Gradio Components: The Key Concepts

In this section, we discuss a few important concepts when it comes to components in Gradio. It's important to understand these concepts when developing your own component. Otherwise, your component may behave very different to other Gradio components!

Tip: You can skip this section if you are familiar with the internals of the Gradio library, such as each component's preprocess and postprocess methods.

Interactive vs Static

Every component in Gradio comes in a static variant, and most come in an interactive version as well. The static version is used when a component is displaying a value, and the user can NOT change that value by interacting with it. The interactive version is used when the user is able to change the value by interacting with the Gradio UI.

Let's see some examples:

import gradio as gr

with gr.Blocks() as demo:
   gr.Textbox(value="Hello", interactive=True)
   gr.Textbox(value="Hello", interactive=False)

demo.launch()

This will display two textboxes. The only difference: you'll be able to edit the value of the Gradio component on top, and you won't be able to edit the variant on the bottom (i.e. the textbox will be disabled).

Perhaps a more interesting example is with the Image component:

import gradio as gr

with gr.Blocks() as demo:
   gr.Image(interactive=True)
   gr.Image(interactive=False)

demo.launch()

The interactive version of the component is much more complex -- you can upload images or snap a picture from your webcam -- while the static version can only be used to display images.

Not every component has a distinct interactive version. For example, the gr.AnnotatedImage only appears as a static version since there's no way to interactively change the value of the annotations or the image.

What you need to remember

Gradio will use the interactive version (if available) of a component if that component is used as the input to any event; otherwise, the static version will be used.
When you design custom components, you must accept the boolean interactive keyword in the constructor of your Python class. In the frontend, you may accept the interactive property, a bool which represents whether the component should be static or interactive. If you do not use this property in the frontend, the component will appear the same in interactive or static mode.

The value and how it is preprocessed/postprocessed

The most important attribute of a component is its value. Every component has a value. The value that is typically set by the user in the frontend (if the component is interactive) or displayed to the user (if it is static). It is also this value that is sent to the backend function when a user triggers an event, or returned by the user's function e.g. at the end of a prediction.

So this value is passed around quite a bit, but sometimes the format of the value needs to change between the frontend and backend. Take a look at this example:

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 = input_img.dot(sepia_filter.T)
    sepia_img /= sepia_img.max()
    return sepia_img

demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
demo.launch()

This will create a Gradio app which has an Image component as the input and the output. In the frontend, the Image component will actually upload the file to the server and send the filepath but this is converted to a numpy array before it is sent to a user's function. Conversely, when the user returns a numpy array from their function, the numpy array is converted to a file so that it can be sent to the frontend and displayed by the Image component.

Tip: By default, the Image component sends numpy arrays to the python function because it is a common choice for machine learning engineers, though the Image component also supports other formats using the type parameter. Read the Image docs here to learn more.

Each component does two conversions:

preprocess: Converts the value from the format sent by the frontend to the format expected by the python function. This usually involves going from a web-friendly JSON structure to a python-native data structure, like a numpy array or PIL image. The Audio, Image components are good examples of preprocess methods.
postprocess: Converts the value returned by the python function to the format expected by the frontend. This usually involves going from a python-native data-structure, like a PIL image to a JSON structure.

What you need to remember

Every component must implement preprocess and postprocess methods. In the rare event that no conversion needs to happen, simply return the value as-is. Textbox and Number are examples of this.
As a component author, YOU control the format of the data displayed in the frontend as well as the format of the data someone using your component will receive. Think of an ergonomic data-structure a python developer will find intuitive, and control the conversion from a Web-friendly JSON data structure (and vice-versa) with preprocess and postprocess.

The "Example Version" of a Component

Gradio apps support providing example inputs -- and these are very useful in helping users get started using your Gradio app. In gr.Interface, you can provide examples using the examples keyword, and in Blocks, you can provide examples using the special gr.Examples component.

At the bottom of this screenshot, we show a miniature example image of a cheetah that, when clicked, will populate the same image in the input Image component:

To enable the example view, you must have the following two files in the top of the frontend directory:

Example.svelte: this corresponds to the "example version" of your component
Index.svelte: this corresponds to the "regular version"

In the backend, you typically don't need to do anything. The user-provided example value is processed using the same .postprocess() method described earlier. If you'd like to do process the data differently (for example, if the .postprocess() method is computationally expensive), then you can write your own .process_example() method for your custom component, which will be used instead.

The Example.svelte file and process_example() method will be covered in greater depth in the dedicated frontend and backend guides respectively.

What you need to remember

If you expect your component to be used as input, it is important to define an "Example" view.
If you don't, Gradio will use a default one but it won't be as informative as it can be!

Conclusion

Now that you know the most important pieces to remember about Gradio components, you can start to design and build your own!