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Make sure you have gradio 5.0 or higher installed as well as node 20+.
As of the time of publication, the latest release is 4.1.1.
Also, please read the [Five Minute Tour](./custom-components-in-five-minutes) of custom components and the [Key Concepts](./key-component-concepts) guide before starting.
| Step 0: Prerequisites | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
Navigate to a directory of your choosing and run the following command:
```bash
gradio cc create PDF
```
Tip: You should change the name of the component.
Some of the screenshots assume the component is called `PDF` but the concepts are the same!
This will create a subdirectory called `pdf` in your current working ... | Step 1: Creating the custom component | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
We're going to use the [pdfjs](https://mozilla.github.io/pdf.js/) javascript library to display the pdfs in the frontend.
Let's start off by adding it to our frontend project's dependencies, as well as adding a couple of other projects we'll need.
From within the `frontend` directory, run `npm install @gradio/client ... | Step 2: Frontend - modify javascript dependencies | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
Run the `dev` command to launch the development server.
This will open the demo in `demo/app.py` in an environment where changes to the `frontend` and `backend` directories will reflect instantaneously in the launched app.
After launching the dev server, you should see a link printed to your console that says `Fronten... | Step 3: Frontend - Launching the Dev Server | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
We're going to start off by first writing the skeleton of our frontend and then adding the pdf rendering logic.
Add the following imports and expose the following properties to the top of your file in the `<script>` tag.
You may get some warnings from your code editor that some props are not used.
That's ok.
```ts
... | Step 4: Frontend - The basic skeleton | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
lets our users upload a new document.
We're going to use the `Upload` and `ModifyUpload` components that come with the `@gradio/upload` package to do this.
Underneath the `</script>` tag, delete all the current code and add the following:
```svelte
<Block {visible} {elem_id} {elem_classes} {container} {scale} {min_wi... | Step 4: Frontend - The basic skeleton | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
The `Upload your PDF` text looks a bit small and barebones.
Lets customize it!
Create a new file called `PdfUploadText.svelte` and copy the following code.
Its creating a new div to display our "upload text" with some custom styling.
Tip: Notice that we're leveraging Gradio core's existing css variables here: `var(-... | Step 5: Frontend - Nicer Upload Text | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
This is the most advanced javascript part.
It took me a while to figure it out!
Do not worry if you have trouble, the important thing is to not be discouraged 💪
Ask for help in the gradio [discord](https://discord.gg/hugging-face-879548962464493619) if you need and ask for help.
With that out of the way, let's start ... | Step 6: PDF Rendering logic | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
$: if(JSON.stringify(old_value) != JSON.stringify(_value)) {
if (_value){
get_doc(_value);
}
old_value = _value;
gradio.dispatch("change");
}
```
Tip: The `$:` syntax in svelte is how you declare statements to be reactive. Whenever any of the inputs of the statement... | Step 6: PDF Rendering logic | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
Now for the fun part - actually rendering the PDF when the file is uploaded!
Add the following functions to the `<script>` tag:
```ts
async function handle_clear() {
_value = null;
await tick();
gradio.dispatch("change");
}
async function handle_upload({detail}: CustomEvent<FileDat... | Step 7: Handling The File Upload And Clear | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
If a user uploads a PDF document with multiple pages, they will only be able to see the first one.
Let's add some buttons to help them navigate the page.
We will use the `BaseButton` from `@gradio/button` so that they look like regular Gradio buttons.
Import the `BaseButton` and add the following functions that will r... | Step 8: Adding buttons to navigate pages | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
We're going to want users of our component to get a preview of the PDF if its used as an `example` in a `gr.Interface` or `gr.Examples`.
To do so, we're going to add some of the pdf rendering logic in `Index.svelte` to `Example.svelte`.
```svelte
<script lang="ts">
export let value: string;
export let type: "galle... | Step 8.5: The Example view | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
The backend changes needed are smaller.
We're almost done!
What we're going to do is:
* Add `change` and `upload` events to our component.
* Add a `height` property to let users control the height of the PDF.
* Set the `data_model` of our component to be `FileData`. This is so that Gradio can automatically cache and s... | Step 9: The backend | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
show_label=show_label, container=container,
scale=scale, min_width=min_width,
interactive=interactive, visible=visible,
elem_id=elem_id, elem_classes=elem_classes,
render=render, load_fn=load_fn, ever... | Step 9: The backend | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
To test our backend code, let's add a more complex demo that performs Document Question and Answering with huggingface transformers.
