Delete app.py

app.py

@@ -1,112 +0,0 @@
-import gradio as gr
-import cv2
-import matplotlib
-import numpy as np
-import os
-from PIL import Image
-import spaces
-import torch
-import tempfile
-from gradio_imageslider import ImageSlider
-from huggingface_hub import hf_hub_download
-
-from ppd.utils.set_seed import set_seed
-from ppd.models.ppd import PixelPerfectDepth
-
-css = """
-#img-display-container {
-    max-height: 100vh;
-}
-#img-display-input {
-    max-height: 100vh;
-}
-#img-display-output {
-    max-height: 100vh;
-}
-#download {
-    height: 62px;
-}
-
-#img-display-output .image-slider-image {
-    object-fit: contain !important;
-    width: 100% !important;
-    height: 100% !important;
-}
-"""
-
-DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
-model = PixelPerfectDepth(sampling_steps=10)
-ckpt_path = hf_hub_download(
-    repo_id="gangweix/Pixel-Perfect-Depth",
-    filename="ppd.pth",
-    repo_type="model"
-)
-state_dict = torch.load(ckpt_path, map_location="cpu")
-model.load_state_dict(state_dict, strict=False)
-model = model.to(DEVICE).eval()
-
-
-
-title = "# Pixel-Perfect Depth"
-description = """Official demo for **Pixel-Perfect Depth**.
-Please refer to our [paper](), [project page](https://pixel-perfect-depth.github.io), and [github](https://github.com/gangweix/pixel-perfect-depth) for more details."""
-
-@spaces.GPU
-def predict_depth(image):
-    return model.infer_image(image)
-
-with gr.Blocks(css=css) as demo:
-    gr.Markdown(title)
-    gr.Markdown(description)
-    gr.Markdown("### Depth Prediction demo")
-
-    with gr.Row():
-        input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
-        depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
-    submit = gr.Button(value="Predict Depth")
-    
-    concat_file = gr.File(label="Concatenated visualization (image+depth)", elem_id="image-depth-download")
-    raw_file = gr.File(label="Raw depth output (saved as .npy)", elem_id="download",)
-
-    cmap = matplotlib.colormaps.get_cmap('Spectral')
-
-    def on_submit(image):
-        original_image = image.copy()
-
-        depth = predict_depth(image[:, :, ::-1])
-
-        # save raw depth (npy)
-        tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
-        np.save(tmp_raw_depth.name, depth)
-
-
-        depth_vis = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
-        depth_vis = depth_vis.astype(np.uint8)
-        colored_depth = (cmap(depth_vis)[:, :, :3] * 255).astype(np.uint8)
-
-        split_region = np.ones((image.shape[0], 50, 3), dtype=np.uint8) * 255
-        combined_result = cv2.hconcat([image[:, :, ::-1], split_region, colored_depth[:, :, ::-1]])
-        tmp_concat = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
-        cv2.imwrite(tmp_concat.name, combined_result)
-
-        return [(original_image, colored_depth), tmp_concat.name, tmp_raw_depth.name]
-
-    submit.click(
-        on_submit, 
-        inputs=[input_image], 
-        outputs=[depth_image_slider, concat_file, raw_file]
-    )
-
-    example_files = os.listdir('assets/examples')
-    example_files.sort()
-    example_files = [os.path.join('assets/examples', filename) for filename in example_files]
-    examples = gr.Examples(
-        examples=example_files, 
-        inputs=[input_image], 
-        outputs=[depth_image_slider, concat_file, raw_file], 
-        fn=on_submit
-    )
-
-
-if __name__ == '__main__':
-    demo.queue().launch(share=True)