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# +
from typing import Dict, List, Any
from PIL import Image
import torch
import os
from io import BytesIO
# from transformers import BlipForConditionalGeneration, BlipProcessor
from transformers import Blip2Processor, Blip2ForConditionalGeneration
# -
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class EndpointHandler():
def __init__(self, path=""):
# load the optimized model
self.processor = Blip2Processor.from_pretrained("ChirathD/Blip-2-test-1")
self.model = Blip2ForConditionalGeneration.from_pretrained("ChirathD/Blip-2-test-1").to(device)
self.model.eval()
self.model = self.model.to(device)
def __call__(self, data: Any) -> Dict[str, Any]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`dict`:. The object returned should be a dict of one list like {"captions": ["A hugging face at the office"]} containing :
- "caption": A string corresponding to the generated caption.
"""
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", {})
raw_images = [Image.open(BytesIO(_img)) for _img in inputs]
processed_image = self.processor(images=raw_images, return_tensors="pt")
processed_image["pixel_values"] = processed_image["pixel_values"].to(device)
processed_image = {**processed_image, **parameters}
with torch.no_grad():
out = self.model.generate(
**processed_image
)
captions = self.processor.batch_decode(out, skip_special_tokens=True)
return {"captions": captions} |