| 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() |
|
|
