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from typing import Dict, List, Any
from transformers import CLIPTokenizer, CLIPModel
import numpy as np
import os
class EndpointHandler:
def __init__(self, path=""):
self.model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
self.artwork_urls = np.load(os.path.join(path, "artwork_urls.npy"), allow_pickle=True)
self.embeddings = np.load(os.path.join(path, "embeddings.npy"), allow_pickle=True)
def __call__(self, data: Dict[str, Any]) -> List[float]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = self.tokenizer(data["inputs"], padding=True, return_tensors="pt")
text_features = self.model.get_text_features(**inputs)
text_features = text_features.detach().numpy()
input_embedding = text_features[0]
input_embedding = input_embedding / np.linalg.norm(input_embedding)
cos_score = self.embeddings @ input_embedding
top_10 = cos_score.argsort()[-100:][::-1]
return self.artwork_urls[top_10].tolist()
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