:
        inputs = processor(images=image_pil, return_tensors="pt", padding=True).to(device)
        with torch.no_grad():
            embeddings = model.get_image_features(**inputs)
        return embeddings
    except Exception as e:
        print(f"Error processing image: {e}")
        return None

# Loop through the dataset and process the images, and add them to a map
# Optionally, you could add more keys here
# Or, just add the embeddings to the full dataset
for item in dataset['train']:
    object_id = item['Object ID']
    image_pil = item['jpg']
    if image_pil:
        embedding = create_embedding(image_pil)
        if embedding is not None:
            embeddings_map[object_id] = embedding.cpu().numpy()

# Convert embeddings map to a new dataset
# Note: I changed to to [embeddings][0] because the examples seemed to like [embeddings] format better
# than [[[embeddings]]]
# ...perhaps that's incorrect?
embedding_dataset = datasets.Dataset.from_dict({
    'Object ID': list(embeddings_map.keys()),
    'Embedding': [embedding.tolist() for embedding in embeddings_map.values()][0]
})

# Save the new dataset to disk
embedding_dataset.save_to_disk('metmuseum_embeddings')