ui-styling

Guide for the visual style, structure, and shared building blocks used by Semantic Link Labs interactive UI tools (HTML widgets and anywidget-based widgets).…

npx skills add https://github.com/microsoft/semantic-link-labs --skill ui-styling

UI Styling for Interactive Tools

This skill describes the styling conventions, shared building blocks, and architectural patterns used by all interactive UI tools in Semantic Link Labs so that every tool has a consistent, elegant design.

When to Use This Skill

Use this skill when you need to:

  • Add a new interactive UI function to the library.
  • Modify an existing interactive UI (e.g. vertipaq_analyzer, delta_analyzer, perspective_editor).
  • Add new shared visual components (icons, theme variables, headers, etc.) that should be reused across tools.
  • Decide between a static-HTML widget and an anywidget-based widget.

The Two UI Patterns

Semantic Link Labs has exactly two supported patterns for interactive UI tools. Always pick one based on whether the UI needs to call back into Python after the initial render.

PatternWhen to useReference implementations
Static-HTML widget (the Vertipaq style)The UI is fully driven by the data computed in Python before render. All interactivity (filtering, sorting, tab switching, theme toggle, column resizing, etc.) is done in pure browser-side JavaScript. No Python code runs after display(HTML(...)).sempy_labs.semantic_model._vertipaq_analyzer.vertipaq_analyzer, sempy_labs._delta_analyzer.delta_analyzer
anywidget widget (the Perspective Editor style)The UI must run Python code in response to user actions (e.g. write back to a semantic model, refresh data from a REST API, perform long-running operations). State is synced between the JS frontend and the Python backend via traitlets.sempy_labs.semantic_model._perspective_editor.perspective_editor

Rule of thumb: if the only thing the user does is view, sort, filter, or switch between pre-computed data, use the static-HTML pattern. If they can change something that must persist (model edits, refresh triggers, server calls), use anywidget.


Shared Building Blocks: sempy_labs._ui_components

src/sempy_labs/_ui_components.py is the single source of truth for everything visual that should be consistent across tools. Both patterns must source their visual primitives from this module. If you need a new shared visual component (a new icon, a new helper, a new themed control), add it here so every tool can pick it up.

