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A typed dictionary to represent metadata for a message in the Chatbot
component. An instance of this dictionary is used for the `metadata` field in
a ChatMessage when the chat message should be displayed as a thought.
Keys ▼
title: str
The title of the 'thought' message. Only required field.
id: int | str
The ID of the message. Only used for nested thoughts. Nested thoughts can be
nested by setting the parent_id to the id of the parent thought.
parent_id: int | str
The ID of the parent message. Only used for nested thoughts.
log: str
A string message to display next to the thought title in a subdued font.
duration: float
The duration of the message in seconds. Appears next to the thought title in a
subdued font inside a parentheses.
status: Literal['pending', 'done']
if set to `'pending'`, a spinner appears next to the thought title and the
accordion is initialized open. If `status` is `'done'`, the thought accordion
is initialized closed. If `status` is not provided, the thought accordion is
initialized open and no spinner is displayed.
|
MetadataDict
|
https://gradio.app/docs/gradio/chatbot
|
Gradio - Chatbot Docs
|
A typed dictionary to represent an option in a ChatMessage. A list of these
dictionaries is used for the `options` field in a ChatMessage.
Keys ▼
value: str
The value to return when the option is selected.
label: str
The text to display in the option, if different from the value.
|
OptionDict
|
https://gradio.app/docs/gradio/chatbot
|
Gradio - Chatbot Docs
|
The gr.LikeData class is a subclass of gr.EventData that specifically
carries information about the `.like()` event. When gr.LikeData is added as a
type hint to an argument of an event listener method, a gr.LikeData object
will automatically be passed as the value of that argument. The attributes of
this object contains information about the event that triggered the listener.
|
Description
|
https://gradio.app/docs/gradio/likedata
|
Gradio - Likedata Docs
|
import gradio as gr
def test(value, like_data: gr.LikeData):
return {
"chatbot_value": value,
"liked_message": like_data.value,
"liked_index": like_data.index,
"liked_or_disliked_as_bool": like_data.liked
}
with gr.Blocks() as demo:
c = gr.Chatbot([("abc", "def")])
t = gr.JSON()
c.like(test, c, t)
demo.launch()
|
Example Usage
|
https://gradio.app/docs/gradio/likedata
|
Gradio - Likedata Docs
|
Parameters ▼
index: int | tuple[int, int]
The index of the liked/disliked item. Is a tuple if the component is two
dimensional.
value: Any
The value of the liked/disliked item.
liked: bool
True if the item was liked, False if disliked, or string value if any other
feedback.
|
Attributes
|
https://gradio.app/docs/gradio/likedata
|
Gradio - Likedata Docs
|
chatbot_core_components_simple
Open in 🎢 ↗ import gradio as gr import random Chatbot demo with multimodal
input (text, markdown, LaTeX, code blocks, image, audio, video, & model3d).
Plus shows support for streaming text. color_map = { "harmful": "crimson",
"neutral": "gray", "beneficial": "green", } def html_src(harm_level): return
f""" <div style="display: flex; gap: 5px;"> <div style="background-color:
{color_map[harm_level]}; padding: 2px; border-radius: 5px;"> {harm_level}
</div> </div> """ def print_like_dislike(x: gr.LikeData): print(x.index,
x.value, x.liked) def add_message(history, message): for x in
message["files"]: history.append(((x,), None)) if message["text"] is not None:
history.append((message["text"], None)) return history,
gr.MultimodalTextbox(value=None, interactive=False) def bot(history,
response_type): if response_type == "gallery": history[-1][1] = gr.Gallery( [
"https://raw.githubusercontent.com/gradio-
app/gradio/main/test/test_files/bus.png",
"https://raw.githubusercontent.com/gradio-
app/gradio/main/test/test_files/bus.png", ] ) elif response_type == "image":
history[-1][1] = gr.Image( "https://raw.githubusercontent.com/gradio-
app/gradio/main/test/test_files/bus.png" ) elif response_type == "video":
history[-1][1] = gr.Video( "https://github.com/gradio-
app/gradio/raw/main/gradio/media_assets/videos/world.mp4", label="test" ) elif
response_type == "audio": history[-1][1] = gr.Audio(
"https://github.com/gradio-
app/gradio/raw/main/gradio/media_assets/audio/audio_sample.wav" ) elif
response_type == "html": history[-1][1] = gr.HTML(
html_src(random.choice(["harmful", "neutral", "beneficial"])) ) elif
response_type == "model3d": history[-1][1] = gr.Model3D(
"https://github.com/gradio-
app/gradio/raw/main/gradio/media_assets/models3d/Fox.gltf" ) else:
history[-1][1] = "Cool!" return history with gr.Blocks(fill_height=True) as
demo: chatbot = gr.Chatbot( elem_id="chatbot", bubble_full_width=False,
scale=1, ) response_type = gr.Radio( [ "ima
|
Demos
|
https://gradio.app/docs/gradio/likedata
|
Gradio - Likedata Docs
|
) else:
history[-1][1] = "Cool!" return history with gr.Blocks(fill_height=True) as
demo: chatbot = gr.Chatbot( elem_id="chatbot", bubble_full_width=False,
scale=1, ) response_type = gr.Radio( [ "image", "text", "gallery", "video",
"audio", "html", "model3d", ], value="text", label="Response Type", )
chat_input = gr.MultimodalTextbox( interactive=True, placeholder="Enter
message or upload file...", show_label=False, ) chat_msg = chat_input.submit(
add_message, [chatbot, chat_input], [chatbot, chat_input] ) bot_msg =
chat_msg.then( bot, [chatbot, response_type], chatbot, api_name="bot_response"
) bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None,
[chat_input]) chatbot.like(print_like_dislike, None, None) if __name__ ==
"__main__": demo.launch()
import gradio as gr
import random
Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, video, & model3d). Plus shows support for streaming text.
