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from typing import Dict, List, Any |
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from PIL import Image |
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import torch |
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import base64 |
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from io import BytesIO |
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from transformers import CLIPProcessor, CLIPModel |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14-336").to(device) |
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self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14-336") |
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def __call__(self, data: Any) -> List[float]: |
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inputs = data.pop("inputs", data) |
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if "image" in inputs: |
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image = Image.open(BytesIO(base64.b64decode(inputs['image']))) |
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inputs = self.processor(images=image, text=None, return_tensors="pt", padding=True).to(device) |
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image_embeds = self.model.get_image_features( |
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pixel_values=inputs["pixel_values"] |
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) |
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return image_embeds[0].tolist() |
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if "text" in inputs: |
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text = inputs['text'] |
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inputs = self.processor(images=None, text=text, return_tensors="pt", padding=True).to(device) |
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text_embeds = self.model.get_text_features( |
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input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"] |
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) |
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return text_embeds[0].tolist() |
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raise Exception("No 'image' or 'text' provided") |
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