.. _asking_for_input: Asking for input (prompts) ========================== This page is about building prompts. Pieces of code that we can embed in a program for asking the user for input. Even if you want to use `prompt_toolkit` for building full screen terminal applications, it is probably still a good idea to read this first, before heading to the :ref:`building full screen applications ` page. In this page, we will cover autocompletion, syntax highlighting, key bindings, and so on. Hello world ----------- The following snippet is the most simple example, it uses the :func:`~prompt_toolkit.shortcuts.prompt` function to ask the user for input and returns the text. Just like ``(raw_)input``. .. code:: python from prompt_toolkit import prompt text = prompt('Give me some input: ') print('You said: %s' % text) .. image:: ../images/hello-world-prompt.png What we get here is a simple prompt that supports the Emacs key bindings like readline, but further nothing special. However, :func:`~prompt_toolkit.shortcuts.prompt` has a lot of configuration options. In the following sections, we will discover all these parameters. The `PromptSession` object -------------------------- Instead of calling the :func:`~prompt_toolkit.shortcuts.prompt` function, it's also possible to create a :class:`~prompt_toolkit.shortcuts.PromptSession` instance followed by calling its :meth:`~prompt_toolkit.shortcuts.PromptSession.prompt` method for every input call. This creates a kind of an input session. .. code:: python from prompt_toolkit import PromptSession # Create prompt object. session = PromptSession() # Do multiple input calls. text1 = session.prompt() text2 = session.prompt() This has mainly two advantages: - The input history will be kept between consecutive :meth:`~prompt_toolkit.shortcuts.PromptSession.prompt` calls. - The :func:`~prompt_toolkit.shortcuts.PromptSession` instance and its :meth:`~prompt_toolkit.shortcuts.PromptSession.prompt` method take about the same arguments, like all the options described below (highlighting, completion, etc...). So if you want to ask for multiple inputs, but each input call needs about the same arguments, they can be passed to the :func:`~prompt_toolkit.shortcuts.PromptSession` instance as well, and they can be overridden by passing values to the :meth:`~prompt_toolkit.shortcuts.PromptSession.prompt` method. Syntax highlighting ------------------- Adding syntax highlighting is as simple as adding a lexer. All of the `Pygments `_ lexers can be used after wrapping them in a :class:`~prompt_toolkit.lexers.PygmentsLexer`. It is also possible to create a custom lexer by implementing the :class:`~prompt_toolkit.lexers.Lexer` abstract base class. .. code:: python from pygments.lexers.html import HtmlLexer from prompt_toolkit.shortcuts import prompt from prompt_toolkit.lexers import PygmentsLexer text = prompt('Enter HTML: ', lexer=PygmentsLexer(HtmlLexer)) print('You said: %s' % text) .. image:: ../images/html-input.png The default Pygments colorscheme is included as part of the default style in prompt_toolkit. If you want to use another Pygments style along with the lexer, you can do the following: .. code:: python from pygments.lexers.html import HtmlLexer from pygments.styles import get_style_by_name from prompt_toolkit.shortcuts import prompt from prompt_toolkit.lexers import PygmentsLexer from prompt_toolkit.styles.pygments import style_from_pygments_cls style = style_from_pygments_cls(get_style_by_name('monokai')) text = prompt('Enter HTML: ', lexer=PygmentsLexer(HtmlLexer), style=style, include_default_pygments_style=False) print('You said: %s' % text) We pass ``include_default_pygments_style=False``, because otherwise, both styles will be merged, possibly giving slightly different colors in the outcome for cases where where our custom Pygments style doesn't specify a color. .. _colors: Colors ------ The colors for syntax highlighting are defined by a :class:`~prompt_toolkit.styles.Style` instance. By default, a neutral built-in style is used, but any style instance can be passed to the :func:`~prompt_toolkit.shortcuts.prompt` function. A simple way to create a style, is by using the :meth:`~prompt_toolkit.styles.Style.from_dict` function: .. code:: python from pygments.lexers.html import HtmlLexer from prompt_toolkit.shortcuts import prompt from prompt_toolkit.styles import Style from prompt_toolkit.lexers import PygmentsLexer our_style = Style.from_dict({ 'pygments.comment': '#888888 bold', 'pygments.keyword': '#ff88ff bold', }) text = prompt('Enter HTML: ', lexer=PygmentsLexer(HtmlLexer), style=our_style) The style dictionary is very similar to the Pygments ``styles`` dictionary, with a few differences: - The `roman`, `sans`, `mono` and `border` options are ignored. - The style has a few additions: ``blink``, ``noblink``, ``reverse`` and ``noreverse``. - Colors can be in the ``#ff0000`` format, but they can be one of the built-in ANSI color names as well. In that case, they map directly to the 16 color palette of the terminal. :ref:`Read more about styling `. Using a Pygments style ^^^^^^^^^^^^^^^^^^^^^^ All Pygments style classes can be used as well, when they are wrapped through :func:`~prompt_toolkit.styles.style_from_pygments_cls`. Suppose we'd like to use a Pygments style, for instance ``pygments.styles.tango.TangoStyle``, that is possible like this: .. code:: python from prompt_toolkit.shortcuts import prompt from prompt_toolkit.styles import style_from_pygments_cls from prompt_toolkit.lexers import PygmentsLexer from pygments.styles.tango import TangoStyle from pygments.lexers.html import HtmlLexer tango_style = style_from_pygments_cls (TangoStyle) text = prompt ('Enter HTML: ', lexer=PygmentsLexer(HtmlLexer), style=tango_style) Creating a custom style could be done like this: .. code:: python from prompt_toolkit.shortcuts import prompt from prompt_toolkit.styles import Style, style_from_pygments_cls, merge_styles from prompt_toolkit.lexers import PygmentsLexer from pygments.styles.tango import TangoStyle from pygments.lexers.html import HtmlLexer our_style = merge_styles([ style_from_pygments_cls(TangoStyle), Style.from_dict({ 'pygments.comment': '#888888 bold', 'pygments.keyword': '#ff88ff bold', }) ]) text = prompt('Enter HTML: ', lexer=PygmentsLexer(HtmlLexer), style=our_style) Coloring the prompt itself ^^^^^^^^^^^^^^^^^^^^^^^^^^ It is possible to add some colors to the prompt itself. For this, we need to build some :ref:`formatted text `. One way of doing this is by creating a list of style/text tuples. In the following example, we use class names to refer to the style. .. code:: python from prompt_toolkit.shortcuts import prompt from prompt_toolkit.styles import Style style = Style.from_dict({ # User input (default text). '': '#ff0066', # Prompt. 'username': '#884444', 'at': '#00aa00', 'colon': '#0000aa', 'pound': '#00aa00', 'host': '#00ffff bg:#444400', 'path': 'ansicyan underline', }) message = [ ('class:username', 'john'), ('class:at', '@'), ('class:host', 'localhost'), ('class:colon', ':'), ('class:path', '/user/john'), ('class:pound', '# '), ] text = prompt(message, style=style) .. image:: ../images/colored-prompt.png The `message` can be any kind of formatted text, as discussed :ref:`here `. It can also be a callable that returns some formatted text. By default, colors are taken from the 256 color palette. If you want to have 24bit true color, this is possible by adding the ``color_depth=ColorDepth.TRUE_COLOR`` option to the :func:`~prompt_toolkit.shortcuts.prompt.prompt` function. .. code:: python from prompt_toolkit.output import ColorDepth text = prompt(message, style=style, color_depth=ColorDepth.TRUE_COLOR) Autocompletion -------------- Autocompletion can be added by passing a ``completer`` parameter. This should be an instance of the :class:`~prompt_toolkit.completion.Completer` abstract base class. :class:`~prompt_toolkit.completion.WordCompleter` is an example of a completer that implements that interface. .. code:: python from prompt_toolkit import prompt from prompt_toolkit.completion import WordCompleter html_completer = WordCompleter(['', '', '', '']) text = prompt('Enter HTML: ', completer=html_completer) print('You said: %s' % text) :class:`~prompt_toolkit.completion.WordCompleter` is a simple completer that completes the last word before the cursor with any of the given words. .. image:: ../images/html-completion.png .. note:: Note that in prompt_toolkit 2.0, the auto completion became synchronous. This means that if it takes a long time to compute the completions, that this will block the event loop and the input processing. For heavy completion algorithms, it is recommended to wrap the completer in a :class:`~prompt_toolkit.completion.ThreadedCompleter` in order to run it in a background thread. Nested completion ^^^^^^^^^^^^^^^^^ Sometimes you have a command line