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You may wish to bin data by time buckets. Use `x_bin` to do so, using a string suffix with "s", "m", "h" or "d", such as "15m" or "1d".
$code_plot_guide_aggregate_temporal
$demo_plot_guide_aggregate_temporal
|
Aggregating by Time
|
https://gradio.app/guides/time-plots
|
Data Science And Plots - Time Plots Guide
|
You can use `gr.DateTime` to accept input datetime data. This works well with plots for defining the x-axis range for the data.
$code_plot_guide_datetime
$demo_plot_guide_datetime
Note how `gr.DateTime` can accept a full datetime string, or a shorthand using `now - [0-9]+[smhd]` format to refer to a past time.
You will often have many time plots in which case you'd like to keep the x-axes in sync. The `DateTimeRange` custom component keeps a set of datetime plots in sync, and also uses the `.select` listener of plots to allow you to zoom into plots while keeping plots in sync.
Because it is a custom component, you first need to `pip install gradio_datetimerange`. Then run the following:
$code_plot_guide_datetimerange
$demo_plot_guide_datetimerange
Try zooming around in the plots and see how DateTimeRange updates. All the plots updates their `x_lim` in sync. You also have a "Back" link in the component to allow you to quickly zoom in and out.
|
DateTime Components
|
https://gradio.app/guides/time-plots
|
Data Science And Plots - Time Plots Guide
|
In many cases, you're working with live, realtime date, not a static dataframe. In this case, you'd update the plot regularly with a `gr.Timer()`. Assuming there's a `get_data` method that gets the latest dataframe:
```python
with gr.Blocks() as demo:
timer = gr.Timer(5)
plot1 = gr.BarPlot(x="time", y="price")
plot2 = gr.BarPlot(x="time", y="price", color="origin")
timer.tick(lambda: [get_data(), get_data()], outputs=[plot1, plot2])
```
You can also use the `every` shorthand to attach a `Timer` to a component that has a function value:
```python
with gr.Blocks() as demo:
timer = gr.Timer(5)
plot1 = gr.BarPlot(get_data, x="time", y="price", every=timer)
plot2 = gr.BarPlot(get_data, x="time", y="price", color="origin", every=timer)
```
|
RealTime Data
|
https://gradio.app/guides/time-plots
|
Data Science And Plots - Time Plots Guide
|
Use any of the standard Gradio form components to filter your data. You can do this via event listeners or function-as-value syntax. Let's look at the event listener approach first:
$code_plot_guide_filters_events
$demo_plot_guide_filters_events
And this would be the function-as-value approach for the same demo.
$code_plot_guide_filters
|
Filters
|
https://gradio.app/guides/filters-tables-and-stats
|
Data Science And Plots - Filters Tables And Stats Guide
|
Add `gr.DataFrame` and `gr.Label` to your dashboard for some hard numbers.
$code_plot_guide_tables_stats
$demo_plot_guide_tables_stats
|
Tables and Stats
|
https://gradio.app/guides/filters-tables-and-stats
|
Data Science And Plots - Filters Tables And Stats Guide
|
```python
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite:///your_database.db')
with gr.Blocks() as demo:
gr.LinePlot(pd.read_sql_query("SELECT time, price from flight_info;", engine), x="time", y="price")
```
Let's see a a more interactive plot involving filters that modify your SQL query:
```python
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite:///your_database.db')
with gr.Blocks() as demo:
origin = gr.Dropdown(["DFW", "DAL", "HOU"], value="DFW", label="Origin")
gr.LinePlot(lambda origin: pd.read_sql_query(f"SELECT time, price from flight_info WHERE origin = {origin};", engine), inputs=origin, x="time", y="price")
```
|
SQLite
|
https://gradio.app/guides/connecting-to-a-database
|
Data Science And Plots - Connecting To A Database Guide
|
If you're using a different database format, all you have to do is swap out the engine, e.g.
```python
engine = create_engine('postgresql://username:password@host:port/database_name')
```
```python
engine = create_engine('mysql://username:password@host:port/database_name')
```
```python
engine = create_engine('oracle://username:password@host:port/database_name')
```
|
Postgres, mySQL, and other databases
|
https://gradio.app/guides/connecting-to-a-database
|
Data Science And Plots - Connecting To A Database Guide
|
Plots accept a pandas Dataframe as their value. The plot also takes `x` and `y` which represent the names of the columns that represent the x and y axes respectively. Here's a simple example:
$code_plot_guide_line
$demo_plot_guide_line
All plots have the same API, so you could swap this out with a `gr.ScatterPlot`:
$code_plot_guide_scatter
$demo_plot_guide_scatter
The y axis column in the dataframe should have a numeric type, but the x axis column can be anything from strings, numbers, categories, or datetimes.
$code_plot_guide_scatter_nominal
$demo_plot_guide_scatter_nominal
|
Creating a Plot with a pd.Dataframe
|
https://gradio.app/guides/creating-plots
|
Data Science And Plots - Creating Plots Guide
|
You can break out your plot into series using the `color` argument.
$code_plot_guide_series_nominal
$demo_plot_guide_series_nominal
If you wish to assign series specific colors, use the `color_map` arg, e.g. `gr.ScatterPlot(..., color_map={'white': 'FF9988', 'asian': '88EEAA', 'black': '333388'})`
The color column can be numeric type as well.
$code_plot_guide_series_quantitative
$demo_plot_guide_series_quantitative
|
Breaking out Series by Color
|
https://gradio.app/guides/creating-plots
|
Data Science And Plots - Creating Plots Guide
|
You can aggregate values into groups using the `x_bin` and `y_aggregate` arguments. If your x-axis is numeric, providing an `x_bin` will create a histogram-style binning:
$code_plot_guide_aggregate_quantitative
$demo_plot_guide_aggregate_quantitative
If your x-axis is a string type instead, they will act as the category bins automatically:
$code_plot_guide_aggregate_nominal
$demo_plot_guide_aggregate_nominal
|
Aggregating Values
|
https://gradio.app/guides/creating-plots
|
Data Science And Plots - Creating Plots Guide
|
You can use the `.select` listener to select regions of a plot. Click and drag on the plot below to select part of the plot.
$code_plot_guide_selection
$demo_plot_guide_selection
You can combine this and the `.double_click` listener to create some zoom in/out effects by changing `x_lim` which sets the bounds of the x-axis:
$code_plot_guide_zoom
$demo_plot_guide_zoom
If you had multiple plots with the same x column, your event listeners could target the x limits of all other plots so that the x-axes stay in sync.
$code_plot_guide_zoom_sync
$demo_plot_guide_zoom_sync
|
Selecting Regions
|
https://gradio.app/guides/creating-plots
|
Data Science And Plots - Creating Plots Guide
|
Take a look how you can have an interactive dashboard where the plots are functions of other Components.
$code_plot_guide_interactive
$demo_plot_guide_interactive
It's that simple to filter and control the data presented in your visualization!
|
Making an Interactive Dashboard
|
https://gradio.app/guides/creating-plots
|
Data Science And Plots - Creating Plots Guide
|
Adding examples to an Interface is as easy as providing a list of lists to the `examples`
keyword argument.
Each sublist is a data sample, where each element corresponds to an input of the prediction function.
The inputs must be ordered in the same order as the prediction function expects them.
If your interface only has one input component, then you can provide your examples as a regular list instead of a list of lists.
Loading Examples from a Directory
You can also specify a path to a directory containing your examples. If your Interface takes only a single file-type input, e.g. an image classifier, you can simply pass a directory filepath to the `examples=` argument, and the `Interface` will load the images in the directory as examples.
In the case of multiple inputs, this directory must
contain a log.csv file with the example values.
In the context of the calculator demo, we can set `examples='/demo/calculator/examples'` and in that directory we include the following `log.csv` file:
```csv
num,operation,num2
5,"add",3
4,"divide",2
5,"multiply",3
```
This can be helpful when browsing flagged data. Simply point to the flagged directory and the `Interface` will load the examples from the flagged data.
Providing Partial Examples
Sometimes your app has many input components, but you would only like to provide examples for a subset of them. In order to exclude some inputs from the examples, pass `None` for all data samples corresponding to those particular components.
|
Providing Examples
|
https://gradio.app/guides/more-on-examples
|
Building Interfaces - More On Examples Guide
|
You may wish to provide some cached examples of your model for users to quickly try out, in case your model takes a while to run normally.
If `cache_examples=True`, your Gradio app will run all of the examples and save the outputs when you call the `launch()` method. This data will be saved in a directory called `gradio_cached_examples` in your working directory by default. You can also set this directory with the `GRADIO_EXAMPLES_CACHE` environment variable, which can be either an absolute path or a relative path to your working directory.
Whenever a user clicks on an example, the output will automatically be populated in the app now, using data from this cached directory instead of actually running the function. This is useful so users can quickly try out your model without adding any load!
Alternatively, you can set `cache_examples="lazy"`. This means that each particular example will only get cached after it is first used (by any user) in the Gradio app. This is helpful if your prediction function is long-running and you do not want to wait a long time for your Gradio app to start.
