Building Demos
Interface

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ChatInterface

Interface

gradio.Interface(···)

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

fn: Callable

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

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

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 "lazy", then examples are cached (for all users of the app) after their first use (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES environment variable, which should be either "true" or "false". In HuggingFace Spaces, this parameter is True (as long as `fn` and `outputs` are also provided). The default option otherwise is False.

cache_mode: Literal['eager', 'lazy'] | None

default = None

if "lazy", examples are cached after their first use. If "eager", all examples are cached at app launch. If None, will use the GRADIO_CACHE_MODE environment variable if defined, or default to "eager".

examples_per_page: int

default = 10

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

example_labels: list[str] | None

default = None

a list of labels for each example. If provided, the length of this list should be the same as the number of examples, and these labels will be used in the UI instead of rendering the example values.

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. If it is an HTTP(S) link to a downloadable remote file, the content of this file is displayed.

theme: Theme | str | None

default = None

a Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the Hugging Face Hub (e.g. "gradio/monochrome"). If None, will use the Default theme.

flagging_mode: Literal['never'] | Literal['auto'] | Literal['manual'] | 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, along with the generated output. If "manual", both the input and outputs are flagged when the user clicks flag button. This parameter can be set with environmental variable GRADIO_FLAGGING_MODE; 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 flagging_mode 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 = ".gradio/flagged"

path to the the directory where flagged data is stored. If the directory does not exist, it will be created.

flagging_callback: FlaggingCallback | None

default = None

either 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

the 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 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).

css: str | Path | None

default = None

Custom css as a code string or pathlib.Path to a css file. This css will be included in the demo webpage.

js: str | Path | None

default = None

Custom js as a code string or pathlib.Path to a js file. The custom js should be in the form of a single js function. This function will automatically be executed when the page loads. For more flexibility, use the head parameter to insert js inside <script> tags.

head: str | Path | None

default = None

Custom html to insert into the head of the demo webpage, either as a code string or a pathlib.Path to an html file. This can be used to add custom meta tags, multiple scripts, stylesheets, etc. to the page.

additional_inputs: str | Component | list[str | Component] | None

default = None

a single Gradio component, or list of Gradio components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. These components will be rendered in an accordion below the main input components. By default, no additional input components will be displayed.

additional_inputs_accordion: str | Accordion | None

default = None

if a string is provided, this is the label of the `gr.Accordion` to use to contain additional inputs. A `gr.Accordion` object can be provided as well to configure other properties of the container holding the additional inputs. Defaults to a `gr.Accordion(label="Additional Inputs", open=False)`. This parameter is only used if `additional_inputs` is provided.

submit_btn: str | Button

default = "Submit"

the button to use for submitting inputs. Defaults to a `gr.Button("Submit", variant="primary")`. This parameter does not apply if the Interface is output-only, in which case the submit button always displays "Generate". Can be set to a string (which becomes the button label) or a `gr.Button` object (which allows for more customization).

stop_btn: str | Button

default = "Stop"

the button to use for stopping the interface. Defaults to a `gr.Button("Stop", variant="stop", visible=False)`. Can be set to a string (which becomes the button label) or a `gr.Button` object (which allows for more customization).

clear_btn: str | Button | None

default = "Clear"

the button to use for clearing the inputs. Defaults to a `gr.Button("Clear", variant="secondary")`. Can be set to a string (which becomes the button label) or a `gr.Button` object (which allows for more customization). Can be set to None, which hides the button.

delete_cache: tuple[int, int] | None

default = None

a tuple corresponding [frequency, age] both expressed in number of seconds. Every `frequency` seconds, the temporary files created by this Blocks instance will be deleted if more than `age` seconds have passed since the file was created. For example, setting this to (86400, 86400) will delete temporary files every day. The cache will be deleted entirely when the server restarts. If None, no cache deletion will occur.

show_progress: Literal['full', 'minimal', 'hidden']

default = "full"

how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all

fill_width: bool

default = False

whether to horizontally expand to fill container fully. If False, centers and constrains app to a maximum width.

allow_flagging: Literal['never'] | Literal['auto'] | Literal['manual'] | None

default = None

Demos

import gradio as gr def greet(name): return "Hello " + name + "!" demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox") if __name__ == "__main__": demo.launch()

import gradio as gr


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


demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")

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

import gradio as gr def greet(name, intensity): return "Hello, " + name + "!" * intensity demo = gr.Interface( fn=greet, inputs=["text", gr.Slider(value=2, minimum=1, maximum=10, step=1)], outputs=[gr.Textbox(label="greeting", lines=3)], ) if __name__ == "__main__": demo.launch()

import gradio as gr

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

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

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

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

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()

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.

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"))

inline: bool | None

default = None

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

inbrowser: bool

default = False

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

share: bool | None

default = None

whether to create a publicly shareable link for the gradio app. 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. Can be set by environment variable GRADIO_SHARE=True.

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

default = None

If provided, username and password (or list of username-password tuples) required to access app. 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

By default, the gradio app blocks the main thread while the server is running. If set to True, the gradio app will not block and the gradio server will terminate as soon as the script finishes.

show_error: bool

default = False

If True, any errors in the gradio app 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 gradio app (used if inline=True)

width: int | str

default = "100%"

The width in pixels of the iframe element containing the gradio app (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. 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. Can be set by comma separated environment variable GRADIO_ALLOWED_PATHS. These files are generally assumed to be secure and will be displayed in the browser when possible.

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. Can be set by comma separated environment variable GRADIO_BLOCKED_PATHS.

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". A full URL beginning with http:// or https:// can be provided, which will be used as the root path in its entirety. 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".

auth_dependency: Callable[[fastapi.Request], str | None] | None

default = None

A function that takes a FastAPI request and returns a string user ID or None. If the function returns None for a specific request, that user is not authorized to access the app (they will see a 401 Unauthorized response). To be used with external authentication systems like OAuth. Cannot be used with `auth`.

max_file_size: str | int | None

default = None

The maximum file size in bytes that can be uploaded. Can be a string of the form "<value><unit>", where value is any positive integer and unit is one of "b", "kb", "mb", "gb", "tb". If None, no limit is set.

enable_monitoring: bool | None

default = None

Enables traffic monitoring of the app through the /monitoring endpoint. By default is None, which enables this endpoint. If explicitly True, will also print the monitoring URL to the console. If False, will disable monitoring altogether.

strict_cors: bool

default = True

If True, prevents external domains from making requests to a Gradio server running on localhost. If False, allows requests to localhost that originate from localhost but also, crucially, from "null". This parameter should normally be True to prevent CSRF attacks but may need to be False when embedding a *locally-running Gradio app* using web components.

node_server_name: str | None

default = None

node_port: int | None

default = None

ssr_mode: bool | None

default = None

If True, the Gradio app will be rendered using server-side rendering mode, which is typically more performant and provides better SEO, but this requires Node 18+ to be installed on the system. If False, the app will be rendered using client-side rendering mode. If None, will use GRADIO_SSR_MODE environment variable or default to False.

load

gradio.Interface.load(block, ···)

Description

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

block: Block | None

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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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"

queue: bool

default = True

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.

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()` and `.key_up()` events) 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 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.

show_api: bool

default = True

whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.

time_limit: int | None

default = None

stream_every: float

default = 0.5

like_user_message: bool

default = False

from_pipeline

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

Description

Class method that constructs an Interface from a Hugging Face transformers.Pipeline or diffusers.DiffusionPipeline 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()

pipeline: Pipeline | DiffusionPipeline

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()

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(···)

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()

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.

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

Sharing Your App

Interface State

Reactive Interfaces

ChatInterface

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