What lives there today

ExportPurpose
ICONSDict of monochrome SVG icons. All use stroke="currentColor" / fill="currentColor" so they adapt to light and dark themes automatically. Keys include tabular-object icons (table, calculation_group, column, column_chunk, measure, hierarchy, calculation_item, partition, relationship), tree/navigation icons (caret_right, folder, level), and UI/action icons (sun, moon, search, plus, play, stop, refresh, swap, sort_asc, sort_desc, panel_collapse, panel_expand, builder, close, fullscreen, fullscreen_exit).
LIGHT_THEME_VARS, DARK_THEME_VARSCSS custom-property blocks defining the Apple-inspired light and dark palettes. Always reference colors via these --ui-* tokens, never hard-coded hex values. Includes semantic tokens for hover backgrounds (--ui-bg-hover), on-accent text (--ui-on-accent), and destructive/error states (--ui-danger*).
SYNTAX_HIGHLIGHT_VARSTheme-independent --ui-syntax-* token block for colorizing DAX/code in an editor. Inject once into the widget's base scope (it is the same in light and dark).
HEADER_CSS, scoped_header_css(root_selector)Standard widget header styles (title + dataset/workspace subtitle) and the four standard header controls (.sl-theme-btn, .sl-change-btn, .sl-reload-btn). scoped_header_css prefixes every rule with the root selector so the styles win against notebook host CSS (e.g. Jupyter's .jp-RenderedHTMLCommon button). Every tool must inject this, even when it builds its own header markup.
render_header_html(title, dataset_name, workspace_name, theme_btn_id, dark_mode, fullscreen_btn_id)Renders the standard header markup. Pass fullscreen_btn_id to include a full-screen toggle button next to the theme toggle.
theme_toggle_script(btn_id, root_selector, dark_class)Returns a <script> block that wires the theme toggle button to flip a dark_class on the root element and swap the sun/moon icon.
fullscreen_css(root_selector, fullscreen_class, container_selector=None, bg_var)Returns the CSS for a widget's full-screen state (covers both the native :fullscreen pseudo-class and the fullscreen_class CSS-overlay fallback). Pass container_selector when an inner element carries the card styling; pass bg_var to match the widget's background token.
fullscreen_toggle_script(btn_id, root_selector, fullscreen_class)Returns a <script> block that wires a full-screen toggle button for static-HTML widgets. Pair with render_header_html(..., fullscreen_btn_id=...) and fullscreen_css.
fullscreen_setup_js(func_name="sllsSetupFullscreen")Returns a JS function definition that wires a full-screen toggle button for anywidget widgets. Embed once at the top of the ESM module, then call func_name(root, btn, fullscreenClass, enterSvg, exitSvg) after creating the button.
display_html_widget(html, fallback=True)Renders a self-contained HTML string (styles + markup + inline <script>) via a lightweight anywidget so it lives in the notebook webview's light DOM instead of the sandboxed srcdoc iframe used for raw display(HTML). Use this for static-HTML widgets so the full-screen toggle gets the native Fullscreen API (matching the anywidget tools). Falls back to display(HTML(html)) when anywidget is unavailable.
ATTRIBUTION_CSS, scoped_attribution_css(root_selector), render_attribution_html(extra_links=None)"Powered by Semantic Link Labs" attribution shown at the bottom of every widget, with an optional list of extra (label, url) links.
SEARCH_SELECT_CSS, SEARCH_SELECT_JSThe standard searchable single-select picker (createSearchSelect({ placeholder, searchPlaceholder, ariaLabel, emptyLabel, onChange })). Every workspace / semantic model / item picker must use this control — never a plain <select> — so long lists can always be filtered by typing. Inject the CSS into the widget stylesheet and the JS into the widget's ESM module, then drive the returned controller with setOptions(items, value), setEmptyLabel(text) and setDisabled(flag). Reference implementations: semantic_model._find_unused_objects, semantic_model._bpa.

When to extend _ui_components

Add a new export to _ui_components.py whenever the same visual element appears (or should appear) in more than one tool. Typical candidates: a new icon, a shared button style, a status-pill style, a confirmation-dialog component, a toast/notification helper. Do not copy-paste CSS or SVGs between widgets — promote them to _ui_components instead.


Standard Header Controls (mandatory)

Four controls recur in nearly every tool. They must look and behave identically everywhere, so both their icon and their chrome come from _ui_components — a tool never defines its own size, radius, border or icon for them. The sempy_labs.semantic_model.test (DAX Perf Optimizer) widget is the reference implementation.

ControlClassIconFootprint
Light/dark modesl-theme-btnICONS["sun"] / ICONS["moon"]32×32 circle, 18px icon
Full screensl-theme-btnICONS["fullscreen"] / ICONS["fullscreen_exit"]32×32 circle, 18px icon
Change model / workspacesl-change-btnICONS["swap"]32×32, 8px radius, 18px icon
Reload (workspaces, models, lists)sl-reload-btnICONS["refresh"]32×32 circle, 14px icon

Rules:

  • Inject scoped_header_css(root_selector) into the widget stylesheet, and use the class names above. Do not re-declare .sl-theme-btn, .sl-change-btn or .sl-reload-btn in a tool — tests/test_ui_header_controls.py fails if a tool restyles them.
  • While a reload is in flight, add sl-spinning to the reload button; the shared CSS animates the icon.
  • If the tool uses --slls-* (or other) palette names, alias the --ui-* tokens the shared CSS reads: --ui-surface, --ui-surface-2, --ui-border-strong, --ui-text, --ui-text-secondary, --ui-text-tertiary, --ui-accent, --ui-accent-soft.
  • Tools rendering their header with render_header_html get these for free via theme_btn_id, fullscreen_btn_id and picker_btn_id. For an extra button that changes the model/workspace, pass "base": "sl-change-btn" in extra_buttons.
  • Buttons that are not one of these four (e.g. expand/collapse, delete, tool-specific actions) keep their own tool-scoped classes.