In our `demo` directory, create a `requirements.txt` file with the following packages
```
torch
transformers
pdf2image
pytesseract
```
Tip: Remember to install these yourself and rest... | Step 10: Add a demo and publish! | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
In order to use our new component in **any** gradio 4.0 app, simply install it with pip, e.g. `pip install gradio-pdf`. Then you can use it like the built-in `gr.File()` component (except that it will only accept and display PDF files).
Here is a simple demo with the Blocks api:
```python
import gradio as gr
from gra... | Conclusion | https://gradio.app/guides/pdf-component-example | Custom Components - Pdf Component Example Guide |
Before using Custom Components, make sure you have Python 3.10+, Node.js v18+, npm 9+, and Gradio 4.0+ (preferably Gradio 5.0+) installed.
| What do I need to install before using Custom Components? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
Custom components built with Gradio 5.0 should be compatible with Gradio 4.0. If you built your custom component in Gradio 4.0 you will have to rebuild your component to be compatible with Gradio 5.0. Simply follow these steps:
1. Update the `@gradio/preview` package. `cd` into the `frontend` directory and run `npm upd... | Are custom components compatible between Gradio 4.0 and 5.0? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
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.
| What templates can I use to create my custom component? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
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.
| What is the development server? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
**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?**... | The development server didn't work for me | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
No! You can start off from an existing gradio component as a template, see the [five minute guide](./custom-components-in-five-minutes).
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 a... | Do I always need to start my component from scratch? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
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.
| Do I need to host my custom component on HuggingFace Spaces? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
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](./backend).
| What methods are mandatory for implementing a custom component in Gradio? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
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.
| What is the purpose of a `data_model` in Gradio custom components? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
Utilizing `FileData` is crucial for components that expect file uploads. It ensures secure file handling, automatic caching, and streamlined client library functionality.
| Why is it important to use `FileData` for components dealing with file uploads? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
You can define event triggers in the `EVENTS` class attribute by listing the desired event names, which automatically adds corresponding methods to your component.
| How can I add event triggers to my custom Gradio component? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
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.
| Can I implement a custom Gradio component without defining a `data_model`? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
We have prepared this [collection](https://huggingface.co/collections/gradio/custom-components-65497a761c5192d981710b12) of custom components on the HuggingFace Hub that you can use to get started!
| Are there sample custom components I can learn from? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
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](https://huggingface.co/search/full-text?q=gradio-custom-component&type=space) | How can I find custom components created by the Gradio community? | https://gradio.app/guides/frequently-asked-questions | Custom Components - Frequently Asked Questions Guide |
Every component in Gradio comes in a `static` variant, and most come in an `interactive` version as well.
The `static` version is used when a component is displaying a value, and the user can **NOT** change that value by interacting with it.
The `interactive` version is used when the user is able to change the value b... | Interactive vs Static | https://gradio.app/guides/key-component-concepts | Custom Components - Key Component Concepts Guide |
The most important attribute of a component is its `value`.
Every component has a `value`.
The value that is typically set by the user in the frontend (if the component is interactive) or displayed to the user (if it is static).
It is also this value that is sent to the backend function when a user triggers an event, ... | The value and how it is preprocessed/postprocessed | https://gradio.app/guides/key-component-concepts | Custom Components - Key Component Concepts Guide |
from the format sent by the frontend to the format expected by the python function. This usually involves going from a web-friendly **JSON** structure to a **python-native** data structure, like a `numpy` array or `PIL` image. The `Audio`, `Image` components are good examples of `preprocess` methods.
2. `postprocess`... | The value and how it is preprocessed/postprocessed | https://gradio.app/guides/key-component-concepts | Custom Components - Key Component Concepts Guide |
Gradio apps support providing example inputs -- and these are very useful in helping users get started using your Gradio app.
In `gr.Interface`, you can provide examples using the `examples` keyword, and in `Blocks`, you can provide examples using the special `gr.Examples` component.
