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))