Workspace / semantic model pickers

Always resolve the workspace with resolve_workspace_name_and_id(workspace) even when the caller passed nothing, and seed the picker with the result, so the workspace dropdown opens pre-selected on the current workspace. Passing None resolves to the attached lakehouse's workspace, or the notebook's.


Design Tokens (the visual language)

All interactive UIs share one visual language. Stick to these tokens — do not introduce one-off colors, fonts, radii, or shadows.

Typography

  • Font stack: -apple-system, BlinkMacSystemFont, "SF Pro Display", "SF Pro Text", "Helvetica Neue", Helvetica, Arial, sans-serif.
  • Enable font smoothing: -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale;.
  • Title (22px / 600 / -0.01em letter-spacing) and subtitle (≈12.5px / --ui-text-secondary) are produced by render_header_html — do not re-implement them.
  • Use font-variant-numeric: tabular-nums for any numeric column or large numeric value.

Color tokens (from LIGHT_THEME_VARS / DARK_THEME_VARS)

TokenUse for
--ui-bg, --ui-bg-secondary, --ui-bg-tertiary, --ui-bg-solidSurfaces, in increasing emphasis levels
--ui-bg-hoverSolid hover background for controls/buttons whose base is --ui-bg
--ui-surface, --ui-surface-2Translucent overlays / hover backgrounds
--ui-border, --ui-border-strongSubtle and strong borders
--ui-text, --ui-text-secondary, --ui-text-tertiaryPrimary, secondary, tertiary text
--ui-accent, --ui-accent-hover, --ui-accent-softBrand accent (links, focus rings, active tab indicator, primary buttons, data bars)
--ui-on-accentText/icon color placed on top of an --ui-accent (or --ui-danger) fill — i.e. "white"
--ui-danger, --ui-danger-hoverDestructive button fill + hover (e.g. a Stop button)
--ui-danger-bg, --ui-danger-border, --ui-danger-textInline error/alert box background, border, and text (themed for light + dark)
--ui-shadow-sm, --ui-shadow-md, --ui-shadow-lgElevation

Never hard-code #fff, red error colors, or any other literal — always go through a token. If a destructive action, error banner, or white-on-accent label is needed, use the danger / on-accent tokens above rather than inlining hex values.

Code / DAX syntax-highlight palette (SYNTAX_HIGHLIGHT_VARS)

For colorizing DAX (or similar code) inside an editor/highlighter, inject the theme-independent SYNTAX_HIGHLIGHT_VARS block once into the widget's base scope (it intentionally renders the same in light and dark, so do not override it in the dark block). Reference these --ui-syntax-* tokens — never the raw hex values.

TokenToken class it colors
--ui-syntax-keyword, --ui-syntax-functionKeywords and function names
--ui-syntax-variableVariables
--ui-syntax-numberNumeric literals
--ui-syntax-virtual-columnVirtual / measure-ref columns
--ui-syntax-stringString literals
--ui-syntax-operator, --ui-syntax-punctuationOperators and punctuation

See sempy_labs.semantic_model._test_dax.test for the reference usage (it injects SYNTAX_HIGHLIGHT_VARS into its .dtx base block alongside LIGHT_THEME_VARS).

If a tool needs its own derived names (e.g. --vpx-text), alias them to the --ui-* tokens inside the tool's scoped block — see _vertipaq_analyzer.py for the pattern. This keeps the palette centralized while letting tools use short, local names.

Shape and motion

  • Radii: 12px (containers), 8px (controls/cards) — exposed as --vpx-radius / --vpx-radius-sm style aliases when needed.
  • Transitions: 0.25s cubic-bezier(0.4, 0, 0.2, 1) for color/border/background; 120ms ease for small button state changes; 80ms for press scale.
  • Subtle hover/active states only — no heavy animations.

