app.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import gc
import os
import shutil
import sys
import time
import json
import base64
import threading
from datetime import datetime
from io import BytesIO

os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"

import cv2
import gradio as gr
import numpy as np
import spaces
import torch
from PIL import Image
from pillow_heif import register_heif_opener

register_heif_opener()

import rerun as rr

try:
    import rerun.blueprint as rrb
except ImportError:
    rrb = None

from gradio_rerun import Rerun

sys.path.append("mapanything/")

from mapanything.utils.geometry import depthmap_to_world_frame, points_to_normals
from mapanything.utils.hf_utils.hf_helpers import initialize_mapanything_model
from mapanything.utils.hf_utils.visual_util import predictions_to_glb
from mapanything.utils.image import load_images, rgb

# ---------------------------------------------------------------------------
# MapAnything Configuration
# ---------------------------------------------------------------------------
high_level_config = {
    "path": "configs/train.yaml",
    "hf_model_name": "facebook/map-anything",
    "model_str": "mapanything",
    "config_overrides": [
        "machine=aws",
        "model=mapanything",
        "model/task=images_only",
        "model.encoder.uses_torch_hub=false",
    ],
    "checkpoint_name": "model.safetensors",
    "config_name": "config.json",
    "trained_with_amp": True,
    "trained_with_amp_dtype": "bf16",
    "data_norm_type": "dinov2",
    "patch_size": 14,
    "resolution": 518,
}

model = None
_model_lock = threading.Lock()

# ---------------------------------------------------------------------------
# Example scenes discovery
# ---------------------------------------------------------------------------
def get_scene_info(examples_dir="examples"):
    import glob
    scenes = []
    if not os.path.exists(examples_dir):
        return scenes
    for scene_folder in sorted(os.listdir(examples_dir)):
        scene_path = os.path.join(examples_dir, scene_folder)
        if not os.path.isdir(scene_path):
            continue
        if scene_folder.startswith("WAI-Logo"):
            continue
        image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp", "*.tiff", "*.tif"]
        image_files = []
        for ext in image_extensions:
            image_files.extend(glob.glob(os.path.join(scene_path, ext)))
            image_files.extend(glob.glob(os.path.join(scene_path, ext.upper())))
        if image_files:
            image_files = sorted(image_files)
            scenes.append({
                "name": scene_folder,
                "path": scene_path,
                "thumbnail": image_files[0],
                "num_images": len(image_files),
                "image_files": image_files,
            })
    return scenes


def build_example_thumbnails_html(scenes):
    if not scenes:
        return ""
    html = '<div style="display:flex;flex-wrap:wrap;gap:10px;padding:8px 0;">'
    for i, scene in enumerate(scenes):
        try:
            with open(scene["thumbnail"], "rb") as f:
                thumb_b64 = base64.b64encode(f.read()).decode()
            ext = os.path.splitext(scene["thumbnail"])[1].lower()
            mime = "image/jpeg" if ext in (".jpg", ".jpeg") else "image/png"
            src = f"data:{mime};base64,{thumb_b64}"
        except Exception:
            src = ""
        html += f'''
        <div class="example-thumb" onclick="window.__loadExampleScene('{scene["name"]}')"
             style="cursor:pointer;border:1px solid #27272a;border-radius:10px;
                    overflow:hidden;width:160px;background:#18181b;
                    transition:all .15s ease;">
            <img src="{src}" style="width:160px;height:110px;object-fit:cover;display:block;" />
            <div style="padding:6px 10px;">
                <div style="font-size:12px;font-weight:600;color:#e4e4e7;
                            white-space:nowrap;overflow:hidden;text-overflow:ellipsis;">
                    {scene["name"]}</div>
                <div style="font-size:11px;color:#71717a;">{scene["num_images"]} images</div>
            </div>
        </div>'''
    html += '</div>'
    return html


# ---------------------------------------------------------------------------
# Core helpers
# ---------------------------------------------------------------------------
def colorize_depth(depth_map, mask=None):
    if depth_map is None:
        return None
    import matplotlib.pyplot as plt
    depth_normalized = depth_map.copy()
    valid_mask = depth_normalized > 0
    if mask is not None:
        valid_mask = valid_mask & mask
    if valid_mask.sum() > 0:
        valid_depths = depth_normalized[valid_mask]
        p5 = np.percentile(valid_depths, 5)
        p95 = np.percentile(valid_depths, 95)
        if p95 > p5:
            depth_normalized[valid_mask] = (depth_normalized[valid_mask] - p5) / (p95 - p5)
        else:
            depth_normalized[valid_mask] = 0.5
    colormap = plt.cm.turbo_r
    colored = colormap(np.clip(depth_normalized, 0, 1))
    colored = (colored[:, :, :3] * 255).astype(np.uint8)
    colored[~valid_mask] = [255, 255, 255]
    return colored


def colorize_normal(normal_map, mask=None):
    if normal_map is None:
        return None
    normal_vis = normal_map.copy()
    if mask is not None:
        normal_vis[~mask] = [0, 0, 0]
    normal_vis = (normal_vis + 1.0) / 2.0
    normal_vis = (np.clip(normal_vis, 0, 1) * 255).astype(np.uint8)
    return normal_vis


def process_predictions_for_visualization(
    predictions, views, config, filter_black_bg=False, filter_white_bg=False
):
    processed_data = {}
    for view_idx, view in enumerate(views):
        image = rgb(view["img"], norm_type=config["data_norm_type"])
        pred_pts3d = predictions["world_points"][view_idx]
        mask = predictions["final_mask"][view_idx].copy()
        if filter_black_bg:
            vc = image[0] * 255 if image[0].max() <= 1.0 else image[0]
            mask = mask & (vc.sum(axis=2) >= 16)
        if filter_white_bg:
            vc = image[0] * 255 if image[0].max() <= 1.0 else image[0]
            mask = mask & ~((vc[:, :, 0] > 240) & (vc[:, :, 1] > 240) & (vc[:, :, 2] > 240))

        depth = predictions["depth"][view_idx].squeeze()
        normals, _ = points_to_normals(pred_pts3d, mask=mask)

        processed_data[view_idx] = {
            "image": image[0],
            "points3d": pred_pts3d,
            "depth": depth,
            "normal": normals,
            "mask": mask,
        }
    return processed_data


def get_view_data(processed_data, view_index):
    if processed_data is None or len(processed_data) == 0:
        return None
    keys = list(processed_data.keys())
    idx = max(0, min(view_index, len(keys) - 1))
    return processed_data[keys[idx]]


def get_depth_vis(processed_data, view_index):
    vd = get_view_data(processed_data, view_index)
    if vd is None or vd["depth"] is None:
        return None
    return colorize_depth(vd["depth"], mask=vd.get("mask"))


def get_normal_vis(processed_data, view_index):
    vd = get_view_data(processed_data, view_index)
    if vd is None or vd["normal"] is None:
        return None
    return colorize_normal(vd["normal"], mask=vd.get("mask"))


def get_measure_image(processed_data, view_index):
    vd = get_view_data(processed_data, view_index)
    if vd is None:
        return None, []
    image = vd["image"].copy()
    if image.dtype != np.uint8:
        image = (image * 255).astype(np.uint8) if image.max() <= 1.0 else image.astype(np.uint8)
    if vd["mask"] is not None:
        invalid = ~vd["mask"]
        if invalid.any():
            overlay = np.array([255, 220, 220], dtype=np.uint8)
            alpha = 0.5
            for c in range(3):
                image[:, :, c] = np.where(
                    invalid,
                    (1 - alpha) * image[:, :, c] + alpha * overlay[c],
                    image[:, :, c],
                ).astype(np.uint8)
    return image, []


def image_to_b64(img_array):
    if img_array is None:
        return ""
    if img_array.dtype != np.uint8:
        if img_array.max() <= 1.0:
            img_array = (img_array * 255).astype(np.uint8)
        else:
            img_array = img_array.astype(np.uint8)
    pil = Image.fromarray(img_array)
    buf = BytesIO()
    pil.save(buf, format="PNG")
    return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()


# ---------------------------------------------------------------------------
# File handling
# ---------------------------------------------------------------------------
def handle_uploads(files, s_time_interval=1.0):
    gc.collect()
    torch.cuda.empty_cache()
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
    target_dir = f"input_images_{timestamp}"
    target_dir_images = os.path.join(target_dir, "images")
    if os.path.exists(target_dir):
        shutil.rmtree(target_dir)
    os.makedirs(target_dir_images)
    image_paths = []
    video_extensions = [".mp4", ".avi", ".mov", ".mkv", ".wmv", ".flv", ".webm", ".m4v", ".3gp"]

    if files is not None:
        for file_data in files:
            file_path = file_data["name"] if isinstance(file_data, dict) and "name" in file_data else str(file_data)
            file_ext = os.path.splitext(file_path)[1].lower()
            if file_ext in video_extensions:
                vs = cv2.VideoCapture(file_path)
                fps = vs.get(cv2.CAP_PROP_FPS)
                frame_interval = max(1, int(fps * s_time_interval))
                count = 0
                frame_num = 0
                while True:
                    gotit, frame = vs.read()
                    if not gotit:
                        break
                    count += 1
                    if count % frame_interval == 0:
                        base_name = os.path.splitext(os.path.basename(file_path))[0]
                        img_path = os.path.join(target_dir_images, f"{base_name}_{frame_num:06}.png")
                        cv2.imwrite(img_path, frame)
                        image_paths.append(img_path)
                        frame_num += 1
                vs.release()
            elif file_ext in [".heic", ".heif"]:
                try:
                    with Image.open(file_path) as img:
                        if img.mode not in ("RGB", "L"):
                            img = img.convert("RGB")
                        base_name = os.path.splitext(os.path.basename(file_path))[0]
                        dst_path = os.path.join(target_dir_images, f"{base_name}.jpg")
                        img.save(dst_path, "JPEG", quality=95)
                        image_paths.append(dst_path)
                except Exception:
                    dst_path = os.path.join(target_dir_images, os.path.basename(file_path))
                    shutil.copy(file_path, dst_path)
                    image_paths.append(dst_path)
            else:
                dst_path = os.path.join(target_dir_images, os.path.basename(file_path))
                shutil.copy(file_path, dst_path)
                image_paths.append(dst_path)

    image_paths = sorted(image_paths)
    return target_dir, image_paths


def load_example_scene(scene_name, examples_dir="examples"):
    scenes = get_scene_info(examples_dir)
    selected = None
    for s in scenes:
        if s["name"] == scene_name:
            selected = s
            break
    if selected is None:
        return "None", [], "Scene not found"
    target_dir, image_paths = handle_uploads(
        [{"name": p} for p in selected["image_files"]], 1.0
    )
    return target_dir, image_paths, f"Loaded scene '{scene_name}' with {selected['num_images']} images. Click Reconstruct to begin."