At the bottom of this screenshot,... | The "Example Version" of a Component | https://gradio.app/guides/key-component-concepts | Custom Components - Key Component Concepts Guide |
Now that you know the most important pieces to remember about Gradio components, you can start to design and build your own!
| Conclusion | https://gradio.app/guides/key-component-concepts | Custom Components - Key Component Concepts Guide |
You will need to have:
* Python 3.10+ (<a href="https://www.python.org/downloads/" target="_blank">install here</a>)
* pip 21.3+ (`python -m pip install --upgrade pip`)
* Node.js 20+ (<a href="https://nodejs.dev/en/download/package-manager/" target="_blank">install here</a>)
* npm 9+ (<a href="https://docs.npmjs.com/d... | Installation | https://gradio.app/guides/custom-components-in-five-minutes | Custom Components - Custom Components In Five Minutes Guide |
The Custom Components workflow consists of 4 steps: create, dev, build, and publish.
1. create: creates a template for you to start developing a custom component.
2. dev: launches a development server with a sample app & hot reloading allowing you to easily develop your custom component
3. build: builds a python packa... | The Workflow | https://gradio.app/guides/custom-components-in-five-minutes | Custom Components - Custom Components In Five Minutes Guide |
Bootstrap a new template by running the following in any working directory:
```bash
gradio cc create MyComponent --template SimpleTextbox
```
Instead of `MyComponent`, give your component any name.
Instead of `SimpleTextbox`, you can use any Gradio component as a template. `SimpleTextbox` is actually a special compo... | 1. create | https://gradio.app/guides/custom-components-in-five-minutes | Custom Components - Custom Components In Five Minutes Guide |
Once you have created your new component, you can start a development server by `entering the directory` and running
```bash
gradio cc dev
```
You'll see several lines that are printed to the console.
The most important one is the one that says:
> Frontend Server (Go here): http://localhost:7861/
The port number mi... | 2. dev | https://gradio.app/guides/custom-components-in-five-minutes | Custom Components - Custom Components In Five Minutes Guide |
Once you are satisfied with your custom component's implementation, you can `build` it to use it outside of the development server.
From your component directory, run:
```bash
gradio cc build
```
This will create a `tar.gz` and `.whl` file in a `dist/` subdirectory.
If you or anyone installs that `.whl` file (`pip i... | 3. build | https://gradio.app/guides/custom-components-in-five-minutes | Custom Components - Custom Components In Five Minutes Guide |
Right now, your package is only available on a `.whl` file on your computer.
You can share that file with the world with the `publish` command!
Simply run the following command from your component directory:
```bash
gradio cc publish
```
This will guide you through the following process:
1. Upload your distribution... | 4. publish | https://gradio.app/guides/custom-components-in-five-minutes | Custom Components - Custom Components In Five Minutes Guide |
Now that you know the high-level workflow of creating custom components, you can go in depth in the next guides!
After reading the guides, check out this [collection](https://huggingface.co/collections/gradio/custom-components-65497a761c5192d981710b12) of custom components on the HuggingFace Hub so you can learn from o... | Conclusion | https://gradio.app/guides/custom-components-in-five-minutes | Custom Components - Custom Components In Five Minutes Guide |
All components inherit from one of three classes `Component`, `FormComponent`, or `BlockContext`.
You need to inherit from one so that your component behaves like all other gradio components.
When you start from a template with `gradio cc create --template`, you don't need to worry about which one to choose since the t... | Which Class to Inherit From | https://gradio.app/guides/backend | Custom Components - Backend Guide |
When you inherit from any of these classes, the following methods must be implemented.
Otherwise the Python interpreter will raise an error when you instantiate your component!
`preprocess` and `postprocess`
Explained in the [Key Concepts](./key-component-conceptsthe-value-and-how-it-is-preprocessed-postprocessed) gu... | The methods you need to implement | https://gradio.app/guides/backend | Custom Components - Backend Guide |
pi_info(self) -> dict[str, list[str]]:
"""
A JSON-schema representation of the value that the `preprocess` expects and the `postprocess` returns.
"""
pass
```
`example_payload`
An example payload for your component, e.g. something that can be passed into the `.preprocess()` method
of your component. T... | The methods you need to implement | https://gradio.app/guides/backend | Custom Components - Backend Guide |
"""
Convert the data from the csv or jsonl file into the component state.
"""
return x
```
| The methods you need to implement | https://gradio.app/guides/backend | Custom Components - Backend Guide |
The `data_model` is how you define the expected data format your component's value will be stored in the frontend.
It specifies the data format your `preprocess` method expects and the format the `postprocess` method returns.
