Gradio Blocks Docs

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Blocks

with gradio.Blocks():

Description

Blocks is Gradio's low-level API that allows you to create more custom web applications and demos than Interfaces (yet still entirely in Python).

Compared to the Interface class, Blocks offers more flexibility and control over: (1) the layout of components (2) the events that trigger the execution of functions (3) data flows (e.g. inputs can trigger outputs, which can trigger the next level of outputs). Blocks also offers ways to group together related demos such as with tabs.

The basic usage of Blocks is as follows: create a Blocks object, then use it as a context (with the "with" statement), and then define layouts, components, or events within the Blocks context. Finally, call the launch() method to launch the demo.

Example Usage

import gradio as gr
def update(name):
    return f"Welcome to Gradio, {name}!"

with gr.Blocks() as demo:
    gr.Markdown("Start typing below and then click **Run** to see the output.")
    with gr.Row():
        inp = gr.Textbox(placeholder="What is your name?")
        out = gr.Textbox()
    btn = gr.Button("Run")
    btn.click(fn=update, inputs=inp, outputs=out)

demo.launch()

Initialization

Parameter	Description
`theme` Theme \| str \| None default: None	a Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the HF Hub (e.g. "gradio/monochrome"). If None, will use the Default theme.
`analytics_enabled` bool \| None default: None	whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable or default to True.
`mode` str default: "blocks"	a human-friendly name for the kind of Blocks or Interface being created.
`title` str default: "Gradio"	The tab title to display when this is opened in a browser window.
`css` str \| None default: None	custom css or path to custom css file to apply to entire Blocks

Demos

import gradio as gr

def welcome(name):
    return f"Welcome to Gradio, {name}!"

with gr.Blocks() as demo:
    gr.Markdown(
    """
    # Hello World!
    Start typing below to see the output.
    """)
    inp = gr.Textbox(placeholder="What is your name?")
    out = gr.Textbox()
    inp.change(welcome, inp, out)

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

import numpy as np
import gradio as gr


def flip_text(x):
    return x[::-1]


def flip_image(x):
    return np.fliplr(x)


with gr.Blocks() as demo:
    gr.Markdown("Flip text or image files using this demo.")
    with gr.Tab("Flip Text"):
        text_input = gr.Textbox()
        text_output = gr.Textbox()
        text_button = gr.Button("Flip")
    with gr.Tab("Flip Image"):
        with gr.Row():
            image_input = gr.Image()
            image_output = gr.Image()
        image_button = gr.Button("Flip")

    with gr.Accordion("Open for More!"):
        gr.Markdown("Look at me...")

    text_button.click(flip_text, inputs=text_input, outputs=text_output)
    image_button.click(flip_image, inputs=image_input, outputs=image_output)

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

from transformers import pipeline

import gradio as gr

asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
classifier = pipeline("text-classification")


def speech_to_text(speech):
    text = asr(speech)["text"]
    return text


def text_to_sentiment(text):
    return classifier(text)[0]["label"]


demo = gr.Blocks()

with demo:
    audio_file = gr.Audio(type="filepath")
    text = gr.Textbox()
    label = gr.Label()

    b1 = gr.Button("Recognize Speech")
    b2 = gr.Button("Classify Sentiment")

    b1.click(speech_to_text, inputs=audio_file, outputs=text)
    b2.click(text_to_sentiment, inputs=text, outputs=label)

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

import gradio as gr

from transformers import pipeline

english_translator = gr.Blocks.load(name="spaces/gradio/english_translator")
english_generator = pipeline("text-generation", model="distilgpt2")


def generate_text(text):
    english_text = english_generator(text)[0]["generated_text"]
    german_text = english_translator(english_text)
    return english_text, german_text


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            seed = gr.Text(label="Input Phrase")
        with gr.Column():
            english = gr.Text(label="Generated English Text")
            german = gr.Text(label="Generated German Text")
    btn = gr.Button("Generate")
    btn.click(generate_text, inputs=[seed], outputs=[english, german])
    gr.Examples(["My name is Clara and I am"], inputs=[seed])

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

import time
import gradio as gr


js_function = "() => {new Audio('file=beep.mp3').play();}"

def task(x):
    time.sleep(2)
    return "Hello, " + x 

with gr.Blocks() as demo:
    name = gr.Textbox(label="name")
    greeting = gr.Textbox(label="greeting")
    name.blur(task, name, greeting)
    greeting.change(None, [], [], _js=js_function)  # Note that _js is a special argument whose usage may change in the future
    
demo.launch()

Methods

launch

gradio.Blocks.launch(···)

Description

Launches a simple web server that serves the demo. Can also be used to create a public link used by anyone to access the demo from their browser by setting share=True.

