app.py

from typing import Dict

import gradio as gr
import torch
from PIL import Image
from transformers import SamModel, SamProcessor

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MODEL = SamModel.from_pretrained("facebook/sam-vit-large").to(DEVICE)
PROCESSOR = SamProcessor.from_pretrained("facebook/sam-vit-large")


def inference(masked_image: Dict[str, Image.Image]) -> Image.Image:
    image = masked_image['image']
    mask = masked_image['mask'].resize((256, 256), Image.Resampling.LANCZOS)
    return image


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(
                image_mode='RGB', type='pil', tool="sketch", interactive=True,
                brush_radius=20.0, brush_color="#FFFFFF", height=500)
            submit_button = gr.Button("Submit")
        output_image = gr.Image(image_mode='RGB', type='pil')

    submit_button.click(
        inference,
        inputs=[input_image],
        outputs=output_image)

demo.launch(debug=False, show_error=True)