It is not necessary to define a `data_model` for your component but it greatly simplifies the ... | The `data_model` | https://gradio.app/guides/backend | Custom Components - Backend Guide |
example, the `Names` model will serialize the data to `{'names': ['freddy', 'pete']}` whereas the `NamesRoot` model will serialize it to `['freddy', 'pete']`.
```python
from typing import List
class Names(GradioModel):
names: List[str]
class NamesRoot(GradioRootModel):
root: List[str]
```
Even if your comp... | The `data_model` | https://gradio.app/guides/backend | Custom Components - Backend Guide |
If your component expects uploaded files as input, or returns saved files to the frontend, you **MUST** use the `FileData` to type the files in your `data_model`.
When you use the `FileData`:
* Gradio knows that it should allow serving this file to the frontend. Gradio automatically blocks requests to serve arbitrary... | Handling Files | https://gradio.app/guides/backend | Custom Components - Backend Guide |
The events triggers for your component are defined in the `EVENTS` class attribute.
This is a list that contains the string names of the events.
Adding an event to this list will automatically add a method with that same name to your component!
You can import the `Events` enum from `gradio.events` to access commonly u... | Adding Event Triggers To Your Component | https://gradio.app/guides/backend | Custom Components - Backend Guide |
Conclusion | https://gradio.app/guides/backend | Custom Components - Backend Guide | |
The documentation will be generated when running `gradio cc build`. You can pass the `--no-generate-docs` argument to turn off this behaviour.
There is also a standalone `docs` command that allows for greater customisation. If you are running this command manually it should be run _after_ the `version` in your `pyproj... | How do I use it? | https://gradio.app/guides/documenting-custom-components | Custom Components - Documenting Custom Components Guide |
The `gradio cc docs` command will generate an interactive Gradio app and a static README file with various features. You can see an example here:
- [Gradio app deployed on Hugging Face Spaces]()
- [README.md rendered by GitHub]()
The README.md and space both have the following features:
- A description.
- Installati... | What gets generated? | https://gradio.app/guides/documenting-custom-components | Custom Components - Documenting Custom Components Guide |
The documentation generator uses existing standards to extract the necessary information, namely Type Hints and Docstrings. There are no Gradio-specific APIs for documentation, so following best practices will generally yield the best results.
If you already use type hints and docstrings in your component source code,... | What do I need to do? | https://gradio.app/guides/documenting-custom-components | Custom Components - Documenting Custom Components Guide |
be typed.
- `preprocess` parameters and return value should be typed.
If you are using `gradio cc create`, these types should already exist, but you may need to tweak them based on any changes you make.
`__init__`
Here, you only need to type the parameters. If you have cloned a template with `gradio` cc create`, the... | What do I need to do? | https://gradio.app/guides/documenting-custom-components | Custom Components - Documenting Custom Components Guide |
offer a rich in-editor experience like type hints, but unlike type hints, they don't have any specific syntax requirements. They are simple strings and can take almost any form. The only requirement is where they appear. Docstrings should be "a string literal that occurs as the first statement in a module, function, cl... | What do I need to do? | https://gradio.app/guides/documenting-custom-components | Custom Components - Documenting Custom Components Guide |
do not need to do anything as they already have descriptions we can extract:
```py
from gradio.events import Events
class ParamViewer(Component):
...
EVENTS = [
Events.change,
Events.upload,
]
```
Custom events
You can define a custom event if the built-in events are unsuitable for your use case. Thi... | What do I need to do? | https://gradio.app/guides/documenting-custom-components | Custom Components - Documenting Custom Components Guide |
ore complex app for your testing purposes. You can also create other spaces, showcasing more complex examples and linking to them from the main class docstring or the `pyproject.toml` description.
Keep the code concise
The 'getting started' snippet utilises the demo code, which should be as short as possible to keep ... | What do I need to do? | https://gradio.app/guides/documenting-custom-components | Custom Components - Documenting Custom Components Guide |
pyproject.toml` urls section might look like this:
```toml
[project.urls]
repository = "https://github.com/user/repo-name"
space = "https://huggingface.co/spaces/user/space-name"
``` | What do I need to do? | https://gradio.app/guides/documenting-custom-components | Custom Components - Documenting Custom Components Guide |
Let's start by using `llama-index` on top of `openai` to build a RAG chatbot on any text or PDF files that you can demo and share in less than 30 lines of code. You'll need to have an OpenAI key for this example (keep reading for the free, open-source equivalent!)
