Gradio logo

New to Gradio? Start here: Getting Started

See the Release History

Interface

gradio.Interface(fn, inputs, outputs, Β·Β·Β·)

Description

Interface is Gradio's main high-level class, and allows you to create a web-based GUI / demo around a machine learning model (or any Python function) in a few lines of code. You must specify three parameters: (1) the function to create a GUI for (2) the desired input components and (3) the desired output components. Additional parameters can be used to control the appearance and behavior of the demo.

Example Usage

import gradio as gr

def image_classifier(inp):
    return {'cat': 0.3, 'dog': 0.7}

demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
demo.launch()

Initialization

Parameter Description
fn

Callable

required

the function to wrap an interface around. 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

str | IOComponent | list[str | IOComponent] | None

required

a single Gradio component, or list of Gradio components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of input components should match the number of parameters in fn. If set to None, then only the output components will be displayed.

outputs

str | IOComponent | list[str | IOComponent] | None

required

a single Gradio component, or list of Gradio components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of output components should match the number of values returned by fn. If set to None, then only the input components will be displayed.

examples

list[Any] | list[list[Any]] | str | None

default: None

sample inputs for the function; if provided, appear below the UI components and can be clicked to populate the interface. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided, but it should be within the directory with the python file running the gradio app. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs.

cache_examples

bool | None

default: None

If True, caches examples in the server for fast runtime in examples. If `fn` is a generator function, then the last yielded value will be used as the output. The default option in HuggingFace Spaces is True. The default option elsewhere is False.

examples_per_page

int

default: 10

If examples are provided, how many to display per page.

live

bool

default: False

whether the interface should automatically rerun if any of the inputs change.

interpretation

Callable | str | None

default: None

function that provides interpretation explaining prediction output. Pass "default" to use simple built-in interpreter, "shap" to use a built-in shapley-based interpreter, or your own custom interpretation function. For more information on the different interpretation methods, see the Advanced Interface Features guide.

num_shap

float

default: 2.0

a multiplier that determines how many examples are computed for shap-based interpretation. Increasing this value will increase shap runtime, but improve results. Only applies if interpretation is "shap".

title

str | None

default: None

a title for the interface; if provided, appears above the input and output components in large font. Also used as the tab title when opened in a browser window.

description

str | None

default: None

a description for the interface; if provided, appears above the input and output components and beneath the title in regular font. Accepts Markdown and HTML content.

article

str | None

default: None

an expanded article explaining the interface; if provided, appears below the input and output components in regular font. Accepts Markdown and HTML content.

thumbnail

str | None

default: None

path or url to image to use as display image when the web demo is shared on social media.

theme

Theme | str | None

default: None

Theme to use, loaded from gradio.themes.

css

str | None

default: None

custom css or path to custom css file to use with interface.

allow_flagging

str | None

default: None

one of "never", "auto", or "manual". If "never" or "auto", users will not see a button to flag an input and output. If "manual", users will see a button to flag. If "auto", every input the user submits will be automatically flagged (outputs are not flagged). If "manual", both the input and outputs are flagged when the user clicks flag button. This parameter can be set with environmental variable GRADIO_ALLOW_FLAGGING; otherwise defaults to "manual".

flagging_options

list[str] | list[tuple[str, str]] | None

default: None

if provided, allows user to select from the list of options when flagging. Only applies if allow_flagging is "manual". Can either be a list of tuples of the form (label, value), where label is the string that will be displayed on the button and value is the string that will be stored in the flagging CSV; or it can be a list of strings ["X", "Y"], in which case the values will be the list of strings and the labels will ["Flag as X", "Flag as Y"], etc.

flagging_dir

str

default: "flagged"

what to name the directory where flagged data is stored.

flagging_callback

FlaggingCallback

default: CSVLogger()

An instance of a subclass of FlaggingCallback which will be called when a sample is flagged. By default logs to a local CSV file.

analytics_enabled

bool | None

default: None

Whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable if defined, or default to True.

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)

api_name

str | Literal[False] | None

default: "predict"

Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False or None, the endpoint will not be exposed in the api docs. If set to a string, the endpoint will be exposed in the api docs with the given name. Default value is "predict".

allow_duplication

bool

default: False

If True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces.

Demos

import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
    
if __name__ == "__main__":
    demo.launch()   

Methods

launch

gradio.Interface.launch(Β·Β·Β·)

Description

Launches a simple web server that serves the demo. Can also be used to create a public link used by anyone to access the demo from their browser by setting share=True.

Example Usage

import gradio as gr
def reverse(text):
    return text[::-1]
demo = gr.Interface(reverse, "text", "text")
demo.launch(share=True, auth=("username", "password"))

Agruments

Parameter Description
inline

bool | None

default: None

whether to display in the interface inline in an iframe. Defaults to True in python notebooks; False otherwise.

inbrowser

bool

default: False

whether to automatically launch the interface in a new tab on the default browser.

share

bool | None

default: None

whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. If not provided, it is set to False by default every time, except when running in Google Colab. When localhost is not accessible (e.g. Google Colab), setting share=False is not supported.

debug

bool

default: False

if True, blocks the main thread from running. If running in Google Colab, this is needed to print the errors in the cell output.

enable_queue

bool | None

default: None

DEPRECATED (use .queue() method instead.) if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. The default option in HuggingFace Spaces is True. The default option elsewhere is False.