# ---------------------------------------------------------------------------
# Model inference
# ---------------------------------------------------------------------------
@spaces.GPU(duration=120)
def run_model(target_dir, apply_mask=True, mask_edges=True, filter_black_bg=False, filter_white_bg=False):
    global model
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    with _model_lock:
        if model is None:
            model = initialize_mapanything_model(high_level_config, device)
        else:
            model = model.to(device)
    model.eval()

    image_folder_path = os.path.join(target_dir, "images")
    views = load_images(image_folder_path)
    if len(views) == 0:
        raise ValueError("No images found.")

    outputs = model.infer(views, apply_mask=apply_mask, mask_edges=True, memory_efficient_inference=False)

    extrinsics, intrinsics, world_pts, depths, images, masks = [], [], [], [], [], []
    for pred in outputs:
        depthmap = pred["depth_z"][0].squeeze(-1)
        K = pred["intrinsics"][0]
        pose = pred["camera_poses"][0]
        pts3d, valid = depthmap_to_world_frame(depthmap, K, pose)
        msk = pred["mask"][0].squeeze(-1).cpu().numpy().astype(bool) if "mask" in pred else np.ones_like(depthmap.cpu().numpy(), dtype=bool)
        msk = msk & valid.cpu().numpy()
        extrinsics.append(pose.cpu().numpy())
        intrinsics.append(K.cpu().numpy())
        world_pts.append(pts3d.cpu().numpy())
        depths.append(depthmap.cpu().numpy())
        images.append(pred["img_no_norm"][0].cpu().numpy())
        masks.append(msk)

    predictions = {
        "extrinsic": np.stack(extrinsics),
        "intrinsic": np.stack(intrinsics),
        "world_points": np.stack(world_pts),
        "depth": np.stack(depths)[..., np.newaxis] if np.stack(depths).ndim == 3 else np.stack(depths),
        "images": np.stack(images),
        "final_mask": np.stack(masks),
    }

    processed_data = process_predictions_for_visualization(
        predictions, views, high_level_config, filter_black_bg, filter_white_bg
    )
    torch.cuda.empty_cache()
    return predictions, processed_data


# ---------------------------------------------------------------------------
# Rerun 3D logging
# ---------------------------------------------------------------------------
def log_predictions_to_rerun(
    predictions, filter_by_frames="All", show_cam=True,
    filter_black_bg=False, filter_white_bg=False
):
    rr.init("MapAnything", spawn=False)
    stream = rr.binary_stream()

    num_views = predictions["world_points"].shape[0]

    if filter_by_frames != "All":
        try:
            idx = int(filter_by_frames.split(":")[0])
            frame_indices = [idx]
        except Exception:
            frame_indices = list(range(num_views))
    else:
        frame_indices = list(range(num_views))

    all_points = []
    all_colors = []

    for vi in frame_indices:
        pts = predictions["world_points"][vi]
        img = predictions["images"][vi]
        mask = predictions["final_mask"][vi]
        H, W = mask.shape[:2]

        if img.max() <= 1.0:
            img_u8 = (img * 255).astype(np.uint8)
        else:
            img_u8 = img.astype(np.uint8)

        if filter_black_bg:
            mask = mask & (img_u8.reshape(H, W, 3).sum(axis=2) >= 16)
        if filter_white_bg:
            c = img_u8.reshape(H, W, 3)
            mask = mask & ~((c[:, :, 0] > 240) & (c[:, :, 1] > 240) & (c[:, :, 2] > 240))

        flat_pts = pts.reshape(-1, 3)
        flat_colors = img_u8.reshape(-1, 3)
        flat_mask = mask.reshape(-1)

        valid_pts = flat_pts[flat_mask]
        valid_colors = flat_colors[flat_mask]
        all_points.append(valid_pts)
        all_colors.append(valid_colors)

        if show_cam:
            extrinsic = predictions["extrinsic"][vi]
            intrinsic = predictions["intrinsic"][vi]

            rotation = extrinsic[:3, :3]
            translation = extrinsic[:3, 3]

            rr.log(
                f"world/camera_{vi}",
                rr.Transform3D(
                    translation=translation,
                    mat3x3=rotation,
                ),
            )

            fx = float(intrinsic[0, 0])
            fy = float(intrinsic[1, 1])
            cx = float(intrinsic[0, 2])
            cy = float(intrinsic[1, 2])

            rr.log(
                f"world/camera_{vi}/image",
                rr.Pinhole(
                    resolution=[W, H],
                    focal_length=[fx, fy],
                    principal_point=[cx, cy],
                ),
            )
            rr.log(
                f"world/camera_{vi}/image/rgb",
                rr.Image(img_u8.reshape(H, W, 3)),
            )

    if all_points:
        combined_pts = np.concatenate(all_points, axis=0)
        combined_colors = np.concatenate(all_colors, axis=0)
        rr.log(
            "world/pointcloud",
            rr.Points3D(
                positions=combined_pts,
                colors=combined_colors,
                radii=np.full(len(combined_pts), 0.005),
            ),
        )

    return stream.read()


# ---------------------------------------------------------------------------
# Reconstruction pipeline
# ---------------------------------------------------------------------------
@spaces.GPU(duration=120)
def gradio_reconstruct(
    target_dir,
    frame_filter="All",
    show_cam=True,
    filter_black_bg=False,
    filter_white_bg=False,
    apply_mask=True,
):
    if not target_dir or target_dir == "None" or not os.path.isdir(target_dir):
        return None, "No valid target directory. Please upload files first.", None, "All", None, None, None, None, "View 1", "View 1", "View 1"

    gc.collect()
    torch.cuda.empty_cache()

    target_dir_images = os.path.join(target_dir, "images")
    all_files = sorted(os.listdir(target_dir_images)) if os.path.isdir(target_dir_images) else []
    all_files_labeled = [f"{i}: {fn}" for i, fn in enumerate(all_files)]
    frame_choices = ["All"] + all_files_labeled

    with torch.no_grad():
        predictions, processed_data = run_model(
            target_dir, apply_mask,
            filter_black_bg=filter_black_bg,
            filter_white_bg=filter_white_bg,
        )

    np.savez(os.path.join(target_dir, "predictions.npz"), **predictions)

    if frame_filter is None:
        frame_filter = "All"

    rerun_data = log_predictions_to_rerun(
        predictions,
        filter_by_frames=frame_filter,
        show_cam=show_cam,
        filter_black_bg=filter_black_bg,
        filter_white_bg=filter_white_bg,
    )

    depth_vis = get_depth_vis(processed_data, 0)
    normal_vis = get_normal_vis(processed_data, 0)
    measure_img, _ = get_measure_image(processed_data, 0)

    num_views = len(processed_data)
    view_choices = [f"View {i + 1}" for i in range(num_views)]

    del predictions
    gc.collect()
    torch.cuda.empty_cache()

    log_msg = f"Reconstruction complete ({len(all_files)} frames)."

    return (
        rerun_data,
        log_msg,
        processed_data,
        gr.Dropdown(choices=frame_choices, value=frame_filter, interactive=True),
        depth_vis,
        normal_vis,
        measure_img,
        "",
        gr.Dropdown(choices=view_choices, value="View 1"),
        gr.Dropdown(choices=view_choices, value="View 1"),
        gr.Dropdown(choices=view_choices, value="View 1"),
    )


def update_rerun_visualization(
    target_dir, frame_filter, show_cam, filter_black_bg, filter_white_bg
):
    if not target_dir or target_dir == "None" or not os.path.isdir(target_dir):
        return None, "No reconstruction available."
    predictions_path = os.path.join(target_dir, "predictions.npz")
    if not os.path.exists(predictions_path):
        return None, "No reconstruction found. Click Reconstruct first."
    loaded = np.load(predictions_path, allow_pickle=True)
    predictions = {key: loaded[key] for key in loaded.keys()}
    rerun_data = log_predictions_to_rerun(
        predictions,
        filter_by_frames=frame_filter or "All",
        show_cam=show_cam,
        filter_black_bg=filter_black_bg,
        filter_white_bg=filter_white_bg,
    )
    return rerun_data, "Visualization updated."


