app.py

import gradio as gr
import torch
from utils import colorize
from PIL import Image
import tempfile

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
model = torch.hub.load('isl-org/ZoeDepth', "ZoeD_N", pretrained=True).to(DEVICE).eval()

def predict_depth(model, image):
    depth = model.infer_pil(image)
    return depth

def on_submit(image):
    depth = predict_depth(model, image)
    colored_depth = colorize(depth, cmap='gray_r')
    tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
    raw_depth = Image.fromarray((depth*256).astype('uint16'))
    raw_depth.save(tmp.name)
    return [colored_depth, tmp.name]

iface = gr.Interface(
    fn=on_submit,
    inputs=gr.inputs.Image(type='pil', label="Input Image"),
    outputs=[
        gr.outputs.Image(type='numpy', label="Depth Map"),
        gr.outputs.File(label="16-bit raw depth, multiplier:256")
    ],
    title="# ZoeDepth",
    description="""Unofficial demo for **ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth**.""",
    css="""
    #img-display-container {
        max-height: 50vh;
        }
    #img-display-input {
        max-height: 40vh;
        }
    #img-display-output {
        max-height: 40vh;
        }
    """
)

if __name__ == '__main__':
    iface.launch()