New to Gradio? Start here: Getting Started
See the Release History
Image
gradio.Image(···)Description
Creates an image component that can be used to upload/draw images (as an input) or display images (as an output).
Behavior
As input: passes the uploaded image as a numpy.array, PIL.Image or str filepath depending on `type` -- unless `tool` is `sketch` AND source is one of `upload` or `webcam`. In these cases, a dict with keys `image` and `mask` is passed, and the format of the corresponding values depends on `type`.
As output: expects a numpy.array, PIL.Image or str or pathlib.Path filepath to an image and displays the image.
Initialization
| Parameter | Description |
|---|---|
value
str | _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. |
shape
tuple[int, int] | None default: None |
(width, height) shape to crop and resize image when passed to function. If None, matches input image size. Pass None for either width or height to only crop and resize the other. |
height
int | None default: None |
Height of the displayed image in pixels. |
width
int | None default: None |
Width of the displayed image in pixels. |
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. |
invert_colors
bool default: False |
whether to invert the image as a preprocessing step. |
source
Literal['upload', 'webcam', 'canvas'] default: "upload" |
Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools. |
tool
Literal['editor', 'select', 'sketch', 'color-sketch'] | None default: None |
Tools used for editing. "editor" allows a full screen editor (and is the default if source is "upload" or "webcam"), "select" provides a cropping and zoom tool, "sketch" allows you to create a binary sketch (and is the default if source="canvas"), and "color-sketch" allows you to created a sketch in different colors. "color-sketch" can be used with source="upload" or "webcam" to allow sketching on an image. "sketch" can also be used with "upload" or "webcam" to create a mask over an image and in that case both the image and mask are passed into the function as a dictionary with keys "image" and "mask" respectively. |
type
Literal['numpy', 'pil', 'filepath'] default: "numpy" |
The format the image is converted to 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. |
label
str | None default: None |
component name in interface. |
every
float | None default: None |
If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute. |
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 width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer. |
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 `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'. |
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. |
mirror_webcam
bool default: True |
If True webcam will be mirrored. Default is True. |
brush_radius
float | None default: None |
Size of the brush for Sketch. Default is None which chooses a sensible default |
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 |
|
"webcam" |
Uses source="webcam", interactive=True |
|
"sketchpad" |
Uses image_mode="L", source="canvas", shape=(28, 28), invert_colors=True, interactive=True |
|
"paint" |
Uses source="canvas", tool="color-sketch", interactive=True |
|
"imagemask" |
Uses source="upload", tool="sketch", interactive=True |
|
"imagepaint" |
Uses source="upload", tool="color-sketch", interactive=True |
|
"pil" |
Uses type="pil" |
Demos
import gradio as gr
import os
def image_mod(image):
return image.rotate(45)
demo = gr.Interface(
image_mod,
gr.Image(type="pil"),
"image",
flagging_options=["blurry", "incorrect", "other"],
examples=[
os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
os.path.join(os.path.dirname(__file__), "images/logo.png"),
os.path.join(os.path.dirname(__file__), "images/tower.jpg"),
],
)
if __name__ == "__main__":
demo.launch()
Methods
change
gradio.Image.change(fn, ···)Description
This listener is triggered when the component's value 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 `.input()` for a listener that is only triggered by user input. This method can be used when this component is in a Gradio Blocks.
Agruments
| Parameter | Description |
|---|---|
fn
Callable | None 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
Component | Sequence[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 | Sequence[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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. |
status_tracker
None default: None |
|
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. |
edit
gradio.Image.edit(fn, ···)Description
This listener is triggered when the user edits the component (e.g. image) using the built-in editor. This method can be used when this component is in a Gradio Blocks.
Agruments
| Parameter | Description |
|---|---|
fn
Callable | None 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
Component | Sequence[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 | Sequence[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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. |
status_tracker
None default: None |
|
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. |
clear
gradio.Image.clear(fn, ···)Description
This listener is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.
Agruments
| Parameter | Description |
|---|---|
fn
Callable | None 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
Component | Sequence[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 | Sequence[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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. |
status_tracker
None default: None |
|
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. |
stream
gradio.Image.stream(fn, ···)Description
This listener is triggered when the user streams the component (e.g. a live webcam component). This method can be used when this component is in a Gradio Blocks.
Agruments
| Parameter | Description |
|---|---|
fn
Callable | None 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
Component | Sequence[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 | Sequence[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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. |
status_tracker
None default: None |
|
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. |
upload
gradio.Image.upload(fn, ···)Description
This listener is triggered when the user uploads a file into the component (e.g. when the user uploads a video into a video component). This method can be used when this component is in a Gradio Blocks.
Agruments
| Parameter | Description |
|---|---|
fn
Callable | None 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
Component | Sequence[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 | Sequence[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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. |
status_tracker
None default: None |
|
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. |
select
gradio.Image.select(fn, ···)Description
Event listener for when the user clicks on a pixel within the image. Uses event data gradio.SelectData to carry `index` to refer to the [x, y] coordinates of the clicked pixel. See EventData documentation on how to use this event data.
Agruments
| Parameter | Description |
|---|---|
fn
Callable | None 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
Component | Sequence[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 | Sequence[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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. |
status_tracker
None default: None |
|
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. |