def update_all_views(
    target_dir, filter_black_bg, filter_white_bg, processed_data,
    d_sel, n_sel, m_sel
):
    if not target_dir or target_dir == "None" or not os.path.isdir(target_dir):
        return processed_data, None, None, None, []
    predictions_path = os.path.join(target_dir, "predictions.npz")
    if not os.path.exists(predictions_path):
        return processed_data, None, None, None, []
    try:
        loaded = np.load(predictions_path, allow_pickle=True)
        predictions = {k: loaded[k] for k in loaded.keys()}
        image_folder = os.path.join(target_dir, "images")
        views = load_images(image_folder)
        new_data = process_predictions_for_visualization(
            predictions, views, high_level_config, filter_black_bg, filter_white_bg
        )
        di = max(0, int(d_sel.split()[1]) - 1) if d_sel else 0
        ni = max(0, int(n_sel.split()[1]) - 1) if n_sel else 0
        mi = max(0, int(m_sel.split()[1]) - 1) if m_sel else 0
        return (
            new_data,
            get_depth_vis(new_data, di),
            get_normal_vis(new_data, ni),
            get_measure_image(new_data, mi)[0],
            [],
        )
    except Exception as e:
        print(f"Error updating views: {e}")
        return processed_data, None, None, None, []


# ---------------------------------------------------------------------------
# Measure logic
# ---------------------------------------------------------------------------
def measure_click(processed_data, measure_points, view_selector, evt: gr.SelectData):
    try:
        if processed_data is None or len(processed_data) == 0:
            return None, [], "No data available"
        try:
            vi = int(view_selector.split()[1]) - 1
        except Exception:
            vi = 0
        vi = max(0, min(vi, len(processed_data) - 1))
        vd = list(processed_data.values())[vi]
        if vd is None:
            return None, [], "No view data"

        px, py = evt.index[0], evt.index[1]

        if vd["mask"] is not None and 0 <= py < vd["mask"].shape[0] and 0 <= px < vd["mask"].shape[1]:
            if not vd["mask"][py, px]:
                img, _ = get_measure_image(processed_data, vi)
                return img, measure_points, "Cannot measure on masked areas (shown in grey overlay)."

        measure_points.append((px, py))
        image, _ = get_measure_image(processed_data, vi)
        if image is None:
            return None, [], "No image"
        image = image.copy()
        pts3d = vd["points3d"]

        for p in measure_points:
            if 0 <= p[0] < image.shape[1] and 0 <= p[1] < image.shape[0]:
                image = cv2.circle(image, p, radius=5, color=(255, 0, 0), thickness=2)

        depth_text = ""
        for i, p in enumerate(measure_points):
            if vd["depth"] is not None and 0 <= p[1] < vd["depth"].shape[0] and 0 <= p[0] < vd["depth"].shape[1]:
                d = vd["depth"][p[1], p[0]]
                depth_text += f"P{i + 1} depth: {d:.2f}m | "

        if len(measure_points) == 2:
            p1, p2 = measure_points
            if all(0 <= p1[c] < image.shape[1 - c] for c in range(2)) and all(0 <= p2[c] < image.shape[1 - c] for c in range(2)):
                image = cv2.line(image, p1, p2, color=(255, 0, 0), thickness=2)
            dist_text = "Distance: N/A"
            if pts3d is not None:
                try:
                    p1_3d = pts3d[p1[1], p1[0]]
                    p2_3d = pts3d[p2[1], p2[0]]
                    distance = np.linalg.norm(p1_3d - p2_3d)
                    dist_text = f"Distance: {distance:.2f}m"
                except Exception:
                    pass
            measure_points = []
            return image, measure_points, depth_text + dist_text
        else:
            return image, measure_points, depth_text + "Click a second point to measure distance."
    except Exception as e:
        return None, [], f"Error: {e}"


# ---------------------------------------------------------------------------
# Navigation helpers
# ---------------------------------------------------------------------------
def nav_depth(processed_data, selector, direction):
    if not processed_data:
        return "View 1", None
    try:
        cur = int(selector.split()[1]) - 1
    except Exception:
        cur = 0
    nv = (cur + direction) % len(processed_data)
    return f"View {nv + 1}", get_depth_vis(processed_data, nv)

def nav_normal(processed_data, selector, direction):
    if not processed_data:
        return "View 1", None
    try:
        cur = int(selector.split()[1]) - 1
    except Exception:
        cur = 0
    nv = (cur + direction) % len(processed_data)
    return f"View {nv + 1}", get_normal_vis(processed_data, nv)

def nav_measure(processed_data, selector, direction):
    if not processed_data:
        return "View 1", None, []
    try:
        cur = int(selector.split()[1]) - 1
    except Exception:
        cur = 0
    nv = (cur + direction) % len(processed_data)
    img, pts = get_measure_image(processed_data, nv)
    return f"View {nv + 1}", img, pts


# ---------------------------------------------------------------------------
# Upload handlers
# ---------------------------------------------------------------------------
def on_files_upload(files, interval):
    if not files:
        return "None", [], "No files uploaded."
    target_dir, paths = handle_uploads(files, interval)
    return target_dir, paths, f"Uploaded {len(paths)} images. Click Reconstruct to process."


def on_example_load(scene_name):
    return load_example_scene(scene_name)


# ---------------------------------------------------------------------------
# CSS
# ---------------------------------------------------------------------------
css = r"""
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&family=JetBrains+Mono:wght@400;500;600&display=swap');

*{box-sizing:border-box;margin:0;padding:0}

body,.gradio-container{
    background:#0f0f13!important;
    font-family:'Inter',system-ui,-apple-system,sans-serif!important;
    font-size:14px!important;
    color:#e4e4e7!important;
    min-height:100vh;
}
.dark body,.dark .gradio-container{
    background:#0f0f13!important;
    color:#e4e4e7!important;
}
footer{display:none!important}

.hidden-component{
    display:none!important;
    height:0!important;
    overflow:hidden!important;
    margin:0!important;
    padding:0!important;
}

/* Main Shell */
.app-shell{
    background:#18181b;
    border:1px solid #27272a;
    border-radius:16px;
    margin:12px auto;
    max-width:1500px;
    overflow:hidden;
    box-shadow:0 25px 50px -12px rgba(0,0,0,.6),
               0 0 0 1px rgba(255,255,255,.03);
}

/* Header */
.app-header{
    background:linear-gradient(135deg,#18181b 0%,#1e1e24 100%);
    border-bottom:1px solid #27272a;
    padding:14px 24px;
    display:flex;
    align-items:center;
    justify-content:space-between;
}
.app-header-left{
    display:flex;
    align-items:center;
    gap:12px;
}
.app-logo{
    width:36px;height:36px;
    background:linear-gradient(135deg,#6366f1,#8b5cf6,#a78bfa);
    border-radius:10px;
    display:flex;align-items:center;justify-content:center;
    font-size:14px;font-weight:800;color:#fff;
    box-shadow:0 4px 12px rgba(99,102,241,.35);
    letter-spacing:-0.5px;
}
.app-title{
    font-size:18px;font-weight:700;
    background:linear-gradient(135deg,#e4e4e7,#a1a1aa);
    -webkit-background-clip:text;
    -webkit-text-fill-color:transparent;
    letter-spacing:-.3px;
}
.app-badge{
    font-size:11px;font-weight:600;
    padding:3px 10px;border-radius:20px;
    background:rgba(99,102,241,.15);
    color:#818cf8;
    border:1px solid rgba(99,102,241,.25);
    letter-spacing:.3px;
}

/* Toolbar */
.app-toolbar{
    background:#18181b;
    border-bottom:1px solid #27272a;
    padding:8px 16px;
    display:flex;gap:4px;align-items:center;flex-wrap:wrap;
}
.tb-sep{
    width:1px;height:28px;background:#27272a;margin:0 8px;
}
.modern-tb-btn{
    display:inline-flex;align-items:center;justify-content:center;gap:6px;
    min-width:32px;height:34px;
    background:transparent;
    border:1px solid transparent;
    border-radius:8px;cursor:pointer;
    font-size:13px;font-weight:600;padding:0 12px;
    font-family:'Inter',sans-serif;
    color:#a1a1aa!important;
    transition:all .15s ease;
}
.modern-tb-btn:hover{
    background:rgba(99,102,241,.15);
    color:#ffffff!important;
    border-color:rgba(99,102,241,.3);
}
.modern-tb-btn:active,.modern-tb-btn.active{
    background:rgba(99,102,241,.25);
    color:#ffffff!important;
    border-color:rgba(99,102,241,.45);
}
.modern-tb-btn .tb-icon{font-size:15px;line-height:1}
.modern-tb-btn .tb-label{font-size:13px;font-weight:600}

/* Main Layout */
.app-main-row{
    display:flex;gap:0;flex:1;overflow:hidden;
    min-height:600px;
}
.app-main-left{
    flex:1;display:flex;flex-direction:column;min-width:0;
    border-right:1px solid #27272a;
}
.app-main-right{
    width:420px;display:flex;flex-direction:column;flex-shrink:0;
    background:#18181b;overflow-y:auto;
}

