| from typing import Dict, List, Any | |
| from transformers import AutoProcessor, Blip2ForConditionalGeneration | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image | |
| import string | |
| import torch | |
| class EndpointHandler: | |
| def __init__(self, path=""): | |
| # load model and processor from path | |
| self.processor = AutoProcessor.from_pretrained(path) | |
| self.model = Blip2ForConditionalGeneration.from_pretrained(path, device_map="auto", load_in_4bit=True) | |
| def __call__(self, data): | |
| """ | |
| Args: | |
| inputs: | |
| Dict of image and text inputs. | |
| """ | |
| # process input | |
| inputs = data.pop("inputs", data) | |
| image = Image.open(BytesIO(base64.b64decode(inputs['image']))) | |
| inputs = self.processor(images=image, text=inputs["text"], return_tensors="pt").to("cuda", torch.float16) | |
| generated_ids = self.model.generate( | |
| **inputs, | |
| temperature=1.0, | |
| length_penalty=1.0, | |
| repetition_penalty=1.5, | |
| max_length=30, | |
| min_length=1, | |
| num_beams=5, | |
| top_p=0.9, | |
| ) | |
| result = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
| if result and result[-1] not in string.punctuation: | |
| result += "." | |
| return [{"generated_text": result}] |