$code_llm_llamaindex
| Llama Index | https://gradio.app/guides/chatinterface-examples | Chatbots - Chatinterface Examples Guide |
Here's an example using `langchain` on top of `openai` to build a general-purpose chatbot. As before, you'll need to have an OpenAI key for this example.
$code_llm_langchain
Tip: For quick prototyping, the community-maintained <a href='https://github.com/AK391/langchain-gradio'>langchain-gradio repo</a> makes it eve... | LangChain | https://gradio.app/guides/chatinterface-examples | Chatbots - Chatinterface Examples Guide |
Of course, we could also use the `openai` library directy. Here a similar example to the LangChain , but this time with streaming as well:
Tip: For quick prototyping, the <a href='https://github.com/gradio-app/openai-gradio'>openai-gradio library</a> makes it even easier to build chatbots on top of OpenAI models.
| OpenAI | https://gradio.app/guides/chatinterface-examples | Chatbots - Chatinterface Examples Guide |
Of course, in many cases you want to run a chatbot locally. Here's the equivalent example using the SmolLM2-135M-Instruct model using the Hugging Face `transformers` library.
$code_llm_hf_transformers
| Hugging Face `transformers` | https://gradio.app/guides/chatinterface-examples | Chatbots - Chatinterface Examples Guide |
The SambaNova Cloud API provides access to full-precision open-source models, such as the Llama family. Here's an example of how to build a Gradio app around the SambaNova API
$code_llm_sambanova
Tip: For quick prototyping, the <a href='https://github.com/gradio-app/sambanova-gradio'>sambanova-gradio library</a> mak... | SambaNova | https://gradio.app/guides/chatinterface-examples | Chatbots - Chatinterface Examples Guide |
The Hyperbolic AI API provides access to many open-source models, such as the Llama family. Here's an example of how to build a Gradio app around the Hyperbolic
$code_llm_hyperbolic
Tip: For quick prototyping, the <a href='https://github.com/HyperbolicLabs/hyperbolic-gradio'>hyperbolic-gradio library</a> makes it ev... | Hyperbolic | https://gradio.app/guides/chatinterface-examples | Chatbots - Chatinterface Examples Guide |
Anthropic's Claude model can also be used via API. Here's a simple 20 questions-style game built on top of the Anthropic API:
$code_llm_claude
| Anthropic's Claude | https://gradio.app/guides/chatinterface-examples | Chatbots - Chatinterface Examples Guide |
The MiniMax API exposes the M-series models through an OpenAI-compatible endpoint, so the standard `openai` client works out of the box. Here's an example of how to build a Gradio app around MiniMax:
$code_llm_minimax
| MiniMax | https://gradio.app/guides/chatinterface-examples | Chatbots - Chatinterface Examples Guide |
Chatbots are a popular application of large language models (LLMs). Using Gradio, you can easily build a chat application and share that with your users, or try it yourself using an intuitive UI.
This tutorial uses `gr.ChatInterface()`, which is a high-level abstraction that allows you to create your chatbot UI fast, ... | Introduction | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
If you have a chat server serving an OpenAI-API compatible endpoint (such as Ollama), you can spin up a ChatInterface in a single line of Python. First, also run `pip install openai`. Then, with your own URL, model, and optional token:
```python
import gradio as gr
gr.load_chat("http://localhost:11434/v1/", model="ll... | Note for OpenAI-API compatible endpoints | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
To create a chat application with `gr.ChatInterface()`, the first thing you should do is define your **chat function**. In the simplest case, your chat function should accept two arguments: `message` and `history` (the arguments can be named anything, but must be in this order).
- `message`: a `str` representing the u... | Defining a chat function | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
t take user input or the previous history into account! Here's another simple example showing how to incorporate a user's input as well as the history.
```python
import gradio as gr
def alternatingly_agree(message, history):
if len([h for h in history if h['role'] == "assistant"]) % 2 == 0:
return f"Yes, ... | Defining a chat function | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
In your chat function, you can use `yield` to generate a sequence of partial responses, each replacing the previous ones. This way, you'll end up with a streaming chatbot. It's that simple!
```python
import time
import gradio as gr
def slow_echo(message, history):
for i in range(len(message)):
time.sleep(... | Streaming chatbots | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
If you're familiar with Gradio's `gr.Interface` class, the `gr.ChatInterface` includes many of the same arguments that you can use to customize the look and feel of your Chatbot. For example, you can:
- add a title and description above your chatbot using `title` and `description` arguments.