/* Upload Area */
.upload-area{
    position:relative;
    background:#09090b;
    min-height:200px;
    display:flex;align-items:center;justify-content:center;
    cursor:pointer;
    border-bottom:1px solid #27272a;
}
.upload-click-area{
    display:flex;flex-direction:column;align-items:center;justify-content:center;
    cursor:pointer;padding:30px 44px;
    border:2px dashed #3f3f46;border-radius:16px;
    background:rgba(99,102,241,.03);
    transition:all .2s ease;
}
.upload-click-area:hover{
    background:rgba(99,102,241,.08);
    border-color:#6366f1;transform:scale(1.02);
}
.upload-click-area:active{
    background:rgba(99,102,241,.12);transform:scale(.98);
}
.upload-click-area svg{width:64px;height:64px;margin-bottom:8px}
.upload-hint{font-size:13px;color:#52525b;margin-top:4px}

/* Gallery Grid */
.gallery-grid{
    display:flex;flex-wrap:wrap;gap:6px;
    padding:10px 12px;
    background:#09090b;
    border-bottom:1px solid #27272a;
    max-height:200px;
    overflow-y:auto;
}
.gallery-grid:empty{display:none}
.gallery-thumb{
    width:80px;height:60px;object-fit:cover;
    border-radius:6px;border:1px solid #27272a;
    cursor:pointer;transition:all .15s;
}
.gallery-thumb:hover{border-color:#6366f1;transform:scale(1.05)}

/* Tabs */
.tab-bar{
    display:flex;gap:0;
    border-bottom:1px solid #27272a;
    background:#18181b;
    overflow-x:auto;
}
.tab-btn{
    padding:10px 20px;
    font-size:13px;font-weight:600;
    color:#71717a;
    background:transparent;
    border:none;border-bottom:2px solid transparent;
    cursor:pointer;
    font-family:'Inter',sans-serif;
    transition:all .15s;
    white-space:nowrap;
}
.tab-btn:hover{color:#a1a1aa;background:rgba(99,102,241,.05)}
.tab-btn.active{
    color:#c7d2fe;
    border-bottom-color:#6366f1;
    background:rgba(99,102,241,.08);
}
.tab-content{display:none;flex:1;flex-direction:column;overflow:hidden}
.tab-content.active{display:flex}

/* Viewer Panel */
.viewer-panel{
    flex:1;background:#09090b;
    display:flex;align-items:center;justify-content:center;
    position:relative;overflow:hidden;
    min-height:400px;
}
.viewer-placeholder{
    color:#3f3f46;font-size:13px;text-align:center;padding:20px;
}
.viewer-panel img{
    max-width:100%;max-height:100%;object-fit:contain;
}

/* Nav Row */
.nav-row{
    display:flex;align-items:center;gap:8px;
    padding:8px 12px;
    background:#18181b;
    border-bottom:1px solid #27272a;
}
.nav-btn{
    display:inline-flex;align-items:center;justify-content:center;
    height:30px;padding:0 14px;
    background:rgba(99,102,241,.1);
    border:1px solid rgba(99,102,241,.2);
    border-radius:6px;cursor:pointer;
    font-size:12px;font-weight:600;color:#c7d2fe;
    font-family:'Inter',sans-serif;
    transition:all .15s;
}
.nav-btn:hover{
    background:rgba(99,102,241,.2);border-color:rgba(99,102,241,.35);
    color:#fff;
}
.nav-select{
    flex:1;
    background:#09090b;border:1px solid #27272a;border-radius:6px;
    padding:5px 10px;font-size:12px;color:#e4e4e7;
    font-family:'JetBrains Mono',monospace;
    outline:none;
}
.nav-select:focus{border-color:#6366f1}

/* Panel Cards */
.panel-card{border-bottom:1px solid #27272a}
.panel-card-title{
    padding:12px 20px;font-size:12px;font-weight:600;
    color:#71717a;text-transform:uppercase;letter-spacing:.8px;
    border-bottom:1px solid rgba(39,39,42,.6);
}
.panel-card-body{
    padding:16px 20px;display:flex;flex-direction:column;gap:10px;
}

/* Settings Group */
.settings-group{
    border:1px solid #27272a;border-radius:10px;
    margin:12px 16px;overflow:hidden;
}
.settings-group-title{
    font-size:12px;font-weight:600;color:#71717a;
    text-transform:uppercase;letter-spacing:.8px;
    padding:10px 16px;border-bottom:1px solid #27272a;
    background:rgba(24,24,27,.5);
}
.settings-group-body{
    padding:14px 16px;display:flex;flex-direction:column;gap:10px;
}
.checkbox-row{
    display:flex;align-items:center;gap:8px;
    font-size:13px;color:#a1a1aa;
}
.checkbox-row input[type="checkbox"]{
    accent-color:#6366f1;width:16px;height:16px;cursor:pointer;
}
.checkbox-row label{color:#a1a1aa;font-size:13px;cursor:pointer}
.slider-row{
    display:flex;align-items:center;gap:10px;min-height:28px;
}
.slider-row label{
    font-size:13px;font-weight:500;color:#a1a1aa;min-width:90px;flex-shrink:0;
}
.slider-row input[type="range"]{
    flex:1;-webkit-appearance:none;appearance:none;
    height:6px;background:#27272a;border-radius:3px;outline:none;
}
.slider-row input[type="range"]::-webkit-slider-thumb{
    -webkit-appearance:none;appearance:none;
    width:16px;height:16px;
    background:linear-gradient(135deg,#6366f1,#7c3aed);
    border-radius:50%;cursor:pointer;
    box-shadow:0 2px 6px rgba(99,102,241,.4);
}
.slider-row .slider-val{
    min-width:40px;text-align:right;
    font-family:'JetBrains Mono',monospace;font-size:12px;
    padding:3px 8px;background:#09090b;
    border:1px solid #27272a;border-radius:6px;color:#a1a1aa;
}

/* Primary Button */
.btn-primary{
    display:flex;align-items:center;justify-content:center;gap:8px;
    width:100%;
    background:linear-gradient(135deg,#6366f1,#7c3aed);
    border:none;border-radius:10px;
    padding:12px 24px;cursor:pointer;
    font-size:15px;font-weight:600;
    font-family:'Inter',sans-serif;color:#fff;
    transition:all .2s ease;
    box-shadow:0 4px 16px rgba(99,102,241,.3),
               inset 0 1px 0 rgba(255,255,255,.1);
    letter-spacing:-.2px;
}
.btn-primary:hover{
    background:linear-gradient(135deg,#7c7cf5,#8b5cf6);
    box-shadow:0 6px 24px rgba(99,102,241,.45);
    transform:translateY(-1px);
}
.btn-primary:active{
    transform:translateY(0);
    box-shadow:0 2px 8px rgba(99,102,241,.3);
}
.btn-primary:disabled{
    opacity:.5;cursor:not-allowed;transform:none;
}

.btn-secondary{
    display:flex;align-items:center;justify-content:center;gap:6px;
    width:100%;
    background:transparent;
    border:1px solid #3f3f46;border-radius:8px;
    padding:8px 16px;cursor:pointer;
    font-size:13px;font-weight:600;
    font-family:'Inter',sans-serif;color:#a1a1aa;
    transition:all .15s;
}
.btn-secondary:hover{
    background:rgba(255,255,255,.05);border-color:#52525b;color:#e4e4e7;
}

/* Loader */
.modern-loader{
    display:none;position:absolute;
    top:0;left:0;right:0;bottom:0;
    background:rgba(9,9,11,.92);z-index:15;
    flex-direction:column;align-items:center;justify-content:center;gap:16px;
    backdrop-filter:blur(4px);
}
.modern-loader.active{display:flex}
.loader-spinner{
    width:36px;height:36px;
    border:3px solid #27272a;border-top-color:#6366f1;
    border-radius:50%;animation:spin .8s linear infinite;
}
@keyframes spin{to{transform:rotate(360deg)}}
.loader-text{font-size:13px;color:#a1a1aa;font-weight:500}
.loader-bar-track{
    width:200px;height:4px;background:#27272a;border-radius:2px;overflow:hidden;
}
.loader-bar-fill{
    height:100%;
    background:linear-gradient(90deg,#6366f1,#8b5cf6,#6366f1);
    background-size:200% 100%;
    animation:shimmer 1.5s ease-in-out infinite;border-radius:2px;
}
@keyframes shimmer{
    0%{background-position:200% 0}100%{background-position:-200% 0}
}

/* Toast */
.toast-notification{
    position:fixed;top:24px;left:50%;
    transform:translateX(-50%) translateY(-120%);
    z-index:9999;padding:10px 24px;border-radius:10px;
    font-family:'Inter',sans-serif;font-size:14px;font-weight:600;
    display:flex;align-items:center;gap:8px;
    box-shadow:0 8px 24px rgba(0,0,0,.5);
    transition:transform .35s cubic-bezier(.34,1.56,.64,1),opacity .35s ease;
    opacity:0;pointer-events:none;
}
.toast-notification.visible{
    transform:translateX(-50%) translateY(0);opacity:1;pointer-events:auto;
}
.toast-notification.error{
    background:linear-gradient(135deg,#dc2626,#b91c1c);color:#fff;
    border:1px solid rgba(255,255,255,.15);
}
.toast-notification.success{
    background:linear-gradient(135deg,#059669,#047857);color:#fff;
    border:1px solid rgba(255,255,255,.15);
}
.toast-notification.info{
    background:linear-gradient(135deg,#2563eb,#1d4ed8);color:#fff;
    border:1px solid rgba(255,255,255,.15);
}

