decoui

Decorator-driven GUI framework for Python. Annotate your methods — decoui generates a full PySide6 desktop app automatically.

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Features

• Zero UI code — decorate a class and its methods, call gui_main()
• Native type mapping — str, int, float, bool, list, dict, Enum → widgets automatically
• Form assist — autocomplete, cascading fill, and lazily evaluated defaults
• Async execution — every tool runs in a thread; stdout and logging are captured in real time
• Execution history — every run is persisted to SQLite with parameters, logs, and status
• Parallel tool tabs — keep multiple tool pages open and switch between running tasks
• Persistent layout — sidebar width is restored from the application database
• Replay — restore any past run's parameters with one click
• Light theme — clean built-in stylesheet, Consolas / 微软雅黑 / Meiryo font stack

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Installation

pip install decoui

Requires Python 3.10+.

decoui depends on PySide6-Essentials (6.6+), not the full PySide6 metapackage. It only ever imports QtCore, QtGui, QtWidgets and QtTest, all of which live in Essentials — so the extra PySide6-Addons payload (Charts, Multimedia, WebEngine, Qt3D, …) is skipped. Measured on Windows / PySide6 6.11: 637 MB → 202 MB.

If your own tool methods need an Addons module, depend on it explicitly:

pip install decoui PySide6-Addons   # or simply: pip install decoui PySide6

Both coexist with decoui — PySide6 is just PySide6-Essentials + PySide6-Addons.

Upgrading an existing environment — all three distributions unpack into the same PySide6/ directory, so uninstalling PySide6 / PySide6-Addons also deletes shared libraries that Essentials still needs, leaving ImportError: DLL load failed while importing QtWidgets. Force a clean reinstall rather than relying on the uninstall:

pip install --force-reinstall PySide6-Essentials
# uv: uv sync --reinstall-package pyside6-essentials

Fresh installs are unaffected.

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Quick Start

import logging
from decoui import tool, toolset, gui_main

@toolset(label="Text Tools", tags=["text"])
class TextTools:

    @tool(label="Count Characters",
          description="Count chars, words and lines.",
          placeholders={"content": "Paste text here…"})
    def count(self, content: str = "") -> str:
        words = len(content.split())
        logging.info("words=%d", words)
        return f"{len(content)} chars / {words} words"

if __name__ == "__main__":
    gui_main(title="My Tools")

gui_main() auto-discovers every @toolset class in the caller's global scope — no explicit registration needed.

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API Reference

@toolset

Applied to a class. Groups its @tool methods under one sidebar entry.

@toolset(
    label="CSV Tools",           # required — sidebar display name
    tags=["file", "batch"],      # optional — used by the tag filter bar
    description="...",           # optional — tooltip on hover in the sidebar
)
class CsvTools:
    ...

Parameter	Type	Description
label	str	Required. Display name in the sidebar.
tags	list[str]	Tag filter bar labels. Multiple tags = AND filter.
description	str	Shown as a tooltip when hovering the toolset in the nav tree.

@tool

Applied to a method inside a @toolset class.

@tool(
    label="Merge Files",
    description="Merge multiple files into one.",
    placeholders={"files": "one path per line"},
    confirm=True,     # show confirmation dialog before running
    timeout=120,      # cancel after N seconds (None = unlimited)
)
def merge(self, files: list, output: str = "out.txt") -> str:
    ...

Parameter	Type	Default	Description
label	str	required	Tool display name.
description	str	""	Shown in a bordered box below the title.
placeholders	dict[str,str]	{}	Placeholder text per parameter name.
labels	dict[str,str]	{}	Form label per parameter name. Defaults to the parameter name.
confirm	bool	False	Show a Yes/No confirmation dialog before running.
timeout	int|None	None	Execution timeout in seconds.
completions	dict[str,list|callable|str]	{}	Autocomplete candidates per parameter. See Form Assist.
cascade	dict[str,callable|str]	{}	Fill other parameters when this one changes.
defaults	dict|callable|str	None	Initial form values, evaluated when the page opens.
completion_debounce_ms	int	250	Idle time before a dynamic completions callback runs.
on_cancel	callable|str	None	Cleanup to run when the tool is cancelled. See Cancellation.

Every key in placeholders, labels, completions, cascade and defaults must name a real parameter of the method, and placeholders/labels values must be strings. A mistake raises at startup, when the toolset tree is built, naming the tool and listing the parameters it does have.

gui_main

gui_main(title="My App", db_path="~/.myapp/history.db", on_startup=connect_backend)

Parameter	Type	Default	Description
title	str	"decoui"	Window title.
db_path	str|Path|None	~/.decoui/history.db	SQLite database path for execution history and application settings.
on_startup	callable|None	None	Application-wide setup, run once before any toolset is created and before the window appears. See Startup.
toolsets	Sequence[type]|None	None	The @toolset classes to load. When omitted, every one visible in the calling namespace is discovered.