- add a theme or custom cs... | Customizing the Chat UI | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
le of how we to apply the parameters we've discussed in this section:
```python
import gradio as gr
def yes_man(message, history):
if message.endswith("?"):
return "Yes"
else:
return "Ask me anything!"
gr.ChatInterface(
yes_man,
chatbot=gr.Chatbot(height=300),
textbox=gr.Textbox(p... | Customizing the Chat UI | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
You may want to add multimodal capabilities to your chat interface. For example, you may want users to be able to upload images or files to your chatbot and ask questions about them. You can make your chatbot "multimodal" by passing in a single parameter (`multimodal=True`) to the `gr.ChatInterface` class.
When `multi... | Multimodal Chat Interface | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
ox` to the `textbox` parameter. You can customize the `MultimodalTextbox` further by passing in the `sources` parameter, which is a list of sources to enable. Here's an example that illustrates how to set up and customize and multimodal chat interface:
```python
import gradio as gr
def count_images(message, history... | Multimodal Chat Interface | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
You may want to add additional inputs to your chat function and expose them to your users through the chat UI. For example, you could add a textbox for a system prompt, or a slider that sets the number of tokens in the chatbot's response. The `gr.ChatInterface` class supports an `additional_inputs` parameter which can ... | Additional Inputs | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
s to the `examples` parameter, where each inner list represents one sample, and each inner list should be `1 + len(additional_inputs)` long. The first element in the inner list should be the example value for the chat message, and each subsequent element should be an example value for one of the additional inputs, in o... | Additional Inputs | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
In the same way that you can accept additional inputs into your chat function, you can also return additional outputs. Simply pass in a list of components to the `additional_outputs` parameter in `gr.ChatInterface` and return additional values for each component from your chat function. Here's an example that extracts ... | Additional Outputs | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
We mentioned earlier that in the simplest case, your chat function should return a `str` response, which will be rendered as Markdown in the chatbot. However, you can also return more complex responses as we discuss below:
**Returning files or Gradio components**
Currently, the following Gradio components can be dis... | Returning Complex Responses | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
ma of the `gr.ChatMessage` data class as well as two internal typed dictionaries:
```py
MessageContent = Union[str, FileDataDict, FileData, Component]
@dataclass
class ChatMessage:
content: MessageContent | list[MessageContent]
metadata: MetadataDict = None
options: list[OptionDict] = None
class Metada... | Returning Complex Responses | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
corresponding to the `options` key should be a list of dictionaries, each with a `value` (a string that is the value that should be sent to the chat function when this response is clicked) and an optional `label` (if provided, is the text displayed as the preset response instead of the `value`).
This example illustr... | Returning Complex Responses | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
You may wish to modify the value of the chatbot with your own events, other than those prebuilt in the `gr.ChatInterface`. For example, you could create a dropdown that prefills the chat history with certain conversations or add a separate button to clear the conversation history. The `gr.ChatInterface` supports these ... | Modifying the Chatbot Value Directly | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
Once you've built your Gradio chat interface and are hosting it on [Hugging Face Spaces](https://hf.space) or somewhere else, then you can query it with a simple API. The API route will be the name of the function you pass to the ChatInterface. So if `gr.ChatInterface(respond)`, then the API route is `/respond`. The en... | Using Your Chatbot via API | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
You can enable persistent chat history for your ChatInterface, allowing users to maintain multiple conversations and easily switch between them. When enabled, conversations are stored locally and privately in the user's browser using local storage. So if you deploy a ChatInterface e.g. on [Hugging Face Spaces](https://... | Chat History | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
To gather feedback on your chat model, set `gr.ChatInterface(flagging_mode="manual")` and users will be able to thumbs-up or thumbs-down assistant responses. Each flagged response, along with the entire chat history, will get saved in a CSV file in the app working directory (this can be configured via the `flagging_dir... | Collecting User Feedback | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
Now that you've learned about the `gr.ChatInterface` class and how it can be used to create chatbot UIs quickly, we recommend reading one of the following:
* [Our next Guide](../guides/chatinterface-examples) shows examples of how to use `gr.ChatInterface` with popular LLM libraries.
