| from typing import Dict, List, Any | |
| from PIL import Image | |
| from io import BytesIO | |
| import base64 | |
| import torch | |
| import open_clip | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.model, self.preprocess, _ = open_clip.create_model_and_transforms('hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K') | |
| self.tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K') | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| image_base64 = data.get("inputs", None) | |
| parameters = data.get("parameters", None) | |
| if image_base64 is None or parameters is None: | |
| raise ValueError("Input data or parameters not provided") | |
| candidate_labels = parameters.get("candidate_labels", None) | |
| if candidate_labels is None: | |
| raise ValueError("Candidate labels not provided") | |
| image = Image.open(BytesIO(base64.b64decode(image_base64))) | |
| image = self.preprocess(image).unsqueeze(0) | |
| text = self.tokenizer(candidate_labels) | |
| with torch.no_grad(): | |
| image_features = self.model.encode_image(image) | |
| text_features = self.model.encode_text(text) | |
| image_features /= image_features.norm(dim=-1, keepdim=True) | |
| text_features /= text_features.norm(dim=-1, keepdim=True) | |
| text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) | |
| results = [{"label": label, "score": score.item()} for label, score in zip(candidate_labels, text_probs[0])] | |
| return results | |