/* Status Bar */
.app-statusbar{
    background:#18181b;border-top:1px solid #27272a;
    padding:6px 20px;display:flex;gap:12px;height:34px;align-items:center;
}
.sb-section{
    padding:0 12px;flex:1;display:flex;align-items:center;
    font-family:'JetBrains Mono',monospace;font-size:12px;color:#52525b;
    overflow:hidden;white-space:nowrap;
}
.sb-section.sb-fixed{
    flex:0 0 auto;min-width:90px;text-align:center;justify-content:center;
    padding:3px 12px;background:rgba(99,102,241,.08);border-radius:6px;
    color:#818cf8;font-weight:500;
}

/* Log Panel */
.log-panel{
    background:#09090b;border-bottom:1px solid #27272a;
    padding:8px 16px;
    font-family:'JetBrains Mono',monospace;
    font-size:12px;color:#71717a;
    min-height:32px;
    display:flex;align-items:center;
}
.log-panel .log-dot{
    width:6px;height:6px;border-radius:50%;
    margin-right:10px;flex-shrink:0;
}
.log-panel .log-dot.idle{background:#52525b}
.log-panel .log-dot.ok{background:#22c55e}
.log-panel .log-dot.busy{background:#eab308;animation:pulse 1s infinite}
@keyframes pulse{0%,100%{opacity:1}50%{opacity:.4}}

/* Measure info */
.measure-info{
    padding:10px 16px;background:rgba(99,102,241,.05);
    border-bottom:1px solid #27272a;
    font-size:13px;color:#a1a1aa;line-height:1.7;
}
.measure-info b{color:#c7d2fe;font-weight:600}
.measure-result{
    padding:10px 16px;
    font-family:'JetBrains Mono',monospace;
    font-size:13px;color:#a1a1aa;
    background:#09090b;
    border-top:1px solid #27272a;
    min-height:36px;
}

/* Example thumbs */
.example-thumb:hover{
    border-color:#6366f1!important;
    box-shadow:0 4px 12px rgba(99,102,241,.2);
    transform:translateY(-2px);
}

/* Rerun viewer override */
.rerun-viewer-wrapper{
    flex:1;min-height:400px;
    background:#09090b;
}

/* Scrollbars */
::-webkit-scrollbar{width:8px;height:8px}
::-webkit-scrollbar-track{background:#09090b}
::-webkit-scrollbar-thumb{background:#27272a;border-radius:4px}
::-webkit-scrollbar-thumb:hover{background:#3f3f46}

/* Hide gradio components used as state */
#gradio-hidden-group{
    position:absolute!important;
    left:-9999px!important;top:-9999px!important;
    width:1px!important;height:1px!important;
    opacity:0.01!important;pointer-events:none!important;
    overflow:hidden!important;
}

@media(max-width:900px){
    .app-main-row{flex-direction:column}
    .app-main-right{width:100%}
    .app-main-left{border-right:none;border-bottom:1px solid #27272a}
}
"""

# ---------------------------------------------------------------------------
# JavaScript
# ---------------------------------------------------------------------------
scenes_data = get_scene_info("examples")
example_thumbs_html = build_example_thumbnails_html(scenes_data)

main_js = r"""
() => {
if (window.__maInitDone) return;
window.__maInitDone = true;

// ---- Toast ----
let toastTimer = null;
function showToast(msg, type) {
    let t = document.getElementById('app-toast');
    if (!t) {
        t = document.createElement('div');
        t.id = 'app-toast';
        t.className = 'toast-notification';
        t.innerHTML = '<span class="toast-icon"></span><span class="toast-text"></span>';
        document.body.appendChild(t);
    }
    t.className = 'toast-notification ' + (type || 'info');
    t.querySelector('.toast-icon').textContent = type === 'error' ? '\u2717' : type === 'success' ? '\u2713' : '\u2139';
    t.querySelector('.toast-text').textContent = msg;
    if (toastTimer) clearTimeout(toastTimer);
    void t.offsetWidth;
    t.classList.add('visible');
    toastTimer = setTimeout(() => t.classList.remove('visible'), 3500);
}
window.__showToast = showToast;

// ---- Tabs ----
function initTabs() {
    const btns = document.querySelectorAll('.tab-btn[data-tab]');
    btns.forEach(btn => {
        btn.addEventListener('click', () => {
            const group = btn.closest('.tab-container');
            if (!group) return;
            group.querySelectorAll('.tab-btn').forEach(b => b.classList.remove('active'));
            group.querySelectorAll('.tab-content').forEach(c => c.classList.remove('active'));
            btn.classList.add('active');
            const target = group.querySelector('#' + btn.dataset.tab);
            if (target) target.classList.add('active');
        });
    });
}
initTabs();

// ---- File Upload ----
const fileInput = document.getElementById('ma-file-input');
const uploadArea = document.getElementById('ma-upload-area');
const uploadPrompt = document.getElementById('ma-upload-prompt');
const galleryGrid = document.getElementById('ma-gallery-grid');

if (uploadArea && fileInput) {
    uploadArea.addEventListener('click', () => fileInput.click());
    uploadArea.addEventListener('dragover', e => {
        e.preventDefault();
        uploadArea.style.outline = '2px solid #6366f1';
        uploadArea.style.outlineOffset = '-2px';
    });
    uploadArea.addEventListener('dragleave', e => {
        e.preventDefault();
        uploadArea.style.outline = '';
    });
    uploadArea.addEventListener('drop', e => {
        e.preventDefault();
        uploadArea.style.outline = '';
        if (e.dataTransfer.files.length) {
            fileInput.files = e.dataTransfer.files;
            fileInput.dispatchEvent(new Event('change', {bubbles: true}));
        }
    });
}

// ---- Gradio value setter ----
function setGradioValue(containerId, value) {
    const container = document.getElementById(containerId);
    if (!container) return;
    const els = container.querySelectorAll('input, textarea');
    els.forEach(el => {
        if (el.type === 'file' || el.type === 'range' || el.type === 'checkbox') return;
        const proto = el.tagName === 'TEXTAREA' ? HTMLTextAreaElement.prototype : HTMLInputElement.prototype;
        const ns = Object.getOwnPropertyDescriptor(proto, 'value');
        if (ns && ns.set) {
            ns.set.call(el, value);
            el.dispatchEvent(new Event('input', {bubbles:true, composed:true}));
            el.dispatchEvent(new Event('change', {bubbles:true, composed:true}));
        }
    });
}

function setGradioCheckbox(containerId, checked) {
    const container = document.getElementById(containerId);
    if (!container) return;
    const cb = container.querySelector('input[type="checkbox"]');
    if (cb && cb.checked !== checked) cb.click();
}

// ---- Sync custom controls to Gradio hidden components ----
function syncSlider(customId, gradioId) {
    const slider = document.getElementById(customId);
    const valSpan = document.getElementById(customId + '-val');
    if (!slider) return;
    slider.addEventListener('input', () => {
        if (valSpan) valSpan.textContent = parseFloat(slider.value).toFixed(
            slider.step && slider.step.includes('.') ? 1 : 0
        );
        const container = document.getElementById(gradioId);
        if (!container) return;
        container.querySelectorAll('input[type="range"],input[type="number"]').forEach(el => {
            const ns = Object.getOwnPropertyDescriptor(HTMLInputElement.prototype, 'value');
            if (ns && ns.set) {
                ns.set.call(el, slider.value);
                el.dispatchEvent(new Event('input', {bubbles:true, composed:true}));
                el.dispatchEvent(new Event('change', {bubbles:true, composed:true}));
            }
        });
    });
}

syncSlider('custom-interval', 'gradio-interval');

// Checkboxes
['show-cam', 'filter-black', 'filter-white', 'apply-mask'].forEach(name => {
    const el = document.getElementById('custom-' + name);
    if (!el) return;
    el.addEventListener('change', () => {
        setGradioCheckbox('gradio-' + name, el.checked);
    });
});

// ---- Loaders ----
function showLoader(id) {
    const l = document.getElementById(id);
    if (l) l.classList.add('active');
}
function hideLoader(id) {
    const l = document.getElementById(id);
    if (l) l.classList.remove('active');
}
window.__showLoader = showLoader;
window.__hideLoader = hideLoader;

function setLogStatus(text, state) {
    const logEl = document.getElementById('log-text');
    const dotEl = document.getElementById('log-dot');
    if (logEl) logEl.textContent = text;
    if (dotEl) {
        dotEl.className = 'log-dot';
        dotEl.classList.add(state || 'idle');
    }
}
window.__setLogStatus = setLogStatus;

// ---- Reconstruct button ----
const reconstructBtn = document.getElementById('custom-reconstruct-btn');
if (reconstructBtn) {
    reconstructBtn.addEventListener('click', () => {
        showLoader('rerun-loader');
        setLogStatus('Reconstructing...', 'busy');
        const sb = document.querySelector('.sb-section.sb-fixed');
        if (sb) sb.textContent = 'Processing...';

        setTimeout(() => {
            const gradioBtn = document.getElementById('gradio-reconstruct-btn');
            if (gradioBtn) {
                const btn = gradioBtn.querySelector('button');
                if (btn) btn.click();
                else gradioBtn.click();
            }
        }, 150);
    });
}

// ---- Clear button ----
const clearBtn = document.getElementById('custom-clear-btn');
if (clearBtn) {
    clearBtn.addEventListener('click', () => {
        if (galleryGrid) galleryGrid.innerHTML = '';
        if (uploadPrompt) uploadPrompt.style.display = '';
        setLogStatus('Ready', 'idle');
        // Click gradio clear
        const gradioClearBtn = document.getElementById('gradio-clear-btn');
        if (gradioClearBtn) {
            const btn = gradioClearBtn.querySelector('button');
            if (btn) btn.click();
        }
    });
}

