Components
Plot

New to Gradio? Start here: Getting Started

To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/02798ec170be7c9e8756dec24ef29c7f46fe2060/gradio-4.41.0-py3-none-any.whl

*Note: Setting share=True in launch() will not work.

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Plot

gradio.Plot(···)

import gradio as gr import matplotlib.pyplot as plt import numpy as np Fs = 8000 f = 5 sample = 8000 x = np.arange(sample) y = np.sin(2 * np.pi * f * x / Fs) plt.plot(x, y) with gr.Blocks() as demo: gr.Plot(value=plt, format="png") demo.launch() matplotlib numpy

Description

Creates a plot component to display various kinds of plots (matplotlib, plotly, altair, or bokeh plots are supported). 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 plot as an PlotData dataclass, which includes the plot information as a JSON string, as well as the type of chart and the plotting library.

Your function should accept one of these types:

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

As output component: Expects plot data in one of these formats: a matplotlib.Figure, bokeh.Model, plotly.Figure, or altair.Chart object.

Your function should return one of these types:

def predict(···) -> Any
	...	
	return value

Initialization

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.Plot`	"plot"	Uses default values

Demos

import pandas as pd import numpy as np import gradio as gr def plot(v, a): g = 9.81 theta = a / 180 * 3.14 tmax = ((2 * v) * np.sin(theta)) / g timemat = tmax * np.linspace(0, 1, 40) x = (v * timemat) * np.cos(theta) y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2)) df = pd.DataFrame({"x": x, "y": y}) return df demo = gr.Blocks() with demo: gr.Markdown( r"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \cdot \frac{\sin(2\theta)}{g}$" ) with gr.Row(): speed = gr.Slider(1, 30, 25, label="Speed") angle = gr.Slider(0, 90, 45, label="Angle") output = gr.LinePlot( x="x", y="y", overlay_point=True, tooltip=["x", "y"], x_lim=[0, 100], y_lim=[0, 60], width=350, height=300, ) btn = gr.Button(value="Run") btn.click(plot, [speed, angle], output) if __name__ == "__main__": demo.launch()

import pandas as pd
import numpy as np

import gradio as gr

def plot(v, a):
    g = 9.81
    theta = a / 180 * 3.14
    tmax = ((2 * v) * np.sin(theta)) / g
    timemat = tmax * np.linspace(0, 1, 40)

    x = (v * timemat) * np.cos(theta)
    y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))
    df = pd.DataFrame({"x": x, "y": y})
    return df

demo = gr.Blocks()

with demo:
    gr.Markdown(
        r"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \cdot \frac{\sin(2\theta)}{g}$"
    )

    with gr.Row():
        speed = gr.Slider(1, 30, 25, label="Speed")
        angle = gr.Slider(0, 90, 45, label="Angle")
    output = gr.LinePlot(
        x="x",
        y="y",
        overlay_point=True,
        tooltip=["x", "y"],
        x_lim=[0, 100],
        y_lim=[0, 60],
        width=350,
        height=300,
    )
    btn = gr.Button(value="Run")
    btn.click(plot, [speed, angle], output)

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

import matplotlib.pyplot as plt import numpy as np import gradio as gr def plot_forecast(final_year, companies, noise, show_legend, point_style): start_year = 2020 x = np.arange(start_year, final_year + 1) year_count = x.shape[0] plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style] fig = plt.figure() ax = fig.add_subplot(111) for i, company in enumerate(companies): series = np.arange(0, year_count, dtype=float) series = series**2 * (i + 1) series += np.random.rand(year_count) * noise ax.plot(x, series, plt_format) if show_legend: plt.legend(companies) return fig demo = gr.Interface( plot_forecast, [ gr.Radio([2025, 2030, 2035, 2040], label="Project to:"), gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="Company Selection"), gr.Slider(1, 100, label="Noise Level"), gr.Checkbox(label="Show Legend"), gr.Dropdown(["cross", "line", "circle"], label="Style"), ], gr.Plot(label="forecast", format="png"), ) if __name__ == "__main__": demo.launch()

import matplotlib.pyplot as plt
import numpy as np

import gradio as gr

def plot_forecast(final_year, companies, noise, show_legend, point_style):
    start_year = 2020
    x = np.arange(start_year, final_year + 1)
    year_count = x.shape[0]
    plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style]
    fig = plt.figure()
    ax = fig.add_subplot(111)
    for i, company in enumerate(companies):
        series = np.arange(0, year_count, dtype=float)
        series = series**2 * (i + 1)
        series += np.random.rand(year_count) * noise
        ax.plot(x, series, plt_format)
    if show_legend:
        plt.legend(companies)
    return fig

demo = gr.Interface(
    plot_forecast,
    [
        gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
        gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="Company Selection"),
        gr.Slider(1, 100, label="Noise Level"),
        gr.Checkbox(label="Show Legend"),
        gr.Dropdown(["cross", "line", "circle"], label="Style"),
    ],
    gr.Plot(label="forecast", format="png"),
)

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 Plot component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.

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

Event Parameters

Guides

Plot Component For Maps

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