By default gui_main() scans the caller's namespace, so a toolset has to be imported and look used:

from mytools.restore import RestoreTools  # noqa: F401 -- discovered by gui_main()

gui_main(title="My App")

Naming them explicitly drops the pragma and the comment that has to explain it:

from mytools.restore import RestoreTools

gui_main(title="My App", toolsets=[RestoreTools])

The list controls what loads, not the order it appears in: the sidebar is always sorted by label.

Startup

Full guide: docs/startup-lifecycle.md — read it before loading anything at startup.

Startup runs in a fixed order, so anything loaded early is available later:

1. db_path is applied and the database is initialised
2. the toolset tree is built and validated
3. gui_main(on_startup=...) runs — application-wide work
4. every @toolset class is instantiated
5. each instance's own on_startup() method runs, if it defines one
6. the window is shown and the event loop starts

The recommended pattern is to declare state in __init__ and load it in on_startup():

@toolset(label="Deploy Tools")
class DeployTools:

    def __init__(self):
        self.config = {"env": "staging"}          # declare, with fallbacks

    def on_startup(self):
        self.config = json.loads(                  # load, before the UI is live
            CONFIG_PATH.read_text(encoding="utf-8")
        )

    @tool(label="Deploy", defaults="load_defaults")
    def deploy(self, env: str = "") -> None:
        ...

    def load_defaults(self) -> dict:
        return {"env": self.config["env"]}         # reads self at call time

Splitting the two matters when loading fails: __init__ still succeeded, so the toolset opens with its declared fallbacks instead of disappearing.

Use the gui_main(on_startup=...) argument for work that belongs to no single toolset — a shared connection, an auth token, a warm cache. It runs before any instance exists, so it cannot write to self; give the toolset its own on_startup() method for that. The application hook always runs before every toolset hook.

Two rules for both hooks

• The event loop is not running yet. QTimer does not fire, queued signals are not delivered, and QThreadPool results never arrive — waiting on a background thread here deadlocks startup. To report a problem, just raise; decoui shows it in a dialog once the window is up.
• They block the window. Both run synchronously before show(), so however long they take is time the user spends looking at nothing. Anything slow belongs in the tool body, where it gets a thread, a progress bar, and a Stop button.

Can the decorator see self?

Decorator arguments are evaluated at import time, when no instance exists — so this is impossible:

@tool(label="Deploy", defaults={"env": self.config["env"]})   # NameError: self

Pass a method name instead. decoui resolves it to a bound method when the tool page is built, and the method reads self at call time — after __init__ and on_startup() have run:

@tool(label="Deploy",
      defaults="load_defaults",          # self.config is available inside
      completions={"service": "search_services"},
      cascade={"service": "describe_service"})

This works for defaults, completions, and cascade alike.

When startup fails

A failure in step 3, 4, or 5 does not stop the application. Every failure is collected and reported in one dialog over the main window, listing what failed with the full traceback behind the Details button, and everything that did load stays usable.

The one exception is step 4: a class whose __init__ raises produces no object, so that toolset is skipped and its tools do not appear. That is the practical reason to keep __init__ trivial and do the real loading in on_startup().

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Type → Widget Mapping

Python annotation	Widget	Notes
str	QLineEdit	Single-line text.
int	QSpinBox	Integer, full int range.
float	QDoubleSpinBox	4 decimal places.
bool	QCheckBox	Checked / unchecked.
list	QTextEdit	One item per line or comma-separated.
dict	QTextEdit	JSON input; parsed with json.loads then ast.literal_eval. Raises on invalid input.
Enum subclass	QComboBox	Dropdown of enum members.
pathlib.Path	QLineEdit + buttons	Text field with File... (file picker) and Folder... (directory picker) buttons. The selected path is passed as a pathlib.Path to the method.

• Required parameters (no default) are marked with a red * in the form label.
• Optional[X] is unwrapped to X.
• Annotated[X, ...] maps on X; the metadata does not affect widget choice.
• Default values are pre-filled into widgets automatically.

Field metadata with Annotated

F attaches a label and placeholder to the type itself, so a field shared across tools carries its wording with it instead of repeating it in every @tool:

from pathlib import Path
from typing import Annotated
from decoui import F, tool, toolset

DumpFile = Annotated[Path, F(label="Dump file", placeholder="Pick a .dump / .tar")]

@toolset(label="Backup")
class Backup:

    @tool(label="Restore")
    def restore(self, archive: DumpFile) -> None: ...