color_map = {
"harmful": "crimson",
"neutral": "gray",
"beneficial": "green",
}
def html_src(harm_level):
return f"""
{harm_level}
"""
def print_like_dislike(x: gr.LikeData):
print(x.index, x.value, x.liked)
def add_message(history, message):
for x in message["files"]:
history.append(((x,), None))
if message["text"] is not None:
history.append((message["text"], None))
return history, gr.MultimodalTextbox(value=None, interactive=False)
def bot(history, response_type):
if response_type == "gallery":
history[-1][1] = gr.Gallery(
[
"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png",
"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png",
]
)
|
Demos
|
https://gradio.app/docs/gradio/likedata
|
Gradio - Likedata Docs
|
sercontent.com/gradio-app/gradio/main/test/test_files/bus.png",
"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png",
]
)
elif response_type == "image":
history[-1][1] = gr.Image(
"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png"
)
elif response_type == "video":
history[-1][1] = gr.Video(
"https://github.com/gradio-app/gradio/raw/main/gradio/media_assets/videos/world.mp4",
label="test"
)
elif response_type == "audio":
history[-1][1] = gr.Audio(
"https://github.com/gradio-app/gradio/raw/main/gradio/media_assets/audio/audio_sample.wav"
)
elif response_type == "html":
history[-1][1] = gr.HTML(
html_src(random.choice(["harmful", "neutral", "beneficial"]))
)
elif response_type == "model3d":
history[-1][1] = gr.Model3D(
"https://github.com/gradio-app/gradio/raw/main/gradio/media_assets/models3d/Fox.gltf"
)
else:
history[-1][1] = "Cool!"
return history
with gr.Blocks(fill_height=True) as demo:
chatbot = gr.Chatbot(
elem_id="chatbot",
bubble_full_width=False,
scale=1,
)
response_type = gr.Radio(
[
"image",
"text",
"gallery",
"video",
"audio",
"html",
"model3d",
],
value="text",
label="Response Type",
)
chat_input = gr.MultimodalTextbox(
interactive=True,
placeholder="Enter message or upload file...",
show_label=False,
)
chat_msg = chat_input.submit(
add_message, [chatbot, ch
|
Demos
|
https://gradio.app/docs/gradio/likedata
|
Gradio - Likedata Docs
|
interactive=True,
placeholder="Enter message or upload file...",
show_label=False,
)
chat_msg = chat_input.submit(
add_message, [chatbot, chat_input], [chatbot, chat_input]
)
bot_msg = chat_msg.then(
bot, [chatbot, response_type], chatbot, api_name="bot_response"
)
bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
chatbot.like(print_like_dislike, None, None)
if __name__ == "__main__":
demo.launch()
|
Demos
|
https://gradio.app/docs/gradio/likedata
|
Gradio - Likedata Docs
|
Creates a set of (string or numeric type) radio buttons of which only one
can be selected.
|
Description
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
**As input component** : Passes the value of the selected radio button as a `str | int | float`, or its index as an `int` into the function, depending on `type`.
Your function should accept one of these types:
def predict(
value: str | int | float | None
)
...
**As output component** : Expects a `str | int | float` corresponding to the value of the radio button to be selected
Your function should return one of these types:
def predict(···) -> str | int | float | None
...
return value
|
Behavior
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
Parameters ▼
choices: list[str | int | float | tuple[str, str | int | float]] | None
default `= None`
A list of string or numeric options to select from. An option can also be a
tuple of the form (name, value), where name is the displayed name of the radio
button and value is the value to be passed to the function, or returned by the
function.
value: str | int | float | Callable | None
default `= None`
The option selected by default. If None, no option is selected by default. If
a function is provided, the function will be called each time the app loads to
set the initial value of this component.
type: Literal['value', 'index']
default `= "value"`
Type of value to be returned by component. "value" returns the string of the
choice selected, "index" returns the index of the choice selected.
label: str | I18nData | None
default `= None`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr.Interface`, the label will be the
name of the parameter this component corresponds to.
info: str | I18nData | None
default `= None`
additional component description, appears below the label in smaller font.
Supports markdown / HTML syntax.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display label.
|
Initialization
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display label.
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, choices in this radio group will be selectable; if False, selection
will be disabled. If not provided, this is inferred based on whether the
component is used as an input or output.
visible: bool | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
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 | tuple[int | str, ...] | None
default `= None`
in a gr.render, Compon
|
Initialization
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
rtl: bool
default `= False`
If True, the radio buttons will be displayed in right-to-left order. Default
is False.