max_threads

int

default: 40

the maximum number of total threads that the Gradio app can generate in parallel. The default is inherited from the starlette library (currently 40). Applies whether the queue is enabled or not. But if queuing is enabled, this parameter is increaseed to be at least the concurrency_count of the queue.

auth

Callable | tuple[str, str] | list[tuple[str, str]] | None

default: None

If provided, username and password (or list of username-password tuples) required to access interface. Can also provide function that takes username and password and returns True if valid login.

auth_message

str | None

default: None

If provided, HTML message provided on login page.

prevent_thread_lock

bool

default: False

If True, the interface will block the main thread while the server is running.

show_error

bool

default: False

If True, any errors in the interface will be displayed in an alert modal and printed in the browser console log

server_name

str | None

default: None

to make app accessible on local network, set this to "0.0.0.0". Can be set by environment variable GRADIO_SERVER_NAME. If None, will use "127.0.0.1".

server_port

int | None

default: None

will start gradio app on this port (if available). Can be set by environment variable GRADIO_SERVER_PORT. If None, will search for an available port starting at 7860.

show_tips

bool

default: False

if True, will occasionally show tips about new Gradio features

height

int

default: 500

The height in pixels of the iframe element containing the interface (used if inline=True)

width

int | str

default: "100%"

The width in pixels of the iframe element containing the interface (used if inline=True)

encrypt

bool | None

default: None

DEPRECATED. Has no effect.

favicon_path

str | None

default: None

If a path to a file (.png, .gif, or .ico) is provided, it will be used as the favicon for the web page.

ssl_keyfile

str | None

default: None

If a path to a file is provided, will use this as the private key file to create a local server running on https.

ssl_certfile

str | None

default: None

If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.

ssl_keyfile_password

str | None

default: None

If a password is provided, will use this with the ssl certificate for https.

ssl_verify

bool

default: True

If False, skips certificate validation which allows self-signed certificates to be used.

quiet

bool

default: False

If True, suppresses most print statements.

show_api

bool

default: True

If True, shows the api docs in the footer of the app. Default True.

file_directories

list[str] | None

default: None

This parameter has been renamed to `allowed_paths`. It will be removed in a future version.

allowed_paths

list[str] | None

default: None

List of complete filepaths or parent directories that gradio is allowed to serve (in addition to the directory containing the gradio python file). Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app.

blocked_paths

list[str] | None

default: None

List of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default.

root_path

str | None

default: None

The root path (or "mount point") of the application, if it's not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy that forwards requests to the application. For example, if the application is served at "https://example.com/myapp", the `root_path` should be set to "/myapp". Can be set by environment variable GRADIO_ROOT_PATH. Defaults to "".

app_kwargs

dict[str, Any] | None

default: None

Additional keyword arguments to pass to the underlying FastAPI app as a dictionary of parameter keys and argument values. For example, `{"docs_url": "/docs"}`

load

gradio.Interface.load(name, Β·Β·Β·)

Description

Warning: this method will be deprecated. Use the equivalent `gradio.load()` instead. This is a class method that constructs a Blocks from a Hugging Face repo. Can accept model repos (if src is "models") or Space repos (if src is "spaces"). The input and output components are automatically loaded from the repo.

Agruments

Parameter Description
name

str

required

the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")

src

str | None

default: None

the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)

api_key

str | None

default: None

optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens. Warning: only provide this if you are loading a trusted private Space as it can be read by the Space you are loading.

alias

str | None

default: None

optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)

from_pipeline

gradio.Interface.from_pipeline(pipeline, Β·Β·Β·)

Description

Class method that constructs an Interface from a Hugging Face transformers.Pipeline object. The input and output components are automatically determined from the pipeline.

Example Usage

import gradio as gr
from transformers import pipeline
pipe = pipeline("image-classification")
gr.Interface.from_pipeline(pipe).launch()

Agruments

Parameter Description
pipeline

Pipeline

required

the pipeline object to use.

integrate

gradio.Interface.integrate(Β·Β·Β·)

Description

A catch-all method for integrating with other libraries. This method should be run after launch()

Agruments

Parameter Description
comet_ml

<class 'inspect._empty'>

default: None

If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard

wandb

ModuleType | None

default: None

If the wandb module is provided, will integrate with it and appear on WandB dashboard

mlflow

ModuleType | None

default: None

If the mlflow module is provided, will integrate with the experiment and appear on ML Flow dashboard

queue

gradio.Interface.queue(Β·Β·Β·)

Description

By enabling the queue you can control the rate of processed requests, let users know their position in the queue, and set a limit on maximum number of events allowed.

Example Usage

demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())
demo.queue(max_size=20)
demo.launch()

Agruments

Parameter Description
concurrency_count

int

default: 1

Number of worker threads that will be processing requests from the queue concurrently. Increasing this number will increase the rate at which requests are processed, but will also increase the memory usage of the queue.

status_update_rate

float | Literal['auto']

default: "auto"

If "auto", Queue will send status estimations to all clients whenever a job is finished. Otherwise Queue will send status at regular intervals set by this parameter as the number of seconds.

client_position_to_load_data

int | None

default: None

DEPRECATED. This parameter is deprecated and has no effect.

default_enabled

bool | None

default: None

Deprecated and has no effect.

api_open

bool

default: True

If True, the REST routes of the backend will be open, allowing requests made directly to those endpoints to skip the queue.

max_size

int | None

default: None

The maximum number of events the queue will store at any given moment. If the queue is full, new events will not be added and a user will receive a message saying that the queue is full. If None, the queue size will be unlimited.