Keep in mind once the cache is generated, it will not be updated automatically in future launches. If the examples or function logic change, delete the cache folder to clear the cache and rebuild it with another `launch()`.
|
Caching examples
|
https://gradio.app/guides/more-on-examples
|
Building Interfaces - More On Examples Guide
|
If the state is something that should be accessible to all function calls and all users, you can create a variable outside the function call and access it inside the function. For example, you may load a large model outside the function and use it inside the function so that every function call does not need to reload the model.
$code_score_tracker
In the code above, the `scores` array is shared between all users. If multiple users are accessing this demo, their scores will all be added to the same list, and the returned top 3 scores will be collected from this shared reference.
|
Global State
|
https://gradio.app/guides/interface-state
|
Building Interfaces - Interface State Guide
|
Another type of data persistence Gradio supports is session state, where data persists across multiple submits within a page session. However, data is _not_ shared between different users of your model. To store data in a session state, you need to do three things:
1. Pass in an extra parameter into your function, which represents the state of the interface.
2. At the end of the function, return the updated value of the state as an extra return value.
3. Add the `'state'` input and `'state'` output components when creating your `Interface`
Here's a simple app to illustrate session state - this app simply stores users previous submissions and displays them back to the user:
$code_interface_state
$demo_interface_state
Notice how the state persists across submits within each page, but if you load this demo in another tab (or refresh the page), the demos will not share chat history. Here, we could not store the submission history in a global variable, otherwise the submission history would then get jumbled between different users.
The initial value of the `State` is `None` by default. If you pass a parameter to the `value` argument of `gr.State()`, it is used as the default value of the state instead.
Note: the `Interface` class only supports a single session state variable (though it can be a list with multiple elements). For more complex use cases, you can use Blocks, [which supports multiple `State` variables](/guides/state-in-blocks/). Alternatively, if you are building a chatbot that maintains user state, consider using the `ChatInterface` abstraction, [which manages state automatically](/guides/creating-a-chatbot-fast).
|
Session State
|
https://gradio.app/guides/interface-state
|
Building Interfaces - Interface State Guide
|
Gradio includes more than 30 pre-built components (as well as many [community-built _custom components_](https://www.gradio.app/custom-components/gallery)) that can be used as inputs or outputs in your demo. These components correspond to common data types in machine learning and data science, e.g. the `gr.Image` component is designed to handle input or output images, the `gr.Label` component displays classification labels and probabilities, the `gr.LinePlot` component displays line plots, and so on.
|
Gradio Components
|
https://gradio.app/guides/the-interface-class
|
Building Interfaces - The Interface Class Guide
|
We used the default versions of the `gr.Textbox` and `gr.Slider`, but what if you want to change how the UI components look or behave?
Let's say you want to customize the slider to have values from 1 to 10, with a default of 2. And you wanted to customize the output text field — you want it to be larger and have a label.
If you use the actual classes for `gr.Textbox` and `gr.Slider` instead of the string shortcuts, you have access to much more customizability through component attributes.
$code_hello_world_2
$demo_hello_world_2
|
Components Attributes
|
https://gradio.app/guides/the-interface-class
|
Building Interfaces - The Interface Class Guide
|
Suppose you had a more complex function, with multiple outputs as well. In the example below, we define a function that takes a string, boolean, and number, and returns a string and number.
$code_hello_world_3
$demo_hello_world_3
Just as each component in the `inputs` list corresponds to one of the parameters of the function, in order, each component in the `outputs` list corresponds to one of the values returned by the function, in order.
|
Multiple Input and Output Components
|
https://gradio.app/guides/the-interface-class
|
Building Interfaces - The Interface Class Guide
|
Gradio supports many types of components, such as `Image`, `DataFrame`, `Video`, or `Label`. Let's try an image-to-image function to get a feel for these!
$code_sepia_filter
$demo_sepia_filter
When using the `Image` component as input, your function will receive a NumPy array with the shape `(height, width, 3)`, where the last dimension represents the RGB values. We'll return an image as well in the form of a NumPy array.
Gradio handles the preprocessing and postprocessing to convert images to NumPy arrays and vice versa. You can also control the preprocessing performed with the `type=` keyword argument. For example, if you wanted your function to take a file path to an image instead of a NumPy array, the input `Image` component could be written as:
```python
gr.Image(type="filepath")
```
You can read more about the built-in Gradio components and how to customize them in the [Gradio docs](https://gradio.app/docs).
|
An Image Example
|
https://gradio.app/guides/the-interface-class
|
Building Interfaces - The Interface Class Guide
|
You can provide example data that a user can easily load into `Interface`. This can be helpful to demonstrate the types of inputs the model expects, as well as to provide a way to explore your dataset in conjunction with your model. To load example data, you can provide a **nested list** to the `examples=` keyword argument of the Interface constructor. Each sublist within the outer list represents a data sample, and each element within the sublist represents an input for each input component. The format of example data for each component is specified in the [Docs](https://gradio.app/docscomponents).
$code_calculator
$demo_calculator
You can load a large dataset into the examples to browse and interact with the dataset through Gradio. The examples will be automatically paginated (you can configure this through the `examples_per_page` argument of `Interface`).
Continue learning about examples in the [More On Examples](https://gradio.app/guides/more-on-examples) guide.
|
Example Inputs
|
https://gradio.app/guides/the-interface-class
|
Building Interfaces - The Interface Class Guide
|
In the previous example, you may have noticed the `title=` and `description=` keyword arguments in the `Interface` constructor that helps users understand your app.
There are three arguments in the `Interface` constructor to specify where this content should go:
- `title`: which accepts text and can display it at the very top of interface, and also becomes the page title.
- `description`: which accepts text, markdown or HTML and places it right under the title.
- `article`: which also accepts text, markdown or HTML and places it below the interface.
Another useful keyword argument is `label=`, which is present in every `Component`. This modifies the label text at the top of each `Component`. You can also add the `info=` keyword argument to form elements like `Textbox` or `Radio` to provide further information on their usage.
```python
gr.Number(label='Age', info='In years, must be greater than 0')
```
|
Descriptive Content
|
https://gradio.app/guides/the-interface-class
|
Building Interfaces - The Interface Class Guide
|
If your prediction function takes many inputs, you may want to hide some of them within a collapsed accordion to avoid cluttering the UI. The `Interface` class takes an `additional_inputs` argument which is similar to `inputs` but any input components included here are not visible by default. The user must click on the accordion to show these components. The additional inputs are passed into the prediction function, in order, after the standard inputs.
You can customize the appearance of the accordion by using the optional `additional_inputs_accordion` argument, which accepts a string (in which case, it becomes the label of the accordion), or an instance of the `gr.Accordion()` class (e.g. this lets you control whether the accordion is open or closed by default).
Here's an example:
$code_interface_with_additional_inputs
$demo_interface_with_additional_inputs
|
Additional Inputs within an Accordion
|
https://gradio.app/guides/the-interface-class
|
Building Interfaces - The Interface Class Guide
|
You can make interfaces automatically refresh by setting `live=True` in the interface. Now the interface will recalculate as soon as the user input changes.
$code_calculator_live
$demo_calculator_live
Note there is no submit button, because the interface resubmits automatically on change.
|
Live Interfaces
|
https://gradio.app/guides/reactive-interfaces
|
Building Interfaces - Reactive Interfaces Guide
|
Some components have a "streaming" mode, such as `Audio` component in microphone mode, or the `Image` component in webcam mode. Streaming means data is sent continuously to the backend and the `Interface` function is continuously being rerun.
The difference between `gr.Audio(source='microphone')` and `gr.Audio(source='microphone', streaming=True)`, when both are used in `gr.Interface(live=True)`, is that the first `Component` will automatically submit data and run the `Interface` function when the user stops recording, whereas the second `Component` will continuously send data and run the `Interface` function _during_ recording.
Here is example code of streaming images from the webcam.
$code_stream_frames
Streaming can also be done in an output component. A `gr.Audio(streaming=True)` output component can take a stream of audio data yielded piece-wise by a generator function and combines them into a single audio file. For a detailed example, see our guide on performing [automatic speech recognition](/guides/real-time-speech-recognition) with Gradio.
|
Streaming Components
|
https://gradio.app/guides/reactive-interfaces
|
Building Interfaces - Reactive Interfaces Guide
|
To create a demo that has both the input and the output components, you simply need to set the values of the `inputs` and `outputs` parameter in `Interface()`. Here's an example demo of a simple image filter:
$code_sepia_filter
$demo_sepia_filter
|
Standard demos
|
https://gradio.app/guides/four-kinds-of-interfaces
|
Building Interfaces - Four Kinds Of Interfaces Guide
|
What about demos that only contain outputs? In order to build such a demo, you simply set the value of the `inputs` parameter in `Interface()` to `None`. Here's an example demo of a mock image generation model:
$code_fake_gan_no_input
$demo_fake_gan_no_input
|
Output-only demos
|
https://gradio.app/guides/four-kinds-of-interfaces
|
Building Interfaces - Four Kinds Of Interfaces Guide
|
Similarly, to create a demo that only contains inputs, set the value of `outputs` parameter in `Interface()` to be `None`. Here's an example demo that saves any uploaded image to disk:
$code_save_file_no_output
$demo_save_file_no_output
|
Input-only demos
|
https://gradio.app/guides/four-kinds-of-interfaces
|
Building Interfaces - Four Kinds Of Interfaces Guide
|
A demo that has a single component as both the input and the output. It can simply be created by setting the values of the `inputs` and `outputs` parameter as the same component. Here's an example demo of a text generation model:
$code_unified_demo_text_generation
$demo_unified_demo_text_generation
It may be the case that none of the 4 cases fulfill your exact needs. In this case, you need to use the `gr.Blocks()` approach!
|
Unified demos
|
https://gradio.app/guides/four-kinds-of-interfaces
|
Building Interfaces - Four Kinds Of Interfaces Guide
|
Gradio demos can be easily shared publicly by setting `share=True` in the `launch()` method. Like this:
```python
import gradio as gr
def greet(name):
return "Hello " + name + "!"
demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
demo.launch(share=True) Share your demo with just 1 extra parameter 🚀
```
This generates a public, shareable link that you can send to anybody! When you send this link, the user on the other side can try out the model in their browser. Because the processing happens on your device (as long as your device stays on), you don't have to worry about any packaging any dependencies.
