| from typing import Dict, List, Any | |
| from PIL import Image | |
| import torch | |
| import base64 | |
| from io import BytesIO | |
| from transformers import AutoProcessor, BlipForQuestionAnswering | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large") | |
| self.model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large").to(device) | |
| def __call__(self, data: Any) -> List[float]: | |
| inputs = data.pop("inputs", data) | |
| image = Image.open(BytesIO(base64.b64decode(inputs['image']))) | |
| inputs = self.processor(image, inputs['question'], return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| outputs = self.model.generate(**inputs) | |
| pooler_output = outputs.pooler_output | |
| return processor.decode(out[0], skip_special_tokens=True) | |