Introducing Gradio ClientsJoin us on Thursday, 9am PST
LivestreamIntroducing Gradio ClientsJoin us on Thursday, 9am PST
LivestreamBefore using Custom Components, make sure you have Python 3.8+, Node.js v16.14+, npm 9+, and Gradio 4.0+ installed.
Run gradio cc show
to see the list of built-in templates.
You can also start off from other's custom components!
Simply git clone
their repository and make your modifications.
When you run gradio cc dev
, a development server will load and run a Gradio app of your choosing.
This is like when you run python <app-file>.py
, however the gradio
command will hot reload so you can instantly see your changes.
1. Check your terminal and browser console
Make sure there are no syntax errors or other obvious problems in your code. Exceptions triggered from python will be displayed in the terminal. Exceptions from javascript will be displayed in the browser console and/or the terminal.
2. Are you developing on Windows?
Chrome on Windows will block the local compiled svelte files for security reasons. We recommend developing your custom component in the windows subsystem for linux (WSL) while the team looks at this issue.
3. Inspect the window.GRADIO_CC variable
In the browser console, print the window.__GRADIO__CC
variable (just type it into the console). If it is an empty object, that means
that the CLI could not find your custom component source code. Typically, this happens when the custom component is installed in a different virtual environment than the one used to run the dev command. Please use the --python-path
and gradio-path
CLI arguments to specify the path of the python and gradio executables for the environment your component is installed in. For example, if you are using a virtualenv located at /Users/mary/venv
, pass in /Users/mary/bin/python
and /Users/mary/bin/gradio
respectively.
If the window.__GRADIO__CC
variable is not empty (see below for an example), then the dev server should be working correctly.
4. Make sure you are using a virtual environment It is highly recommended you use a virtual environment to prevent conflicts with other python dependencies installed in your system.
No! You can start off from an existing gradio component as a template, see the five minute guide.
You can also start from an existing custom component if you'd like to tweak it further. Once you find the source code of a custom component you like, clone the code to your computer and run gradio cc install
. Then you can run the development server to make changes.If you run into any issues, contact the author of the component by opening an issue in their repository. The gallery is a good place to look for published components. For example, to start from the PDF component, clone the space with git clone https://huggingface.co/spaces/freddyaboulton/gradio_pdf
, cd
into the src
directory, and run gradio cc install
.
You can develop and build your custom component without hosting or connecting to HuggingFace. If you would like to share your component with the gradio community, it is recommended to publish your package to PyPi and host a demo on HuggingFace so that anyone can install it or try it out.
You must implement the preprocess
, postprocess
, example_payload
, and example_value
methods. If your component does not use a data model, you must also define the api_info
, flag
, and read_from_flag
methods. Read more in the backend guide.
data_model
in Gradio custom components?A data_model
defines the expected data format for your component, simplifying the component development process and self-documenting your code. It streamlines API usage and example caching.
FileData
for components dealing with file uploads?Utilizing FileData
is crucial for components that expect file uploads. It ensures secure file handling, automatic caching, and streamlined client library functionality.
You can define event triggers in the EVENTS
class attribute by listing the desired event names, which automatically adds corresponding methods to your component.
data_model
?Yes, it is possible to create custom components without a data_model
, but you are going to have to manually implement api_info
, flag
, and read_from_flag
methods.
We have prepared this collection of custom components on the HuggingFace Hub that you can use to get started!
We're working on creating a gallery to make it really easy to discover new custom components.
In the meantime, you can search for HuggingFace Spaces that are tagged as a gradio-custom-component
here