Anatomy of a Widget

Every interactive UI, regardless of pattern, should be composed of these regions in this order:

  1. Root container with a per-instance unique class (e.g. vpx-<uid>) and an optional dark-mode modifier class (e.g. vpx-dark). The unique id (uid = uuid.uuid4().hex[:8]) keeps multiple widgets on the same page from colliding.
  2. Standard header rendered via render_header_html(...) — title + dataset/workspace subtitle + theme toggle button.
  3. Summary cards (optional) — a horizontal row of (label, value) cards summarizing the result set.
  4. Tab bar (optional) — for multi-section views; active tab uses --ui-accent text + a 2px accent underline.
  5. Toolbar (optional) — search box (using ICONS["search"]), row count, view toggles (e.g. data-bars on/off).
  6. Main content — table, tree, form, etc.
  7. Attribution rendered via render_attribution_html(...) at the bottom, with optional extra_links for upstream credit (e.g. SQLBI's Vertipaq Analyzer).

Pattern A: Static-HTML Widget (Vertipaq / Delta Analyzer style)

Use this for read-only / pre-computed UIs. End-to-end recipe:

1. Compute data in Python, then render

Build the pandas.DataFrame(s) or dict of dataframes in Python. Pass them to a private visualize_* / _render_* helper that assembles HTML and JS strings and calls IPython.display.display(HTML(...)).

2. Generate a per-instance uid

uid = uuid.uuid4().hex[:8]
root_selector = f".vpx-{uid}"            # used for scoping CSS + JS lookups
theme_btn_id = f"vpx-theme-{uid}"

Every CSS class, every DOM id, and every global JS function name must include uid so multiple instances on one notebook page never clash.

3. Import the shared building blocks

Pull from sempy_labs._ui_components — do not re-define icons, colors, headers, theme-toggle JS, or the attribution footer.

from sempy_labs._ui_components import (
    ICONS as _UI_ICONS,
    LIGHT_THEME_VARS as _UI_LIGHT_VARS,
    DARK_THEME_VARS as _UI_DARK_VARS,
    scoped_header_css as _ui_scoped_header_css,
    scoped_attribution_css as _ui_scoped_attribution_css,
    render_header_html as _ui_render_header_html,
    render_attribution_html as _ui_render_attribution_html,
    theme_toggle_script as _ui_theme_toggle_script,
)

4. Scope all CSS under the root selector

Inline the light palette inside .vpx-<uid> { ... } and the dark palette inside .vpx-<uid>.vpx-dark { ... }, so dark mode is a class toggle on the root. Include _ui_scoped_header_css(root_selector) and _ui_scoped_attribution_css(root_selector) in your <style> block. Scoping is required so that high-specificity notebook host styles (Jupyter, VS Code, Fabric) do not override your widget.

5. Build the markup in order: header → cards → tabs → toolbar → content → attribution

Use render_header_html(...) for the header and render_attribution_html(...) for the footer. Re-use icons from _UI_ICONS (apply your own class via .replace("<svg ", '<svg class="vpx-tab-icon" ', 1) for sizing).

6. Wire all interactivity in inline JavaScript

Filter/sort/resize/tab-switch/bar-toggle logic lives in a <script> block whose function names are also uid-suffixed (e.g. window.vpxSort_<uid>). The theme toggle button is wired by appending _ui_theme_toggle_script(btn_id=theme_btn_id, root_selector=root_selector, dark_class="vpx-dark").

7. Render

display(HTML(styles + "\n".join(html_parts) + script + theme_script))

Reference

  • src/sempy_labs/semantic_model/_vertipaq_analyzer.pyvisualize_vertipaq (full implementation: cards, tabs, toolbar, sortable/filterable/resizable table with optional data bars).
  • src/sempy_labs/_delta_analyzer.py — same pattern applied to delta-table analysis output.

Pattern B: anywidget Widget (Perspective Editor style)

Use this when the UI must call back into Python after render (e.g. to edit a semantic model, trigger a refresh, run an API call). The widget is implemented as a subclass of anywidget.AnyWidget with state synced via traitlets.