Example Usage

import gradio as gr
def reverse(text):
    return text[::-1]
with gr.Blocks() as demo:
    button = gr.Button(value="Reverse")
    button.click(reverse, gr.Textbox(), gr.Textbox())
demo.launch(share=True, auth=("username", "password"))

Agruments

Parameter	Description
`inline` bool \| None default: None	whether to display in the interface inline in an iframe. Defaults to True in python notebooks; False otherwise.
`inbrowser` bool default: False	whether to automatically launch the interface in a new tab on the default browser.
`share` bool \| None default: None	whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. If not provided, it is set to False by default every time, except when running in Google Colab. When localhost is not accessible (e.g. Google Colab), setting share=False is not supported.
`debug` bool default: False	if True, blocks the main thread from running. If running in Google Colab, this is needed to print the errors in the cell output.
`enable_queue` bool \| None default: None	DEPRECATED (use .queue() method instead.) if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
`max_threads` int default: 40	the maximum number of total threads that the Gradio app can generate in parallel. The default is inherited from the starlette library (currently 40). Applies whether the queue is enabled or not. But if queuing is enabled, this parameter is increaseed to be at least the concurrency_count of the queue.
`auth` Callable \| tuple[str, str] \| list[tuple[str, str]] \| None default: None	If provided, username and password (or list of username-password tuples) required to access interface. Can also provide function that takes username and password and returns True if valid login.
`auth_message` str \| None default: None	If provided, HTML message provided on login page.
`prevent_thread_lock` bool default: False	If True, the interface will block the main thread while the server is running.
`show_error` bool default: False	If True, any errors in the interface will be displayed in an alert modal and printed in the browser console log
`server_name` str \| None default: None	to make app accessible on local network, set this to "0.0.0.0". Can be set by environment variable GRADIO_SERVER_NAME. If None, will use "127.0.0.1".
`server_port` int \| None default: None	will start gradio app on this port (if available). Can be set by environment variable GRADIO_SERVER_PORT. If None, will search for an available port starting at 7860.
`show_tips` bool default: False	if True, will occasionally show tips about new Gradio features
`height` int default: 500	The height in pixels of the iframe element containing the interface (used if inline=True)
`width` int \| str default: "100%"	The width in pixels of the iframe element containing the interface (used if inline=True)
`encrypt` bool \| None default: None	DEPRECATED. Has no effect.
`favicon_path` str \| None default: None	If a path to a file (.png, .gif, or .ico) is provided, it will be used as the favicon for the web page.
`ssl_keyfile` str \| None default: None	If a path to a file is provided, will use this as the private key file to create a local server running on https.
`ssl_certfile` str \| None default: None	If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.
`ssl_keyfile_password` str \| None default: None	If a password is provided, will use this with the ssl certificate for https.
`ssl_verify` bool default: True	If False, skips certificate validation which allows self-signed certificates to be used.
`quiet` bool default: False	If True, suppresses most print statements.
`show_api` bool default: True	If True, shows the api docs in the footer of the app. Default True. If the queue is enabled, then api_open parameter of .queue() will determine if the api docs are shown, independent of the value of show_api.
`file_directories` list[str] \| None default: None	This parameter has been renamed to `allowed_paths`. It will be removed in a future version.
`allowed_paths` list[str] \| None default: None	List of complete filepaths or parent directories that gradio is allowed to serve (in addition to the directory containing the gradio python file). Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app.
`blocked_paths` list[str] \| None default: None	List of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default.
`root_path` str default: ""	The root path (or "mount point") of the application, if it's not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy that forwards requests to the application. For example, if the application is served at "https://example.com/myapp", the `root_path` should be set to "/myapp".
`app_kwargs` dict[str, Any] \| None default: None	Additional keyword arguments to pass to the underlying FastAPI app as a dictionary of parameter keys and argument values. For example, `{"docs_url": "/docs"}`

queue

gradio.Blocks.queue(···)

Description

You can control the rate of processed requests by creating a queue. This will allow you to set the number of requests to be processed at one time, and will let users know their position in the queue.

