# The `Interface` class

As mentioned in the [Quickstart](/main/guides/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:

$code_hello_world_4


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.

In this Guide, we'll dive into `gr.Interface` and the various ways it can be customized, but before we do that, let's get a better understanding of Gradio components.

## Gradio Components

Gradio includes more than 30 pre-built components (as well as many [community-built _custom components_](https://www.gradio.app/custom-components/gallery)) that can be used as inputs or outputs in your demo. These components correspond to common data types in machine learning and data science, e.g. the `gr.Image` component is designed to handle input or output images, the `gr.Label` component displays classification labels and probabilities, the `gr.Plot` component displays various kinds of plots, and so on. 

**Static and Interactive Components**

Every component has a _static_ version that is designed to *display* data, and most components also have an _interactive_ version designed to let users input or modify the data. Typically, you don't need to think about this distinction, because when you build a Gradio demo, Gradio automatically figures out whether the component should be static or interactive based on whether it is being used as an input or output. However, you can set this manually using the `interactive` argument that every component supports.

**Preprocessing and Postprocessing**

When a component is used as an input, Gradio automatically handles the _preprocessing_ needed to convert the data from a type sent by the user's browser (such as an uploaded image) to a form that can be accepted by your function (such as a `numpy` array).


Similarly, when a component is used as an output, Gradio automatically handles the _postprocessing_ needed to convert the data from what is returned by your function (such as a list of image paths) to a form that can be displayed in the user's browser (a gallery of images).

## 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 classes for `gr.Textbox` and `gr.Slider` instead of the string shortcuts, you have access to much more customizability through component attributes.

$code_hello_world_2
$demo_hello_world_2

## 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. 

$code_hello_world_3
$demo_hello_world_3

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!

$code_sepia_filter
$demo_sepia_filter

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. 

As mentioned above, Gradio handles the preprocessing and postprocessing to convert images to NumPy arrays and vice versa. You can also control the preprocessing performed 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:

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

You can read more about the built-in Gradio components and how to customize them in the [Gradio docs](https://gradio.app/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](https://gradio.app/docs#components).

$code_calculator
$demo_calculator

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](https://gradio.app/guides/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.

![annotated](https://github.com/gradio-app/gradio/blob/main/guides/assets/annotated.png?raw=true)

Note: if you're using the `Blocks` class, you can insert text, markdown, or HTML anywhere in your application using the `gr.Markdown(...)` or `gr.HTML(...)` components.

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.

```python
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:

$code_interface_with_additional_inputs
$demo_interface_with_additional_inputs