|
Initialization
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.Radio` | "radio" | Uses default values
|
Shortcuts
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
sentence_builderblocks_essay
Open in 🎢 ↗ import gradio as gr def sentence_builder(quantity, animal,
countries, place, activity_list, morning): return f"""The {quantity} {animal}s
from {" and ".join(countries)} went to the {place} where they {" and
".join(activity_list)} until the {"morning" if morning else "night"}""" demo =
gr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label="Count",
info="Choose between 2 and 20"), gr.Dropdown( ["cat", "dog", "bird"],
label="Animal", info="Will add more animals later!" ),
gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where
are they from?"), gr.Radio(["park", "zoo", "road"], label="Location",
info="Where did they go?"), gr.Dropdown( ["ran", "swam", "ate", "slept"],
value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum
dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies
aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl." ),
gr.Checkbox(label="Morning", info="Did they do it in the morning?"), ],
"text", examples=[ [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"],
True], [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False], [10, "bird",
["USA", "Pakistan"], "road", ["ran"], False], [8, "cat", ["Pakistan"], "zoo",
["ate"], True], ] ) if __name__ == "__main__": demo.launch()
import gradio as gr
def sentence_builder(quantity, animal, countries, place, activity_list, morning):
return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
demo = gr.Interface(
sentence_builder,
[
gr.Slider(2, 20, value=4, label="Count", info="Choose between 2 and 20"),
gr.Dropdown(
["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
),
gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="
|
Demos
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
,
gr.Dropdown(
["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
),
gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
gr.Dropdown(
["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
),
gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
],
"text",
examples=[
[2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
[4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
[10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
[8, "cat", ["Pakistan"], "zoo", ["ate"], True],
]
)
if __name__ == "__main__":
demo.launch()
Open in 🎢 ↗ import gradio as gr countries_cities_dict = { "USA": ["New York",
"Los Angeles", "Chicago"], "Canada": ["Toronto", "Montreal", "Vancouver"],
"Pakistan": ["Karachi", "Lahore", "Islamabad"], } def change_textbox(choice):
if choice == "short": return gr.Textbox(lines=2, visible=True),
gr.Button(interactive=True) elif choice == "long": return gr.Textbox(lines=8,
visible=True, value="Lorem ipsum dolor sit amet"), gr.Button(interactive=True)
else: return gr.Textbox(visible=False), gr.Button(interactive=False) with
gr.Blocks() as demo: radio = gr.Radio( ["short", "long", "none"], label="What
kind of essay would you like to write?" ) text = gr.Textbox(lines=2,
interactive=True, show_copy_button=True) with gr.Row(): num =
gr.Number(minimum=0, maximum=100, label="input") out =
gr.Number(label="output") minimum_slider = gr.Slider(0, 100, 0, label="min"
|
Demos
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
ines=2,
interactive=True, show_copy_button=True) with gr.Row(): num =
gr.Number(minimum=0, maximum=100, label="input") out =
gr.Number(label="output") minimum_slider = gr.Slider(0, 100, 0, label="min")
maximum_slider = gr.Slider(0, 100, 100, label="max") submit_btn =
gr.Button("Submit", variant="primary") with gr.Row(): country =
gr.Dropdown(list(countries_cities_dict.keys()), label="Country") cities =
gr.Dropdown([], label="Cities") @country.change(inputs=country,
outputs=cities) def update_cities(country): cities =
list(countries_cities_dict[country]) return gr.Dropdown(choices=cities,
value=cities[0], interactive=True) def reset_bounds(minimum, maximum): return
gr.Number(minimum=minimum, maximum=maximum) radio.change(fn=change_textbox,
inputs=radio, outputs=[text, submit_btn]) gr.on( [minimum_slider.change,
maximum_slider.change], reset_bounds, [minimum_slider, maximum_slider],
outputs=num, ) num.submit(lambda x: x, num, out) if __name__ == "__main__":
demo.launch()
import gradio as gr
countries_cities_dict = {
"USA": ["New York", "Los Angeles", "Chicago"],
"Canada": ["Toronto", "Montreal", "Vancouver"],
"Pakistan": ["Karachi", "Lahore", "Islamabad"],
}
def change_textbox(choice):
if choice == "short":
return gr.Textbox(lines=2, visible=True), gr.Button(interactive=True)
elif choice == "long":
return gr.Textbox(lines=8, visible=True, value="Lorem ipsum dolor sit amet"), gr.Button(interactive=True)
else:
return gr.Textbox(visible=False), gr.Button(interactive=False)
with gr.Blocks() as demo:
radio = gr.Radio(
["short", "long", "none"], label="What kind of essay would you like to write?"
)
text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)
with gr.Row():
num = gr.Number(minimum=0, maximum=100, label="input")
out = gr.Number(label="output")
m
|
Demos
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
x(lines=2, interactive=True, show_copy_button=True)
with gr.Row():
num = gr.Number(minimum=0, maximum=100, label="input")
out = gr.Number(label="output")
minimum_slider = gr.Slider(0, 100, 0, label="min")
maximum_slider = gr.Slider(0, 100, 100, label="max")
submit_btn = gr.Button("Submit", variant="primary")
with gr.Row():
country = gr.Dropdown(list(countries_cities_dict.keys()), label="Country")
cities = gr.Dropdown([], label="Cities")
@country.change(inputs=country, outputs=cities)
def update_cities(country):
cities = list(countries_cities_dict[country])
return gr.Dropdown(choices=cities, value=cities[0], interactive=True)
def reset_bounds(minimum, maximum):
return gr.Number(minimum=minimum, maximum=maximum)
radio.change(fn=change_textbox, inputs=radio, outputs=[text, submit_btn])
gr.on(
[minimum_slider.change, maximum_slider.change],
reset_bounds,
[minimum_slider, maximum_slider],
outputs=num,
)
num.submit(lambda x: x, num, out)
if __name__ == "__main__":
demo.launch()
|
Demos
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
Description
Event listeners allow you to 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 Radio component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`Radio.select(fn, ···)` | Event listener for when the user selects or deselects the Radio. Uses event data gradio.SelectData to carry `value` referring to the label of the Radio, and `selected` to refer to state of the Radio. See EventData documentation on how to use this event data
`Radio.change(fn, ···)` | Triggered when the value of the Radio 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.
`Radio.input(fn, ···)` | This listener is triggered when the user changes the value of the Radio.
Event Parameters
Parameters ▼
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`
Li
|
Event Listeners
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
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.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
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
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
If True, will place the request on the queue, if the queue has been enabled.
If False,
|
Event Listeners
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
will show the progress animation on all of the output components.
