# baased on https://huggingface.co./blog/getting-started-with-embeddings
import torch
from datasets import load_dataset
from sentence_transformers import SentenceTransformer, util


embeddings = load_dataset('metmuseum/openaccess_embeddings')

with torch.no_grad():
    embeddings.set_format("torch", columns=['Embedding'], output_all_columns=True)

#First, we load the respective CLIP model
model = SentenceTransformer('clip-ViT-B-32')

def search(query, k=3):
    # First, we encode the query (which can either be an image or a text string)
    query_emb = model.encode([query], convert_to_tensor=True, show_progress_bar=False)
    
    # Then, we use the util.semantic_search function, which computes the cosine-similarity
    # between the query embedding and all image embeddings.
    # It then returns the top_k highest ranked images, which we output
    hits = util.semantic_search(query_emb, embeddings["train"]["Embedding"], top_k=k)[0]
    print("Results for '"+query+"'")
    for hit in hits:
        # print(hit)
        print("https://www.metmuseum.org/art/collection/search/"+str(embeddings["train"][hit['corpus_id']]["Object ID"]))
        print("score: "+str(hit["score"]))

search("Painting of a sunset")
print("\n")
search("Angry cat")