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from typing import Dict, List, Any |
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from transformers import CLIPTokenizer, CLIPModel |
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import numpy as np |
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class EndpointHandler: |
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def __init__(self, path=""): |
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repo_id = "openai/clip-vit-large-patch14-336" |
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self.model = CLIPModel.from_pretrained(repo_id) |
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self.tokenizer = CLIPTokenizer.from_pretrained(repo_id) |
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def __call__(self, data: Dict[str, Any]) -> List[float]: |
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""" |
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data args: |
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inputs (:obj: `str` | `PIL.Image` | `np.array`) |
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kwargs |
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Return: |
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A :obj:`list` | `dict`: will be serialized and returned |
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""" |
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query = data["inputs"] |
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inputs = self.tokenizer(query, padding=True, return_tensors="pt") |
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text_features = self.model.get_text_features(**inputs) |
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text_features = text_features.detach().numpy() |
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input_embedding = text_features[0] |
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input_embedding = input_embedding / np.linalg.norm(input_embedding) |
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return input_embedding.tolist() |
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