* If you'd like to build very cust... | What's Next? | https://gradio.app/guides/creating-a-chatbot-fast | Chatbots - Creating A Chatbot Fast Guide |
Every element of the chatbot value is a dictionary of `role` and `content` keys. You can always use plain python dictionaries to add new values to the chatbot but Gradio also provides the `ChatMessage` dataclass to help you with IDE autocompletion. The schema of `ChatMessage` is as follows:
```py
MessageContent = Uni... | The `ChatMessage` dataclass | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
tion`: an optional numeric value representing the duration of the thought/tool usage, in seconds. Displayed in a subdued font next inside parentheses next to the thought title.
* `status`: if set to `"pending"`, a spinner appears next to the thought title and the accordion is initialized open. If `status` is `"done"`,... | The `ChatMessage` dataclass | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
A real example using transformers.agents
We'll create a Gradio application simple agent that has access to a text-to-image tool.
Tip: Make sure you read the [smolagents documentation](https://huggingface.co/docs/smolagents/index) first
We'll start by importing the necessary classes from transformers and gradio.
``... | Building with Agents | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
om/freddyaboulton/freddyboulton/assets/41651716/c8d21336-e0e6-4878-88ea-e6fcfef3552d)
A real example using langchain agents
We'll create a UI for langchain agent that has access to a search engine.
We'll begin with imports and setting up the langchain agent. Note that you'll need an .env file with the following env... | Building with Agents | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
🦜⛓️ and see its thoughts 💭")
chatbot = gr.Chatbot(
label="Agent",
avatar_images=(
None,
"https://em-content.zobj.net/source/twitter/141/parrot_1f99c.png",
),
)
input = gr.Textbox(lines=1, label="Chat Message")
input.submit(interact_with_langchain_agent, ... | Building with Agents | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
The Gradio Chatbot can natively display intermediate thoughts of a _thinking_ LLM. This makes it perfect for creating UIs that show how an AI model "thinks" while generating responses. Below guide will show you how to build a chatbot that displays Gemini AI's thought process in real-time.
A real example using Gemini ... | Building with Visibly Thinking LLMs | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
if len(parts) == 2 and not thinking_complete:
Complete thought and start response
thought_buffer += current_chunk
messages[-1] = ChatMessage(
role="assistant",
content=thought_buffer,
metadata={"title": "⏳Thinking: *The thoughts produc... | Building with Visibly Thinking LLMs | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
message
input_box.submit(
lambda msg: (msg, msg, ""), Store message and clear input
inputs=[input_box],
outputs=[msg_store, input_box, input_box],
queue=False
).then(
user_message, Add user message to chat
inputs=[msg_store, chatbot],
outputs=[inpu... | Building with Visibly Thinking LLMs | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
document preparation:
```python
def encode_pdf_to_base64(file_obj) -> str:
"""Convert uploaded PDF file to base64 string."""
if file_obj is None:
return None
with open(file_obj.name, 'rb') as f:
return base64.b64encode(f.read()).decode('utf-8')
def format_message_history(
history: list... | Building with Visibly Thinking LLMs | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
latest_message["content"].append({"type": "text", "text": history[-1]["content"]})
formatted_messages.append(latest_message)
return formatted_messages
```
Then, let's create our bot response handler that processes citations:
```python
def bot_response(
history: list,
enable_citations: bool,
d... | Building with Visibly Thinking LLMs | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
your request."
})
return history
```
Finally, let's create the Gradio interface:
```python
with gr.Blocks() as demo:
gr.Markdown("Chat with Citations")
with gr.Row(scale=1):
with gr.Column(scale=4):
chatbot = gr.Chatbot(bubble_full_width=False, show_label=False, scale... | Building with Visibly Thinking LLMs | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
tbot that not only responds to users but also shows its sources, creating a more transparent and trustworthy interaction. See our finished Citations demo [here](https://huggingface.co/spaces/ysharma/anthropic-citations-with-gradio-metadata-key).
| Building with Visibly Thinking LLMs | https://gradio.app/guides/agents-and-tool-usage | Chatbots - Agents And Tool Usage Guide |
First, we'll build the UI without handling these events and build from there.
We'll use the Hugging Face InferenceClient in order to get started without setting up
any API keys.
This is what the first draft of our application looks like:
```python
from huggingface_hub import InferenceClient
import gradio as gr
clie... | The UI | https://gradio.app/guides/chatbot-specific-events | Chatbots - Chatbot Specific Events Guide |
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