handler.py

from typing import Dict, List, Any
import base64
from io import BytesIO
import torch
from loadimg import load_img
from torchvision import transforms
from transformers import AutoModelForImageSegmentation

torch.set_float32_matmul_precision(["high", "highest"][0])

birefnet = AutoModelForImageSegmentation.from_pretrained(
    "ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to("cuda")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

transform_image = transforms.Compose(
    [
        transforms.Resize((1024, 1024)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ]
)

class EndpointHandler():
    def __init__(self, path=""):
        self.birefnet = AutoModelForImageSegmentation.from_pretrained(
            "ZhengPeng7/BiRefNet", trust_remote_code=True
        )
        self.birefnet.to(device)

    def __call__(self, data: Dict[str, Any]):
        """
       data args:
            inputs (:obj: `str`)
            date (:obj: `str`)
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        print('data["inputs"] = ',data["inputs"])
        image =  load_img(data["inputs"]).convert("RGB")
        image_size = image.size
        input_images = transform_image(image).unsqueeze(0).to("cuda")
        # Prediction
        with torch.no_grad():
            preds = birefnet(input_images)[-1].sigmoid().cpu()
        pred = preds[0].squeeze()
        pred_pil = transforms.ToPILImage()(pred)
        mask = pred_pil.resize(image_size)
        image.putalpha(mask)
        # buffered = BytesIO()
        # image.save(buffered, format="JPEG")
        # img_str = base64.b64encode(buffered.getvalue())
        return image