| from typing import Dict, List, Any | |
| from transformers import CLIPModel, AutoProcessor, AutoTokenizer | |
| import torch | |
| from PIL import Image | |
| import requests | |
| class EndpointHandler: | |
| def __init__(self, path=""): | |
| self.model = CLIPModel.from_pretrained("patrickjohncyh/fashion-clip") | |
| self.processor = AutoProcessor.from_pretrained("patrickjohncyh/fashion-clip") | |
| self.tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32") | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| parameters = data.pop("parameters", {"mode": "text"}) | |
| inputs = data.pop("inputs", data) | |
| with torch.no_grad(): | |
| if parameters["mode"] == "text": | |
| inputs = self.tokenizer(inputs, padding=True, return_tensors="pt") | |
| features = self.model.get_text_features(**inputs) | |
| if parameters["mode"] == "image": | |
| image = Image.open(requests.get(inputs, stream=True).raw) | |
| inputs = self.processor(images=image, return_tensors="pt") | |
| features = self.model.get_image_features(**inputs) | |
| return features[0].tolist() | |