interface where the completion depends on the previous words from the input. Examples are the CLIs from routers and switches. A simple :class:`~prompt_toolkit.completion.WordCompleter` is not enough in that case. We want to to be able to define completions at multiple hierarchical levels. :class:`~prompt_toolkit.completion.NestedCompleter` solves this issue: .. code:: python from prompt_toolkit import prompt from prompt_toolkit.completion import NestedCompleter completer = NestedCompleter.from_nested_dict({ 'show': { 'version': None, 'clock': None, 'ip': { 'interface': {'brief'} } }, 'exit': None, }) text = prompt('# ', completer=completer) print('You said: %s' % text) Whenever there is a ``None`` value in the dictionary, it means that there is no further nested completion at that point. When all values of a dictionary would be ``None``, it can also be replaced with a set. A custom completer ^^^^^^^^^^^^^^^^^^ For more complex examples, it makes sense to create a custom completer. For instance: .. code:: python from prompt_toolkit import prompt from prompt_toolkit.completion import Completer, Completion class MyCustomCompleter(Completer): def get_completions(self, document, complete_event): yield Completion('completion', start_position=0) text = prompt('> ', completer=MyCustomCompleter()) A :class:`~prompt_toolkit.completion.Completer` class has to implement a generator named :meth:`~prompt_toolkit.completion.Completer.get_completions` that takes a :class:`~prompt_toolkit.document.Document` and yields the current :class:`~prompt_toolkit.completion.Completion` instances. Each completion contains a portion of text, and a position. The position is used for fixing text before the cursor. Pressing the tab key could for instance turn parts of the input from lowercase to uppercase. This makes sense for a case insensitive completer. Or in case of a fuzzy completion, it could fix typos. When ``start_position`` is something negative, this amount of characters will be deleted and replaced. Styling individual completions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Each completion can provide a custom style, which is used when it is rendered in the completion menu or toolbar. This is possible by passing a style to each :class:`~prompt_toolkit.completion.Completion` instance. .. code:: python from prompt_toolkit.completion import Completer, Completion class MyCustomCompleter(Completer): def get_completions(self, document, complete_event): # Display this completion, black on yellow. yield Completion('completion1', start_position=0, style='bg:ansiyellow fg:ansiblack') # Underline completion. yield Completion('completion2', start_position=0, style='underline') # Specify class name, which will be looked up in the style sheet. yield Completion('completion3', start_position=0, style='class:special-completion') The "colorful-prompts.py" example uses completion styling: .. image:: ../images/colorful-completions.png Finally, it is possible to pass :ref:`formatted text <formatted_text>` for the ``display`` attribute of a :class:`~prompt_toolkit.completion.Completion`. This provides all the freedom you need to display the text in any possible way. It can also be combined with the ``style`` attribute. For instance: .. code:: python from prompt_toolkit.completion import Completer, Completion from prompt_toolkit.formatted_text import HTML class MyCustomCompleter(Completer): def get_completions(self, document, complete_event): yield Completion( 'completion1', start_position=0, display=HTML('<b>completion</b><ansired>1</ansired>'), style='bg:ansiyellow') Fuzzy completion ^^^^^^^^^^^^^^^^ If one possible completions is "django_migrations", a fuzzy completer would allow you to get this by typing "djm" only, a subset of characters for this string. Prompt_toolkit ships with a :class:`~prompt_toolkit.completion.FuzzyCompleter` and :class:`~prompt_toolkit.completion.FuzzyWordCompleter` class. These provide the means for doing this kind of "fuzzy completion". The first one can take any completer instance and wrap it so that it becomes a fuzzy completer. The second one behaves like a :class:`~prompt_toolkit.completion.WordCompleter` wrapped into a :class:`~prompt_toolkit.completion.FuzzyCompleter`. Complete while typing ^^^^^^^^^^^^^^^^^^^^^ Autcompletions can be generated automatically while typing or when the user presses the tab key. This can be configured with the ``complete_while_typing`` option: .. code:: python text = prompt('Enter HTML: ', completer=my_completer, complete_while_typing=True) Notice that this setting is incompatible with the ``enable_history_search`` option. The reason for this is that the up and down key bindings would conflict otherwise. So, make sure to disable history search for this. Asynchronous completion ^^^^^^^^^^^^^^^^^^^^^^^ When generating the completions takes a lot of time, it's better to do this in a background thread. This is possible by wrapping the completer in a :class:`~prompt_toolkit.completion.ThreadedCompleter`, but also by passing the `complete_in_thread=True` argument. .. code:: python text = prompt('> ', completer=MyCustomCompleter(), complete_in_thread=True) Input validation ---------------- A prompt can have a validator attached. This is some code that will check whether the given input is acceptable and it will only return it if that's the case. Otherwise it will show an error message and move the cursor to a given position. A validator should implements the :class:`~prompt_toolkit.validation.Validator` abstract base class. This requires only one method, named ``validate`` that takes a :class:`~prompt_toolkit.document.Document` as input and raises :class:`~prompt_toolkit.validation.ValidationError` when the validation fails. .. code:: python from prompt_toolkit.validation import Validator, ValidationError from prompt_toolkit import prompt class NumberValidator(Validator): def validate(self, document): text = document.text if text and not text.isdigit(): i = 0 # Get index of first non numeric character. # We want to move the cursor here. for i, c in enumerate(text): if not c.isdigit(): break raise ValidationError(message='This input contains non-numeric characters', cursor_position=i) number = int(prompt('Give a number: ', validator=NumberValidator())) print('You said: %i' % number) .. image:: ../images/number-validator.png By default, the input is validated in real-time while the user is typing, but prompt_toolkit can also validate after the user presses the enter key: .. code:: python prompt('Give a number: ', validator=NumberValidator(), validate_while_typing=False) If the input validation contains some heavy CPU intensive code, but you don't want to block the event loop, then it's recommended to wrap the validator class in a :class:`~prompt_toolkit.validation.ThreadedValidator`. Validator from a callable ^^^^^^^^^^^^^^^^^^^^^^^^^ Instead of implementing the :class:`~prompt_toolkit.validation.Validator` abstract base class, it is also possible to start from a simple function and use the :meth:`~prompt_toolkit.validation.Validator.from_callable` classmethod. This is easier and sufficient for probably 90% of the validators. It looks as follows: .. code:: python from prompt_toolkit.validation import Validator from prompt_toolkit import prompt def is_number(text): return text.isdigit() validator = Validator.from_callable( is_number, error_message='This input contains non-numeric characters', move_cursor_to_end=True) number = int(prompt('Give a number: ', validator=validator)) print('You said: %i' % number) We define a function that takes a string, and tells whether it's valid input or not by returning a boolean. :meth:`~prompt_toolkit.validation.Validator.from_callable` turns that into a :class:`~prompt_toolkit.validation.Validator` instance. Notice that setting the cursor position is not possible this way. History ------- A :class:`~prompt_toolkit.history.History` object keeps track of all the previously entered strings, so that the up-arrow can reveal previously entered items. The recommended way is to use a :class:`~prompt_toolkit.shortcuts.PromptSession`, which uses an :class:`~prompt_toolkit.history.InMemoryHistory` for the entire session by default. The following example has a history out of the box: .. code:: python from prompt_toolkit import PromptSession session = PromptSession() while True: session.prompt() To persist a history to disk, use a :class:`~prompt_toolkit.history.FileHistory` instead of the default :class:`~prompt_toolkit.history.InMemoryHistory`. This history object can be passed either to a :class:`~prompt_toolkit.shortcuts.PromptSession` or to the :meth:`~prompt_toolkit.shortcuts.prompt` function. For instance: .. code:: python from prompt_toolkit import PromptSession from prompt_toolkit.history import FileHistory session = PromptSession(history=FileHistory('~/.myhistory')) while True: session.prompt() Auto suggestion --------------- Auto suggestion is a way to propose some input completions to the user like the `fish shell <http://fishshell.com/>`_. Usually, the input is compared to the history and when there is another entry starting with the given text, the completion will be shown as gray text behind the current input. Pressing the right arrow :kbd:`→` or :kbd:`c-e` will insert this suggestion, :kbd:`alt-f` will insert the first word of the suggestion. .. note:: When suggestions are based on the history, don't forget to share one :class:`~prompt_toolkit.history.History` object between consecutive :func:`~prompt_toolkit.shortcuts.prompt` calls. Using a :class:`~prompt_toolkit.shortcuts.PromptSession` does this for you. Example: .. code:: python from prompt_toolkit import PromptSession from prompt_toolkit.history import InMemoryHistory from prompt_toolkit.auto_suggest import AutoSuggestFromHistory session = PromptSession() while True: text = session.prompt('> ', auto_suggest=AutoSuggestFromHistory()) print('You said: %s' % text) .. image:: ../images/auto-suggestion.png A suggestion does not have to come from the history. Any implementation of the :class:`~prompt_toolkit.auto_suggest.AutoSuggest` abstract base class can be passed as an argument. Adding a bottom toolbar ----------------------- Adding a bottom toolbar is as easy as passing a ``bottom_toolbar`` argument to :func:`~prompt_toolkit.shortcuts.prompt`. This argument be either plain text, :ref:`formatted text <formatted_text>` or a callable that returns plain or formatted text. When a function is given, it will be called every time the prompt is rendered, so the bottom toolbar can be used to display dynamic information. The toolbar is always erased when the prompt returns. Here we have an example of a callable that returns an :class:`~prompt_toolkit.formatted_text.HTML` object. By default, the toolbar has the **reversed style**, which is why we are setting the background instead of the foreground. .. code:: python from prompt_toolkit import prompt from prompt_toolkit.formatted_text import HTML def bottom_toolbar(): return HTML('This is a <b><style bg="ansired">Toolbar</style></b>!') text = prompt('> ', bottom_toolbar=bottom_toolbar) print('You said: %s' % text) .. image:: ../images/bottom-toolbar.png Similar, we could use a list of style/text tuples. .. code:: python from prompt_toolkit import prompt from prompt_toolkit.styles import Style def bottom_toolbar(): return [('class:bottom-toolbar', ' This is a toolbar. ')] style = Style.from_dict({ 'bottom-toolbar': '#ffffff bg:#333333', }) text = prompt('> ', bottom_toolbar=bottom_toolbar, style=style) print('You said: %s' % text) The default class name is ``bottom-toolbar`` and that will also be used to fill the background of the toolbar. Adding a right prompt --------------------- The :func:`~prompt_toolkit.shortcuts.prompt` function has out of the box support for right prompts as well. People familiar to ZSH could recognize this as the `RPROMPT` option. So, similar to adding a bottom toolbar, we can pass an ``rprompt`` argument. This can be either plain text, :ref:`formatted text <formatted_text>` or a callable which returns either. .. code:: python from prompt_toolkit import prompt from prompt_toolkit.styles import Style example_style = Style.from_dict({ 'rprompt': 'bg:#ff0066 #ffffff', }) def get_rprompt(): return '<rprompt>' answer = prompt('> ', rprompt=get_rprompt, style=example_style) .. image:: ../images/rprompt.png The ``get_rprompt`` function can return any kind of formatted text such as :class:`~prompt_toolkit.formatted_text.HTML`. it is also possible to pass text directly to the ``rprompt`` argument of the :func:`~prompt_toolkit.shortcuts.prompt` function. It does not have to be a callable. Vi input mode ------------- Prompt-toolkit supports both Emacs and Vi key bindings, similar to Readline. The :func:`~prompt_toolkit.shortcuts.prompt` function will use Emacs bindings by default. This is done because on most operating systems, also the Bash shell uses Emacs bindings by default, and that is more intuitive. If however, Vi binding are required, just pass ``vi_mode=True``. .. code:: python from prompt_toolkit import prompt prompt('> ', vi_mode=True) Adding custom key bindings -------------------------- By default, every prompt already has a set of key bindings which implements the usual Vi or Emacs behavior. We can extend this