app.py

import gradio as gr
import torch
from PIL import Image
import cv2
import numpy as np
from geobench.modeling.archs.dam.dam import DepthAnything
from geobench.utils.image_util import colorize_depth_maps
from geobench.midas.transforms import Resize, NormalizeImage, PrepareForNet
from torchvision.transforms import Compose
import os

# Helper function to load model (same as your original code)
def load_model_by_name(arch_name, checkpoint_path, device):
    if arch_name == 'depthanything':
        if '.safetensors' in checkpoint_path:
            model = DepthAnything.from_pretrained(os.path.dirname(checkpoint_path)).to(device)
        else:
            raise NotImplementedError("Model architecture not implemented.")
    else:
        raise NotImplementedError(f"Unknown architecture: {arch_name}")
    return model

# Image processing function (same as your original code, modified for Gradio)
def process_image(image, model, device, mode='rel_depth'):
    # Preprocess the image
    image_np = np.array(image)[..., ::-1] / 255
    transform = Compose([
        Resize(512, 512, resize_target=None, keep_aspect_ratio=False, ensure_multiple_of=32, image_interpolation_method=cv2.INTER_CUBIC),
        NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
        PrepareForNet()
    ])
    
    image_tensor = transform({'image': image_np})['image']
    image_tensor = torch.from_numpy(image_tensor).unsqueeze(0).to(device)
    
    with torch.no_grad():  # Disable autograd since we don't need gradients on CPU
        pred_disp, _ = model(image_tensor)
        pred_disp_np = pred_disp.cpu().detach().numpy()[0, :, :, :].transpose(1, 2, 0)
        pred_disp = (pred_disp_np - pred_disp_np.min()) / (pred_disp_np.max() - pred_disp_np.min())

    # Colorize depth map
    cmap = "Spectral_r" if mode != 'metric' else 'Spectral_r'
    depth_colored = colorize_depth_maps(pred_disp[None, ...], 0, 1, cmap=cmap).squeeze()
    depth_colored = (depth_colored * 255).astype(np.uint8)
    
    depth_image = Image.fromarray(depth_colored)
    return depth_image

# Gradio interface function
def gradio_interface(image, mode='rel_depth'):
    # Set device to CPU explicitly
    device = torch.device("cpu")  # Force using CPU
    model = load_model_by_name("depthanything", "your_checkpoint_path_here", device)

    # Process image and return output
    return process_image(image, model, device, mode)

# Create Gradio interface
iface = gr.Interface(
    fn=gradio_interface,
    inputs=[gr.Image(type="pil"), gr.Dropdown(choices=['rel_depth', 'metric_depth', 'disparity'], label="Mode")],
    outputs=gr.Image(type="pil"),
    title="Depth Estimation Demo",
    description="Upload an image to see the depth estimation results."
)

# Launch the Gradio interface
iface.launch()