A share link usually looks something like this: **https://07ff8706ab.gradio.live**. Although the link is served through the Gradio Share Servers, these servers are only a proxy for your local server, and do not store any data sent through your app. Share links expire after 1 week. (it is [also possible to set up your own Share Server](https://github.com/huggingface/frp/) on your own cloud server to overcome this restriction.)
Tip: Keep in mind that share links are publicly accessible, meaning that anyone can use your model for prediction! Therefore, make sure not to expose any sensitive information through the functions you write, or allow any critical changes to occur on your device. Or you can [add authentication to your Gradio app](authentication) as discussed below.
Note that by default, `share=False`, which means that your server is only running locally. (This is the default, except in Google Colab notebooks, where share links are automatically created). As an alternative to using share links, you can use use [SSH port-forwarding](https://www.ssh.com/ssh/tunneling/example) to share your local server with specific users.
|
Sharing Demos
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
If you'd like to have a permanent link to your Gradio demo on the internet, use Hugging Face Spaces. [Hugging Face Spaces](http://huggingface.co/spaces/) provides the infrastructure to permanently host your machine learning model for free!
After you have [created a free Hugging Face account](https://huggingface.co/join), you have two methods to deploy your Gradio app to Hugging Face Spaces:
1. From terminal: run `gradio deploy` in your app directory. The CLI will gather some basic metadata, upload all the files in the current directory (respecting any `.gitignore` file that may be present in the root of the directory), and then launch your app on Spaces. To update your Space, you can re-run this command or enable the Github Actions option in the CLI to automatically update the Spaces on `git push`.
2. From your browser: Drag and drop a folder containing your Gradio model and all related files [here](https://huggingface.co/new-space). See [this guide how to host on Hugging Face Spaces](https://huggingface.co/blog/gradio-spaces) for more information, or watch the embedded video:
<video autoplay muted loop>
<source src="https://github.com/gradio-app/gradio/blob/main/guides/assets/hf_demo.mp4?raw=true" type="video/mp4" />
</video>
|
Hosting on HF Spaces
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
You can add a button to your Gradio app that creates a unique URL you can use to share your app and all components **as they currently are** with others. This is useful for sharing unique and interesting generations from your application , or for saving a snapshot of your app at a particular point in time.
To add a deep link button to your app, place the `gr.DeepLinkButton` component anywhere in your app.
For the URL to be accessible to others, your app must be available at a public URL. So be sure to host your app like Hugging Face Spaces or use the `share=True` parameter when launching your app.
Let's see an example of how this works. Here's a simple Gradio chat ap that uses the `gr.DeepLinkButton` component. After a couple of messages, click the deep link button and paste it into a new browser tab to see the app as it is at that point in time.
$code_deep_link
$demo_deep_link
|
Sharing Deep Links
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
Once you have hosted your app on Hugging Face Spaces (or on your own server), you may want to embed the demo on a different website, such as your blog or your portfolio. Embedding an interactive demo allows people to try out the machine learning model that you have built, without needing to download or install anything — right in their browser! The best part is that you can embed interactive demos even in static websites, such as GitHub pages.
There are two ways to embed your Gradio demos. You can find quick links to both options directly on the Hugging Face Space page, in the "Embed this Space" dropdown option:
Embedding with Web Components
Web components typically offer a better experience to users than IFrames. Web components load lazily, meaning that they won't slow down the loading time of your website, and they automatically adjust their height based on the size of the Gradio app.
To embed with Web Components:
1. Import the gradio JS library into into your site by adding the script below in your site (replace {GRADIO_VERSION} in the URL with the library version of Gradio you are using).
```html
<script
type="module"
src="https://gradio.s3-us-west-2.amazonaws.com/{GRADIO_VERSION}/gradio.js"
></script>
```
2. Add
```html
<gradio-app src="https://$your_space_host.hf.space"></gradio-app>
```
element where you want to place the app. Set the `src=` attribute to your Space's embed URL, which you can find in the "Embed this Space" button. For example:
```html
<gradio-app
src="https://abidlabs-pytorch-image-classifier.hf.space"
></gradio-app>
```
<script>
fetch("https://pypi.org/pypi/gradio/json"
).then(r => r.json()
).then(obj => {
let v = obj.info.version;
content = document.querySelector('.prose');
content.innerHTML = content.innerHTML.replaceAll("{GRADIO_VERSION}", v);
});
</script>
You can see examples of h
|
Embedding Hosted Spaces
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
=> {
let v = obj.info.version;
content = document.querySelector('.prose');
content.innerHTML = content.innerHTML.replaceAll("{GRADIO_VERSION}", v);
});
</script>
You can see examples of how web components look <a href="https://www.gradio.app">on the Gradio landing page</a>.
You can also customize the appearance and behavior of your web component with attributes that you pass into the `<gradio-app>` tag:
- `src`: as we've seen, the `src` attributes links to the URL of the hosted Gradio demo that you would like to embed
- `space`: an optional shorthand if your Gradio demo is hosted on Hugging Face Space. Accepts a `username/space_name` instead of a full URL. Example: `gradio/Echocardiogram-Segmentation`. If this attribute attribute is provided, then `src` does not need to be provided.
- `control_page_title`: a boolean designating whether the html title of the page should be set to the title of the Gradio app (by default `"false"`)
- `initial_height`: the initial height of the web component while it is loading the Gradio app, (by default `"300px"`). Note that the final height is set based on the size of the Gradio app.
- `container`: whether to show the border frame and information about where the Space is hosted (by default `"true"`)
- `info`: whether to show just the information about where the Space is hosted underneath the embedded app (by default `"true"`)
- `autoscroll`: whether to autoscroll to the output when prediction has finished (by default `"false"`)
- `eager`: whether to load the Gradio app as soon as the page loads (by default `"false"`)
- `theme_mode`: whether to use the `dark`, `light`, or default `system` theme mode (by default `"system"`)
- `render`: an event that is triggered once the embedded space has finished rendering.
Here's an example of how to use these attributes to create a Gradio app that does not lazy load and has an initial height of 0px.
```html
<gradio-app
space="gradio/Echocardiogram-Segmentation"
eager="true"
init
|
Embedding Hosted Spaces
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
ple of how to use these attributes to create a Gradio app that does not lazy load and has an initial height of 0px.
```html
<gradio-app
space="gradio/Echocardiogram-Segmentation"
eager="true"
initial_height="0px"
></gradio-app>
```
Here's another example of how to use the `render` event. An event listener is used to capture the `render` event and will call the `handleLoadComplete()` function once rendering is complete.
```html
<script>
function handleLoadComplete() {
console.log("Embedded space has finished rendering");
}
const gradioApp = document.querySelector("gradio-app");
gradioApp.addEventListener("render", handleLoadComplete);
</script>
```
_Note: While Gradio's CSS will never impact the embedding page, the embedding page can affect the style of the embedded Gradio app. Make sure that any CSS in the parent page isn't so general that it could also apply to the embedded Gradio app and cause the styling to break. Element selectors such as `header { ... }` and `footer { ... }` will be the most likely to cause issues._
Embedding with IFrames
To embed with IFrames instead (if you cannot add javascript to your website, for example), add this element:
```html
<iframe src="https://$your_space_host.hf.space"></iframe>
```
Again, you can find the `src=` attribute to your Space's embed URL, which you can find in the "Embed this Space" button.
Note: if you use IFrames, you'll probably want to add a fixed `height` attribute and set `style="border:0;"` to remove the border. In addition, if your app requires permissions such as access to the webcam or the microphone, you'll need to provide that as well using the `allow` attribute.
|
Embedding Hosted Spaces
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
You can use almost any Gradio app as an API! In the footer of a Gradio app [like this one](https://huggingface.co/spaces/gradio/hello_world), you'll see a "Use via API" link.
This is a page that lists the endpoints that can be used to query the Gradio app, via our supported clients: either [the Python client](https://gradio.app/guides/getting-started-with-the-python-client/), or [the JavaScript client](https://gradio.app/guides/getting-started-with-the-js-client/). For each endpoint, Gradio automatically generates the parameters and their types, as well as example inputs, like this.
