Gradio LinePlot Docs

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LinePlot

gradio.LinePlot(···)

Description

Creates a line plot component to display data from a pandas DataFrame (as output). As this component does not accept user input, it is rarely used as an input component.

Behavior

As input component: (Rarely used) passes the data displayed in the line plot as an AltairPlotData dataclass, which includes the plot information as a JSON string, as well as the type of plot (in this case, "line").

Your function should accept one of these types:

def predict(
	value: AltairPlotData | None
)
	...

As output component: Expects a pandas DataFrame containing the data to display in the line plot. The DataFrame should contain at least two columns, one for the x-axis (corresponding to this component's x argument) and one for the y-axis (corresponding to y).

Your function should return one of these types:

def predict(···) -> pd.DataFrame | dict | None
	...	
	return value

Initialization

Parameter	Description
`value` pd.DataFrame \| Callable \| None default: None	The pandas dataframe containing the data to display in a scatter plot.
`x` str \| None default: None	Column corresponding to the x axis.
`y` str \| None default: None	Column corresponding to the y axis.
`color` str \| None default: None	The column to determine the point color. If the column contains numeric data, gradio will interpolate the column data so that small values correspond to light colors and large values correspond to dark values.
`stroke_dash` str \| None default: None	The column to determine the symbol used to draw the line, e.g. dashed lines, dashed lines with points.
`overlay_point` bool \| None default: None	Whether to draw a point on the line for each (x, y) coordinate pair.
`title` str \| None default: None	The title to display on top of the chart.
`tooltip` list[str] \| str \| None default: None	The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.
`x_title` str \| None default: None	The title given to the x axis. By default, uses the value of the x parameter.
`y_title` str \| None default: None	The title given to the y axis. By default, uses the value of the y parameter.
`x_label_angle` float \| None default: None	The angle for the x axis labels. Positive values are clockwise, and negative values are counter-clockwise.
`y_label_angle` float \| None default: None	The angle for the y axis labels. Positive values are clockwise, and negative values are counter-clockwise.
`color_legend_title` str \| None default: None	The title given to the color legend. By default, uses the value of color parameter.
`stroke_dash_legend_title` str \| None default: None	The title given to the stroke_dash legend. By default, uses the value of the stroke_dash parameter.
`color_legend_position` Literal['left', 'right', 'top', 'bottom', 'top-left', 'top-right', 'bottom-left', 'bottom-right', 'none'] \| None default: None	The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
`stroke_dash_legend_position` Literal['left', 'right', 'top', 'bottom', 'top-left', 'top-right', 'bottom-left', 'bottom-right', 'none'] \| None default: None	The position of the stoke_dash legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
`height` int \| str \| None default: None	The height of the plot, specified in pixels if a number is passed, or in CSS units if a string is passed.
`width` int \| str \| None default: None	The width of the plot, specified in pixels if a number is passed, or in CSS units if a string is passed.
`x_lim` list[int] \| None default: None	A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].
`y_lim` list[int] \| None default: None	A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
`caption` str \| None default: None	The (optional) caption to display below the plot.
`interactive` bool \| None default: True	Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
`label` str \| None default: None	The (optional) label to display on the top left corner of the plot.
`show_label` bool \| None default: None	Whether the label should be displayed.
`container` bool default: True
`scale` int \| None default: None
`min_width` int default: 160
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`visible` bool default: True	Whether the plot should be visible.
`elem_id` str \| None default: 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 default: 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 default: 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.
`show_actions_button` bool default: False	Whether to show the actions button on the top right corner of the plot.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.LinePlot`	"lineplot"	Uses default values

Demos

import gradio as gr
from vega_datasets import data

stocks = data.stocks()
gapminder = data.gapminder()
gapminder = gapminder.loc[
    gapminder.country.isin(["Argentina", "Australia", "Afghanistan"])
]
climate = data.climate()
seattle_weather = data.seattle_weather()

