# Slider

```python
gradio.Slider(···)
```

### Description

Creates a slider that ranges from {minimum} to {maximum} with a step size of {step}.

### Behavior

### Initialization

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `minimum` | `float` | `0` | minimum value for slider. When used as an input, if a user provides a smaller value, a gr.Error exception is raised by the backend. |
| `maximum` | `float` | `100` | maximum value for slider. When used as an input, if a user provides a larger value, a gr.Error exception is raised by the backend. |
| `value` | `float \| Callable \| None` | `None` | default value for slider. If a function is provided, the function will be called each time the app loads to set the initial value of this component. Ignored if randomized=True. |
| `step` | `float \| None` | `None` | increment between slider values. |
| `precision` | `int \| None` | `None` | Precision to round input/output to. If set to 0, will round to nearest integer and convert type to int. If None, no rounding happens. |
| `label` | `str \| I18nData \| None` | `None` | the label for this component, displayed above the component if `show_label` is `True` and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component corresponds to. |
| `info` | `str \| I18nData \| None` | `None` | additional component description, appears below the label in smaller font. Supports markdown / HTML syntax. |
| `every` | `Timer \| float \| None` | `None` | Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer. |
| `inputs` | `Component \| list[Component] \| set[Component] \| None` | `None` | Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change. |
| `show_label` | `bool \| None` | `None` | if True, will display label. |
| `container` | `bool` | `True` | If True, will place the component in a container - providing some extra padding around the border. |
| `scale` | `int \| None` | `None` | relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True. |
| `min_width` | `int` | `160` | minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first. |
| `interactive` | `bool \| None` | `None` | if True, slider will be adjustable; if False, adjusting will be disabled. If not provided, this is inferred based on whether the component is used as an input or output. |
| `visible` | `bool \| Literal['hidden']` | `True` | If False, component will be hidden. If "hidden", component will be visually hidden and not take up space in the layout but still exist in the DOM |
| `elem_id` | `str \| None` | `None` | An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. |
| `elem_classes` | `list[str] \| str \| None` | `None` | An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. |
| `render` | `bool` | `True` | If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later. |
| `key` | `int \| str \| tuple[int \| str, ...] \| None` | `None` | in a gr.render, Components with the same key across re-renders are treated as the same component, not a new component. Properties set in 'preserved_by_key' are not reset across a re-render. |
| `preserved_by_key` | `list[str] \| str \| None` | `"value"` | A list of parameters from this component's constructor. Inside a gr.render() function, if a component is re-rendered with the same key, these (and only these) parameters will be preserved in the UI (if they have been changed by the user or an event listener) instead of re-rendered based on the values provided during constructor. |
| `randomize` | `bool` | `False` | If True, the value of the slider when the app loads is taken uniformly at random from the range given by the minimum and maximum. |
| `buttons` | `list[Literal['reset']] \| None` | `None` | A list of buttons to show for the component. Currently, the only valid option is "reset". The "reset" button allows the user to reset the slider to its default value. By default, no buttons are shown. |
### Shortcuts

| Class | Interface String Shortcut | Initialization |
|-------|--------------------------|----------------|
| `gradio.Slider` | `"slider"` | Uses default values |
### Common Patterns

#### Logarithmic scale

Sliders are linear by default. For parameters that vary over several orders of magnitude (e.g. learning rate), map the slider value inside your function:

```python
import gradio as gr

def train(lr_exp):
    lr = 10 ** lr_exp  # slider value -5 → lr 0.00001
    return f"Training with lr={lr}"

demo = gr.Interface(
    fn=train,
    inputs=gr.Slider(-5, 0, value=-3, step=0.5, label="Learning rate (log₁₀)"),
    outputs="text",
)
```

#### Reacting on release only

By default, the slider triggers a `change` event on every movement. For expensive operations, listen to `release` instead so the function only runs when the user lets go:

```python
import gradio as gr

with gr.Blocks() as demo:
    slider = gr.Slider(0, 100, label="Epochs")
    output = gr.Textbox()
    slider.release(fn=lambda v: f"Will train for {int(v)} epochs", inputs=slider, outputs=output)
```

### Demos

**sentence_builder**

[See demo on Hugging Face Spaces](https://huggingface.co/spaces/gradio/sentence_builder)

```python
import gradio as gr

def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""

demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose between 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    api_name="predict",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()
```