The endpoints are automatically created when you launch a Gradio application. If you are using Gradio `Blocks`, you can also name each event listener, such as
```python
btn.click(add, [num1, num2], output, api_name="addition")
```
This will add and document the endpoint `/addition/` to the automatically generated API page. Read more about the [API page here](./view-api-page).
|
API Page
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
When a user makes a prediction to your app, you may need the underlying network request, in order to get the request headers (e.g. for advanced authentication), log the client's IP address, getting the query parameters, or for other reasons. Gradio supports this in a similar manner to FastAPI: simply add a function parameter whose type hint is `gr.Request` and Gradio will pass in the network request as that parameter. Here is an example:
```python
import gradio as gr
def echo(text, request: gr.Request):
if request:
print("Request headers dictionary:", request.headers)
print("IP address:", request.client.host)
print("Query parameters:", dict(request.query_params))
return text
io = gr.Interface(echo, "textbox", "textbox").launch()
```
Note: if your function is called directly instead of through the UI (this happens, for
example, when examples are cached, or when the Gradio app is called via API), then `request` will be `None`.
You should handle this case explicitly to ensure that your app does not throw any errors. That is why
we have the explicit check `if request`.
|
Accessing the Network Request Directly
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
In some cases, you might have an existing FastAPI app, and you'd like to add a path for a Gradio demo.
You can easily do this with `gradio.mount_gradio_app()`.
Here's a complete example:
$code_custom_path
Note that this approach also allows you run your Gradio apps on custom paths (`http://localhost:8000/gradio` in the example above).
|
Mounting Within Another FastAPI App
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
Password-protected app
You may wish to put an authentication page in front of your app to limit who can open your app. With the `auth=` keyword argument in the `launch()` method, you can provide a tuple with a username and password, or a list of acceptable username/password tuples; Here's an example that provides password-based authentication for a single user named "admin":
```python
demo.launch(auth=("admin", "pass1234"))
```
For more complex authentication handling, you can even pass a function that takes a username and password as arguments, and returns `True` to allow access, `False` otherwise.
Here's an example of a function that accepts any login where the username and password are the same:
```python
def same_auth(username, password):
return username == password
demo.launch(auth=same_auth)
```
If you have multiple users, you may wish to customize the content that is shown depending on the user that is logged in. You can retrieve the logged in user by [accessing the network request directly](accessing-the-network-request-directly) as discussed above, and then reading the `.username` attribute of the request. Here's an example:
```python
import gradio as gr
def update_message(request: gr.Request):
return f"Welcome, {request.username}"
with gr.Blocks() as demo:
m = gr.Markdown()
demo.load(update_message, None, m)
demo.launch(auth=[("Abubakar", "Abubakar"), ("Ali", "Ali")])
```
Note: For authentication to work properly, third party cookies must be enabled in your browser. This is not the case by default for Safari or for Chrome Incognito Mode.
If users visit the `/logout` page of your Gradio app, they will automatically be logged out and session cookies deleted. This allows you to add logout functionality to your Gradio app as well. Let's update the previous example to include a log out button:
```python
import gradio as gr
def update_message(request: gr.Request):
return f"Welcome, {request.username}"
with gr.Blocks() as
|
Authentication
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
Let's update the previous example to include a log out button:
```python
import gradio as gr
def update_message(request: gr.Request):
return f"Welcome, {request.username}"
with gr.Blocks() as demo:
m = gr.Markdown()
logout_button = gr.Button("Logout", link="/logout")
demo.load(update_message, None, m)
demo.launch(auth=[("Pete", "Pete"), ("Dawood", "Dawood")])
```
By default, visiting `/logout` logs the user out from **all sessions** (e.g. if they are logged in from multiple browsers or devices, all will be signed out). If you want to log out only from the **current session**, add the query parameter `all_session=false` (i.e. `/logout?all_session=false`).
Note: Gradio's built-in authentication provides a straightforward and basic layer of access control but does not offer robust security features for applications that require stringent access controls (e.g. multi-factor authentication, rate limiting, or automatic lockout policies).
OAuth (Login via Hugging Face)
Gradio natively supports OAuth login via Hugging Face. In other words, you can easily add a _"Sign in with Hugging Face"_ button to your demo, which allows you to get a user's HF username as well as other information from their HF profile. Check out [this Space](https://huggingface.co/spaces/Wauplin/gradio-oauth-demo) for a live demo.
To enable OAuth, you must set `hf_oauth: true` as a Space metadata in your README.md file. This will register your Space
as an OAuth application on Hugging Face. Next, you can use `gr.LoginButton` to add a login button to
your Gradio app. Once a user is logged in with their HF account, you can retrieve their profile by adding a parameter of type
`gr.OAuthProfile` to any Gradio function. The user profile will be automatically injected as a parameter value. If you want
to perform actions on behalf of the user (e.g. list user's private repos, create repo, etc.), you can retrieve the user
token by adding a parameter of type `gr.OAuthToken`. You must def
|
Authentication
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
e. If you want
to perform actions on behalf of the user (e.g. list user's private repos, create repo, etc.), you can retrieve the user
token by adding a parameter of type `gr.OAuthToken`. You must define which scopes you will use in your Space metadata
(see [documentation](https://huggingface.co/docs/hub/spaces-oauthscopes) for more details).
Here is a short example:
$code_login_with_huggingface
When the user clicks on the login button, they get redirected in a new page to authorize your Space.
<center>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/oauth_sign_in.png" style="width:300px; max-width:80%">
</center>
Users can revoke access to their profile at any time in their [settings](https://huggingface.co/settings/connected-applications).
As seen above, OAuth features are available only when your app runs in a Space. However, you often need to test your app
locally before deploying it. To test OAuth features locally, your machine must be logged in to Hugging Face. Please run `huggingface-cli login` or set `HF_TOKEN` as environment variable with one of your access token. You can generate a new token in your settings page (https://huggingface.co/settings/tokens). Then, clicking on the `gr.LoginButton` will log in to your local Hugging Face profile, allowing you to debug your app with your Hugging Face account before deploying it to a Space.
**Security Note**: It is important to note that adding a `gr.LoginButton` does not restrict users from using your app, in the same way that adding [username-password authentication](/guides/sharing-your-apppassword-protected-app) does. This means that users of your app who have not logged in with Hugging Face can still access and run events in your Gradio app -- the difference is that the `gr.OAuthProfile` or `gr.OAuthToken` will be `None` in the corresponding functions.
OAuth (with external providers)
It is also possible to authenticate with external OAuth pr
|
Authentication
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
erence is that the `gr.OAuthProfile` or `gr.OAuthToken` will be `None` in the corresponding functions.
OAuth (with external providers)
It is also possible to authenticate with external OAuth providers (e.g. Google OAuth) in your Gradio apps. To do this, first mount your Gradio app within a FastAPI app ([as discussed above](mounting-within-another-fast-api-app)). Then, you must write an *authentication function*, which gets the user's username from the OAuth provider and returns it. This function should be passed to the `auth_dependency` parameter in `gr.mount_gradio_app`.
Similar to [FastAPI dependency functions](https://fastapi.tiangolo.com/tutorial/dependencies/), the function specified by `auth_dependency` will run before any Gradio-related route in your FastAPI app. The function should accept a single parameter: the FastAPI `Request` and return either a string (representing a user's username) or `None`. If a string is returned, the user will be able to access the Gradio-related routes in your FastAPI app.
First, let's show a simplistic example to illustrate the `auth_dependency` parameter:
```python
from fastapi import FastAPI, Request
import gradio as gr
app = FastAPI()
def get_user(request: Request):
return request.headers.get("user")
demo = gr.Interface(lambda s: f"Hello {s}!", "textbox", "textbox")
app = gr.mount_gradio_app(app, demo, path="/demo", auth_dependency=get_user)
if __name__ == '__main__':
uvicorn.run(app)
```
In this example, only requests that include a "user" header will be allowed to access the Gradio app. Of course, this does not add much security, since any user can add this header in their request.
Here's a more complete example showing how to add Google OAuth to a Gradio app (assuming you've already created OAuth Credentials on the [Google Developer Console](https://console.cloud.google.com/project)):
```python
import os
from authlib.integrations.starlette_client import OAuth, OAuthError
from fastapi import FastA
|
Authentication
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
entials on the [Google Developer Console](https://console.cloud.google.com/project)):
```python
import os
from authlib.integrations.starlette_client import OAuth, OAuthError
from fastapi import FastAPI, Depends, Request
from starlette.config import Config
from starlette.responses import RedirectResponse
from starlette.middleware.sessions import SessionMiddleware
import uvicorn
import gradio as gr
app = FastAPI()
Replace these with your own OAuth settings
GOOGLE_CLIENT_ID = "..."
GOOGLE_CLIENT_SECRET = "..."