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 ar
|
Event Listeners
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
'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 | Literal[True] | 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`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-rende
|
Event Listeners
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
|
Event Listeners
|
https://gradio.app/docs/gradio/radio
|
Gradio - Radio Docs
|
Button that clears the value of a component or a list of components when
clicked. It is instantiated with the list of components to clear.
|
Description
|
https://gradio.app/docs/gradio/clearbutton
|
Gradio - Clearbutton Docs
|
**As input component** : (Rarely used) the `str` corresponding to the
button label when the button is clicked
Your function should accept one of these types:
def predict(
value: str | None
)
...
**As output component** : string corresponding to the button label
Your function should return one of these types:
def predict(···) -> str | None
...
return value
|
Behavior
|
https://gradio.app/docs/gradio/clearbutton
|
Gradio - Clearbutton Docs
|
Parameters ▼
components: None | list[Component] | Component
default `= None`
value: str
default `= "Clear"`
default text for the button to display. If a function is provided, the
function will be called each time the app loads to set the initial value of
this component.
every: Timer | float | None
default `= None`
continuously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
variant: Literal['primary', 'secondary', 'stop']
default `= "secondary"`
sets the background and text color of the button. Use 'primary' for main call-
to-action buttons, 'secondary' for a more subdued style, 'stop' for a stop
button, 'huggingface' for a black background with white text, consistent with
Hugging Face's button styles.
size: Literal['sm', 'md', 'lg']
default `= "lg"`
size of the button. Can be "sm", "md", or "lg".
icon: str | Path | None
default `= None`
URL or path to the icon file to display within the button. If None, no icon
will be displayed.
link: str | None
default `= None`
URL to open when the button is clicked. If None, no link will be used.
visible: bool | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
interactive: bool
default `= True`
if False, the Button will be in a disabled state.
elem_id: str | None
default `= None`
an optional string that is assigned as the id o
|
Initialization
|
https://gradio.app/docs/gradio/clearbutton
|
Gradio - Clearbutton Docs
|
interactive: bool
default `= True`
if False, the Button will be in a disabled state.
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 | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
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 | None
default `= None`
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.
api_name: str
|
Initialization
|
https://gradio.app/docs/gradio/clearbutton
|
Gradio - Clearbutton Docs
|
ent 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.
api_name: str | None | Literal['False']
default `= None`
show_api: bool
default `= False`
|
Initialization
|
https://gradio.app/docs/gradio/clearbutton
|
Gradio - Clearbutton Docs
|
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.ClearButton` | "clearbutton" | Uses default values
|
Shortcuts
|
https://gradio.app/docs/gradio/clearbutton
|
Gradio - Clearbutton Docs
|
Description
Event listeners allow you to 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 ClearButton component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`ClearButton.add(fn, ···)` | Adds a component or list of components to the list of components that will be cleared when the button is clicked.
`ClearButton.click(fn, ···)` | Triggered when the Button is clicked.
Event Parameters
Parameters ▼
components: None | Component | list[Component]
|
Event Listeners
|
https://gradio.app/docs/gradio/clearbutton
|
Gradio - Clearbutton Docs
|
The gr.DownloadData class is a subclass of gr.EventData that specifically
carries information about the `.download()` event. When gr.DownloadData is
added as a type hint to an argument of an event listener method, a
gr.DownloadData object will automatically be passed as the value of that
argument. The attributes of this object contains information about the event
that triggered the listener.
|
Description
|
https://gradio.app/docs/gradio/downloaddata
|
Gradio - Downloaddata Docs
|
import gradio as gr
def on_download(download_data: gr.DownloadData):
return f"Downloaded file: {download_data.file.path}"
with gr.Blocks() as demo:
files = gr.File()
textbox = gr.Textbox()
files.download(on_download, None, textbox)
demo.launch()
|
Example Usage
|
https://gradio.app/docs/gradio/downloaddata
|
Gradio - Downloaddata Docs
|
Parameters ▼
file: FileData
The file that was downloaded, as a FileData object.
|
Attributes
|
https://gradio.app/docs/gradio/downloaddata
|
Gradio - Downloaddata Docs
|
Component to select a date and (optionally) a time.
|
Description
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
**As input component** : Passes text value as a `str` into the function.
Your function should accept one of these types:
def predict(
value: float | datetime | str | None
)
...
**As output component** : Expects a tuple pair of datetimes.
Your function should return one of these types:
def predict(···) -> float | datetime | str | None
...
return value
|
Behavior
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
Parameters ▼
value: float | str | datetime | None
default `= None`
default value for datetime.
include_time: bool
default `= True`
If True, the component will include time selection. If False, only date
selection will be available.
type: Literal['timestamp', 'datetime', 'string']
default `= "timestamp"`
The type of the value. Can be "timestamp", "datetime", or "string". If
"timestamp", the value will be a number representing the start and end date in
seconds since epoch. If "datetime", the value will be a datetime object. If
"string", the value will be the date entered by the user.
timezone: str | None
default `= None`
The timezone to use for timestamps, such as "US/Pacific" or "Europe/Paris". If
None, the timezone will be the local timezone.
label: str | I18nData | None
default `= None`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr.Interface`, the label will be the
name of the parameter this component corresponds to.
show_label: bool | None
default `= None`
if True, will display label.
info: str | I18nData | None
default `= None`
additional component description, appears below the label in smaller font.
Supports markdown / HTML syntax.
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. The event can be accessed
(e.g. to cancel it) via this component's .load_event attribute.
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 B
|
Initialization
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
nents. 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.
visible: bool | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
interactive: bool | None
default `= None`
elem_id: str | None
default `= None`
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 | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
|
Initialization
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
y have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
|
Initialization
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.DateTime` | "datetime" | Uses default values
|
Shortcuts
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
Description
Event listeners allow you to 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 DateTime component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`DateTime.change(fn, ···)` | Triggered when the value of the DateTime 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.
`DateTime.submit(fn, ···)` | This listener is triggered when the user presses the Enter key while the DateTime is focused.