by passing another :class:`~prompt_toolkit.key_binding.KeyBindings` instance to the ``key_bindings`` argument of the :func:`~prompt_toolkit.shortcuts.prompt` function or the :class:`~prompt_toolkit.shortcuts.PromptSession` class. An example of a prompt that prints ``'hello world'`` when :kbd:`Control-T` is pressed. .. code:: python from prompt_toolkit import prompt from prompt_toolkit.application import run_in_terminal from prompt_toolkit.key_binding import KeyBindings bindings = KeyBindings() @bindings.add('c-t') def _(event): " Say 'hello' when `c-t` is pressed. " def print_hello(): print('hello world') run_in_terminal(print_hello) @bindings.add('c-x') def _(event): " Exit when `c-x` is pressed. " event.app.exit() text = prompt('> ', key_bindings=bindings) print('You said: %s' % text) Note that we use :meth:`~prompt_toolkit.application.run_in_terminal` for the first key binding. This ensures that the output of the print-statement and the prompt don't mix up. If the key bindings doesn't print anything, then it can be handled directly without nesting functions. Enable key bindings according to a condition ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Often, some key bindings can be enabled or disabled according to a certain condition. For instance, the Emacs and Vi bindings will never be active at the same time, but it is possible to switch between Emacs and Vi bindings at run time. In order to enable a key binding according to a certain condition, we have to pass it a :class:`~prompt_toolkit.filters.Filter`, usually a :class:`~prompt_toolkit.filters.Condition` instance. (:ref:`Read more about filters <filters>`.) .. code:: python from prompt_toolkit import prompt from prompt_toolkit.filters import Condition from prompt_toolkit.key_binding import KeyBindings bindings = KeyBindings() @Condition def is_active(): " Only activate key binding on the second half of each minute. " return datetime.datetime.now().second > 30 @bindings.add('c-t', filter=is_active) def _(event): # ... pass prompt('> ', key_bindings=bindings) Dynamically switch between Emacs and Vi mode ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The :class:`~prompt_toolkit.application.Application` has an ``editing_mode`` attribute. We can change the key bindings by changing this attribute from ``EditingMode.VI`` to ``EditingMode.EMACS``. .. code:: python from prompt_toolkit import prompt from prompt_toolkit.application.current import get_app from prompt_toolkit.enums import EditingMode from prompt_toolkit.key_binding import KeyBindings def run(): # Create a set of key bindings. bindings = KeyBindings() # Add an additional key binding for toggling this flag. @bindings.add('f4') def _(event): " Toggle between Emacs and Vi mode. " app = event.app if app.editing_mode == EditingMode.VI: app.editing_mode = EditingMode.EMACS else: app.editing_mode = EditingMode.VI # Add a toolbar at the bottom to display the current input mode. def bottom_toolbar(): " Display the current input mode. " text = 'Vi' if get_app().editing_mode == EditingMode.VI else 'Emacs' return [ ('class:toolbar', ' [F4] %s ' % text) ] prompt('> ', key_bindings=bindings, bottom_toolbar=bottom_toolbar) run() :ref:`Read more about key bindings ...<key_bindings>` Using control-space for completion ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ An popular short cut that people sometimes use it to use control-space for opening the autocompletion menu instead of the tab key. This can be done with the following key binding. .. code:: python kb = KeyBindings() @kb.add('c-space') def _(event): " Initialize autocompletion, or select the next completion. " buff = event.app.current_buffer if buff.complete_state: buff.complete_next() else: buff.start_completion(select_first=False) Other prompt options -------------------- Multiline input ^^^^^^^^^^^^^^^ Reading multiline input is as easy as passing the ``multiline=True`` parameter. .. code:: python from prompt_toolkit import prompt prompt('> ', multiline=True) A side effect of this is that the enter key will now insert a newline instead of accepting and returning the input. The user will now have to press :kbd:`Meta+Enter` in order to accept the input. (Or :kbd:`Escape` followed by :kbd:`Enter`.) It is possible to specify a continuation prompt. This works by passing a ``prompt_continuation`` callable to :func:`~prompt_toolkit.shortcuts.prompt`. This function is supposed to return :ref:`formatted