// ---- Example scenes ----
window.__loadExampleScene = function(sceneName) {
    setGradioValue('gradio-example-name', sceneName);
    setLogStatus('Loading example: ' + sceneName + '...', 'busy');
    setTimeout(() => {
        const btn = document.getElementById('gradio-load-example-btn');
        if (btn) {
            const b = btn.querySelector('button');
            if (b) b.click(); else btn.click();
        }
    }, 150);
};

// ---- View navigation buttons ----
['depth', 'normal', 'measure'].forEach(tab => {
    const prevBtn = document.getElementById('nav-prev-' + tab);
    const nextBtn = document.getElementById('nav-next-' + tab);
    if (prevBtn) {
        prevBtn.addEventListener('click', () => {
            const gradioBtn = document.getElementById('gradio-prev-' + tab);
            if (gradioBtn) { const b = gradioBtn.querySelector('button'); if (b) b.click(); }
        });
    }
    if (nextBtn) {
        nextBtn.addEventListener('click', () => {
            const gradioBtn = document.getElementById('gradio-next-' + tab);
            if (gradioBtn) { const b = gradioBtn.querySelector('button'); if (b) b.click(); }
        });
    }
});

// ---- Watch gradio outputs and sync to custom UI ----
function watchOutputs() {
    // Log output
    const logGradio = document.getElementById('gradio-log-output');
    if (logGradio) {
        const obs = new MutationObserver(() => {
            const md = logGradio.querySelector('.markdown-text, .prose, p, span');
            if (md && md.textContent) {
                const text = md.textContent.trim();
                if (text) {
                    setLogStatus(text, text.toLowerCase().includes('error') ? 'idle' : 'ok');
                    const sb = document.querySelector('.sb-section.sb-fixed');
                    if (sb) sb.textContent = text.includes('complete') || text.includes('Success') ? 'Done' : 'Ready';
                    hideLoader('rerun-loader');
                }
            }
        });
        obs.observe(logGradio, {childList:true, subtree:true, characterData:true});
    }

    // Depth image
    syncImageOutput('gradio-depth-image', 'custom-depth-img');
    syncImageOutput('gradio-normal-image', 'custom-normal-img');
    syncImageOutput('gradio-measure-image', 'custom-measure-img');

    // Measure text
    const measureTextGradio = document.getElementById('gradio-measure-text');
    if (measureTextGradio) {
        const obs = new MutationObserver(() => {
            const md = measureTextGradio.querySelector('.markdown-text, .prose, p, span');
            const target = document.getElementById('custom-measure-result');
            if (md && target) target.textContent = md.textContent || '';
        });
        obs.observe(measureTextGradio, {childList:true, subtree:true, characterData:true});
    }

    // View selectors
    ['depth', 'normal', 'measure'].forEach(tab => {
        const gradioSel = document.getElementById('gradio-' + tab + '-selector');
        const customSel = document.getElementById('custom-' + tab + '-select');
        if (gradioSel && customSel) {
            const obs = new MutationObserver(() => {
                // Sync dropdown options and value from gradio to custom
                const gradioInput = gradioSel.querySelector('input');
                if (gradioInput && gradioInput.value) {
                    customSel.value = gradioInput.value;
                }
                // Sync options
                const options = gradioSel.querySelectorAll('li, option, [role="option"]');
                if (options.length > 0) {
                    const currentOptions = new Set(Array.from(customSel.options).map(o => o.value));
                    const newOptions = Array.from(options).map(o => o.textContent.trim()).filter(Boolean);
                    if (newOptions.length > 0 && !newOptions.every(o => currentOptions.has(o))) {
                        customSel.innerHTML = '';
                        newOptions.forEach(text => {
                            const opt = document.createElement('option');
                            opt.value = text;
                            opt.textContent = text;
                            customSel.appendChild(opt);
                        });
                    }
                }
            });
            obs.observe(gradioSel, {childList:true, subtree:true, attributes:true, characterData:true});
        }
    });

    // Gallery sync from gradio-gallery
    const gradioGallery = document.getElementById('gradio-gallery');
    if (gradioGallery && galleryGrid) {
        const gobs = new MutationObserver(() => {
            const imgs = gradioGallery.querySelectorAll('img');
            if (imgs.length > 0) {
                galleryGrid.innerHTML = '';
                imgs.forEach(img => {
                    const thumb = document.createElement('img');
                    thumb.src = img.src;
                    thumb.className = 'gallery-thumb';
                    galleryGrid.appendChild(thumb);
                });
                if (uploadPrompt) uploadPrompt.style.display = 'none';
            }
        });
        gobs.observe(gradioGallery, {childList:true, subtree:true, attributes:true, attributeFilter:['src']});
    }
}

function syncImageOutput(gradioId, customImgId) {
    const gradioContainer = document.getElementById(gradioId);
    const customImg = document.getElementById(customImgId);
    if (!gradioContainer || !customImg) return;
    const obs = new MutationObserver(() => {
        const img = gradioContainer.querySelector('img');
        if (img && img.src && customImg.src !== img.src) {
            customImg.src = img.src;
            customImg.style.display = 'block';
            const ph = customImg.parentElement.querySelector('.viewer-placeholder');
            if (ph) ph.style.display = 'none';
        }
    });
    obs.observe(gradioContainer, {childList:true, subtree:true, attributes:true, attributeFilter:['src']});
}

watchOutputs();

// Custom select change -> gradio
['depth', 'normal', 'measure'].forEach(tab => {
    const sel = document.getElementById('custom-' + tab + '-select');
    if (!sel) return;
    sel.addEventListener('change', () => {
        setGradioValue('gradio-' + tab + '-selector', sel.value);
        // Trigger the gradio dropdown change
        const gradioSel = document.getElementById('gradio-' + tab + '-selector');
        if (gradioSel) {
            const input = gradioSel.querySelector('input');
            if (input) {
                const ns = Object.getOwnPropertyDescriptor(HTMLInputElement.prototype, 'value');
                if (ns && ns.set) {
                    ns.set.call(input, sel.value);
                    input.dispatchEvent(new Event('input', {bubbles:true, composed:true}));
                    input.dispatchEvent(new Event('change', {bubbles:true, composed:true}));
                }
            }
        }
    });
});

}
"""

# ---------------------------------------------------------------------------
# Build Gradio App
# ---------------------------------------------------------------------------
with gr.Blocks() as demo:

    # -- Hidden Gradio components for state and data binding --
    with gr.Group(elem_id="gradio-hidden-group"):
        target_dir_state = gr.Textbox(value="None", elem_id="gradio-target-dir", elem_classes="hidden-component", container=False)
        example_name_state = gr.Textbox(value="", elem_id="gradio-example-name", elem_classes="hidden-component", container=False)
        is_example_state = gr.Textbox(value="None", elem_id="gradio-is-example", elem_classes="hidden-component", container=False)
        log_output = gr.Markdown("Ready", elem_id="gradio-log-output", elem_classes="hidden-component", container=False)

        unified_upload = gr.File(file_count="multiple", elem_id="gradio-file-upload", elem_classes="hidden-component", container=False, file_types=["image", "video"])
        interval_slider = gr.Slider(minimum=0.1, maximum=5.0, value=1.0, step=0.1, elem_id="gradio-interval", elem_classes="hidden-component", container=False)

        show_cam_cb = gr.Checkbox(value=True, elem_id="gradio-show-cam", elem_classes="hidden-component", container=False)
        filter_black_cb = gr.Checkbox(value=False, elem_id="gradio-filter-black", elem_classes="hidden-component", container=False)
        filter_white_cb = gr.Checkbox(value=False, elem_id="gradio-filter-white", elem_classes="hidden-component", container=False)
        apply_mask_cb = gr.Checkbox(value=True, elem_id="gradio-apply-mask", elem_classes="hidden-component", container=False)

        frame_filter_dd = gr.Dropdown(choices=["All"], value="All", elem_id="gradio-frame-filter", elem_classes="hidden-component", container=False, allow_custom_value=True)

        image_gallery = gr.Gallery(elem_id="gradio-gallery", elem_classes="hidden-component", container=False)

        depth_image = gr.Image(type="numpy", elem_id="gradio-depth-image", elem_classes="hidden-component", container=False, format="png")
        normal_image = gr.Image(type="numpy", elem_id="gradio-normal-image", elem_classes="hidden-component", container=False, format="png")
        measure_image = gr.Image(type="numpy", elem_id="gradio-measure-image", elem_classes="hidden-component", container=False, format="png", interactive=False, sources=[])
        measure_text_md = gr.Markdown("", elem_id="gradio-measure-text", elem_classes="hidden-component", container=False)

        depth_selector = gr.Dropdown(choices=["View 1"], value="View 1", elem_id="gradio-depth-selector", elem_classes="hidden-component", container=False, allow_custom_value=True)
        normal_selector = gr.Dropdown(choices=["View 1"], value="View 1", elem_id="gradio-normal-selector", elem_classes="hidden-component", container=False, allow_custom_value=True)
        measure_selector = gr.Dropdown(choices=["View 1"], value="View 1", elem_id="gradio-measure-selector", elem_classes="hidden-component", container=False, allow_custom_value=True)

        processed_data_state = gr.State(value=None)
        measure_points_state = gr.State(value=[])

        reconstruct_btn = gr.Button("Reconstruct", elem_id="gradio-reconstruct-btn", elem_classes="hidden-component")
        clear_btn = gr.Button("Clear", elem_id="gradio-clear-btn", elem_classes="hidden-component")
        load_example_btn = gr.Button("Load Example", elem_id="gradio-load-example-btn", elem_classes="hidden-component")

        prev_depth_btn = gr.Button("Prev", elem_id="gradio-prev-depth", elem_classes="hidden-component")
        next_depth_btn = gr.Button("Next", elem_id="gradio-next-depth", elem_classes="hidden-component")
        prev_normal_btn = gr.Button("Prev", elem_id="gradio-prev-normal", elem_classes="hidden-component")
        next_normal_btn = gr.Button("Next", elem_id="gradio-next-normal", elem_classes="hidden-component")
        prev_measure_btn = gr.Button("Prev", elem_id="gradio-prev-measure", elem_classes="hidden-component")
        next_measure_btn = gr.Button("Next", elem_id="gradio-next-measure", elem_classes="hidden-component")