1. Guard the optional dependency

anywidget is not a hard dependency of the library — keep it that way. Import lazily inside the public function and raise a friendly ImportError if missing:

try:
    import anywidget
    import traitlets
except ImportError as e:
    raise ImportError(
        "The '<my_tool>' function requires the 'anywidget' package. "
        "Install it with: pip install anywidget"
    ) from e

2. Subclass anywidget.AnyWidget

Define the widget with class attributes _esm (the JS module string — typically a top-level function render({ model, el }) { ... } block) and _css (the CSS string). Declare every piece of state that must cross the Python/JS boundary as a synced traitlet:

class MyWidget(anywidget.AnyWidget):
    _esm = _WIDGET_JS
    _css = _WIDGET_CSS

    data = traitlets.Dict().tag(sync=True)
    selected = traitlets.Unicode("").tag(sync=True)
    status = traitlets.Dict().tag(sync=True)         # { "message": ..., "kind": "success"|"error" }
    pending_action = traitlets.Dict().tag(sync=True) # what JS asked Python to do
    run = traitlets.Int(0).tag(sync=True)            # bump to trigger the Python callback
    dataset_name = traitlets.Unicode("").tag(sync=True)
    workspace_name = traitlets.Unicode("").tag(sync=True)
    dark_mode = traitlets.Bool(False).tag(sync=True)

3. Use the "bump run + observe" callback pattern

JS triggers Python work by setting pending_action (a dict describing what to do) and then incrementing run and calling model.save_changes(). Python observes run and dispatches based on pending_action["action"]. On completion it writes results back to other traitlets (including a user-visible status) which the JS observer renders.

def _on_run(change):
    data = dict(widget.pending_action or {})
    action = data.get("action")
    if not action:
        return
    try:
        ...  # do Python work, then update widget.status / other traitlets
    except Exception as e:
        widget.status = {"message": f"Error: {e}", "kind": "error"}

widget.observe(_on_run, names=["run"])

4. Display once, keep the reference alive

After display(widget), keep the local widget reference inside the closure (Python's GC must not collect it, or observers stop firing). Do not also return widget — that causes Jupyter to render it a second time.

5. Visual conventions on the JS side

The frontend render({ model, el }) function should:

  • Create a root <div> with a stable namespace class (e.g. slls-pe for the perspective editor). Add slls-pe-dark when dark_mode === true and slls-pe-auto (which uses @media (prefers-color-scheme: dark)) when dark_mode is null/undefined.
  • Build the header with the same title + dataset/workspace subtitle + sun/moon theme-toggle button shape used by the static widgets. The theme button toggles the dark_mode traitlet (model.set("dark_mode", ...); model.save_changes();) so the preference round-trips to Python.
  • Use the same color/typography/radius tokens as _ui_components (light + dark palettes, Apple font stack, 12px / 8px radii). When you need an icon also used elsewhere, embed the SVG from ICONS (e.g. via a Python-side template-substitution placeholder like __SLLS_ICON_TABLE__) so there is one source of truth.
  • Always render the "Powered by Semantic Link Labs" attribution at the bottom, matching render_attribution_html.

If you need a new visual primitive in an anywidget tool (a new icon, a new button style, a new theme token), add it to _ui_components and substitute it into the _WIDGET_JS/_WIDGET_CSS strings — do not fork the design.

Reference

  • src/sempy_labs/semantic_model/_perspective_editor.pyperspective_editor. Full implementation showing widget class definition, traitlets, the pending_action + run callback pattern, dark-mode round-trip, and lazy-import guard.
  • src/sempy_labs/semantic_model/_direct_lake_manager.pydirect_lake_manager. Multi-screen anywidget with model-selection / model-management screens, popover menus, modals, pending-change tracking, and a save bar. Demonstrates icon-template-substitution from _ui_components.ICONS.