text <formatted_text>`, or a list of ``(style, text)`` tuples. The width of the returned text should not exceed the given width. (The width of the prompt margin is defined by the prompt.) .. code:: python from prompt_toolkit import prompt def prompt_continuation(width, line_number, is_soft_wrap): return '.' * width # Or: return [('', '.' * width)] prompt('multiline input> ', multiline=True, prompt_continuation=prompt_continuation) .. image:: ../images/multiline-input.png Passing a default ^^^^^^^^^^^^^^^^^ A default value can be given: .. code:: python from prompt_toolkit import prompt import getpass prompt('What is your name: ', default='%s' % getpass.getuser()) Mouse support ^^^^^^^^^^^^^ There is limited mouse support for positioning the cursor, for scrolling (in case of large multiline inputs) and for clicking in the autocompletion menu. Enabling can be done by passing the ``mouse_support=True`` option. .. code:: python from prompt_toolkit import prompt prompt('What is your name: ', mouse_support=True) Line wrapping ^^^^^^^^^^^^^ Line wrapping is enabled by default. This is what most people are used to and this is what GNU Readline does. When it is disabled, the input string will scroll horizontally. .. code:: python from prompt_toolkit import prompt prompt('What is your name: ', wrap_lines=False) Password input ^^^^^^^^^^^^^^ When the ``is_password=True`` flag has been given, the input is replaced by asterisks (``*`` characters). .. code:: python from prompt_toolkit import prompt prompt('Enter password: ', is_password=True) Cursor shapes ------------- Many terminals support displaying different types of cursor shapes. The most common are block, beam or underscore. Either blinking or not. It is possible to decide which cursor to display while asking for input, or in case of Vi input mode, have a modal prompt for which its cursor shape changes according to the input mode. .. code:: python from prompt_toolkit import prompt from prompt_toolkit.cursor_shapes import CursorShape, ModalCursorShapeConfig # Several possible values for the `cursor_shape_config` parameter: prompt('>', cursor=CursorShape.BLOCK) prompt('>', cursor=CursorShape.UNDERLINE) prompt('>', cursor=CursorShape.BEAM) prompt('>', cursor=CursorShape.BLINKING_BLOCK) prompt('>', cursor=CursorShape.BLINKING_UNDERLINE) prompt('>', cursor=CursorShape.BLINKING_BEAM) prompt('>', cursor=ModalCursorShapeConfig()) Prompt in an `asyncio` application ---------------------------------- .. note:: New in prompt_toolkit 3.0. (In prompt_toolkit 2.0 this was possible using a work-around). For `asyncio <https://docs.python.org/3/library/asyncio.html>`_ applications, it's very important to never block the eventloop. However, :func:`~prompt_toolkit.shortcuts.prompt` is blocking, and calling this would freeze the whole application. Asyncio actually won't even allow us to run that function within a coroutine. The answer is to call :meth:`~prompt_toolkit.shortcuts.PromptSession.prompt_async` instead of :meth:`~prompt_toolkit.shortcuts.PromptSession.prompt`. The async variation returns a coroutines and is awaitable. .. code:: python from prompt_toolkit import PromptSession from prompt_toolkit.patch_stdout import patch_stdout async def my_coroutine(): session = PromptSession() while True: with patch_stdout(): result = await session.prompt_async('Say something: ') print('You said: %s' % result) The :func:`~prompt_toolkit.patch_stdout.patch_stdout` context manager is optional, but it's recommended, because other coroutines could print to stdout. This ensures that other output won't destroy the prompt. Reading keys from stdin, one key at a time, but without a prompt ---------------------------------------------------------------- Suppose that you want to use prompt_toolkit to read the keys from stdin, one key at a time, but not render a prompt to the output, that is also possible: .. code:: python import asyncio from prompt_toolkit.input import create_input from prompt_toolkit.keys import Keys async def main() -> None: done = asyncio.Event() input = create_input() def keys_ready(): for key_press in input.read_keys(): print(key_press) if key_press.key == Keys.ControlC: done.set() with input.raw_mode(): with input.attach(keys_ready): await done.wait() if __name__ == "__main__": asyncio.run(main()) The above snippet will print the `KeyPress` object whenever a key is pressed. This is also cross platform, and should work on Windows.