    # -- Rerun Viewer (hidden, data source only) --
    rerun_viewer = Rerun(
        label="Rerun 3D Viewer",
        elem_id="gradio-rerun-viewer",
        streaming=True,
    )

    # -- Main HTML UI --
    gr.HTML(f"""
    <div class="app-shell">

        <!-- Header -->
        <div class="app-header">
            <div class="app-header-left">
                <div class="app-logo">MA</div>
                <span class="app-title">MapAnything</span>
                <span class="app-badge">3D Reconstruction</span>
            </div>
        </div>

        <!-- Toolbar -->
        <div class="app-toolbar">
            <button id="custom-reconstruct-btn" class="modern-tb-btn">
                <span class="tb-icon">&#9654;</span><span class="tb-label">Reconstruct</span>
            </button>
            <div class="tb-sep"></div>
            <button id="custom-clear-btn" class="modern-tb-btn">
                <span class="tb-icon">&#10005;</span><span class="tb-label">Clear</span>
            </button>
            <div class="tb-sep"></div>
            <span style="font-size:12px;color:#52525b;font-family:'JetBrains Mono',monospace;">
                MapAnything -- Metric 3D Reconstruction
            </span>
        </div>

        <!-- Log Bar -->
        <div class="log-panel">
            <span class="log-dot idle" id="log-dot"></span>
            <span id="log-text">Ready. Upload images or video to begin.</span>
        </div>

        <!-- Main Content -->
        <div class="app-main-row">

            <!-- Left: Viewer Tabs -->
            <div class="app-main-left tab-container">
                <div class="tab-bar">
                    <button class="tab-btn active" data-tab="tab-3d">3D View</button>
                    <button class="tab-btn" data-tab="tab-depth">Depth</button>
                    <button class="tab-btn" data-tab="tab-normal">Normal</button>
                    <button class="tab-btn" data-tab="tab-measure">Measure</button>
                </div>

                <!-- 3D View Tab -->
                <div class="tab-content active" id="tab-3d">
                    <div class="viewer-panel rerun-viewer-wrapper" style="position:relative;">
                        <div class="modern-loader" id="rerun-loader">
                            <div class="loader-spinner"></div>
                            <div class="loader-text">Running reconstruction...</div>
                            <div class="loader-bar-track"><div class="loader-bar-fill"></div></div>
                        </div>
                        <div class="viewer-placeholder" id="rerun-placeholder">
                            3D point cloud will appear here after reconstruction
                        </div>
                    </div>
                </div>

                <!-- Depth Tab -->
                <div class="tab-content" id="tab-depth">
                    <div class="nav-row">
                        <button class="nav-btn" id="nav-prev-depth">Prev</button>
                        <select class="nav-select" id="custom-depth-select">
                            <option value="View 1">View 1</option>
                        </select>
                        <button class="nav-btn" id="nav-next-depth">Next</button>
                    </div>
                    <div class="viewer-panel">
                        <img id="custom-depth-img" style="display:none;max-width:100%;max-height:100%;object-fit:contain;" />
                        <div class="viewer-placeholder">Depth map will appear after reconstruction</div>
                    </div>
                </div>

                <!-- Normal Tab -->
                <div class="tab-content" id="tab-normal">
                    <div class="nav-row">
                        <button class="nav-btn" id="nav-prev-normal">Prev</button>
                        <select class="nav-select" id="custom-normal-select">
                            <option value="View 1">View 1</option>
                        </select>
                        <button class="nav-btn" id="nav-next-normal">Next</button>
                    </div>
                    <div class="viewer-panel">
                        <img id="custom-normal-img" style="display:none;max-width:100%;max-height:100%;object-fit:contain;" />
                        <div class="viewer-placeholder">Normal map will appear after reconstruction</div>
                    </div>
                </div>

                <!-- Measure Tab -->
                <div class="tab-content" id="tab-measure">
                    <div class="measure-info">
                        <b>How to measure:</b> Click two points on the image to measure the 3D distance between them.
                        Grey areas indicate regions without depth data.
                    </div>
                    <div class="nav-row">
                        <button class="nav-btn" id="nav-prev-measure">Prev</button>
                        <select class="nav-select" id="custom-measure-select">
                            <option value="View 1">View 1</option>
                        </select>
                        <button class="nav-btn" id="nav-next-measure">Next</button>
                    </div>
                    <div class="viewer-panel" style="cursor:crosshair;">
                        <img id="custom-measure-img" style="display:none;max-width:100%;max-height:100%;object-fit:contain;" />
                        <div class="viewer-placeholder">Measurement view will appear after reconstruction</div>
                    </div>
                    <div class="measure-result" id="custom-measure-result"></div>
                </div>
            </div>

            <!-- Right: Upload, Settings, Examples -->
            <div class="app-main-right">

                <!-- Upload Area -->
                <div class="panel-card">
                    <div class="panel-card-title">Input</div>
                    <div class="panel-card-body" style="padding:0;">
                        <div id="ma-upload-area" class="upload-area">
                            <div id="ma-upload-prompt" class="upload-click-area">
                                <svg viewBox="0 0 64 64" fill="none" xmlns="http://www.w3.org/2000/svg">
                                    <rect x="6" y="12" width="52" height="40" rx="4" fill="none" stroke="#6366f1" stroke-width="1.5" stroke-dasharray="4 3"/>
                                    <polygon points="10,48 24,32 34,40 44,26 54,48" fill="rgba(99,102,241,0.12)" stroke="#6366f1" stroke-width="1"/>
                                    <circle cx="22" cy="24" r="4" fill="rgba(99,102,241,0.15)" stroke="#6366f1" stroke-width="1"/>
                                    <line x1="32" y1="8" x2="32" y2="18" stroke="#6366f1" stroke-width="1.5"/>
                                    <line x1="27" y1="13" x2="32" y2="8" stroke="#6366f1" stroke-width="1.5"/>
                                    <line x1="37" y1="13" x2="32" y2="8" stroke="#6366f1" stroke-width="1.5"/>
                                </svg>
                                <div class="upload-hint">Click or drag files to upload</div>
                                <div class="upload-hint">Images, videos (MP4, AVI, MOV), HEIC</div>
                            </div>
                        </div>
                        <input id="ma-file-input" type="file" accept="image/*,video/*,.heic,.heif" multiple style="display:none;" />
                        <div class="gallery-grid" id="ma-gallery-grid"></div>
                    </div>
                </div>

                <!-- Reconstruct Button -->
                <div style="padding:12px 20px;">
                    <button id="custom-reconstruct-btn-side" class="btn-primary"
                            onclick="document.getElementById('custom-reconstruct-btn').click()">
                        Reconstruct
                    </button>
                </div>
                <div style="padding:0 20px 12px;">
                    <button class="btn-secondary"
                            onclick="document.getElementById('custom-clear-btn').click()">
                        Clear All
                    </button>
                </div>

                <!-- Settings -->
                <div class="settings-group">
                    <div class="settings-group-title">Reconstruction Settings</div>
                    <div class="settings-group-body">
                        <div class="checkbox-row">
                            <input type="checkbox" id="custom-apply-mask" checked>
                            <label for="custom-apply-mask">Apply ambiguity mask</label>
                        </div>
                        <div class="slider-row">
                            <label>Frame interval</label>
                            <input type="range" id="custom-interval" min="0.1" max="5.0" step="0.1" value="1.0">
                            <span class="slider-val" id="custom-interval-val">1.0</span>
                        </div>
                    </div>
                </div>

                <div class="settings-group">
                    <div class="settings-group-title">Visualization Options</div>
                    <div class="settings-group-body">
                        <div class="checkbox-row">
                            <input type="checkbox" id="custom-show-cam" checked>
                            <label for="custom-show-cam">Show cameras</label>
                        </div>
                        <div class="checkbox-row">
                            <input type="checkbox" id="custom-filter-black">
                            <label for="custom-filter-black">Filter black background</label>
                        </div>
                        <div class="checkbox-row">
                            <input type="checkbox" id="custom-filter-white">
                            <label for="custom-filter-white">Filter white background</label>
                        </div>
                    </div>
                </div>

                <!-- Example Scenes -->
                <div class="panel-card">
                    <div class="panel-card-title">Example Scenes</div>
                    <div class="panel-card-body">
                        {example_thumbs_html if example_thumbs_html else '<div style="color:#52525b;font-size:13px;">No example scenes found</div>'}
                    </div>
                </div>