SECRET_KEY = "..."
config_data = {'GOOGLE_CLIENT_ID': GOOGLE_CLIENT_ID, 'GOOGLE_CLIENT_SECRET': GOOGLE_CLIENT_SECRET}
starlette_config = Config(environ=config_data)
oauth = OAuth(starlette_config)
oauth.register(
name='google',
server_metadata_url='https://accounts.google.com/.well-known/openid-configuration',
client_kwargs={'scope': 'openid email profile'},
)
SECRET_KEY = os.environ.get('SECRET_KEY') or "a_very_secret_key"
app.add_middleware(SessionMiddleware, secret_key=SECRET_KEY)
Dependency to get the current user
def get_user(request: Request):
user = request.session.get('user')
if user:
return user['name']
return None
@app.get('/')
def public(user: dict = Depends(get_user)):
if user:
return RedirectResponse(url='/gradio')
else:
return RedirectResponse(url='/login-demo')
@app.route('/logout')
async def logout(request: Request):
request.session.pop('user', None)
return RedirectResponse(url='/')
@app.route('/login')
async def login(request: Request):
redirect_uri = request.url_for('auth')
If your app is running on https, you should ensure that the
`redirect_uri` is https, e.g. uncomment the following lines:
from urllib.parse import urlparse, urlunparse
redirect_uri = urlunparse(urlparse(str(redirect_uri))._replace(scheme='https'))
return await oauth.google.authorize_redirect(request, redirect_uri)
@app.route('/auth')
async def auth(request: Reque
|
Authentication
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
direct_uri = urlunparse(urlparse(str(redirect_uri))._replace(scheme='https'))
return await oauth.google.authorize_redirect(request, redirect_uri)
@app.route('/auth')
async def auth(request: Request):
try:
access_token = await oauth.google.authorize_access_token(request)
except OAuthError:
return RedirectResponse(url='/')
request.session['user'] = dict(access_token)["userinfo"]
return RedirectResponse(url='/')
with gr.Blocks() as login_demo:
gr.Button("Login", link="/login")
app = gr.mount_gradio_app(app, login_demo, path="/login-demo")
def greet(request: gr.Request):
return f"Welcome to Gradio, {request.username}"
with gr.Blocks() as main_demo:
m = gr.Markdown("Welcome to Gradio!")
gr.Button("Logout", link="/logout")
main_demo.load(greet, None, m)
app = gr.mount_gradio_app(app, main_demo, path="/gradio", auth_dependency=get_user)
if __name__ == '__main__':
uvicorn.run(app)
```
There are actually two separate Gradio apps in this example! One that simply displays a log in button (this demo is accessible to any user), while the other main demo is only accessible to users that are logged in. You can try this example out on [this Space](https://huggingface.co/spaces/gradio/oauth-example).
|
Authentication
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
Gradio apps can function as MCP (Model Context Protocol) servers, allowing LLMs to use your app's functions as tools. By simply setting `mcp_server=True` in the `.launch()` method, Gradio automatically converts your app's functions into MCP tools that can be called by MCP clients like Claude Desktop, Cursor, or Cline. The server exposes tools based on your function names, docstrings, and type hints, and can handle file uploads, authentication headers, and progress updates. You can also create MCP-only functions using `gr.api` and expose resources and prompts using decorators. For a comprehensive guide on building MCP servers with Gradio, see [Building an MCP Server with Gradio](https://www.gradio.app/guides/building-mcp-server-with-gradio).
|
MCP Servers
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
When publishing your app publicly, and making it available via API or via MCP server, you might want to set rate limits to prevent users from abusing your app. You can identify users using their IP address (using the `gr.Request` object [as discussed above](accessing-the-network-request-directly)) or, if they are logged in via Hugging Face OAuth, using their username. To see a complete example of how to set rate limits, please see [this Gradio app](https://github.com/gradio-app/gradio/blob/main/demo/rate_limit/run.py).
|
Rate Limits
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
By default, Gradio collects certain analytics to help us better understand the usage of the `gradio` library. This includes the following information:
* What environment the Gradio app is running on (e.g. Colab Notebook, Hugging Face Spaces)
* What input/output components are being used in the Gradio app
* Whether the Gradio app is utilizing certain advanced features, such as `auth` or `show_error`
* The IP address which is used solely to measure the number of unique developers using Gradio
* The version of Gradio that is running
No information is collected from _users_ of your Gradio app. If you'd like to disable analytics altogether, you can do so by setting the `analytics_enabled` parameter to `False` in `gr.Blocks`, `gr.Interface`, or `gr.ChatInterface`. Or, you can set the GRADIO_ANALYTICS_ENABLED environment variable to `"False"` to apply this to all Gradio apps created across your system.
*Note*: this reflects the analytics policy as of `gradio>=4.32.0`.
|
Analytics
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
[Progressive Web Apps (PWAs)](https://developer.mozilla.org/en-US/docs/Web/Progressive_web_apps) are web applications that are regular web pages or websites, but can appear to the user like installable platform-specific applications.
Gradio apps can be easily served as PWAs by setting the `pwa=True` parameter in the `launch()` method. Here's an example:
```python
import gradio as gr
def greet(name):
return "Hello " + name + "!"
demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
demo.launch(pwa=True) Launch your app as a PWA
```
This will generate a PWA that can be installed on your device. Here's how it looks:
When you specify `favicon_path` in the `launch()` method, the icon will be used as the app's icon. Here's an example:
```python
demo.launch(pwa=True, favicon_path="./hf-logo.svg") Use a custom icon for your PWA
```
|
Progressive Web App (PWA)
|
https://gradio.app/guides/sharing-your-app
|
Additional Features - Sharing Your App Guide
|
By default, each event listener has its own queue, which handles one request at a time. This can be configured via two arguments:
- `concurrency_limit`: This sets the maximum number of concurrent executions for an event listener. By default, the limit is 1 unless configured otherwise in `Blocks.queue()`. You can also set it to `None` for no limit (i.e., an unlimited number of concurrent executions). For example:
```python
import gradio as gr
with gr.Blocks() as demo:
prompt = gr.Textbox()
image = gr.Image()
generate_btn = gr.Button("Generate Image")
generate_btn.click(image_gen, prompt, image, concurrency_limit=5)
```
In the code above, up to 5 requests can be processed simultaneously for this event listener. Additional requests will be queued until a slot becomes available.
If you want to manage multiple event listeners using a shared queue, you can use the `concurrency_id` argument:
- `concurrency_id`: This allows event listeners to share a queue by assigning them the same ID. For example, if your setup has only 2 GPUs but multiple functions require GPU access, you can create a shared queue for all those functions. Here's how that might look:
```python
import gradio as gr
with gr.Blocks() as demo:
prompt = gr.Textbox()
image = gr.Image()
generate_btn_1 = gr.Button("Generate Image via model 1")
generate_btn_2 = gr.Button("Generate Image via model 2")
generate_btn_3 = gr.Button("Generate Image via model 3")
generate_btn_1.click(image_gen_1, prompt, image, concurrency_limit=2, concurrency_id="gpu_queue")
generate_btn_2.click(image_gen_2, prompt, image, concurrency_id="gpu_queue")
generate_btn_3.click(image_gen_3, prompt, image, concurrency_id="gpu_queue")
```
In this example, all three event listeners share a queue identified by `"gpu_queue"`. The queue can handle up to 2 concurrent requests at a time, as defined by the `concurrency_limit`.
Notes
- To ensure unlimited concurrency for an event listener, se
|
Configuring the Queue
|
https://gradio.app/guides/queuing
|
Additional Features - Queuing Guide
|
identified by `"gpu_queue"`. The queue can handle up to 2 concurrent requests at a time, as defined by the `concurrency_limit`.
Notes
- To ensure unlimited concurrency for an event listener, set `concurrency_limit=None`. This is useful if your function is calling e.g. an external API which handles the rate limiting of requests itself.
- The default concurrency limit for all queues can be set globally using the `default_concurrency_limit` parameter in `Blocks.queue()`.
These configurations make it easy to manage the queuing behavior of your Gradio app.
|
Configuring the Queue
|
https://gradio.app/guides/queuing
|
Additional Features - Queuing Guide
|
**API endpoint names**
When you create a Gradio application, the API endpoint names are automatically generated based on the function names. You can change this by using the `api_name` parameter in `gr.Interface` or `gr.ChatInterface`. If you are using Gradio `Blocks`, you can name each event listener, like this:
```python
btn.click(add, [num1, num2], output, api_name="addition")
```
**Hiding API endpoints**
When building a complex Gradio app, you might want to hide certain API endpoints from appearing on the view API page, e.g. if they correspond to functions that simply update the UI. You can set the `show_api` parameter to `False` in any `Blocks` event listener to achieve this, e.g.
```python
btn.click(add, [num1, num2], output, show_api=False)
```
**Disabling API endpoints**
Hiding the API endpoint doesn't disable it. A user can still programmatically call the API endpoint if they know the name. If you want to disable an API endpoint altogether, set `api_name=False`, e.g.
```python
btn.click(add, [num1, num2], output, api_name=False)
```
Note: setting an `api_name=False` also means that downstream apps will not be able to load your Gradio app using `gr.load()` as this function uses the Gradio API under the hood.
**Adding API endpoints**
You can also add new API routes to your Gradio application that do not correspond to events in your UI.
