Custom Components GalleryNEW


The Interface class

As mentioned in the Quickstart, the gr.Interface class is a high-level abstraction in Gradio that allows you to quickly create a demo for any Python function simply by specifying the input types and the output types. Revisiting our first demo:

import gradio as gr

def greet(name, intensity):
    return "Hello, " + name + "!" * int(intensity)

demo = gr.Interface(
    inputs=["text", "slider"],


We see that the Interface class is initialized with three required parameters:

  • fn: the function to wrap a user interface (UI) around
  • inputs: which Gradio component(s) to use for the input. The number of components should match the number of arguments in your function.
  • outputs: which Gradio component(s) to use for the output. The number of components should match the number of return values from your function.

Let’s take a closer look at these components used to provide input and output.

Components Attributes

We used the default versions of the gr.Textbox and gr.Slider, but what if you want to change how the UI components look or behave?

Let’s say you want to customize the slider to have values from 1 to 10, with a default of 2. And you wanted to customize the output text field — you want it to be larger and have a label.

If you use the actual class for gr.Textbox and gr.Slider instead of using the string shortcut, you have access to much more customizability through component attributes.

import gradio as gr

def greet(name, intensity):
    return "Hello, " + name + "!" * intensity

demo = gr.Interface(
    inputs=["text", gr.Slider(value=2, minimum=1, maximum=10, step=1)],
    outputs=[gr.Textbox(label="greeting", lines=3)],


Multiple Input and Output Components

Suppose you had a more complex function, with multiple outputs as well. In the example below, we define a function that takes a string, boolean, and number, and returns a string and number.

import gradio as gr

def greet(name, is_morning, temperature):
    salutation = "Good morning" if is_morning else "Good evening"
    greeting = f"{salutation} {name}. It is {temperature} degrees today"
    celsius = (temperature - 32) * 5 / 9
    return greeting, round(celsius, 2)

demo = gr.Interface(
    inputs=["text", "checkbox", gr.Slider(0, 100)],
    outputs=["text", "number"],

Just as each component in the inputs list corresponds to one of the parameters of the function, in order, each component in the outputs list corresponds to one of the values returned by the function, in order.

An Image Example

Gradio supports many types of components, such as Image, DataFrame, Video, or Label. Let’s try an image-to-image function to get a feel for these!

import numpy as np
import gradio as gr

def sepia(input_img):
    sepia_filter = np.array([
        [0.393, 0.769, 0.189], 
        [0.349, 0.686, 0.168], 
        [0.272, 0.534, 0.131]
    sepia_img =
    sepia_img /= sepia_img.max()
    return sepia_img

demo = gr.Interface(sepia, gr.Image(), "image")

When using the Image component as input, your function will receive a NumPy array with the shape (height, width, 3), where the last dimension represents the RGB values. We’ll return an image as well in the form of a NumPy array.

You can also set the datatype used by the component with the type= keyword argument. For example, if you wanted your function to take a file path to an image instead of a NumPy array, the input Image component could be written as:

gr.Image(type="filepath", shape=...)

Also note that our input Image component comes with an edit button 🖉, which allows for cropping and zooming into images. Manipulating images in this way can help reveal biases or hidden flaws in a machine learning model!

You can read more about the many components and how to use them in the Gradio docs.

Example Inputs

You can provide example data that a user can easily load into Interface. This can be helpful to demonstrate the types of inputs the model expects, as well as to provide a way to explore your dataset in conjunction with your model. To load example data, you can provide a nested list to the examples= keyword argument of the Interface constructor. Each sublist within the outer list represents a data sample, and each element within the sublist represents an input for each input component. The format of example data for each component is specified in the Docs.

import gradio as gr
#from foo import BAR
def calculator(num1, operation, num2):
    if operation == "add":
        return num1 + num2
    elif operation == "subtract":
        return num1 - num2
    elif operation == "multiply":
        return num1 * num2
    elif operation == "divide":
        if num2 == 0:
            raise gr.Error("Cannot divide by zero!")
        return num1 / num2

demo = gr.Interface(
        gr.Radio(["add", "subtract", "multiply", "divide"]),
        [45, "add", 3],
        [3.14, "divide", 2],
        [144, "multiply", 2.5],
        [0, "subtract", 1.2],
    title="Toy Calculator",
    description="Here's a sample toy calculator. Allows you to calculate things like $2+2=4$",


You can load a large dataset into the examples to browse and interact with the dataset through Gradio. The examples will be automatically paginated (you can configure this through the examples_per_page argument of Interface).

Continue learning about examples in the More On Examples guide.

Descriptive Content

In the previous example, you may have noticed the title= and description= keyword arguments in the Interface constructor that helps users understand your app.

There are three arguments in the Interface constructor to specify where this content should go:

  • title: which accepts text and can display it at the very top of interface, and also becomes the page title.
  • description: which accepts text, markdown or HTML and places it right under the title.
  • article: which also accepts text, markdown or HTML and places it below the interface.


If you’re using the Blocks API instead, you can insert text, markdown, or HTML anywhere using the gr.Markdown(...) or gr.HTML(...) components, with descriptive content inside the Component constructor.

Another useful keyword argument is label=, which is present in every Component. This modifies the label text at the top of each Component. You can also add the info= keyword argument to form elements like Textbox or Radio to provide further information on their usage.

gr.Number(label='Age', info='In years, must be greater than 0')

Additional Inputs within an Accordion

If your prediction function takes many inputs, you may want to hide some of them within a collapsed accordion to avoid cluttering the UI. The Interface class takes an additional_inputs argument which is similar to inputs but any input components included here are not visible by default. The user must click on the accordion to show these components. The additional inputs are passed into the prediction function, in order, after the standard inputs.

You can customize the appearance of the accordion by using the optional additional_inputs_accordion argument, which accepts a string (in which case, it becomes the label of the accordion), or an instance of the gr.Accordion() class (e.g. this lets you control whether the accordion is open or closed by default).

Here’s an example:

import gradio as gr

def generate_fake_image(prompt, seed, initial_image=None):
    return f"Used seed: {seed}", ""

demo = gr.Interface(
    outputs=["textbox", "image"],
        gr.Slider(0, 1000),



By default, an Interface will have “Flag” button. When a user testing your Interface sees input with interesting output, such as erroneous or unexpected model behaviour, they can flag the input for you to review. Within the directory provided by the flagging_dir= argument to the Interface constructor, a CSV file will log the flagged inputs. If the interface involves file data, such as for Image and Audio components, folders will be created to store those flagged data as well.

For example, with the calculator interface shown above, we would have the flagged data stored in the flagged directory shown below:

+-- flagged/
|   +-- logs.csv



With the sepia interface shown earlier, we would have the flagged data stored in the flagged directory shown below:

+-- flagged/
|   +-- logs.csv
|   +-- im/
|   |   +-- 0.png
|   |   +-- 1.png
|   +-- Output/
|   |   +-- 0.png
|   |   +-- 1.png



If you wish for the user to provide a reason for flagging, you can pass a list of strings to the flagging_options argument of Interface. Users will have to select one of the strings when flagging, which will be saved as an additional column to the CSV.