Event Parameters
Parameters ▼
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, Non
|
Event Listeners
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
nction 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.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
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
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
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 i
|
Event Listeners
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
eue 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 | Literal[True] | None
default `= None`
Optional frontend js meth
|
Event Listeners
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | 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`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes succe
|
Event Listeners
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
tor: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
|
Event Listeners
|
https://gradio.app/docs/gradio/datetime
|
Gradio - Datetime Docs
|
Creates an image component that can be used to upload images (as an input)
or display images (as an output).
|
Description
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
**As input component** : Passes the uploaded image as a tuple of
`numpy.array`, `PIL.Image` or `str` filepath depending on `type`.
Your function should accept one of these types:
def predict(
value: tuple[
str | PIL.Image.Image | np.ndarray | None, str | PIL.Image.Image | np.ndarray | None
]
)
...
**As output component** : Expects a tuple of `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 value
|
Behavior
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
Parameters ▼
value: image_tuple | Callable | None
default `= None`
A tuple of PIL Image, numpy array, path or URL for the default value that
ImageSlider component is going to take, this pair of images should be of equal
size. If a function is provided, the function will be called each time the app
loads to set the initial value of this component.
format: str
default `= "webp"`
File format (e.g. "png" or "gif"). Used 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.
Applies both when this component is used as an input or output. This parameter
has no effect on SVG files.
height: int | str | None
default `= None`
The height of the component, specified in pixels if a number is passed, or in
CSS units if a string is passed. This has no effect on the preprocessed tuple
of image file or numpy array, but will affect the displayed image.
width: int | str | None
default `= None`
The width of the component, specified in pixels if a number is passed, or in
CSS units if a string is passed. This has no effect on the preprocessed tuple
of image file or numpy array, but will affect the displayed image.
image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None
default `= "RGB"`
The pixel format and color depth that the image should be loaded and
preprocessed as. "RGB" will load the image as a color image, or "L" as black-
and-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html
for other supported image modes and their meaning. This parameter has no
effect on SVG or GIF files. If set to None, the image_mode will be inferred
from the image file types (e.g. "RGBA" for a .png image, "RGB" in most other
cases).
type: Literal['numpy', 'pil', 'filepath']
default `= "numpy"`
The for
|
Initialization
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
image_mode will be inferred
from the image file types (e.g. "RGBA" for a .png image, "RGB" in most other
cases).
type: Literal['numpy', 'pil', 'filepath']
default `= "numpy"`
The format the images are converted to before being passed into the prediction
function. "numpy" converts the images to numpy arrays with shape (height,
width, 3) and values from 0 to 255, "pil" converts the images to PIL image
objects, "filepath" passes str paths to temporary files containing the images.
To support animated GIFs in input, the `type` should be set to "filepath" or
"pil". To support SVGs, the `type` should be set to "filepath".
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 `gr.Interface`, the label will be the name of the parameter this
component is assigned to.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
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. Only applies if interactive is
False (e.g. if the component is used as an output).
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 Comp
|
Initialization
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
= 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 | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
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 | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
|
Initialization
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
le[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
show_fullscreen_button: bool
default `= True`
If True, will show a fullscreen icon in the corner of the component that
allows user to view the image in fullscreen mode. If False, icon does not
appear.
slider_position: float
default `= 50`
The position of the slider as a percentage of the width of the image, between
0 and 100.
max_height: int
default `= 500`
The maximum height of the image.
|
Initialization
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.ImageSlider` | "imageslider" | Uses default values
|
Shortcuts
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
imageslider
Open in 🎢 ↗ import gradio as gr from PIL import ImageFilter def
img_to_slider(im): if not im: return im return (im,
im.filter(filter=ImageFilter.GaussianBlur(radius=10))) def slider_to_self(im):
if not im or not im[0]: return im return (im[0],
im[0].filter(filter=ImageFilter.GaussianBlur(radius=10))) def
slider_to_self_two(im): return im def position_to_slider(pos): return
gr.ImageSlider(slider_position=pos) with gr.Blocks() as demo: gr.Markdown("
img to image slider") with gr.Row(): img1 = gr.Image(label="Blur image",
type="pil") img2 = gr.ImageSlider(label="Blur image", type="pil") btn =
gr.Button("Blur image") btn.click(img_to_slider, inputs=img1, outputs=img2)
gr.Markdown("unified image slider") with gr.Row(): img3 =
gr.ImageSlider(label="Blur image", type="pil") img3.upload(slider_to_self,
inputs=img3, outputs=img3) pos = gr.Slider(label="Position", value=50,
minimum=0, maximum=100, step=0.01) pos.change(position_to_slider, inputs=pos,
outputs=img3, show_progress="hidden") if __name__ == "__main__": demo.launch()
import gradio as gr
from PIL import ImageFilter
def img_to_slider(im):
if not im:
return im
return (im, im.filter(filter=ImageFilter.GaussianBlur(radius=10)))
def slider_to_self(im):
if not im or not im[0]:
return im
return (im[0], im[0].filter(filter=ImageFilter.GaussianBlur(radius=10)))
def slider_to_self_two(im):
return im
def position_to_slider(pos):
return gr.ImageSlider(slider_position=pos)
with gr.Blocks() as demo:
gr.Markdown("img to image slider")
with gr.Row():
img1 = gr.Image(label="Blur image", type="pil")
img2 = gr.ImageSlider(label="Blur image", type="pil")
btn = gr.Button("Blur image")
btn.click(img_to_slider, inputs=img1, outputs=img2)
gr.Markdown("unified image slider")
with gr.Row()
|
Demos
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
r(label="Blur image", type="pil")
btn = gr.Button("Blur image")
btn.click(img_to_slider, inputs=img1, outputs=img2)
gr.Markdown("unified image slider")
with gr.Row():
img3 = gr.ImageSlider(label="Blur image", type="pil")
img3.upload(slider_to_self, inputs=img3, outputs=img3)
pos = gr.Slider(label="Position", value=50, minimum=0, maximum=100, step=0.01)
pos.change(position_to_slider, inputs=pos, outputs=img3, show_progress="hidden")
if __name__ == "__main__":
demo.launch()
|
Demos
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
Description
Event listeners allow you to 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 ImageSlider component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`ImageSlider.clear(fn, ···)` | This listener is triggered when the user clears the ImageSlider using the clear button for the component.
