| import requests |
| from PIL import Image |
| from transformers import Blip2Processor, Blip2ForConditionalGeneration |
| from typing import Dict, List, Any |
| import torch |
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| self.processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") |
| self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b") |
|
|
| self.device = "cuda" if torch.cuda.is_available() else "cpu" |
| self.model.to(self.device) |
|
|
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
| image = data.pop("inputs", data) |
|
|
| processed = self.processor(images=image, return_tensors="pt").to(self.device) |
|
|
| out = self.model.generate(**processed) |
|
|
| return self.processor.decode(out[0], skip_special_tokens=True) |
