Building Demos
ChatInterface

New to Gradio? Start here: Getting Started

To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/3fcd23a09df280468351eac4fb081e79c8e58216/gradio-6.0.1-py3-none-any.whl

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

←

Interface

TabbedInterface

→

ChatInterface

gradio.ChatInterface(fn, type="messages", ···)

Description

ChatInterface is Gradio's high-level abstraction for creating chatbot UIs, and allows you to create a web-based demo around a chatbot model in a few lines of code. Only one parameter is required: fn, which takes a function that governs the response of the chatbot based on the user input and chat history. Additional parameters can be used to control the appearance and behavior of the demo.

Example Usage

Basic Example: A chatbot that echoes back the users’s message

import gradio as gr

def echo(message, history):
    return message

demo = gr.ChatInterface(fn=echo, type="messages", examples=["hello", "hola", "merhaba"], title="Echo Bot")
demo.launch()

Custom Chatbot: A gr.ChatInterface with a custom gr.Chatbot that includes a placeholder as well as upvote/downvote buttons. The upvote/downvote buttons are automatically added when a .like() event is attached to a gr.Chatbot. In order to attach event listeners to your custom chatbot, wrap the gr.Chatbot as well as the gr.ChatInterface inside of a gr.Blocks like this:

import gradio as gr

def yes(message, history):
    return "yes"

def vote(data: gr.LikeData):
    if data.liked:
        print("You upvoted this response: " + data.value["value"])
    else:
        print("You downvoted this response: " + data.value["value"])

with gr.Blocks() as demo:
    chatbot = gr.Chatbot(placeholder="<strong>Your Personal Yes-Man</strong><br>Ask Me Anything")
    chatbot.like(vote, None, None)
    gr.ChatInterface(fn=yes, type="messages", chatbot=chatbot)
    
demo.launch()

Initialization

🔗

fn: Callable

the function to wrap the chat interface around. The function should accept two parameters: a `str` representing the input message and `list` of openai-style dictionaries: {"role": "user" | "assistant", "content": `str` | {"path": `str`} | `gr.Component`} representing the chat history. The function should return/yield a `str` (for a simple message), a supported Gradio component (e.g. gr.Image to return an image), a `dict` (for a complete openai-style message response), or a `list` of such messages.

🔗

multimodal: bool

default = False

if True, the chat interface will use a `gr.MultimodalTextbox` component for the input, which allows for the uploading of multimedia files. If False, the chat interface will use a gr.Textbox component for the input. If this is True, the first argument of `fn` should accept not a `str` message but a `dict` message with keys "text" and "files"

🔗

chatbot: Chatbot | None

default = None

an instance of the gr.Chatbot component to use for the chat interface, if you would like to customize the chatbot properties. If not provided, a default gr.Chatbot component will be created.

🔗

textbox: Textbox | MultimodalTextbox | None

default = None

an instance of the gr.Textbox or gr.MultimodalTextbox component to use for the chat interface, if you would like to customize the textbox properties. If not provided, a default gr.Textbox or gr.MultimodalTextbox component will be created.

🔗

additional_inputs: str | Component | list[str | Component] | None

default = None

an instance or list of instances of gradio components (or their string shortcuts) to use as additional inputs to the chatbot. If the components are not already rendered in a surrounding Blocks, then the components will be displayed under the chatbot, in an accordion. The values of these components will be passed into `fn` as arguments in order after the chat history.

🔗

additional_inputs_accordion: str | Accordion | None

default = None

if a string is provided, this is the label of the `gr.Accordion` to use to contain additional inputs. A `gr.Accordion` object can be provided as well to configure other properties of the container holding the additional inputs. Defaults to a `gr.Accordion(label="Additional Inputs", open=False)`. This parameter is only used if `additional_inputs` is provided.

🔗

additional_outputs: Component | list[Component] | None

default = None

an instance or list of instances of gradio components to use as additional outputs from the chat function. These must be components that are already defined in the same Blocks scope. If provided, the chat function should return additional values for these components. See $demo/chatinterface_artifacts.

🔗

editable: bool

default = False

if True, users can edit past messages to regenerate responses.

🔗

examples: list[str] | list[MultimodalValue] | list[list] | None

default = None

sample inputs for the function; if provided, appear within the chatbot and can be clicked to populate the chatbot input. Should be a list of strings representing text-only examples, or a list of dictionaries (with keys `text` and `files`) representing multimodal examples. If `additional_inputs` are provided, the examples must be a list of lists, where the first element of each inner list is the string or dictionary example message and the remaining elements are the example values for the additional inputs -- in this case, the examples will appear under the chatbot.