`ImageSlider.change(fn, ···)` | Triggered when the value of the ImageSlider 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.
`ImageSlider.stream(fn, ···)` | This listener is triggered when the user streams the ImageSlider.
`ImageSlider.select(fn, ···)` | Event listener for when the user selects or deselects the ImageSlider. Uses event data gradio.SelectData to carry `value` referring to the label of the ImageSlider, and `selected` to refer to state of the ImageSlider. See EventData documentation on how to use this event data
`ImageSlider.upload(fn, ···)` | This listener is triggered when the user uploads a file into the ImageSlider.
`ImageSlider.input(fn, ···)` | This listener is triggered when the user changes the value of the ImageSlider.
Event Parameters
Parameters ▼
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 out
|
Event Listeners
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
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.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
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, "hi
|
Event Listeners
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
ss 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
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
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. Functio
|
Event Listeners
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
ts 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 | Literal[True] | 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 automati
|
Event Listeners
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
thod 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`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
|
Event Listeners
|
https://gradio.app/docs/gradio/imageslider
|
Gradio - Imageslider Docs
|
Creates a "Sign In" button that redirects the user to sign in with Hugging
Face OAuth. Once the user is signed in, the button will act as a logout
button, and you can retrieve a signed-in user's profile by adding a parameter
of type `gr.OAuthProfile` to any Gradio function. This will only work if this
Gradio app is running in a Hugging Face Space. Permissions for the OAuth app
can be configured in the Spaces README file, as described here:
<https://huggingface.co/docs/hub/en/spaces-oauth.> For local development,
instead of OAuth, the local Hugging Face account that is logged in (via `hf
auth login`) will be available through the `gr.OAuthProfile` object.
|
Description
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
**As input component** : (Rarely used) the `str` corresponding to the
button label when the button is clicked
Your function should accept one of these types:
def predict(
value: str | None
)
...
**As output component** : string corresponding to the button label
Your function should return one of these types:
def predict(···) -> str | None
...
return value
|
Behavior
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
Parameters ▼
value: str
default `= "Sign in with Hugging Face"`
logout_value: str
default `= "Logout ({})"`
The text to display when the user is signed in. The string should contain a
placeholder for the username with a call-to-action to logout, e.g. "Logout
({})".
every: Timer | float | None
default `= None`
inputs: Component | list[Component] | set[Component] | None
default `= None`
variant: Literal['primary', 'secondary', 'stop', 'huggingface']
default `= "huggingface"`
size: Literal['sm', 'md', 'lg']
default `= "lg"`
icon: str | Path | None
default `= "/home/runner/work/gradio/gradio/gradio/icons/huggingface-
logo.svg"`
link: str | None
default `= None`
visible: bool | Literal['hidden']
default `= True`
interactive: bool
default `= True`
elem_id: str | None
default `= None`
elem_classes: list[str] | str | None
default `= None`
render: bool
default `= True`
key: int | str | tuple[int | str, ...] | None
default `= None`
preserved_by_key: list[str] | str | None
default `= "value"`
scale: int | None
default `= None`
min_width: int | None
default `= None`
|
Initialization
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.LoginButton` | "loginbutton" | Uses default values
|
Shortcuts
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
login_with_huggingface
Open in 🎢 ↗ from __future__ import annotations import gradio as gr from huggingface_hub import whoami def hello(profile: gr.OAuthProfile | None) -> str: if profile is None: return "I don't know you." return f"Hello {profile.name}" def list_organizations(oauth_token: gr.OAuthToken | None) -> str: if oauth_token is None: return "Please deploy this on Spaces and log in to list organizations." org_names = [org["name"] for org in whoami(oauth_token.token)["orgs"]] return f"You belong to {', '.join(org_names)}." with gr.Blocks() as demo: gr.LoginButton() m1 = gr.Markdown() m2 = gr.Markdown() demo.load(hello, inputs=None, outputs=m1) demo.load(list_organizations, inputs=None, outputs=m2) if __name__ == "__main__": demo.launch()
from __future__ import annotations
import gradio as gr
from huggingface_hub import whoami
def hello(profile: gr.OAuthProfile | None) -> str:
if profile is None:
return "I don't know you."
return f"Hello {profile.name}"
def list_organizations(oauth_token: gr.OAuthToken | None) -> str:
if oauth_token is None:
return "Please deploy this on Spaces and log in to list organizations."
org_names = [org["name"] for org in whoami(oauth_token.token)["orgs"]]
return f"You belong to {', '.join(org_names)}."
with gr.Blocks() as demo:
gr.LoginButton()
m1 = gr.Markdown()
m2 = gr.Markdown()
demo.load(hello, inputs=None, outputs=m1)
demo.load(list_organizations, inputs=None, outputs=m2)
if __name__ == "__main__":
demo.launch()
|
Demos
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
Description
Event listeners allow you to 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 LoginButton component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`LoginButton.click(fn, ···)` | Triggered when the Button is clicked.
