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.

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

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.

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)

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.py — visualize_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.py — perspective_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.py — direct_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

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).
• 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.