            </div>
        </div>

        <!-- Status Bar -->
        <div class="app-statusbar">
            <div class="sb-section" id="status-info">MapAnything -- Metric 3D Reconstruction from Images</div>
            <div class="sb-section sb-fixed">Ready</div>
        </div>

    </div>
    """)

    # -- Load JavaScript --
    demo.load(fn=None, js=main_js)

    # -- Wire custom file input to Gradio file upload --
    # This JavaScript bridges the custom file input to the hidden Gradio file component
    file_bridge_js = r"""
    () => {
        const customInput = document.getElementById('ma-file-input');
        const gradioUpload = document.getElementById('gradio-file-upload');
        if (!customInput || !gradioUpload) { setTimeout(arguments.callee, 500); return; }

        customInput.addEventListener('change', () => {
            // Find the actual Gradio file input inside the hidden component
            const gradioInput = gradioUpload.querySelector('input[type="file"]');
            if (gradioInput && customInput.files.length > 0) {
                // Create a DataTransfer to set files
                const dt = new DataTransfer();
                Array.from(customInput.files).forEach(f => dt.items.add(f));
                gradioInput.files = dt.files;
                gradioInput.dispatchEvent(new Event('change', {bubbles: true}));
            }
            customInput.value = '';
        });
    }
    """
    demo.load(fn=None, js=file_bridge_js)

    # -- Wire Rerun viewer into the 3D tab --
    rerun_mount_js = r"""
    () => {
        function mountRerun() {
            const rerunContainer = document.getElementById('gradio-rerun-viewer');
            const targetPanel = document.querySelector('#tab-3d .rerun-viewer-wrapper');
            if (!rerunContainer || !targetPanel) {
                setTimeout(mountRerun, 500);
                return;
            }
            // Move rerun viewer into the custom panel
            rerunContainer.style.display = '';
            rerunContainer.style.width = '100%';
            rerunContainer.style.height = '100%';
            rerunContainer.style.position = 'absolute';
            rerunContainer.style.top = '0';
            rerunContainer.style.left = '0';
            rerunContainer.style.zIndex = '5';
            targetPanel.style.position = 'relative';
            targetPanel.appendChild(rerunContainer);

            // Hide placeholder when rerun has content
            const placeholder = document.getElementById('rerun-placeholder');
            const obs = new MutationObserver(() => {
                const hasCanvas = rerunContainer.querySelector('canvas, iframe, .rerun');
                if (hasCanvas && placeholder) placeholder.style.display = 'none';
            });
            obs.observe(rerunContainer, {childList: true, subtree: true});
        }
        mountRerun();
    }
    """
    demo.load(fn=None, js=rerun_mount_js)

    # -- Measure image click bridge --
    # The measure_image gr.Image is hidden; we need clicks on custom-measure-img
    # to route through the Gradio component
    measure_bridge_js = r"""
    () => {
        function bridgeMeasure() {
            const customImg = document.getElementById('custom-measure-img');
            const gradioMeasure = document.getElementById('gradio-measure-image');
            if (!customImg || !gradioMeasure) {
                setTimeout(bridgeMeasure, 500);
                return;
            }
            // Forward clicks from custom image to the gradio image component
            customImg.addEventListener('click', (e) => {
                const rect = customImg.getBoundingClientRect();
                const x = e.clientX - rect.left;
                const y = e.clientY - rect.top;
                const scaleX = customImg.naturalWidth / rect.width;
                const scaleY = customImg.naturalHeight / rect.height;
                const imgX = Math.round(x * scaleX);
                const imgY = Math.round(y * scaleY);

                // Find the gradio image element and simulate click
                const gradioImg = gradioMeasure.querySelector('img');
                if (gradioImg) {
                    const gRect = gradioImg.getBoundingClientRect();
                    // We need to compute position relative to the gradio image
                    const gScaleX = gradioImg.naturalWidth ? rect.width / gradioImg.naturalWidth : 1;
                    const gScaleY = gradioImg.naturalHeight ? rect.height / gradioImg.naturalHeight : 1;

                    const clickEvent = new MouseEvent('click', {
                        clientX: gRect.left + imgX / scaleX,
                        clientY: gRect.top + imgY / scaleY,
                        bubbles: true,
                        cancelable: true,
                    });
                    gradioImg.dispatchEvent(clickEvent);
                }
            });
        }
        bridgeMeasure();
    }
    """
    demo.load(fn=None, js=measure_bridge_js)

    # -----------------------------------------------------------------------
    # Event Wiring
    # -----------------------------------------------------------------------

    # File upload
    unified_upload.change(
        fn=on_files_upload,
        inputs=[unified_upload, interval_slider],
        outputs=[target_dir_state, image_gallery, log_output],
    )

    # Reconstruct
    reconstruct_btn.click(
        fn=lambda: (None, "Reconstructing..."),
        inputs=[],
        outputs=[rerun_viewer, log_output],
    ).then(
        fn=gradio_reconstruct,
        inputs=[
            target_dir_state, frame_filter_dd, show_cam_cb,
            filter_black_cb, filter_white_cb, apply_mask_cb,
        ],
        outputs=[
            rerun_viewer, log_output, processed_data_state,
            frame_filter_dd,
            depth_image, normal_image, measure_image, measure_text_md,
            depth_selector, normal_selector, measure_selector,
        ],
    ).then(
        fn=lambda: "False",
        outputs=[is_example_state],
    )

    # Clear
    clear_btn.click(
        fn=lambda: ("None", None, None, None, "Ready", None, None, None, "", [], None),
        outputs=[
            target_dir_state, rerun_viewer, image_gallery, unified_upload,
            log_output,
            depth_image, normal_image, measure_image, measure_text_md,
            measure_points_state, processed_data_state,
        ],
    )

    # Load example
    load_example_btn.click(
        fn=lambda name: load_example_scene(name) if name else ("None", [], "No scene selected"),
        inputs=[example_name_state],
        outputs=[target_dir_state, image_gallery, log_output],
    )

    # Visualization updates on checkbox changes
    for cb_component in [show_cam_cb, filter_black_cb, filter_white_cb]:
        cb_component.change(
            fn=update_rerun_visualization,
            inputs=[target_dir_state, frame_filter_dd, show_cam_cb, filter_black_cb, filter_white_cb],
            outputs=[rerun_viewer, log_output],
        )

    filter_black_cb.change(
        fn=update_all_views,
        inputs=[target_dir_state, filter_black_cb, filter_white_cb, processed_data_state,
                depth_selector, normal_selector, measure_selector],
        outputs=[processed_data_state, depth_image, normal_image, measure_image, measure_points_state],
    )
    filter_white_cb.change(
        fn=update_all_views,
        inputs=[target_dir_state, filter_black_cb, filter_white_cb, processed_data_state,
                depth_selector, normal_selector, measure_selector],
        outputs=[processed_data_state, depth_image, normal_image, measure_image, measure_points_state],
    )

    frame_filter_dd.change(
        fn=update_rerun_visualization,
        inputs=[target_dir_state, frame_filter_dd, show_cam_cb, filter_black_cb, filter_white_cb],
        outputs=[rerun_viewer, log_output],
    )

    # Depth navigation
    prev_depth_btn.click(
        fn=lambda pd, sel: nav_depth(pd, sel, -1),
        inputs=[processed_data_state, depth_selector],
        outputs=[depth_selector, depth_image],
    )
    next_depth_btn.click(
        fn=lambda pd, sel: nav_depth(pd, sel, 1),
        inputs=[processed_data_state, depth_selector],
        outputs=[depth_selector, depth_image],
    )
    depth_selector.change(
        fn=lambda pd, sel: get_depth_vis(pd, int(sel.split()[1]) - 1) if pd and sel else None,
        inputs=[processed_data_state, depth_selector],
        outputs=[depth_image],
    )

    # Normal navigation
    prev_normal_btn.click(
        fn=lambda pd, sel: nav_normal(pd, sel, -1),
        inputs=[processed_data_state, normal_selector],
        outputs=[normal_selector, normal_image],
    )
    next_normal_btn.click(
        fn=lambda pd, sel: nav_normal(pd, sel, 1),
        inputs=[processed_data_state, normal_selector],
        outputs=[normal_selector, normal_image],
    )
    normal_selector.change(
        fn=lambda pd, sel: get_normal_vis(pd, int(sel.split()[1]) - 1) if pd and sel else None,
        inputs=[processed_data_state, normal_selector],
        outputs=[normal_image],
    )

    # Measure navigation
    prev_measure_btn.click(
        fn=lambda pd, sel: nav_measure(pd, sel, -1),
        inputs=[processed_data_state, measure_selector],
        outputs=[measure_selector, measure_image, measure_points_state],
    )
    next_measure_btn.click(
        fn=lambda pd, sel: nav_measure(pd, sel, 1),
        inputs=[processed_data_state, measure_selector],
        outputs=[measure_selector, measure_image, measure_points_state],
    )
    measure_selector.change(
        fn=lambda pd, sel: get_measure_image(pd, int(sel.split()[1]) - 1) if pd and sel else (None, []),
        inputs=[processed_data_state, measure_selector],
        outputs=[measure_image, measure_points_state],
    )

    # Measure click
    measure_image.select(
        fn=measure_click,
        inputs=[processed_data_state, measure_points_state, measure_selector],
        outputs=[measure_image, measure_points_state, measure_text_md],
    )


if __name__ == "__main__":
    demo.queue(max_size=20).launch(
        css=css,
        show_error=True,
        share=True,
        ssr_mode=False,
        mcp_server=True,
    )