handler.py

from PIL import Image
import matplotlib.pyplot as plt
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
from typing import Dict, List, Any
from io import BytesIO
import base64

class EndpointHandler:
    def __init__(self):
        # Initialize the image segmentation pipeline
        self.pipeline = AutoModelForImageSegmentation.from_pretrained('.', trust_remote_code=True)
        torch.set_float32_matmul_precision(['high', 'highest'][0])
        self.image_size = (1024, 1024)
    
    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        self.pipeline.eval()
        image_size = (1024, 1024)
        transform_image = transforms.Compose([
            transforms.Resize(image_size),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])
        print(1)
        # Extract the image path from the input data
        image_b64 = data.get("inputs", "")
        image_data = base64.b64decode(image_b64)
        image = Image.open(BytesIO(image_data)).convert("RGB")  # Convert to RGB instead of RGBA
        input_images = transform_image(image).unsqueeze(0)
        print(2)
        # Prediction
        with torch.no_grad():
            preds = self.pipeline(input_images)[-1].sigmoid()
        pred = preds[0].squeeze()
        pred_pil = transforms.ToPILImage()(pred)
        
        # Resize the mask to the original image size
        original_image = Image.open(BytesIO(image_data)).convert("RGBA")  # Load original RGBA image for alpha
        mask = pred_pil.resize(original_image.size)
        original_image.putalpha(mask)
        print(3)
    
        # Convert the image with alpha mask to base64
        buffered = BytesIO()
        original_image.save(buffered, format="PNG")
        base64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
        
        # Return the result as a list of dictionaries
        return [{"image": base64_image}]