6. Icon template-substitution recipe (anywidget)

Because _WIDGET_JS is a raw string passed to anywidget's _esm, you cannot directly call Python at JS render time. To keep icons centralized in _ui_components.ICONS, use placeholder substitution at module-import time:

  1. In _WIDGET_JS, refer to icons through uppercase placeholders, e.g.:

    const SUN_SVG = `__SLLS_ICON_SUN__`;
    const ICON_SVG = {
        table: `__SLLS_ICON_TABLE__`,
        column: `__SLLS_ICON_COLUMN__`,
        // ...
    };
    
  2. Immediately after _WIDGET_JS = r"""..."""", substitute each placeholder from ICONS:

    from sempy_labs._ui_components import ICONS as _UI_ICONS
    
    _WIDGET_JS = (
        _WIDGET_JS
        .replace("__SLLS_ICON_SUN__", _UI_ICONS["sun"])
        .replace("__SLLS_ICON_MOON__", _UI_ICONS["moon"])
        .replace("__SLLS_ICON_TABLE__", _UI_ICONS["table"])
        .replace("__SLLS_ICON_COLUMN__", _UI_ICONS["column"])
        # ...one .replace per icon used
    )
    

Do not inline raw SVG strings inside _WIDGET_JS. If you need an icon that isn't in ICONS yet, add it to _ui_components.ICONS first, then substitute it in.

7. Minimal anywidget template

from typing import Optional
from uuid import UUID
from sempy._utils._log import log

_WIDGET_CSS = """
.my-widget { /* root container styles, using --ui-* tokens */ }
.my-widget.my-widget-dark { /* DARK_THEME_VARS-equivalent overrides */ }
"""

_WIDGET_JS = r"""
function render({ model, el }) {
    const root = document.createElement("div");
    root.className = "my-widget";

    function applyTheme() {
        root.classList.remove("my-widget-dark", "my-widget-auto");
        const dm = model.get("dark_mode");
        if (dm === true) root.classList.add("my-widget-dark");
        else if (dm == null) root.classList.add("my-widget-auto");
    }
    applyTheme();
    model.on("change:dark_mode", applyTheme);
    el.appendChild(root);

    const SUN = `__SLLS_ICON_SUN__`;
    const MOON = `__SLLS_ICON_MOON__`;

    // ... build header, body, attribution ...

    // Trigger a Python action:
    function runAction(payload) {
        model.set("pending_action", payload);
        model.set("run", model.get("run") + 1);
        model.save_changes();
    }
}
export default { render };
"""

from sempy_labs._ui_components import ICONS as _UI_ICONS  # noqa: E402

_WIDGET_JS = (
    _WIDGET_JS
    .replace("__SLLS_ICON_SUN__", _UI_ICONS["sun"])
    .replace("__SLLS_ICON_MOON__", _UI_ICONS["moon"])
)


@log
def my_widget_function(
    dataset: str | UUID,
    workspace: Optional[str | UUID] = None,
    dark_mode: bool = False,
):
    """One-line description.

    Parameters
    ----------
    dataset : str | uuid.UUID
        ...
    workspace : str | uuid.UUID, default=None
        The Fabric workspace name or ID. Defaults to the attached lakehouse
        workspace or the notebook workspace.
    dark_mode : bool, default=False
        If True, renders with a dark color theme.
    """
    try:
        import anywidget
        import traitlets
    except ImportError as e:
        raise ImportError(
            "The 'my_widget_function' function requires the 'anywidget' "
            "package. Install it with: pip install anywidget"
        ) from e

    from IPython.display import display
    from sempy_labs._helper_functions import (
        resolve_workspace_name_and_id,
        resolve_dataset_name_and_id,
    )

    ws_name, ws_id = resolve_workspace_name_and_id(workspace)
    ds_name, ds_id = resolve_dataset_name_and_id(dataset, ws_id)

    class _Widget(anywidget.AnyWidget):
        _esm = _WIDGET_JS
        _css = _WIDGET_CSS
        dataset_name = traitlets.Unicode("").tag(sync=True)
        workspace_name = traitlets.Unicode("").tag(sync=True)
        dark_mode = traitlets.Bool(False).tag(sync=True)
        status = traitlets.Dict().tag(sync=True)
        pending_action = traitlets.Dict().tag(sync=True)
        run = traitlets.Int(0).tag(sync=True)

    widget = _Widget(
        dataset_name=ds_name,
        workspace_name=ws_name or "",
        dark_mode=bool(dark_mode),
    )

    def _on_run(_change):
        action = (widget.pending_action or {}).get("action")
        if not action:
            return
        try:
            # ... dispatch on action, mutate traitlets ...
            widget.status = {"message": "Done.", "kind": "success"}
        except Exception as e:
            widget.status = {"message": f"Error: {e}", "kind": "error"}

    widget.observe(_on_run, names=["run"])
    display(widget)  # do NOT return widget

