Components
Chatbot

New to Gradio? Start here: Getting Started

To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/d8c939444451e8ce80a6256daba3ab7fbc1dcac3/gradio-4.44.1-py3-none-any.whl

*Note: Setting share=True in launch() will not work.

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Chatbot

gradio.Chatbot(···)

import gradio as gr with gr.Blocks() as demo: gr.Chatbot(value=[["Hello World","Hey Gradio!"],["❤️","😍"],["🔥","🤗"]]) demo.launch()

Description

Creates a chatbot that displays user-submitted messages and responses. Supports a subset of Markdown including bold, italics, code, tables. Also supports audio/video/image files, which are displayed in the Chatbot, and other kinds of files which are displayed as links. This component is usually used as an output component.

Behavior

The data format accepted by the Chatbot is dictated by the type parameter. This parameter can take two values, 'tuples' and 'messages'.

If type is 'tuples', then the data sent to/from the chatbot will be a list of tuples. The first element of each tuple is the user message and the second element is the bot’s response. Each element can be a string (markdown/html is supported), a tuple (in which case the first element is a filepath that will be displayed in the chatbot), or a gradio component (see the Examples section for more details).

If the type is 'messages', then the data sent to/from the chatbot will be a list of dictionaries with role and content keys. This format is compliant with the format expected by most LLM APIs (HuggingChat, OpenAI, Claude). The role key is either 'user' or 'assistant’and thecontentkey can be a string (markdown/html supported), aFileDataDict` (to represent a file that is displayed in the chatbot - documented below), or a gradio component.

For convenience, you can use the ChatMessage dataclass so that your text editor can give you autocomplete hints and typechecks.

from gradio import ChatMessage

def generate_response(history):
    history.append(
        ChatMessage(role="assistant",
                    content="How can I help you?")
        )
    return history

Additionally, when type is messages, you can provide additional metadata regarding any tools used to generate the response. This is useful for displaying the thought process of LLM agents. For example,

def generate_response(history):
    history.append(
        ChatMessage(role="assistant",
                    content="The weather API says it is 20 degrees Celcius in New York.",
                    metadata={"title": "🛠️ Used tool Weather API"})
        )
    return history

Would be displayed as following:

All of the types expected by the messages format are documented below:

class MetadataDict(TypedDict):
    title: Union[str, None]

class FileDataDict(TypedDict):
    path: str  # server filepath
    url: NotRequired[Optional[str]]  # normalised server url
    size: NotRequired[Optional[int]]  # size in bytes
    orig_name: NotRequired[Optional[str]]  # original filename
    mime_type: NotRequired[Optional[str]]
    is_stream: NotRequired[bool]
    meta: dict[Literal["_type"], Literal["gradio.FileData"]]


class MessageDict(TypedDict):
    content: str | FileDataDict | Component
    role: Literal["user", "assistant", "system"]
    metadata: NotRequired[MetadataDict]


@dataclass
class Metadata:
    title: Optional[str] = None


@dataclass
class ChatMessage:
    role: Literal["user", "assistant", "system"]
    content: str | FileData | Component | FileDataDict | tuple | list
    metadata: MetadataDict | Metadata = field(default_factory=Metadata)

As input component: If type is 'tuples', passes the messages in the chatbot as a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list has 2 elements: the user message and the response message. Each message can be (1) a string in valid Markdown, (2) a tuple if there are displayed files: (a filepath or URL to a file, [optional string alt text]), or (3) None, if there is no message displayed. If type is 'messages', passes the value as a list of dictionaries with 'role' and 'content' keys. The `content` key's value supports everything the `tuples` format supports.

Your function should accept one of these types:

If type is tuples -

from gradio import Component

def predict(
	value: list[list[str | tuple[str, str] | Component | None]] | None
):
	...

If type is messages -

from gradio import MessageDict

def predict(value: list[MessageDict] | None):
    ...

As output component: If type is `tuples`, expects a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message. The individual messages can be (1) strings in valid Markdown, (2) tuples if sending files: (a filepath or URL to a file, [optional string alt text]) -- if the file is image/video/audio, it is displayed in the Chatbot, or (3) None, in which case the message is not displayed. If type is 'messages', passes the value as a list of dictionaries with 'role' and 'content' keys. The `content` key's value supports everything the `tuples` format supports.

Your function should return one of these types:

If type is tuples -

def predict(···) -> list[list[str | tuple[str] | tuple[str, str] | None] | tuple] | None
	...	
	return value

If type is messages -

from gradio import ChatMessage, MessageDict

def predict(···) - > list[MessageDict] | list[ChatMessage]:
    ...

Initialization

value: list[list[str | GradioComponent | tuple[str] | tuple[str | Path, str] | None]] | Callable | None

default = None

Default value to show in chatbot. If callable, the function will be called whenever the app loads to set the initial value of the component.

type: Literal['messages', 'tuples']

default = "tuples"

The format of the messages. If 'tuples', expects a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message. The individual messages can be (1) strings in valid Markdown, (2) tuples if sending files: (a filepath or URL to a file, [optional string alt text]) -- if the file is image/video/audio, it is displayed in the Chatbot, or (3) None, in which case the message is not displayed. If 'messages', passes the value as a list of dictionaries with 'role' and 'content' keys. The `content' key's value supports everything the 'tuples' format supports. The 'role' key should be one of 'user' or 'assistant'. Any other roles will not be displayed in the output.

