| 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.base_model_name = "Salesforce/blip2-opt-2.7b" | |
| self.model_name = "sooh-j/blip2-vizwizqa" | |
| self.base_model = Blip2ForConditionalGeneration.from_pretrained(self.base_model_name, | |
| load_in_8bit=True) | |
| self.processor = Blip2Processor.from_pretrained(self.base_model_name) | |
| self.model = PeftModel.from_pretrained(self.model_name, self.base_model_name) | |
| 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]]: | |
| data = data.pop("inputs", data) | |
| image = data.image | |
| question = data.question | |
| prompt = f"Question: {question}, Answer:" | |
| processed = self.processor(images=image, prompt, return_tensors="pt").to(self.device) | |
| out = self.model.generate(**processed) | |
| return self.processor.decode(out[0], skip_special_tokens=True) |