    # One tool can reword the shared field; the decorator wins.
    @tool(label="Verify", labels={"archive": "Archive to check"})
    def verify(self, archive: DumpFile) -> None: ...

Resolution order for both label and placeholder:

@tool(labels=…) / @tool(placeholders=…) → Annotated[…, F(…)] → parameter name (label) or empty (placeholder)

Adding F to an annotation is purely additive: widget selection, required-field marking and value conversion all read the bare type underneath, so existing code keeps working unchanged.

Note — with from __future__ import annotations, annotations are strings at runtime and decoui resolves them with typing.get_type_hints(). An alias like DumpFile must therefore be importable at runtime; putting the import under if TYPE_CHECKING: makes resolution fail, and decoui falls back to the raw strings, losing the F metadata along with the type mapping.

pathlib.Path example

import pathlib
from decoui import tool, toolset

@toolset(label="File Tools")
class FileTools:

    @tool(label="File Info", placeholders={"path": "Select a file or folder…"})
    def file_info(self, path: pathlib.Path) -> None:
        import logging
        p = pathlib.Path(path)
        logging.info("size: %d bytes", p.stat().st_size)
        logging.info("resolved: %s", p.resolve())

---

Form Assist

Three opt-in @tool arguments make the parameter form react to what the user types.

_CATALOG = {
    "web-frontend": {"version": "2.4.1", "owner": "frontend-team"},
    "auth-service": {"version": "3.0.2", "owner": "identity-team"},
}

@toolset(label="Deploy Tools")
class DeployTools:

    @tool(
        label="Deploy Service",
        completions={
            "env": ["production", "staging", "development"],   # static list
            "service": "search_services",                       # method on this class
        },
        cascade={"service": "describe_service"},                # fill other fields
        defaults={"env": "staging"},                            # initial values
    )
    def deploy(self, service: str = "", env: str = "",
               version: str = "", owner: str = "") -> None:
        ...

    def search_services(self, text: str) -> list[str]:
        return [name for name in _CATALOG if text.casefold() in name]

    def describe_service(self, value: str, form: dict) -> dict:
        return dict(_CATALOG.get(value, {"version": "", "owner": ""}))

completions — autocomplete

Value	Behaviour
list[str]	Fixed candidates, filtered by Qt (case-insensitive, substring match).
callable	Re-evaluated as the user types, debounced. The returned list replaces the popup contents; your callback controls matching and ordering.
str	Name of a method on the toolset class, so the callback can use self.

Clicking into the field opens the popup right away, before anything is typed — a static list shows every option, and a callback is called with an empty text so it can return its default set. The list is not narrowed by a value already in the field, since re-entering one is usually a prelude to changing it.

Supported on str and pathlib.Path parameters only. Declaring it elsewhere raises at startup.

cascade — fill other fields

Keyed by the source parameter. The callback returns {target_name: value}, or None to change nothing. It fires when the source is committed (focus loss, Enter, picking a candidate, toggling a checkbox), not on every keystroke, and is skipped when the value did not actually change.

Targets are always overwritten, including fields the user typed into — the callback receives form with every current value, so it can return one unchanged to preserve it. A written target may itself be a cascade source; chains are capped at 5 levels and cycles terminate after one round. Replay never triggers cascades, so restored parameters survive.

defaults — lazy initial values

Signature defaults (def deploy(self, env: str = "staging")) are evaluated at import time. Use defaults= when the initial value must be read after the application has started — for example from ~/.decoui/history.db, which does not exist until gui_main() runs.

@tool(label="Deploy", defaults=lambda: {"env": get_setting("deploy.env", "staging")})

Accepts a dict, a callable taking nothing or the current form, or a method name. Runs once per tool page.

Callback rules

• Completion and cascade callbacks run on a background thread. They must not touch Qt objects. Keep them to a lookup: query, return data.
• Callbacks may take 0, 1, or 2 positional arguments — (text, form) for completions, (value, form) for cascade.
• Any exception is caught, reported as a WARNING in the output console, and never aborts the form.
• Results from superseded requests are discarded, so a slow lookup cannot overwrite a newer one.

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Output & Logging

Tool methods can use print() and the standard logging module. Both are captured and rendered in the output console with colour coding:

Source	Colour
print / stdout	White
logging.DEBUG	Gray
logging.INFO	Phosphor Green
logging.WARNING	Yellow
logging.ERROR	Red
logging.CRITICAL	Bold Red

Return values from tool methods are not displayed in the GUI. Use logging or print for any output you want users to see.

