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