Upload example.py

Added example use with larger Open Access Dataset

example.py

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+import torch
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+import numpy as np
+import os
+from datasets import load_dataset, Dataset
+
+# prevent OMP error?
+os.environ['KMP_DUPLICATE_LIB_OK']='True'
+
+# ignore images? Do this unless you have a lot of memory!
+remove_images = True
+verbose = True
+
+# Save locally for faster use next time
+cache_on_disk = True
+
+# Load datasets and remove images (or not)
+def load_and_prepare_data():
+    if verbose:
+       print("Loading")
+    # Load from HF
+    embeddings_data = load_dataset("metmuseum/openaccess_embeddings", split='train')
+    collection_data = load_dataset("metmuseum/openaccess", split='train')
+
+    # Strip out image binary data (or not)
+    if remove_images:
+        cd_cleaned = collection_data.remove_columns(['jpg'])
+        # Convert collection to pandas dataframes
+        collection_df = cd_cleaned.to_pandas()
+    else:
+        # Convert collection to pandas dataframes
+        collection_df = collection_data.to_pandas()
+        
+    # Convert embeddings to pandas dataframes
+    embedding_df = embeddings_data.to_pandas()
+
+    # Merge the datasets on "Object ID"
+    if verbose:
+        print("Merging")
+
+    merged_df = collection_df.merge(embedding_df, on="Object ID", how="left")
+
+    if verbose:
+        print("Merged")
+
+    # Convert back to Huggingface dataset
+    first_dataset = Dataset.from_pandas(merged_df)
+
+    # Remove empty embeddings - note, this will result in about 1/2 of the samples being tossed
+    # But make our lives easier when passing to FAISS etc
+    merged_dataset = first_dataset.filter(lambda example: example['Embedding'] is not None)
+    
+    if cache_on_disk:
+        merged_dataset.save_to_disk('metmuseum_merged')
+    
+    return merged_dataset
+
+# Function to build the FAISS index & (optionally) save
+def build_faiss_index(dataset, index_file):
+    dataset.add_faiss_index('Embedding')
+    if cache_on_disk:
+        dataset.save_faiss_index('Embedding', index_file)
+
+# Function to load the FAISS on-disk index
+def load_faiss_index(dataset, index_file):
+    dataset.load_faiss_index('Embedding',index_file)
+
+def search_embeddings(dataset, query_embedding, k=5):
+    # """Search for the top k closest embeddings in the index."""
+    scores, samples = dataset.get_nearest_examples(
+        "Embedding", query_embedding, k
+    )
+    return scores, samples
+
+def query_text(processor, model, text):
+    """Convert a text query into an embedding."""
+    inputs = processor(text=text, return_tensors="pt")
+    with torch.no_grad():
+        text_embedding = model.get_text_features(**inputs).numpy()
+    return text_embedding
+
+def query_image(processor, model, image_path):
+    """Convert an image query into an embedding."""
+    image = Image.open(image_path)
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        image_embedding = model.get_image_features(**inputs).numpy()
+    print(image_embedding.shape)
+    return image_embedding[0]
+
+if __name__ == "__main__":
+    index_file = "faiss_index_file.index"
+    dataset_path = "metmuseum_merged"
+
+    # Try to load cahced data & cahced FAISS index
+    if os.path.exists(dataset_path):
+        dataset = Dataset.load_from_disk(dataset_path)
+    else:
+        dataset = load_and_prepare_data()
+
+    if not os.path.exists(index_file):
+        if verbose:
+           print("Building index")
+        build_faiss_index(dataset, index_file)
+    else:
+        load_faiss_index(dataset, index_file)
+
+    # Load CLIP to embed text / images to search
+    model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+    # Example usage for text query
+    # This doesn't really seem to work right now...
+    text_query = "A painting of a sunflower"
+    text_embedding = query_text(processor, model, text_query)
+
+    # K = how many results to get
+    scores, samples = search_embeddings(dataset, text_embedding, k=5)
+
+    print("\Text Query Results:")
+    print(scores)
+    # The results are dataset columns -- you could loop through all fields,
+    # Or just get a URL like below
+    for result in samples["Object ID"]:
+        print("https://metmuseum.org/art/collection/search/" + str(result))
+
+    # Example usage for image query
+    image_path = "DP355692.jpg"  # Replace with the path to your image file
+    image_embedding = query_image(processor, model, image_path)
+
+    # K = how many results to get
+    scores, samples = search_embeddings(dataset, image_embedding, k=5)
+
+    print("\nImage Query Results:")
+    print(scores)
+    for result in samples["Object ID"]:
+        print("https://metmuseum.org/art/collection/search/" + str(result))