Gradio Interface Docs

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Interface

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

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 \| Component \| list[str \| Component] \| 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 \| Component \| list[str \| Component] \| 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.
`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 \| None default: None	None or an instance of a subclass of FlaggingCallback which will be called when a sample is flagged. If set to None, an instance of gradio.flagging.CSVLogger will be created and logs will be saved to a local CSV file in flagging_dir. Default to None.
`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 set to a string, the endpoint will be exposed in the API docs with the given name. If None, the name of the prediction function will be used as the API endpoint. If False, the endpoint will not be exposed in the API docs and downstream apps (including those that `gr.load` this app) will not be able to use this event.
`allow_duplication` bool default: False	If True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces.
`concurrency_limit` int \| None \| Literal['default'] default: "default"	If set, this this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `.queue()`, which itself is 1 by default).

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(show_api=False)

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(
    fn=greet,
    inputs=["text", "checkbox", gr.Slider(0, 100)],
    outputs=["text", "number"],
)
if __name__ == "__main__":
    demo.launch()

import gradio as gr

title = "gpt2-xl"

examples = [
    ["The tower is 324 metres (1,063 ft) tall,"],
    ["The Moon's orbit around Earth has"],
    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
]

demo = gr.load(
    "huggingface/gpt2-xl",
    inputs=gr.Textbox(lines=5, max_lines=6, label="Input Text"),
    title=title,
    examples=examples,
)

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

Methods

launch

gradio.Interface.launch(···)

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. <br>

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.
`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).
`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.
`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)
`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.
`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"`
`state_session_capacity` int default: 10000	The maximum number of sessions whose information to store in memory. If the number of sessions exceeds this number, the oldest sessions will be removed. Reduce capacity to reduce memory usage when using gradio.State or returning updated components from functions. Defaults to 10000.
`share_server_address` str \| None default: None	Use this to specify a custom FRP server and port for sharing Gradio apps (only applies if share=True). If not provided, will use the default FRP server at https://gradio.live. See https://github.com/huggingface/frp for more information.
`share_server_protocol` Literal[('http', 'https')] \| None default: None	Use this to specify the protocol to use for the share links. Defaults to "https", unless a custom share_server_address is provided, in which case it defaults to "http". If you are using a custom share_server_address and want to use https, you must set this to "https".

load

gradio.Interface.load(block, ···)

Description

This listener is triggered when the Interface initially loads in the browser.

Agruments

Parameter	Description
`block` Block \| None required
`fn` Callable \| None \| Literal['decorator'] default: "decorator"	the function to call when this event is triggered. 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` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None \| Literal[False] default: None	defines how the endpoint appears in the API docs. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. If False, the endpoint will not be exposed in the API docs and downstream apps (including those that `gr.load` this app) will not be able to use this event.
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal[('full', 'minimal', 'hidden')] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`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)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
`trigger_mode` Literal[('once', 'multiple', 'always_last')] \| None default: None	If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` event) would allow a second submission after the pending event is complete.
`js` str \| None default: None	Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
`concurrency_limit` int \| None \| Literal['default'] default: "default"	If set, this this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
`concurrency_id` str \| None default: None	If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.

from_pipeline

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

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

Parameter	Description
`pipeline` Pipeline required	the pipeline object to use.

pipeline

Pipeline

required

the pipeline object to use.

integrate

gradio.Interface.integrate(···)

Description

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

Agruments

Parameter Description

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

comet_ml

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(···)

Description

By enabling the queue you can control when 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
`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.
`api_open` bool \| None default: None	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.
`concurrency_count` int \| None default: None	Deprecated and has no effect. Set the concurrency_limit directly on event listeners e.g. btn.click(fn, ..., concurrency_limit=10) or gr.Interface(concurrency_limit=10). If necessary, the total number of workers can be configured via `max_threads` in launch().
`default_concurrency_limit` int \| None \| Literal['not_set'] default: "not_set"	The default value of `concurrency_limit` to use for event listeners that don't specify a value. Can be set by environment variable GRADIO_DEFAULT_CONCURRENCY_LIMIT. Defaults to 1 if not set otherwise.

Guides

Quickstart

Key Features

Sharing Your App

Interface State

Reactive Interfaces

Advanced Interface Features

ChatInterface →

Interface

Description
Example Usage
Initialization
Demos
Methods
launch
load
from_pipeline
integrate
queue
Guides