Progress

Long, quiet work looks indistinguishable from a hang. progress() drives the page's progress bar and the status text next to it:

from decoui import progress, tool, toolset

@tool(label="Dump")
def dump(self, schema: str) -> None:
    for index, table in enumerate(tables):
        progress(index, len(tables), f"dumping {table}")
        dump_table(table)
    progress(len(tables), len(tables), "done")

Argument	Meaning
done	Units completed so far.
total	Total units, or 0 when unknown — the bar stays indeterminate and only the message updates.
message	Short status text. Replaces "Running…" while set.

Calls closer than 100 ms apart are dropped so a tight loop cannot flood the GUI; the final call (done >= total) is always delivered.

Outside a running tool — when you instantiate the toolset and call the method directly, as in a test — progress() does nothing at all. It prints nothing and raises nothing, so a tool stays callable as a plain method.

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Tool Page Buttons

Button	Action
Run	Type-coerce parameters, run the method in a background thread.
Reset	Expand the parameter panel and clear the output console. Parameters are kept.
Stop	Request cancellation of the running task.
Replay	Open the History panel pre-filtered to this tool's past runs.
Copy	Copy current console output to clipboard.
View Log	Open the current console output in a resizable log viewer window.

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Cancellation

If your tool starts a subprocess, declare on_cancel. Without it, pressing Stop leaves the child running.

Cancellation works by injecting an exception into the worker thread. That has two consequences most cleanup code gets wrong:

1. except Exception does not catch it. The injected _WorkerCancelled derives from BaseException, so an ordinary handler lets it pass straight through.

2. finally does not save you either. The injected exception is only raised at a Python bytecode boundary. A thread parked in proc.wait(), socket.recv() or any other C call does not reach one until that call returns on its own — so a finally block waiting to terminate the child does not run until the child has already finished. That is the entire problem: the child is exactly what needs to be stopped.

on_cancel runs on the GUI thread, before the interrupt is injected, while the worker is still blocked. That is the only moment anything can reach the child:

@toolset(label="Database")
class RestoreTools:

    @tool(label="Restore", on_cancel="stop")
    def restore(self, archive: Path) -> None:
        self.proc = subprocess.Popen(["pg_restore", str(archive)], encoding="utf-8")
        self.proc.wait()

    def stop(self) -> None:
        proc = getattr(self, "proc", None)
        if proc is not None and proc.poll() is None:
            proc.terminate()

3. The hook runs concurrently with your tool. It is on the GUI thread while the tool body is still on the worker thread, so:

• keep it to idempotent interruption — terminate(), close(), cancel() — and do not touch state the tool body is writing;
• tolerate state that does not exist yet: Stop can be pressed before the tool assigns it, which is why the example reads getattr(self, "proc", None);
• keep it fast. It blocks the GUI. Slow cleanup belongs in the tool's own finally.

A hook that raises is reported as an ERROR line in that run's log; cancellation still completes.

4. After cancellation the run is recorded as cancelled and the return value is dropped. The Popen object is left unusable, too: the injection can land between waitpid() returning and Popen recording the status, so proc.returncode may stay None even though the child is gone. Check the child through the OS if you need its fate.

timeout= uses this same path — from the tool's side a timeout and a Stop press are the same event.

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Execution History

Every run is stored in SQLite. The History panel (sidebar button) shows:

• Timestamp, tool name, status badge, duration
• Per-row checkboxes with Select All / Deselect All / Delete Selected
• Filter by tool, status, and time range
• Click any row to see the parameter snapshot
• Replay Params — restores that run's parameters to the tool's form
• View Full Log — opens the full log in a resizable window with level filters and search
• DB size readout — the current on-disk size of the history database, next to the filters
• Clear History — drops every record, parameter, and log, then vacuums the database to reclaim the space. Application settings are preserved.

History is stored at ~/.decoui/history.db by default. Override with db_path in gui_main().

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Example

See src/decoui/example.py for a complete demo covering all supported widget types.

Two tools there are worth reading as a pair before writing anything that runs for a while:

Tool	Shows
Demo Tools → Slow Task	progress() driving the bar, confirm=True, and what a failed run looks like. Cancellable on its own, because it only sleeps.
Demo Tools → Run Child Process	on_cancel around a real subprocess. Press Stop while it runs: the hook is what actually kills the child, and the with Popen(...) block is what reaps it.

Run it with:

uv run python main.py
# or
python main.py