Event Parameters
Parameters ▼
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.
api_descripti
|
Event Listeners
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
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.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
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
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
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`
Maximu
|
Event Listeners
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
ed* 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 | Literal[True] | 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 c
|
Event Listeners
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
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`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
|
Event Listeners
|
https://gradio.app/docs/gradio/loginbutton
|
Gradio - Loginbutton Docs
|
Creates a navigation bar component for multipage Gradio apps. The navbar
component allows customizing the appearance of the navbar for that page. Only
one Navbar component can exist per page in a Blocks app, and it can be placed
anywhere within the page.
The Navbar component is designed to control the appearance of the navigation
bar in multipage applications. When present in a Blocks app, its properties
override the default navbar behavior.
|
Description
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
**As input component** : The preprocessed input data sent to the user's
function in the backend.
Your function should accept one of these types:
def predict(
value: list[tuple[str, str]] | None
)
...
**As output component** : The output data received by the component from
the user's function in the backend.
Your function should return one of these types:
def predict(···) -> list[tuple[str, str]] | None
...
return value
|
Behavior
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
Parameters ▼
value: list[tuple[str, str]] | None
default `= None`
If a list of tuples of (page_name, page_path) are provided, these additional
pages will be added to the navbar alongside the existing pages defined in the
Blocks app. The page_path can be either a relative path for internal Gradio
app pages (e.g., "analytics") or an absolute URL for external links (e.g.,
"https://twitter.com/username"). Otherwise, only the pages defined using the
`Blocks.route` method will be displayed. Example: [("Dashboard", "dashboard"),
("About", "https://twitter.com/abidlabs")]
visible: bool
default `= True`
If True, the navbar will be visible. If False, the navbar will be hidden.
main_page_name: str | Literal[False]
default `= "Home"`
The title to display in the navbar for the main page of the Gradio. If False,
the main page will not be displayed in the navbar.
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 | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component.
|
Initialization
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.Navbar` | "navbar" | Uses default values
|
Shortcuts
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
Description
Event listeners allow you to 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 Navbar component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`Navbar.change(fn, ···)` | Triggered when the value of the Navbar 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.
Event Parameters
Parameters ▼
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 t
|
Event Listeners
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
efines 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.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
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
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
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
|
Event Listeners
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
e, 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 | Literal[True] | 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 value
|
Event Listeners
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
r | Literal[True] | 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`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.valid
|
Event Listeners
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
ll be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
|
Event Listeners
|
https://gradio.app/docs/gradio/navbar
|
Gradio - Navbar Docs
|
Special component that ticks at regular intervals when active. It is not
visible, and only used to trigger events at a regular interval through the
`tick` event listener.
|
Description
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
**As input component** : The interval of the timer as a float.
Your function should accept one of these types:
def predict(
value: float | None
)
...
**As output component** : The interval of the timer as a float or None.
Your function should return one of these types:
def predict(···) -> float | None
...
return value
|
Behavior
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
Parameters ▼
value: float
default `= 1`
Interval in seconds between each tick.
active: bool
default `= True`
Whether the timer is active.
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.
|
Initialization
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.Timer` | "timer" | Uses default values
|
Shortcuts
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
Description
Event listeners allow you to 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 Timer component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`Timer.tick(fn, ···)` | This listener is triggered at regular intervals defined by the Timer.
Event Parameters
Parameters ▼
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.
|
Event Listeners
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
ill 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.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "hidden"`
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
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
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
|
Event Listeners
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
ction 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 | Literal[True] | 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
|
Event Listeners
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
t 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`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
|
Event Listeners
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
each input value.
|
Event Listeners
|
https://gradio.app/docs/gradio/timer
|
Gradio - Timer Docs
|
Creates an image component that can be used to upload images (as an input)
or display images (as an output).
|
Description
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
**As input component** : Passes the uploaded image as a `numpy.array`,
`PIL.Image` or `str` filepath depending on `type`.
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 value
|
Behavior
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
Parameters ▼
value: str | PIL.Image.Image | np.ndarray | Callable | None
default `= None`
A `PIL.Image`, `numpy.array`, `pathlib.Path`, or `str` filepath or URL for the
default value that Image component is going to take. If a function is
provided, the function will be called each time the app loads to set the
initial value of this component.
format: str
default `= "webp"`
File format (e.g. "png" or "gif"). Used 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.
Applies both when this component is used as an input or output. This parameter
has no effect on SVG files.
height: int | str | None
default `= None`
The height of the component, specified in pixels if a number is passed, or in
CSS units if a string is passed. This has no effect on the preprocessed image
file or numpy array, but will affect the displayed image.
width: int | str | None
default `= None`
The width of the component, specified in pixels if a number is passed, or in
CSS units if a string is passed. This has no effect on the preprocessed image
file or numpy array, but will affect the displayed image.
image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None
default `= "RGB"`
The pixel format and color depth that the image should be loaded and
preprocessed as. "RGB" will load the image as a color image, or "L" as black-
and-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html
for other supported image modes and their meaning. This parameter has no
effect on SVG or GIF files. If set to None, the image_mode will be inferred
from the image file type (e.g. "RGBA" for a .png image, "RGB" in most other
cases).
sources: list[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboa
|
Initialization
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
erred
from the image file type (e.g. "RGBA" for a .png image, "RGB" in most other
cases).
sources: list[Literal['upload', 'webcam', 'clipboard']] | 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.
To support animated GIFs in input, the `type` should be set to "filepath" or
"pil". To support SVGs, the `type` should be set to "filepath".
label: str | I18nData | 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 `gr.Interface`, the label will be the name of the parameter this
component is assigned to.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will disp
|
Initialization
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
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. Only applies if interactive is
False (e.g. if the component is used as an output).
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 | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
streaming: bool
default `= False`
If True when used in a `live` interface, will automatically stream webcam
feed. Only valid is source is 'webcam'. If the component is an output
component, will automatically convert images to base64.
elem_id: str | None
default `= None`
An optional string that is assigned as the id of this component in the HTML
DOM
|
Initialization
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
an output
component, will automatically convert images to base64.
