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To install Gradio from main, run the following command:
pip install https://gradio-builds.s3.amazonaws.com/3ab30eefe0cfd2e9c4a23989381e5c583a92c69d/gradio-4.29.0-py3-none-any.whl*Note: Setting share=True in
launch() will not work.
Image %20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)
gradio.Image(···)Description
Creates an image component that can be used to upload images (as an input) or display images (as an output).
Behavior
As input component: Passes the uploaded image as a numpy.array, PIL.Image or str filepath depending on type. For SVGs, the type parameter is ignored and the filepath of the SVG is returned.
Your function should accept one of these types:
def predict(
value: np.ndarray | PIL.Image.Image | str | None
)
...As output component: Expects a numpy.array, PIL.Image, or str or pathlib.Path filepath to an image which is displayed.
Your function should return one of these types:
def predict(···) -> np.ndarray | PIL.Image.Image | str | Path | None
...
return valueInitialization
| Parameter | Description |
|---|---|
value str | PIL.Image.Image | np.ndarray | None default: None | A PIL Image, numpy array, path or URL for the default value that Image component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component. |
format str default: "webp" | Format to save image if it does not already have a valid format (e.g. if the image is being returned to the frontend as a numpy array or PIL Image). The format should be supported by the PIL library. This parameter has no effect on SVG files. |
height int | str | None default: None | The height of the displayed image, specified in pixels if a number is passed, or in CSS units if a string is passed. |
width int | str | None default: None | The width of the displayed image, specified in pixels if a number is passed, or in CSS units if a string is passed. |
image_mode Literal[('1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F')] default: "RGB" | "RGB" if color, or "L" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning. |
sources list[Literal[('upload', 'webcam', 'clipboard')]] | None default: None | List of sources for the image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "clipboard" allows users to paste an image from the clipboard. If None, defaults to ["upload", "webcam", "clipboard"] if streaming is False, otherwise defaults to ["webcam"]. |
type Literal[('numpy', 'pil', 'filepath')] default: "numpy" | The format the image is converted before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. If the image is SVG, the |
label str | None default: None | The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a |
every float | None default: None | If |
show_label bool | None default: None | if True, will display label. |
show_download_button bool default: True | If True, will display button to download image. |
container bool default: True | If True, will place the component in a container - providing some extra padding around the border. |
scale int | None default: None | relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True. |
min_width int default: 160 | minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first. |
interactive bool | None default: None | if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output. |
visible bool default: True | If False, component will be hidden. |
streaming bool default: False | If True when used in a |
elem_id str | None default: None | An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. |
elem_classes list[str] | str | None default: None | An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. |
render bool default: True | If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later. |
key int | str | None default: None | if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved. |
mirror_webcam bool default: True | If True webcam will be mirrored. Default is True. |
show_share_button bool | None default: None | If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise. |
Shortcuts
| Class | Interface String Shortcut | Initialization |
|---|---|---|
| "image" | Uses default values |
Demos
Event Listeners
Description
Event listeners allow you to capture and respond to user interactions with the UI components you've defined in a Gradio Blocks app. When a user interacts with an element, such as changing a slider value or uploading an image, a function is called.
Supported Event Listeners
The Image component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Arguments table below.
| Listener | Description |
|---|---|
| This listener is triggered when the user clears the Image using the X button for the component. |
| Triggered when the value of the Image changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See |
| This listener is triggered when the user streams the Image. |
| Event listener for when the user selects or deselects the Image. Uses event data gradio.SelectData to carry |
| This listener is triggered when the user uploads a file into the Image. |
Event Arguments
| Parameter | Description |
|---|---|
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 |
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 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 |
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. |
trigger_mode Literal[('once', 'multiple', 'always_last')] | None default: None | If "once" (default for all events except |
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 |
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 to use this event. If fn is None, show_api will automatically be set to False. |