**slider_release**

[See demo on Hugging Face Spaces](https://huggingface.co/spaces/gradio/slider_release)

```python
import gradio as gr

def identity(x, state):
    state += 1
    return x, state, state

with gr.Blocks() as demo:
    slider = gr.Slider(0, 100, step=0.1)
    state = gr.State(value=0)
    with gr.Row():
        number = gr.Number(label="On release")
        number2 = gr.Number(label="Number of events fired")
    slider.release(identity, inputs=[slider, state], outputs=[number, state, number2], api_name="predict")

if __name__ == "__main__":
    print("here")
    demo.launch()
    print(demo.server_port)
```

**interface_random_slider**

[See demo on Hugging Face Spaces](https://huggingface.co/spaces/gradio/interface_random_slider)

```python
import gradio as gr

def func(slider_1, slider_2, *args):
    return slider_1 + slider_2 * 5

demo = gr.Interface(
    func,
    [
        gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label="Random Big Range"),
        gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label="Random only multiple of 0.05 allowed"),
        gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label="Random only multiples of 0.25 allowed"),
        gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label="Random between -100 and 100 step 3"),
        gr.Slider(minimum=-100, maximum=100, randomize=True, label="Random between -100 and 100"),
        gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),
    ],
    "number",
    api_name="predict"
)

if __name__ == "__main__":
    demo.launch()
```

**blocks_random_slider**

[See demo on Hugging Face Spaces](https://huggingface.co/spaces/gradio/blocks_random_slider)

```python

import gradio as gr

def func(slider_1, slider_2):
    return slider_1 * 5 + slider_2

with gr.Blocks() as demo:
    slider = gr.Slider(minimum=-10.2, maximum=15, label="Random Slider (Static)", randomize=True)
    slider_1 = gr.Slider(minimum=100, maximum=200, label="Random Slider (Input 1)", randomize=True)
    slider_2 = gr.Slider(minimum=10, maximum=23.2, label="Random Slider (Input 2)", randomize=True)
    slider_3 = gr.Slider(value=3, label="Non random slider")
    btn = gr.Button("Run")
    btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())

if __name__ == "__main__":
    demo.launch()
```

### Event Listeners

#### Description

Event listeners allow you to respond to user interactions with the UI components you've defined in a Gradio Blocks app. When a user interacts with an element, such as changing a slider value or uploading an image, a function is called.

#### Supported Event Listeners

The `Slider` component supports the following event listeners:

- `Slider.change(fn, ...)`: Triggered when the value of the Slider changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
- `Slider.input(fn, ...)`: This listener is triggered when the user changes the value of the Slider.
- `Slider.release(fn, ...)`: This listener is triggered when the user releases the mouse on this Slider.

#### Event Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `fn` | `Callable \| None \| Literal['decorator']` | `"decorator"` | the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component. |
| `inputs` | `Component \| BlockContext \| list[Component \| BlockContext] \| Set[Component \| BlockContext] \| None` | `None` | List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. |
| `outputs` | `Component \| BlockContext \| list[Component \| BlockContext] \| Set[Component \| BlockContext] \| None` | `None` | List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. |
| `api_name` | `str \| None` | `None` | defines how the endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. |
| `api_description` | `str \| None \| Literal[False]` | `None` | Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs. |
| `scroll_to_output` | `bool` | `False` | If True, will scroll to output component on completion |
| `show_progress` | `Literal['full', 'minimal', 'hidden']` | `"full"` | how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all |
| `show_progress_on` | `Component \| list[Component] \| None` | `None` | Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components. |
| `queue` | `bool` | `True` | If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app. |
| `batch` | `bool` | `False` | If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component. |
| `max_batch_size` | `int` | `4` | Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) |
| `preprocess` | `bool` | `True` | If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component). |
| `postprocess` | `bool` | `True` | If False, will not run postprocessing of component data before returning 'fn' output to the browser. |
| `cancels` | `dict[str, Any] \| list[dict[str, Any]] \| None` | `None` | A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. |
| `trigger_mode` | `Literal['once', 'multiple', 'always_last'] \| None` | `None` | If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete. |
| `js` | `str \| Literal[True] \| None` | `None` | Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components. |
| `concurrency_limit` | `int \| None \| Literal['default']` | `"default"` | If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default). |
| `concurrency_id` | `str \| None` | `None` | If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit. |
| `api_visibility` | `Literal['public', 'private', 'undocumented']` | `"public"` | controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by clients), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private". |
| `time_limit` | `int \| None` | `None` |  |
| `stream_every` | `float` | `0.5` |  |
| `key` | `int \| str \| tuple[int \| str, ...] \| None` | `None` | A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical. |
| `validator` | `Callable \| None` | `None` | Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function and should return a `gr.validate()` for each input value. |