| | 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") |
| |
|