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 | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
mirror_webcam: bool | None
default `= None`
If True webcam will be mirrored. Default is True.
webcam_options: WebcamOptions | None
default `= None`
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.
placeholder: str | None
default `= None`
Custom text for the upload area. Overrides default upload messages
|
Initialization
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
icon appears if this
Gradio app is launched on Spaces, but not otherwise.
placeholder: str | None
default `= None`
Custom text for the upload area. Overrides default upload messages when
provided. Accepts new lines and `` to designate a heading.
show_fullscreen_button: bool
default `= True`
If True, will show a fullscreen icon in the corner of the component that
allows user to view the image in fullscreen mode. If False, icon does not
appear.
webcam_constraints: dict[str, Any] | None
default `= None`
A dictionary that allows developers to specify custom media constraints for
the webcam stream. This parameter provides flexibility to control the video
stream's properties, such as resolution and front or rear camera on mobile
devices. See
[demo/webcam_constraints](https://gradio.app/playground?demo=Blank&code=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
|
Initialization
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
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%3D%3D&reqs=b3BlbmN2LXB5dGhvbg%3D%3D)
watermark: WatermarkOptions | None
default `= None`
If provided and this component is used to display a `value` image, the
`watermark` image will be displayed on the bottom right of the `value` image,
10 pixels from the bottom and 10 pixels from the right. The watermark image
will not be resized. Supports `PIL.Image`, `numpy.array`, `pathlib.Path`, and
`str` filepaths. SVGs and GIFs are not supported as `watermark` images nor can
they be watermarked.
|
Initialization
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.Image` | "image" | Uses default values
|
Shortcuts
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
The `gr.Image` component can process or display any image format that is
[supported by the PIL
library](https://pillow.readthedocs.io/en/stable/handbook/image-file-
formats.html), including animated GIFs. In addition, it also supports the SVG
image format.
When the `gr.Image` component is used as an input component, the image is
converted into a `str` filepath, a `PIL.Image` object, or a `numpy.array`,
depending on the `type` parameter. However, animated GIF and SVG images are
treated differently:
* Animated `GIF` images can only be converted to `str` filepaths or `PIL.Image` objects. If they are converted to a `numpy.array` (which is the default behavior), only the first frame will be used. So if your demo expects an input `GIF` image, make sure to set the `type` parameter accordingly, e.g.
import gradio as gr
demo = gr.Interface(
fn=lambda x:x,
inputs=gr.Image(type="filepath"),
outputs=gr.Image()
)
demo.launch()
* For `SVG` images, the `type` parameter is ignored altogether and the image is always returned as an image filepath. This is because `SVG` images cannot be processed as `PIL.Image` or `numpy.array` objects.
|
`GIF` and `SVG` Image Formats
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
sepia_filterfake_diffusion
Open in 🎢 ↗ import numpy as np import gradio as gr def sepia(input_img):
sepia_filter = np.array([ [0.393, 0.769, 0.189], [0.349, 0.686, 0.168],
[0.272, 0.534, 0.131] ]) sepia_img = input_img.dot(sepia_filter.T) sepia_img
/= sepia_img.max() return sepia_img demo = gr.Interface(sepia, gr.Image(),
"image") if __name__ == "__main__": demo.launch()
import numpy as np
import gradio as gr
def sepia(input_img):
sepia_filter = np.array([
[0.393, 0.769, 0.189],
[0.349, 0.686, 0.168],
[0.272, 0.534, 0.131]
])
sepia_img = input_img.dot(sepia_filter.T)
sepia_img /= sepia_img.max()
return sepia_img
demo = gr.Interface(sepia, gr.Image(), "image")
if __name__ == "__main__":
demo.launch()
Open in 🎢 ↗ import gradio as gr import numpy as np import time def
fake_diffusion(steps): rng = np.random.default_rng() for i in range(steps):
time.sleep(1) image = rng.random(size=(600, 600, 3)) yield image image =
np.ones((1000,1000,3), np.uint8) image[:] = [255, 124, 0] yield image demo =
gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3, step=1),
outputs="image") if __name__ == "__main__": demo.launch()
import gradio as gr
import numpy as np
import time
def fake_diffusion(steps):
rng = np.random.default_rng()
for i in range(steps):
time.sleep(1)
image = rng.random(size=(600, 600, 3))
yield image
image = np.ones((1000,1000,3), np.uint8)
image[:] = [255, 124, 0]
yield image
demo = gr.Interface(fake_diffusion,
inputs=gr.Slider(1, 10, 3, step=1),
outputs="image")
if __name__ == "__main__":
demo.launch()
|
Demos
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
Description
Event listeners allow you to 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 Parameters
table below.
Listener | Description
---|---
`Image.clear(fn, ···)` | This listener is triggered when the user clears the Image using the clear button for the component.
`Image.change(fn, ···)` | 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 `.input()` for a listener that is only triggered by user input.
`Image.stream(fn, ···)` | This listener is triggered when the user streams the Image.
`Image.select(fn, ···)` | Event listener for when the user selects or deselects the Image. Uses event data gradio.SelectData to carry `value` referring to the label of the Image, and `selected` to refer to state of the Image. See EventData documentation on how to use this event data
`Image.upload(fn, ···)` | This listener is triggered when the user uploads a file into the Image.
`Image.input(fn, ···)` | This listener is triggered when the user changes the value of the Image.
Event Parameters
Parameters ▼
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 | BlockCo
|
Event Listeners
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
ion 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.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
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
show_progress_on: Component | lis
|
Event Listeners
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
ent 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
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
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
functio
|
Event Listeners
|
https://gradio.app/docs/gradio/image
|
Gradio - Image Docs
|
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