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from transformers import CLIPModel, CLIPProcessor |
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from typing import Dict, List, Any |
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from PIL import Image |
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from transformers import pipeline |
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import requests |
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import torch |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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""" |
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path: |
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""" |
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self.device = "cuda" if torch.cuda.is_available() else "cpu" |
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self.processor = CLIPProcessor.from_pretrained(path) |
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self.model = CLIPModel.from_pretrained(path).to(self.device) |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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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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result = {} |
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inputs = data.pop("inputs", data) |
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image_url = inputs['image_url'] |
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image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB') |
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processed_image = self.processor(images=image, return_tensors="pt").to(self.device) |
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output = self.model.get_image_features(processed_image["pixel_values"])[0].tolist() |
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result["embedding"] = output |
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return result |
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