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