🔗

example_labels: list[str] | None

default = None

labels for the examples, to be displayed instead of the examples themselves. If provided, should be a list of strings with the same length as the examples list. Only applies when examples are displayed within the chatbot (i.e. when `additional_inputs` is not provided).

🔗

example_icons: list[str] | None

default = None

icons for the examples, to be displayed above the examples. If provided, should be a list of string URLs or local paths with the same length as the examples list. Only applies when examples are displayed within the chatbot (i.e. when `additional_inputs` is not provided).

🔗

run_examples_on_click: bool

default = True

if True, clicking on an example will run the example through the chatbot fn and the response will be displayed in the chatbot. If False, clicking on an example will only populate the chatbot input with the example message. Has no effect if `cache_examples` is True

🔗

cache_examples: bool | None

default = None

if True, caches examples in the server for fast runtime in examples. The default option in HuggingFace Spaces is True. The default option elsewhere is False. Note that examples are cached separately from Gradio's queue() so certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will not be displayed in Gradio's UI for cached examples.

🔗

cache_mode: Literal['eager', 'lazy'] | None

default = None

if "eager", all examples are cached at app launch. If "lazy", examples are cached for all users after the first use by any user of the app. If None, will use the GRADIO_CACHE_MODE environment variable if defined, or default to "eager".

🔗

title: str | I18nData | None

default = None

a title for the interface; if provided, appears above chatbot in large font. Also used as the tab title when opened in a browser window.

🔗

description: str | None

default = None

a description for the interface; if provided, appears above the chatbot and beneath the title in regular font. Accepts Markdown and HTML content.

🔗

flagging_mode: Literal['never', 'manual'] | None

default = None

one of "never", "manual". If "never", users will not see a button to flag an input and output. If "manual", users will see a button to flag.

🔗

flagging_options: list[str] | tuple[str, ...] | None

default = ('Like', 'Dislike')

a list of strings representing the options that users can choose from when flagging a message. Defaults to ["Like", "Dislike"]. These two case-sensitive strings will render as "thumbs up" and "thumbs down" icon respectively next to each bot message, but any other strings appear under a separate flag icon.

🔗

flagging_dir: str

default = ".gradio/flagged"

path to the the directory where flagged data is stored. If the directory does not exist, it will be created.

🔗

analytics_enabled: bool | None

default = None

whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable if defined, or default to True.

🔗

autofocus: bool

default = True

if True, autofocuses to the textbox when the page loads.

🔗

autoscroll: bool

default = True

If True, will automatically scroll to the bottom of the chatbot when a new message appears, unless the user scrolls up. If False, will not scroll to the bottom of the chatbot automatically.

🔗

submit_btn: str | bool | None

default = True

If True, will show a submit button with a submit icon within the textbox. If a string, will use that string as the submit button text in place of the icon. If False, will not show a submit button.

🔗

stop_btn: str | bool | None

default = True

If True, will show a button with a stop icon during generator executions, to stop generating. If a string, will use that string as the submit button text in place of the stop icon. If False, will not show a stop button.

🔗

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

default = "default"

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

🔗

delete_cache: tuple[int, int] | None

default = None

a tuple corresponding [frequency, age] both expressed in number of seconds. Every `frequency` seconds, the temporary files created by this Blocks instance will be deleted if more than `age` seconds have passed since the file was created. For example, setting this to (86400, 86400) will delete temporary files every day. The cache will be deleted entirely when the server restarts. If None, no cache deletion will occur.

🔗

show_progress: Literal['full', 'minimal', 'hidden']

default = "minimal"

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

🔗

fill_height: bool

default = True

if True, the chat interface will expand to the height of window.

🔗

fill_width: bool

default = False

Whether to horizontally expand to fill container fully. If False, centers and constrains app to a maximum width.

🔗

api_name: str | None

default = None

defines how the chat endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None, the name of the function will be used.

🔗

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.

🔗

api_visibility: Literal['public', 'private', 'undocumented']

default = "public"

Controls the visibility of the chat endpoint. Can be "public" (shown in API docs and callable), "private" (hidden from API docs and not callable), or "undocumented" (hidden from API docs but callable).

🔗

save_history: bool

default = False

if True, will save the chat history to the browser's local storage and display previous conversations in a side panel.

🔗

validator: Callable | None

default = None

a function that takes in the inputs and can optionally return a gr.validate() object for each input.

Demos

Guides

Creating A Chatbot Fast

Sharing Your App

←

Interface

TabbedInterface

→

Building Demos

Blocks Layout

Components

Helpers

Modals

Routes

Other

ChatInterface

Description

Example Usage

Initialization

Demos

Guides