For example, in this Gradio application, we add a new route that adds numbers and slices a list:
```py
import gradio as gr
with gr.Blocks() as demo:
with gr.Row():
input = gr.Textbox()
button = gr.Button("Submit")
output = gr.Textbox()
def fn(a: int, b: int, c: list[str]) -> tuple[int, str]:
return a + b, c[a:b]
gr.api(fn, api_name="add_and_slice")
_, url, _ = demo.launch()
```
This will create a new route `/add_and_slice` which will show up in the "view API" page. It can be programmatically called by the Python or JS Clients (discussed below) like this:
```py
from grad
|
Configuring the API Page
|
https://gradio.app/guides/view-api-page
|
Additional Features - View Api Page Guide
|
``
This will create a new route `/add_and_slice` which will show up in the "view API" page. It can be programmatically called by the Python or JS Clients (discussed below) like this:
```py
from gradio_client import Client
client = Client(url)
result = client.predict(
a=3,
b=5,
c=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
api_name="/add_and_slice"
)
print(result)
```
|
Configuring the API Page
|
https://gradio.app/guides/view-api-page
|
Additional Features - View Api Page Guide
|
This API page not only lists all of the endpoints that can be used to query the Gradio app, but also shows the usage of both [the Gradio Python client](https://gradio.app/guides/getting-started-with-the-python-client/), and [the Gradio JavaScript client](https://gradio.app/guides/getting-started-with-the-js-client/).
For each endpoint, Gradio automatically generates a complete code snippet with the parameters and their types, as well as example inputs, allowing you to immediately test an endpoint. Here's an example showing an image file input and `str` output:
|
The Clients
|
https://gradio.app/guides/view-api-page
|
Additional Features - View Api Page Guide
|
Instead of reading through the view API page, you can also use Gradio's built-in API recorder to generate the relevant code snippet. Simply click on the "API Recorder" button, use your Gradio app via the UI as you would normally, and then the API Recorder will generate the code using the Clients to recreate your all of your interactions programmatically.
|
The API Recorder 🪄
|
https://gradio.app/guides/view-api-page
|
Additional Features - View Api Page Guide
|
The API page also includes instructions on how to use the Gradio app as an Model Context Protocol (MCP) server, which is a standardized way to expose functions as tools so that they can be used by LLMs.
For the MCP sever, each tool, its description, and its parameters are listed, along with instructions on how to integrate with popular MCP Clients. Read more about Gradio's [MCP integration here](https://www.gradio.app/guides/building-mcp-server-with-gradio).
|
MCP Server
|
https://gradio.app/guides/view-api-page
|
Additional Features - View Api Page Guide
|
You can access the complete OpenAPI (formerly Swagger) specification of your Gradio app's API at the endpoint `<your-gradio-app-url>/gradio_api/openapi.json`. The OpenAPI specification is a standardized, language-agnostic interface description for REST APIs that enables both humans and computers to discover and understand the capabilities of your service.
|
OpenAPI Specification
|
https://gradio.app/guides/view-api-page
|
Additional Features - View Api Page Guide
|
Let's create a demo where a user can choose a filter to apply to their webcam stream. Users can choose from an edge-detection filter, a cartoon filter, or simply flipping the stream vertically.
$code_streaming_filter
$demo_streaming_filter
You will notice that if you change the filter value it will immediately take effect in the output stream. That is an important difference of stream events in comparison to other Gradio events. The input values of the stream can be changed while the stream is being processed.
Tip: We set the "streaming" parameter of the image output component to be "True". Doing so lets the server automatically convert our output images into base64 format, a format that is efficient for streaming.
|
A Realistic Image Demo
|
https://gradio.app/guides/streaming-inputs
|
Additional Features - Streaming Inputs Guide
|
For some image streaming demos, like the one above, we don't need to display separate input and output components. Our app would look cleaner if we could just display the modified output stream.
We can do so by just specifying the input image component as the output of the stream event.
$code_streaming_filter_unified
$demo_streaming_filter_unified
|
Unified Image Demos
|
https://gradio.app/guides/streaming-inputs
|
Additional Features - Streaming Inputs Guide
|
Your streaming function should be stateless. It should take the current input and return its corresponding output. However, there are cases where you may want to keep track of past inputs or outputs. For example, you may want to keep a buffer of the previous `k` inputs to improve the accuracy of your transcription demo. You can do this with Gradio's `gr.State()` component.
Let's showcase this with a sample demo:
```python
def transcribe_handler(current_audio, state, transcript):
next_text = transcribe(current_audio, history=state)
state.append(current_audio)
state = state[-3:]
return state, transcript + next_text
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
mic = gr.Audio(sources="microphone")
state = gr.State(value=[])
with gr.Column():
transcript = gr.Textbox(label="Transcript")
mic.stream(transcribe_handler, [mic, state, transcript], [state, transcript],
time_limit=10, stream_every=1)
demo.launch()
```
|
Keeping track of past inputs or outputs
|
https://gradio.app/guides/streaming-inputs
|
Additional Features - Streaming Inputs Guide
|
For an end-to-end example of streaming from the webcam, see the object detection from webcam [guide](/main/guides/object-detection-from-webcam-with-webrtc).
|
End-to-End Examples
|
https://gradio.app/guides/streaming-inputs
|
Additional Features - Streaming Inputs Guide
|
Client side functions are ideal for updating component properties (like visibility, placeholders, interactive state, or styling).
Here's a basic example:
```py
import gradio as gr
with gr.Blocks() as demo:
with gr.Row() as row:
btn = gr.Button("Hide this row")
This function runs in the browser without a server roundtrip
btn.click(
lambda: gr.Row(visible=False),
None,
row,
js=True
)
demo.launch()
```
|
When to Use Client Side Functions
|
https://gradio.app/guides/client-side-functions
|
Additional Features - Client Side Functions Guide
|
Client side functions have some important restrictions:
* They can only update component properties (not values)
* They cannot take any inputs
Here are some functions that will work with `js=True`:
```py
Simple property updates
lambda: gr.Textbox(lines=4)
Multiple component updates
lambda: [gr.Textbox(lines=4), gr.Button(interactive=False)]
Using gr.update() for property changes
lambda: gr.update(visible=True, interactive=False)
```
We are working to increase the space of functions that can be transpiled to JavaScript so that they can be run in the browser. [Follow the Groovy library for more info](https://github.com/abidlabs/groovy-transpiler).
|
Limitations
|
https://gradio.app/guides/client-side-functions
|
Additional Features - Client Side Functions Guide
|
Here's a more complete example showing how client side functions can improve the user experience:
$code_todo_list_js
|
Complete Example
|
https://gradio.app/guides/client-side-functions
|
Additional Features - Client Side Functions Guide
|
When you set `js=True`, Gradio:
1. Transpiles your Python function to JavaScript
2. Runs the function directly in the browser
3. Still sends the request to the server (for consistency and to handle any side effects)
This provides immediate visual feedback while ensuring your application state remains consistent.
|
Behind the Scenes
|
https://gradio.app/guides/client-side-functions
|
Additional Features - Client Side Functions Guide
|
- **1. Static files**. You can designate static files or directories using the `gr.set_static_paths` function. Static files are not be copied to the Gradio cache (see below) and will be served directly from your computer. This can help save disk space and reduce the time your app takes to launch but be mindful of possible security implications as any static files are accessible to all useres of your Gradio app.
- **2. Files in the `allowed_paths` parameter in `launch()`**. This parameter allows you to pass in a list of additional directories or exact filepaths you'd like to allow users to have access to. (By default, this parameter is an empty list).
- **3. Files in Gradio's cache**. After you launch your Gradio app, Gradio copies certain files into a temporary cache and makes these files accessible to users. Let's unpack this in more detail below.
|
Files Gradio allows users to access
|
https://gradio.app/guides/file-access
|
Additional Features - File Access Guide
|
First, it's important to understand why Gradio has a cache at all. Gradio copies files to a cache directory before returning them to the frontend. This prevents files from being overwritten by one user while they are still needed by another user of your application. For example, if your prediction function returns a video file, then Gradio will move that video to the cache after your prediction function runs and returns a URL the frontend can use to show the video. Any file in the cache is available via URL to all users of your running application.
Tip: You can customize the location of the cache by setting the `GRADIO_TEMP_DIR` environment variable to an absolute path, such as `/home/usr/scripts/project/temp/`.
Files Gradio moves to the cache
Gradio moves three kinds of files into the cache
1. Files specified by the developer before runtime, e.g. cached examples, default values of components, or files passed into parameters such as the `avatar_images` of `gr.Chatbot`
2. File paths returned by a prediction function in your Gradio application, if they ALSO meet one of the conditions below:
* It is in the `allowed_paths` parameter of the `Blocks.launch` method.
* It is in the current working directory of the python interpreter.
* It is in the temp directory obtained by `tempfile.gettempdir()`.
**Note:** files in the current working directory whose name starts with a period (`.`) will not be moved to the cache, even if they are returned from a prediction function, since they often contain sensitive information.
If none of these criteria are met, the prediction function that is returning that file will raise an exception instead of moving the file to cache. Gradio performs this check so that arbitrary files on your machine cannot be accessed.
3. Files uploaded by a user to your Gradio app (e.g. through the `File` or `Image` input components).
Tip: If at any time Gradio blocks a file that you would like it to process, add its path to the `allowed_paths` p
|
The Gradio cache
|
https://gradio.app/guides/file-access
|
Additional Features - File Access Guide
|
d by a user to your Gradio app (e.g. through the `File` or `Image` input components).
Tip: If at any time Gradio blocks a file that you would like it to process, add its path to the `allowed_paths` parameter.
|
The Gradio cache
|
https://gradio.app/guides/file-access
|
Additional Features - File Access Guide
|
While running, Gradio apps will NOT ALLOW users to access:
- **Files that you explicitly block via the `blocked_paths` parameter in `launch()`**. You can pass in a list of additional directories or exact filepaths to the `blocked_paths` parameter in `launch()`. This parameter takes precedence over the files that Gradio exposes by default, or by the `allowed_paths` parameter or the `gr.set_static_paths` function.
