| from PIL import Image | |
| from transformers import Blip2Processor, Blip2ForConditionalGeneration | |
| import torch | |
| from models import load_transformers | |
| class blip2: | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| def __init__(self, model_pretrain:str = "Salesforce/blip2-opt-2.7b"): | |
| self.processor = Blip2Processor.from_pretrained(model_pretrain) | |
| self.model = Blip2ForConditionalGeneration.from_pretrained( | |
| model_pretrain, device_map={"": 0}, torch_dtype=torch.float16 | |
| ) | |
| def image_captioning(self, image: Image.Image) -> str: | |
| inputs = self.processor(images=image, return_tensors="pt").to(self.device, torch.float16) | |
| generated_ids = self.model.generate(**inputs) | |
| generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
| return generated_text | |
| def visual_question_answering(self, image: Image.Image, prompt: str) -> str: | |
| inputs = self.processor(images=image, text=prompt, return_tensors="pt").to(device=self.device, dtype=torch.float16) | |
| generated_ids = self.model.generate(**inputs) | |
| generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
| return generated_text | |