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from io import BytesIO |
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import base64 |
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import traceback |
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import logging |
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from PIL import Image |
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import torch |
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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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logger = logging.getLogger(__name__) |
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logger.setLevel('INFO') |
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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").to(device) |
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self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") |
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def __call__(self, data): |
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try: |
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inputs = data.pop("inputs", None) |
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text_input = None |
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image_data = None |
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if isinstance(inputs, Image.Image): |
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logger.info('image sent directly') |
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image = inputs |
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else: |
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text_input = inputs["text"] if "text" in inputs else None |
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image_data = inputs['image'] if 'image' in inputs else None |
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if image_data is not None: |
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logger.info('image is encoded') |
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image = Image.open(BytesIO(base64.b64decode(image_data))) |
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if text_input: |
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processor = self.processor(text=text_input, return_tensors="pt", padding=True).to(device) |
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with torch.no_grad(): |
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return {"embeddings": self.model.get_text_features(**processor).tolist()} |
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elif image: |
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processor = self.processor(images=image, return_tensors="pt").to(device) |
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with torch.no_grad(): |
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return {"embeddings": self.model.get_image_features(**processor).tolist()} |
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else: |
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return {'embeddings':None} |
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except Exception as ex: |
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logger.error('error doing request: %s', ex) |
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logger.exception(ex) |
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stack_info = traceback.format_exc() |
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logger.error('stack trace:\n%s',stack_info) |
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return {'Error':stack_info} |
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