- **Any other paths on the host machine**. Users should NOT be able to access other arbitrary paths on the host.
|
The files Gradio will not allow others to access
|
https://gradio.app/guides/file-access
|
Additional Features - File Access Guide
|
Sharing your Gradio application will also allow users to upload files to your computer or server. You can set a maximum file size for uploads to prevent abuse and to preserve disk space. You can do this with the `max_file_size` parameter of `.launch`. For example, the following two code snippets limit file uploads to 5 megabytes per file.
```python
import gradio as gr
demo = gr.Interface(lambda x: x, "image", "image")
demo.launch(max_file_size="5mb")
or
demo.launch(max_file_size=5 * gr.FileSize.MB)
```
|
Uploading Files
|
https://gradio.app/guides/file-access
|
Additional Features - File Access Guide
|
* Set a `max_file_size` for your application.
* Do not return arbitrary user input from a function that is connected to a file-based output component (`gr.Image`, `gr.File`, etc.). For example, the following interface would allow anyone to move an arbitrary file in your local directory to the cache: `gr.Interface(lambda s: s, "text", "file")`. This is because the user input is treated as an arbitrary file path.
* Make `allowed_paths` as small as possible. If a path in `allowed_paths` is a directory, any file within that directory can be accessed. Make sure the entires of `allowed_paths` only contains files related to your application.
* Run your gradio application from the same directory the application file is located in. This will narrow the scope of files Gradio will be allowed to move into the cache. For example, prefer `python app.py` to `python Users/sources/project/app.py`.
|
Best Practices
|
https://gradio.app/guides/file-access
|
Additional Features - File Access Guide
|
Both `gr.set_static_paths` and the `allowed_paths` parameter in launch expect absolute paths. Below is a minimal example to display a local `.png` image file in an HTML block.
```txt
├── assets
│ └── logo.png
└── app.py
```
For the example directory structure, `logo.png` and any other files in the `assets` folder can be accessed from your Gradio app in `app.py` as follows:
```python
from pathlib import Path
import gradio as gr
gr.set_static_paths(paths=[Path.cwd().absolute()/"assets"])
with gr.Blocks() as demo:
gr.HTML("<img src='/gradio_api/file=assets/logo.png'>")
demo.launch()
```
|
Example: Accessing local files
|
https://gradio.app/guides/file-access
|
Additional Features - File Access Guide
|
Gradio can stream audio and video directly from your generator function.
This lets your user hear your audio or see your video nearly as soon as it's `yielded` by your function.
All you have to do is
1. Set `streaming=True` in your `gr.Audio` or `gr.Video` output component.
2. Write a python generator that yields the next "chunk" of audio or video.
3. Set `autoplay=True` so that the media starts playing automatically.
For audio, the next "chunk" can be either an `.mp3` or `.wav` file or a `bytes` sequence of audio.
For video, the next "chunk" has to be either `.mp4` file or a file with `h.264` codec with a `.ts` extension.
For smooth playback, make sure chunks are consistent lengths and larger than 1 second.
We'll finish with some simple examples illustrating these points.
Streaming Audio
```python
import gradio as gr
from time import sleep
def keep_repeating(audio_file):
for _ in range(10):
sleep(0.5)
yield audio_file
gr.Interface(keep_repeating,
gr.Audio(sources=["microphone"], type="filepath"),
gr.Audio(streaming=True, autoplay=True)
).launch()
```
Streaming Video
```python
import gradio as gr
from time import sleep
def keep_repeating(video_file):
for _ in range(10):
sleep(0.5)
yield video_file
gr.Interface(keep_repeating,
gr.Video(sources=["webcam"], format="mp4"),
gr.Video(streaming=True, autoplay=True)
).launch()
```
|
Streaming Media
|
https://gradio.app/guides/streaming-outputs
|
Additional Features - Streaming Outputs Guide
|
For an end-to-end example of streaming media, see the object detection from video [guide](/main/guides/object-detection-from-video) or the streaming AI-generated audio with [transformers](https://huggingface.co/docs/transformers/index) [guide](/main/guides/streaming-ai-generated-audio).
|
End-to-End Examples
|
https://gradio.app/guides/streaming-outputs
|
Additional Features - Streaming Outputs Guide
|
You can initialize the `I18n` class with multiple language dictionaries to add custom translations:
```python
import gradio as gr
Create an I18n instance with translations for multiple languages
i18n = gr.I18n(
en={"greeting": "Hello, welcome to my app!", "submit": "Submit"},
es={"greeting": "¡Hola, bienvenido a mi aplicación!", "submit": "Enviar"},
fr={"greeting": "Bonjour, bienvenue dans mon application!", "submit": "Soumettre"}
)
with gr.Blocks() as demo:
Use the i18n method to translate the greeting
gr.Markdown(i18n("greeting"))
with gr.Row():
input_text = gr.Textbox(label="Input")
output_text = gr.Textbox(label="Output")
submit_btn = gr.Button(i18n("submit"))
Pass the i18n instance to the launch method
demo.launch(i18n=i18n)
```
|
Setting Up Translations
|
https://gradio.app/guides/internationalization
|
Additional Features - Internationalization Guide
|
When you use the `i18n` instance with a translation key, Gradio will show the corresponding translation to users based on their browser's language settings or the language they've selected in your app.
If a translation isn't available for the user's locale, the system will fall back to English (if available) or display the key itself.
|
How It Works
|
https://gradio.app/guides/internationalization
|
Additional Features - Internationalization Guide
|
Locale codes should follow the BCP 47 format (e.g., 'en', 'en-US', 'zh-CN'). The `I18n` class will warn you if you use an invalid locale code.
|
Valid Locale Codes
|
https://gradio.app/guides/internationalization
|
Additional Features - Internationalization Guide
|
The following component properties typically support internationalization:
- `description`
- `info`
- `title`
- `placeholder`
- `value`
- `label`
Note that support may vary depending on the component, and some properties might have exceptions where internationalization is not applicable. You can check this by referring to the typehint for the parameter and if it contains `I18nData`, then it supports internationalization.
|
Supported Component Properties
|
https://gradio.app/guides/internationalization
|
Additional Features - Internationalization Guide
|
When a user closes their browser tab, Gradio will automatically delete any `gr.State` variables associated with that user session after 60 minutes. If the user connects again within those 60 minutes, no state will be deleted.
You can control the deletion behavior further with the following two parameters of `gr.State`:
1. `delete_callback` - An arbitrary function that will be called when the variable is deleted. This function must take the state value as input. This function is useful for deleting variables from GPU memory.
2. `time_to_live` - The number of seconds the state should be stored for after it is created or updated. This will delete variables before the session is closed, so it's useful for clearing state for potentially long running sessions.
|
Automatic deletion of `gr.State`
|
https://gradio.app/guides/resource-cleanup
|
Additional Features - Resource Cleanup Guide
|
Your Gradio application will save uploaded and generated files to a special directory called the cache directory. Gradio uses a hashing scheme to ensure that duplicate files are not saved to the cache but over time the size of the cache will grow (especially if your app goes viral 😉).
Gradio can periodically clean up the cache for you if you specify the `delete_cache` parameter of `gr.Blocks()`, `gr.Interface()`, or `gr.ChatInterface()`.
This parameter is a tuple of the form `[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 if they are older than a day old.
Additionally, the cache will be deleted entirely when the server restarts.
|
Automatic cache cleanup via `delete_cache`
|
https://gradio.app/guides/resource-cleanup
|
Additional Features - Resource Cleanup Guide
|
Additionally, Gradio now includes a `Blocks.unload()` event, allowing you to run arbitrary cleanup functions when users disconnect (this does not have a 60 minute delay).
Unlike other gradio events, this event does not accept inputs or outptus.
You can think of the `unload` event as the opposite of the `load` event.
|
The `unload` event
|
https://gradio.app/guides/resource-cleanup
|
Additional Features - Resource Cleanup Guide
|
The following demo uses all of these features. When a user visits the page, a special unique directory is created for that user.
As the user interacts with the app, images are saved to disk in that special directory.
When the user closes the page, the images created in that session are deleted via the `unload` event.
The state and files in the cache are cleaned up automatically as well.
$code_state_cleanup
$demo_state_cleanup
|
Putting it all together
|
https://gradio.app/guides/resource-cleanup
|
Additional Features - Resource Cleanup Guide
|
1. `GRADIO_SERVER_PORT`
- **Description**: Specifies the port on which the Gradio app will run.
- **Default**: `7860`
- **Example**:
```bash
export GRADIO_SERVER_PORT=8000
```
2. `GRADIO_SERVER_NAME`
- **Description**: Defines the host name for the Gradio server. To make Gradio accessible from any IP address, set this to `"0.0.0.0"`
- **Default**: `"127.0.0.1"`
- **Example**:
```bash
export GRADIO_SERVER_NAME="0.0.0.0"
```
3. `GRADIO_NUM_PORTS`
- **Description**: Defines the number of ports to try when starting the Gradio server.