The Full-Screen Toggle

Every interactive widget should offer a full-screen button in its header (next to the theme toggle) that expands the tool to fill the screen and toggles back. The behavior is centralized in sempy_labs._ui_components so all widgets stay in sync — never re-implement it.

The toggle is host-aware because notebook hosts constrain what "full screen" can mean. The native Fullscreen API only works for an element in the top-level document or in an <iframe> carrying allowfullscreen. Notebook output webviews (where anywidget content and anything rendered via display_html_widget live) permit it, so the toggle gets true edge-to-edge fullscreen there. By contrast, raw display(HTML(...)) with a <script> is isolated in a nested, sandboxed srcdoc iframe without allowfullscreen, where requestFullscreen() is rejected and a position: fixed overlay collapses the content-sized iframe into an unreadable strip. This is exactly why static-HTML widgets should render via display_html_widget (see below) rather than display(HTML(...)).

The shared logic in _FULLSCREEN_BODY is a faithful port of the reference implementation in sempy_labs.semantic_model._test_dax.test, and is intentionally simple — two states:

  1. Native fullscreen — try root.requestFullscreen(). This gives true edge-to-edge fullscreen, and the native :fullscreen CSS rules style it. This is the normal path for every tool, since they all render in the webview (anywidget content directly, static-HTML widgets via display_html_widget).
  2. CSS-overlay fallback — if requestFullscreen() rejects or is unavailable, apply the fullscreenClass fixed overlay (position: fixed; inset: 0), which fills the viewport. This only matters in degraded hosts (e.g. the display(HTML(...)) fallback when anywidget isn't installed).

The button stays in sync when the user leaves native full screen via the Esc key (a fullscreenchange listener re-renders it). The button icon swaps between ICONS["fullscreen"] (enter) and ICONS["fullscreen_exit"] (exit). The behavior is exposed via fullscreen_setup_js (anywidget) / fullscreen_toggle_script (static HTML) — never re-implement it.

Static-HTML widgets (Vertipaq / Delta style)

  1. Allocate a button id and a fullscreen class alongside the other per-instance ids:
    fullscreen_btn_id = f"vpx-fullscreen-{uid}"
    fullscreen_class = "vpx-fullscreen"   # scoped under the uid'd root
    
  2. Include the full-screen CSS in your <style> block, pointing at the root, the fullscreen class, the inner container (if any), and the widget's background token:
    ui_fullscreen_css = fullscreen_css(
        root_selector, fullscreen_class,
        container_selector=".vpx-container", bg_var="var(--vpx-bg)",
    )
    
  3. Render the header with fullscreen_btn_id= so the button appears next to the theme toggle:
    header_html = render_header_html(..., theme_btn_id=theme_btn_id,
                                      fullscreen_btn_id=fullscreen_btn_id)
    
  4. Append the wiring script alongside the theme script, and render through display_html_widget (not display(HTML(...))). The helper hosts the markup in a lightweight anywidget so it lives in the notebook webview's light DOM rather than the nested, sandboxed srcdoc iframe used for raw HTML output. That sandbox blocks the native Fullscreen API and collapses fixed overlays; the webview permits real fullscreen, so the toggle expands edge-to-edge just like the anywidget tools. It falls back to display(HTML(...)) automatically when anywidget isn't installed.
    fullscreen_script = fullscreen_toggle_script(
        btn_id=fullscreen_btn_id, root_selector=root_selector,
        fullscreen_class=fullscreen_class,
    )
    display_html_widget(styles + html + script + theme_script + fullscreen_script)
    

anywidget widgets (Perspective Editor / Direct Lake Manager style)