## Or generate your own fake data, here's an example for stocks:
#
# import pandas as pd
# import random
#
# stocks = pd.DataFrame(
#     {
#         "symbol": [
#             random.choice(
#                 [
#                     "MSFT",
#                     "AAPL",
#                     "AMZN",
#                     "IBM",
#                     "GOOG",
#                 ]
#             )
#             for _ in range(120)
#         ],
#         "date": [
#             pd.Timestamp(year=2000 + i, month=j, day=1)
#             for i in range(10)
#             for j in range(1, 13)
#         ],
#         "price": [random.randint(10, 200) for _ in range(120)],
#     }
# )


def line_plot_fn(dataset):
    if dataset == "stocks":
        return gr.LinePlot(
            stocks,
            x="date",
            y="price",
            color="symbol",
            color_legend_position="bottom",
            title="Stock Prices",
            tooltip=["date", "price", "symbol"],
            height=300,
            width=500,
        )
    elif dataset == "climate":
        return gr.LinePlot(
            climate,
            x="DATE",
            y="HLY-TEMP-NORMAL",
            y_lim=[250, 500],
            title="Climate",
            tooltip=["DATE", "HLY-TEMP-NORMAL"],
            height=300,
            width=500,
        )
    elif dataset == "seattle_weather":
        return gr.LinePlot(
            seattle_weather,
            x="date",
            y="temp_min",
            tooltip=["weather", "date"],
            overlay_point=True,
            title="Seattle Weather",
            height=300,
            width=500,
        )
    elif dataset == "gapminder":
        return gr.LinePlot(
            gapminder,
            x="year",
            y="life_expect",
            color="country",
            title="Life expectancy for countries",
            stroke_dash="cluster",
            x_lim=[1950, 2010],
            tooltip=["country", "life_expect"],
            stroke_dash_legend_title="Country Cluster",
            height=300,
            width=500,
        )


with gr.Blocks() as line_plot:
    with gr.Row():
        with gr.Column():
            dataset = gr.Dropdown(
                choices=["stocks", "climate", "seattle_weather", "gapminder"],
                value="stocks",
            )
        with gr.Column():
            plot = gr.LinePlot()
    dataset.change(line_plot_fn, inputs=dataset, outputs=plot)
    line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)


if __name__ == "__main__":
    line_plot.launch()

import math

import pandas as pd

import gradio as gr
import datetime
import numpy as np


def get_time():
    return datetime.datetime.now()


plot_end = 2 * math.pi


def get_plot(period=1):
    global plot_end
    x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)
    y = np.sin(2 * math.pi * period * x)
    update = gr.LinePlot(
        value=pd.DataFrame({"x": x, "y": y}),
        x="x",
        y="y",
        title="Plot (updates every second)",
        width=600,
        height=350,
    )
    plot_end += 2 * math.pi
    if plot_end > 1000:
        plot_end = 2 * math.pi
    return update


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            c_time2 = gr.Textbox(label="Current Time refreshed every second")
            gr.Textbox(
                "Change the value of the slider to automatically update the plot",
                label="",
            )
            period = gr.Slider(
                label="Period of plot", value=1, minimum=0, maximum=10, step=1
            )
            plot = gr.LinePlot(show_label=False)
        with gr.Column():
            name = gr.Textbox(label="Enter your name")
            greeting = gr.Textbox(label="Greeting")
            button = gr.Button(value="Greet")
            button.click(lambda s: f"Hello {s}", name, greeting)

    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)
    dep = demo.load(get_plot, None, plot, every=1)
    period.change(get_plot, period, plot, every=1, cancels=[dep])

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

Event Listeners

Description

Event listeners allow you to capture and 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 LinePlot component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Arguments table below.

Listener	Description
`gradio.LinePlot.change(fn, ···)`	Triggered when the value of the Plot 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.
`gradio.LinePlot.clear(fn, ···)`	This listener is triggered when the user clears the Plot using the X button for the component.

Event Arguments

Parameter	Description
`fn` Callable \| None \| Literal['decorator'] default: "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 \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: 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 \| Literal[False] default: None	defines how the endpoint appears in the API docs. Can be a string, None, or False. 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. If False, the endpoint will not be exposed in the API docs and downstream apps (including those that `gr.load` this app) will not be able to use this event.
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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 default: 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 default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: 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 default: 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 default: 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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.
`trigger_mode` Literal['once', 'multiple', 'always_last'] \| None default: 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 \| None default: 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: "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 default: 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.
`show_api` bool default: True	whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps to use this event. If fn is None, show_api will automatically be set to False.

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LinePlot

Description
Behavior
Initialization
Shortcuts
Demos
Event Listeners