"""gr.Gallery() component.""" from __future__ import annotations from pathlib import Path from typing import Any, Callable, List, Literal, Optional, Tuple, Union from urllib.parse import urlparse import numpy as np from gradio_client.documentation import document, set_documentation_group from gradio_client.utils import is_http_url_like from PIL import Image as _Image # using _ to minimize namespace pollution from gradio import processing_utils, utils from gradio.components.base import Component from gradio.data_classes import FileData, GradioModel, GradioRootModel from gradio.events import Events set_documentation_group("component") GalleryImageType = Union[np.ndarray, _Image.Image, Path, str] CaptionedGalleryImageType = Tuple[GalleryImageType, str] class GalleryImage(GradioModel): image: FileData caption: Optional[str] = None class GalleryData(GradioRootModel): root: List[GalleryImage] from gradio.events import Dependency @document() class Carousel(Component): """ Used to display a list of images as a gallery that can be scrolled through. Preprocessing: A list of (image, caption) tuples. Each image is a filepath, numpy array or PIL.image depending on the `type` parameter. {List[tuple[str | PIL.Image | numpy.array, str | None]]}. Postprocessing: expects a list of images in any format, {List[numpy.array | PIL.Image | str | pathlib.Path]}, or a {List} of (image, {str} caption) tuples and displays them. Demos: fake_gan """ EVENTS = [Events.select, Events.upload, Events.change] data_model = GalleryData def __init__( self, value: list[np.ndarray | _Image.Image | str | Path | tuple] | Callable | None = None, *, label: str | None = None, every: float | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, visible: bool = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, columns: int | tuple | None = 2, rows: int | tuple | None = None, height: int | float | None = None, allow_preview: bool = True, preview: bool | None = None, selected_index: int | None = None, object_fit: Literal["contain", "cover", "fill", "none", "scale-down"] | None = None, show_share_button: bool | None = None, show_download_button: bool | None = True, interactive: bool | None = None, type: Literal["numpy", "pil", "filepath"] = "filepath", ): """ Parameters: value: List of images to display in the gallery by default. If callable, the function will be called whenever the app loads to set the initial value of the component. label: 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: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute. show_label: if True, will display label. container: If True, will place the component in a container - providing some extra padding around the border. scale: 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: 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: If False, component will be hidden. elem_id: 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: 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: 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. columns: Represents the number of images that should be shown in one row, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). If fewer than 6 are given then the last will be used for all subsequent breakpoints rows: Represents the number of rows in the image grid, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). If fewer than 6 are given then the last will be used for all subsequent breakpoints height: The height of the gallery component, specified in pixels if a number is passed, or in CSS units if a string is passed. If more images are displayed than can fit in the height, a scrollbar will appear. allow_preview: If True, images in the gallery will be enlarged when they are clicked. Default is True. preview: If True, Carousel will start in preview mode, which shows all of the images as thumbnails and allows the user to click on them to view them in full size. Only works if allow_preview is True. selected_index: The index of the image that should be initially selected. If None, no image will be selected at start. If provided, will set Carousel to preview mode unless allow_preview is set to False. object_fit: CSS object-fit property for the thumbnail images in the gallery. Can be "contain", "cover", "fill", "none", or "scale-down". show_share_button: 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. show_download_button: If True, will show a download button in the corner of the selected image. If False, the icon does not appear. Default is True. interactive: If True, the gallery will be interactive, allowing the user to upload images. If False, the gallery will be static. Default is True. type: The format the image is converted to before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. If the image is SVG, the `type` is ignored and the filepath of the SVG is returned. """ self.columns = columns self.rows = rows self.height = height self.preview = preview self.object_fit = object_fit self.allow_preview = allow_preview self.show_download_button = ( (utils.get_space() is not None) if show_download_button is None else show_download_button ) self.selected_index = selected_index self.type = type self.show_share_button = ( (utils.get_space() is not None) if show_share_button is None else show_share_button ) super().__init__( label=label, every=every, show_label=show_label, container=container, scale=scale, min_width=min_width, visible=visible, elem_id=elem_id, elem_classes=elem_classes, render=render, value=value, interactive=interactive, ) def postprocess( self, value: list[GalleryImageType | CaptionedGalleryImageType] | None, ) -> GalleryData: """ Parameters: value: list of images, or list of (image, caption) tuples Returns: list of string file paths to images in temp directory """ if value is None: return GalleryData(root=[]) output = [] for img in value: url = None caption = None orig_name = None if isinstance(img, (tuple, list)): img, caption = img if isinstance(img, np.ndarray): file = processing_utils.save_img_array_to_cache( img, cache_dir=self.GRADIO_CACHE ) file_path = str(utils.abspath(file)) elif isinstance(img, _Image.Image): file = processing_utils.save_pil_to_cache( img, cache_dir=self.GRADIO_CACHE ) file_path = str(utils.abspath(file)) elif isinstance(img, str): file_path = img if is_http_url_like(img): url = img orig_name = Path(urlparse(img).path).name else: url = None orig_name = Path(img).name elif isinstance(img, Path): file_path = str(img) orig_name = img.name else: raise ValueError(f"Cannot process type as image: {type(img)}") entry = GalleryImage( image=FileData(path=file_path, url=url, orig_name=orig_name), caption=caption, ) output.append(entry) return GalleryData(root=output) @staticmethod def convert_to_type(img: str, type: Literal["filepath", "numpy", "pil"]): if type == "filepath": return img else: converted_image = _Image.open(img) if type == "numpy": converted_image = np.array(converted_image) return converted_image def preprocess( self, payload: GalleryData | None ) -> ( List[tuple[str, str | None]] | List[tuple[_Image.Image, str | None]] | List[tuple[np.ndarray, str | None]] | None ): if payload is None or not payload.root: return None data = [] for gallery_element in payload.root: image = self.convert_to_type(gallery_element.image.path, self.type) # type: ignore data.append((image, gallery_element.caption)) return data def example_inputs(self) -> Any: return [ "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png" ] def select(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | None = None, api_name: str | None | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", queue: bool | None = None, batch: bool = False, max_batch_size: int = 4, preprocess: bool = True, postprocess: bool = True, cancels: dict[str, Any] | list[dict[str, Any]] | None = None, every: float | None = None, trigger_mode: Literal["once", "multiple", "always_last"] | None = None, js: str | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True) -> Dependency: """ Parameters: fn: 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: List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. outputs: List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. api_name: Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. scroll_to_output: If True, will scroll to output component on completion show_progress: If True, will show progress animation while pending queue: 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: 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: Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: 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: If False, will not run postprocessing of component data before returning 'fn' output to the browser. cancels: A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. trigger_mode: If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` event) would allow a second submission after the pending event is complete. js: 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: 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: 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: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps to use this event. If fn is None, show_api will automatically be set to False. """ ... def upload(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | None = None, api_name: str | None | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", queue: bool | None = None, batch: bool = False, max_batch_size: int = 4, preprocess: bool = True, postprocess: bool = True, cancels: dict[str, Any] | list[dict[str, Any]] | None = None, every: float | None = None, trigger_mode: Literal["once", "multiple", "always_last"] | None = None, js: str | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True) -> Dependency: """ Parameters: fn: 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: List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. outputs: List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. api_name: Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. scroll_to_output: If True, will scroll to output component on completion show_progress: If True, will show progress animation while pending queue: 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: 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: Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: 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: If False, will not run postprocessing of component data before returning 'fn' output to the browser. cancels: A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. trigger_mode: If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` event) would allow a second submission after the pending event is complete. js: 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: 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: 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: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps to use this event. If fn is None, show_api will automatically be set to False. """ ... def change(self, fn: Callable | None, inputs: Component | Sequence[Component] | set[Component] | None = None, outputs: Component | Sequence[Component] | None = None, api_name: str | None | Literal[False] = None, scroll_to_output: bool = False, show_progress: Literal["full", "minimal", "hidden"] = "full", queue: bool | None = None, batch: bool = False, max_batch_size: int = 4, preprocess: bool = True, postprocess: bool = True, cancels: dict[str, Any] | list[dict[str, Any]] | None = None, every: float | None = None, trigger_mode: Literal["once", "multiple", "always_last"] | None = None, js: str | None = None, concurrency_limit: int | None | Literal["default"] = "default", concurrency_id: str | None = None, show_api: bool = True) -> Dependency: """ Parameters: fn: 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: List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list. outputs: List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list. api_name: Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name. scroll_to_output: If True, will scroll to output component on completion show_progress: If True, will show progress animation while pending queue: 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: 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: Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) preprocess: 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: If False, will not run postprocessing of component data before returning 'fn' output to the browser. cancels: A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish. every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled. trigger_mode: If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` event) would allow a second submission after the pending event is complete. js: 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: 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: 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: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps to use this event. If fn is None, show_api will automatically be set to False. """ ...