  1. Prepend the shared helper to the ESM module and append the full-screen CSS (the root usually carries the card styling itself, so no container_selector):
    _WIDGET_JS = fullscreen_setup_js() + _WIDGET_JS.replace(
        "__SLLS_ICON_FULLSCREEN__", ICONS["fullscreen"]
    ).replace("__SLLS_ICON_FULLSCREEN_EXIT__", ICONS["fullscreen_exit"])
    _WIDGET_CSS = _WIDGET_CSS + "\n" + fullscreen_css(
        ".slls-pe", "slls-pe-fullscreen", bg_var="var(--slls-bg-solid)"
    )
    
  2. In the JS render, create the button next to the theme button and call the helper:
    const fullscreenBtn = document.createElement("button");
    fullscreenBtn.className = "slls-pe-btn slls-pe-btn-icon";
    fullscreenBtn.type = "button";
    header.appendChild(fullscreenBtn);
    sllsSetupFullscreen(root, fullscreenBtn, "slls-pe-fullscreen",
                        `__SLLS_ICON_FULLSCREEN__`, `__SLLS_ICON_FULLSCREEN_EXIT__`);
    

Reference

  • src/sempy_labs/semantic_model/_test_dax.py — original full-screen implementation.
  • src/sempy_labs/semantic_model/_vertipaq_analyzer.py, src/sempy_labs/_delta_analyzer.py — static-HTML usage.
  • src/sempy_labs/semantic_model/_perspective_editor.py, src/sempy_labs/semantic_model/_direct_lake_manager.py — anywidget usage.

Public API Conventions for Interactive Tools

All interactive UI functions follow the same Python signature conventions as the rest of the library (see the Add Function skill), with these additions:

  • Apply the @log decorator and write a numpydoc docstring.
  • Accept a dark_mode: bool = False parameter so users can opt into dark on first render. Document it.
  • Accept the standard workspace: Optional[str | UUID] = None parameter and resolve it via resolve_workspace_name_and_id.
  • For functions that operate on a semantic model, accept dataset: str | UUID and use connect_semantic_model (read-only when the UI is view-only, read-write only when it actually needs to mutate the model).
  • The function's job is to display the widget, not return it. Do not return the widget object (it causes double-rendering in Jupyter).

Checklist for a New Interactive UI

  • Picked the correct pattern: static-HTML if no Python callbacks are needed, anywidget if they are.
  • All icons come from sempy_labs._ui_components.ICONS (no inlined one-off SVGs). For anywidget tools, icons are injected into _WIDGET_JS via __SLLS_ICON_*__ placeholder substitution at module-import time.
  • All colors come from LIGHT_THEME_VARS / DARK_THEME_VARS (no hard-coded hex values).
  • Standard header rendered via render_header_html (static) or built in JS using the same layout/tokens (anywidget).
  • Standard "Powered by Semantic Link Labs" attribution rendered at the bottom.
  • Theme toggle wired via theme_toggle_script (static) or via a synced dark_mode traitlet (anywidget).
  • Full-screen toggle wired via render_header_html(..., fullscreen_btn_id=...) + fullscreen_css + fullscreen_toggle_script (static) or fullscreen_setup_js + fullscreen_css (anywidget).
  • CSS is scoped under a per-instance uid (static) or under a stable namespace class (anywidget) so multiple instances on one page do not collide and notebook host styles do not bleed in.
  • Apple-inspired font stack and antialiasing are applied to the root.
  • Public function accepts dark_mode: bool = False, resolves workspace, has @log + numpydoc docstring, and calls display(...) (does not return the widget).
  • Any genuinely reusable new component was promoted to _ui_components rather than duplicated.
  • For anywidget tools: anywidget is imported lazily with a friendly ImportError.

More skills from microsoft

oss-growth
microsoft
OSS growth hacker persona
agent-framework-azure-ai-py
microsoft
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
devops
azure-ai-anomalydetector-java
microsoft
Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
development
azure-ai-language-conversations-py
microsoft
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development