Example Usage

with gr.Blocks() as demo:
    button = gr.Button(label="Generate Image")
    button.click(fn=image_generator, inputs=gr.Textbox(), outputs=gr.Image())
demo.queue(concurrency_count=3)
demo.launch()

Agruments

Parameter	Description
`concurrency_count` int default: 1	Number of worker threads that will be processing requests from the queue concurrently. Increasing this number will increase the rate at which requests are processed, but will also increase the memory usage of the queue.
`status_update_rate` float \| Literal['auto'] default: "auto"	If "auto", Queue will send status estimations to all clients whenever a job is finished. Otherwise Queue will send status at regular intervals set by this parameter as the number of seconds.
`client_position_to_load_data` int \| None default: None	DEPRECATED. This parameter is deprecated and has no effect.
`default_enabled` bool \| None default: None	Deprecated and has no effect.
`api_open` bool default: True	If True, the REST routes of the backend will be open, allowing requests made directly to those endpoints to skip the queue.
`max_size` int \| None default: None	The maximum number of events the queue will store at any given moment. If the queue is full, new events will not be added and a user will receive a message saying that the queue is full. If None, the queue size will be unlimited.

integrate

gradio.Blocks.integrate(···)

Description

A catch-all method for integrating with other libraries. This method should be run after launch()

Agruments

Parameter Description

Parameter	Description
`comet_ml` <class 'inspect._empty'> default: None	If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard
`wandb` ModuleType \| None default: None	If the wandb module is provided, will integrate with it and appear on WandB dashboard
`mlflow` ModuleType \| None default: None	If the mlflow module is provided, will integrate with the experiment and appear on ML Flow dashboard

comet_ml

default: None

If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard

wandb

ModuleType | None

default: None

If the wandb module is provided, will integrate with it and appear on WandB dashboard

mlflow

ModuleType | None

default: None

If the mlflow module is provided, will integrate with the experiment and appear on ML Flow dashboard

load

gradio.Blocks.load(···)

Description

For reverse compatibility reasons, this is both a class method and an instance method, the two of which, confusingly, do two completely different things.

Class method: loads a demo from a Hugging Face Spaces repo and creates it locally and returns a block instance. Warning: this method will be deprecated. Use the equivalent `gradio.load()` instead.

Instance method: adds event that runs as soon as the demo loads in the browser. Example usage below.

Example Usage

import gradio as gr
import datetime
with gr.Blocks() as demo:
    def get_time():
        return datetime.datetime.now().time()
    dt = gr.Textbox(label="Current time")
    demo.load(get_time, inputs=None, outputs=dt)
demo.launch()

Agruments

Parameter	Description
`fn` Callable \| None default: None	Instance Method - the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` list[Component] \| None default: None	Instance Method - List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` list[Component] \| None default: None	Instance Method - List of gradio.components to use as inputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None \| Literal[False] default: None	Instance Method - 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` bool default: False	Instance Method - If True, will scroll to output component on completion
`show_progress` str default: "full"	Instance Method - If True, will show progress animation while pending
`queue` <class 'inspect._empty'> default: None	Instance Method - If True, will place the request on the queue, if the queue exists
`batch` bool default: False	Instance Method - 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	Instance Method - Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	Instance Method - 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	Instance Method - If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`every` float \| None default: None	Instance Method - Run this event 'every' number of seconds. Interpreted in seconds. Queue must be enabled.
`name` str \| None default: None	Class Method - the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
`src` str \| None default: None	Class Method - the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
`api_key` str \| None default: None	Class Method - optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens. Warning: only provide this if you are loading a trusted private Space as it can be read by the Space you are loading.
`alias` str \| None default: None	Class Method - optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)

Guides

Blocks And Event Listeners

Controlling Layout

State In Blocks

Custom CSS And JS

Custom Interpretations With Blocks

Using Blocks Like Functions

Blocks

Description
Example Usage
Initialization
Demos
Methods
launch
queue
integrate
load
Guides

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