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.

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.

visible: bool

default = True

If False, component will be hidden.

elem_id: str | None

default = None

An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

elem_classes: list[str] | str | None

default = None

An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

render: bool

default = True

If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.

key: int | str | None

default = None

if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.

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.

latex_delimiters: list[dict[str, str | bool]] | None

default = None

A list of dicts of the form {"left": open delimiter (str), "right": close delimiter (str), "display": whether to display in newline (bool)} that will be used to render LaTeX expressions. If not provided, `latex_delimiters` is set to `[{ "left": "$$", "right": "$$", "display": True }]`, so only expressions enclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass in an empty list to disable LaTeX rendering. For more information, see the [KaTeX documentation](https://katex.org/docs/autorender.html).

rtl: bool

default = False

If True, sets the direction of the rendered text to right-to-left. Default is False, which renders text left-to-right.

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.

show_copy_button: bool

default = False

If True, will show a copy button for each chatbot message.

avatar_images: tuple[str | Path | None, str | Path | None] | None

default = None

Tuple of two avatar image paths or URLs for user and bot (in that order). Pass None for either the user or bot image to skip. Must be within the working directory of the Gradio app or an external URL.

sanitize_html: bool

default = True

If False, will disable HTML sanitization for chatbot messages. This is not recommended, as it can lead to security vulnerabilities.

render_markdown: bool

default = True

If False, will disable Markdown rendering for chatbot messages.

bubble_full_width: bool

default = True

If False, the chat bubble will fit to the content of the message. If True (default), the chat bubble will be the full width of the component.

line_breaks: bool

default = True

If True (default), will enable Github-flavored Markdown line breaks in chatbot messages. If False, single new lines will be ignored. Only applies if `render_markdown` is True.

likeable: bool

default = False

Whether the chat messages display a like or dislike button. Set automatically by the .like method but has to be present in the signature for it to show up in the config.

layout: Literal['panel', 'bubble'] | None

default = None

If "panel", will display the chatbot in a llm style layout. If "bubble", will display the chatbot with message bubbles, with the user and bot messages on alterating sides. Will default to "bubble".

placeholder: str | None

default = None

a placeholder message to display in the chatbot when it is empty. Centered vertically and horizontally in the Chatbot. Supports Markdown and HTML. If None, no placeholder is displayed.

show_copy_all_button: <class 'inspect._empty'>

default = False

If True, will show a copy all button that copies all chatbot messages to the clipboard.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.Chatbot`	"chatbot"	Uses default values

Examples

Using Gradio Components Inside gr.Chatbot

The Chatbot component supports using many of the core Gradio components (such as gr.Image, gr.Plot, gr.Audio, and gr.HTML) inside of the chatbot. Simply include one of these components in your list of tuples. Here’s an example:

import gradio as gr

def load():
    return [
        ("Here's an audio", gr.Audio("https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav")),
        ("Here's an video", gr.Video("https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4"))
    ]

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    button = gr.Button("Load audio and video")
    button.click(load, None, chatbot)

demo.launch()

Demos

import gradio as gr import random import time with gr.Blocks() as demo: chatbot = gr.Chatbot() msg = gr.Textbox() clear = gr.ClearButton([msg, chatbot]) def respond(message, chat_history): bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"]) chat_history.append((message, bot_message)) time.sleep(2) return "", chat_history msg.submit(respond, [msg, chatbot], [msg, chatbot]) if __name__ == "__main__": demo.launch()

import gradio as gr
import random
import time

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.ClearButton([msg, chatbot])

    def respond(message, chat_history):
        bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
        chat_history.append((message, bot_message))
        time.sleep(2)
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])

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

import gradio as gr import random # Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). 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/demo/video_component/files/world.mp4" ) elif response_type == "audio": history[-1][1] = gr.Audio( "https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav" ) elif response_type == "html": history[-1][1] = gr.HTML( html_src(random.choice(["harmful", "neutral", "beneficial"])) ) 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", ], 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). 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",
            ]
        )
    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/demo/video_component/files/world.mp4"
        )
    elif response_type == "audio":
        history[-1][1] = gr.Audio(
            "https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav"
        )
    elif response_type == "html":
        history[-1][1] = gr.HTML(
            html_src(random.choice(["harmful", "neutral", "beneficial"]))
        )
    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",
        ],
        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()

Event Listeners

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 Chatbot component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.

Listener	Description
`Chatbot.change(fn, ···)`	Triggered when the value of the Chatbot 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.
`Chatbot.select(fn, ···)`	Event listener for when the user selects or deselects the Chatbot. Uses event data gradio.SelectData to carry `value` referring to the label of the Chatbot, and `selected` to refer to state of the Chatbot. See EventData documentation on how to use this event data
`Chatbot.like(fn, ···)`	This listener is triggered when the user likes/dislikes from within the Chatbot. This event has EventData of type gradio.LikeData that carries information, accessible through LikeData.index and LikeData.value. See EventData documentation on how to use this event data.

Event 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.

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

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.

every: float | None

default = None

Will be deprecated in favor of gr.Timer. Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.

trigger_mode: Literal['once', 'multiple', 'always_last'] | None

default = None

If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.

js: str | None

default = None

Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.

concurrency_limit: int | None | Literal['default']

default = "default"

If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).

concurrency_id: str | None

default = None

If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.

show_api: bool

default = True

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

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