- **Default**: `100`
- **Example**:
```bash
export GRADIO_NUM_PORTS=200
```
4. `GRADIO_ANALYTICS_ENABLED`
- **Description**: Whether Gradio should provide
- **Default**: `"True"`
- **Options**: `"True"`, `"False"`
- **Example**:
```sh
export GRADIO_ANALYTICS_ENABLED="True"
```
5. `GRADIO_DEBUG`
- **Description**: Enables or disables debug mode in Gradio. If debug mode is enabled, the main thread does not terminate allowing error messages to be printed in environments such as Google Colab.
- **Default**: `0`
- **Example**:
```sh
export GRADIO_DEBUG=1
```
6. `GRADIO_FLAGGING_MODE`
- **Description**: Controls whether users can flag inputs/outputs in the Gradio interface. See [the Guide on flagging](/guides/using-flagging) for more details.
- **Default**: `"manual"`
- **Options**: `"never"`, `"manual"`, `"auto"`
- **Example**:
```sh
export GRADIO_FLAGGING_MODE="never"
```
7. `GRADIO_TEMP_DIR`
- **Description**: Specifies the directory where temporary files created by Gradio are stored.
- **Default**: System default temporary directory
- **Example**:
```sh
export GRADIO_TEMP_DIR="/path/to/temp"
```
8. `GRADIO_ROOT_PATH`
- **Description**: Sets the root path for the Gradio application. Useful if running Gradio [behind a reverse proxy](/guides/running-gradio-on-your-web-server-with-nginx).
- **Default**: `""`
- **Example**:
```sh
export GRADIO_ROOT_PATH=
|
Key Environment Variables
|
https://gradio.app/guides/environment-variables
|
Additional Features - Environment Variables Guide
|
r the Gradio application. Useful if running Gradio [behind a reverse proxy](/guides/running-gradio-on-your-web-server-with-nginx).
- **Default**: `""`
- **Example**:
```sh
export GRADIO_ROOT_PATH="/myapp"
```
9. `GRADIO_SHARE`
- **Description**: Enables or disables sharing the Gradio app.
- **Default**: `"False"`
- **Options**: `"True"`, `"False"`
- **Example**:
```sh
export GRADIO_SHARE="True"
```
10. `GRADIO_ALLOWED_PATHS`
- **Description**: Sets a list of complete filepaths or parent directories that gradio is allowed to serve. 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. Multiple items can be specified by separating items with commas.
- **Default**: `""`
- **Example**:
```sh
export GRADIO_ALLOWED_PATHS="/mnt/sda1,/mnt/sda2"
```
11. `GRADIO_BLOCKED_PATHS`
- **Description**: Sets a 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. Multiple items can be specified by separating items with commas.
- **Default**: `""`
- **Example**:
```sh
export GRADIO_BLOCKED_PATHS="/users/x/gradio_app/admin,/users/x/gradio_app/keys"
```
12. `FORWARDED_ALLOW_IPS`
- **Description**: This is not a Gradio-specific environment variable, but rather one used in server configurations, specifically `uvicorn` which is used by Gradio internally. This environment variable is useful when deploying applications behind a reverse proxy. It defines a list of IP addresses that are trusted to forward traffic to your application. When set, the application will trust the `X-Forwarded-For` header from these IP addresses to determine the original IP address of the user making the request. This means that if you use the `gr.Request` [objec
|
Key Environment Variables
|
https://gradio.app/guides/environment-variables
|
Additional Features - Environment Variables Guide
|
the application will trust the `X-Forwarded-For` header from these IP addresses to determine the original IP address of the user making the request. This means that if you use the `gr.Request` [object's](https://www.gradio.app/docs/gradio/request) `client.host` property, it will correctly get the user's IP address instead of the IP address of the reverse proxy server. Note that only trusted IP addresses (i.e. the IP addresses of your reverse proxy servers) should be added, as any server with these IP addresses can modify the `X-Forwarded-For` header and spoof the client's IP address.
- **Default**: `"127.0.0.1"`
- **Example**:
```sh
export FORWARDED_ALLOW_IPS="127.0.0.1,192.168.1.100"
```
13. `GRADIO_CACHE_EXAMPLES`
- **Description**: Whether or not to cache examples by default in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()` when no explicit argument is passed for the `cache_examples` parameter. You can set this environment variable to either the string "true" or "false".
- **Default**: `"false"`
- **Example**:
```sh
export GRADIO_CACHE_EXAMPLES="true"
```
14. `GRADIO_CACHE_MODE`
- **Description**: How to cache examples. Only applies if `cache_examples` is set to `True` either via enviornment variable or by an explicit parameter, AND no no explicit argument is passed for the `cache_mode` parameter in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()`. Can be set to either the strings "lazy" or "eager." If "lazy", examples are cached after their first use for all users of the app. If "eager", all examples are cached at app launch.
- **Default**: `"eager"`
- **Example**:
```sh
export GRADIO_CACHE_MODE="lazy"
```
15. `GRADIO_EXAMPLES_CACHE`
- **Description**: If you set `cache_examples=True` in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()`, Gradio will run your prediction function and save the results to disk. By default, this is in the `.gradio/cached_examples//` subdirectory within your
|
Key Environment Variables
|
https://gradio.app/guides/environment-variables
|
Additional Features - Environment Variables Guide
|
e()`, `gr.ChatInterface()` or in `gr.Examples()`, Gradio will run your prediction function and save the results to disk. By default, this is in the `.gradio/cached_examples//` subdirectory within your app's working directory. You can customize the location of cached example files created by Gradio by setting the environment variable `GRADIO_EXAMPLES_CACHE` to an absolute path or a path relative to your working directory.
- **Default**: `".gradio/cached_examples/"`
- **Example**:
```sh
export GRADIO_EXAMPLES_CACHE="custom_cached_examples/"
```
16. `GRADIO_SSR_MODE`
- **Description**: Controls whether server-side rendering (SSR) is enabled. When enabled, the initial HTML is rendered on the server rather than the client, which can improve initial page load performance and SEO.
- **Default**: `"False"` (except on Hugging Face Spaces, where this environment variable sets it to `True`)
- **Options**: `"True"`, `"False"`
- **Example**:
```sh
export GRADIO_SSR_MODE="True"
```
17. `GRADIO_NODE_SERVER_NAME`
- **Description**: Defines the host name for the Gradio node server. (Only applies if `ssr_mode` is set to `True`.)
- **Default**: `GRADIO_SERVER_NAME` if it is set, otherwise `"127.0.0.1"`
- **Example**:
```sh
export GRADIO_NODE_SERVER_NAME="0.0.0.0"
```
18. `GRADIO_NODE_NUM_PORTS`
- **Description**: Defines the number of ports to try when starting the Gradio node server. (Only applies if `ssr_mode` is set to `True`.)
- **Default**: `100`
- **Example**:
```sh
export GRADIO_NODE_NUM_PORTS=200
```
19. `GRADIO_RESET_EXAMPLES_CACHE`
- **Description**: If set to "True", Gradio will delete and recreate the examples cache directory when the app starts instead of reusing the cached example if they already exist.
- **Default**: `"False"`
- **Options**: `"True"`, `"False"`
- **Example**:
```sh
export GRADIO_RESET_EXAMPLES_CACHE="True"
```
20. `GRADIO_CHAT_FLAGGING_MODE`
- **Description**: Controls whether users can flag
|
Key Environment Variables
|
https://gradio.app/guides/environment-variables
|
Additional Features - Environment Variables Guide
|
e"`
- **Options**: `"True"`, `"False"`
- **Example**:
```sh
export GRADIO_RESET_EXAMPLES_CACHE="True"
```
20. `GRADIO_CHAT_FLAGGING_MODE`
- **Description**: Controls whether users can flag messages in `gr.ChatInterface` applications. Similar to `GRADIO_FLAGGING_MODE` but specifically for chat interfaces.
- **Default**: `"never"`
- **Options**: `"never"`, `"manual"`
- **Example**:
```sh
export GRADIO_CHAT_FLAGGING_MODE="manual"
```
21. `GRADIO_WATCH_DIRS`
- **Description**: Specifies directories to watch for file changes when running Gradio in development mode. When files in these directories change, the Gradio app will automatically reload. Multiple directories can be specified by separating them with commas. This is primarily used by the `gradio` CLI command for development workflows.
- **Default**: `""`
- **Example**:
```sh
export GRADIO_WATCH_DIRS="/path/to/src,/path/to/templates"
```
22. `GRADIO_VIBE_MODE`
- **Description**: Enables the Vibe editor mode, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. When enabled, anyone who can access the Gradio endpoint can modify files and run arbitrary code on the host machine. Use with extreme caution in production environments.
- **Default**: `""`
- **Options**: Any non-empty string enables the mode
- **Example**:
```sh
export GRADIO_VIBE_MODE="1"
```
23. `GRADIO_MCP_SERVER`
- **Description**: Enables the MCP (Model Context Protocol) server functionality in Gradio. When enabled, the Gradio app will be set up as an MCP server and documented functions will be added as MCP tools that can be used by LLMs. This allows LLMs to interact with your Gradio app's functionality through the MCP protocol.
- **Default**: `"False"`
- **Options**: `"True"`, `"False"`
- **Example**:
```sh
export GRADIO_MCP_SERVER="True"
```
|
Key Environment Variables
|
https://gradio.app/guides/environment-variables
|
Additional Features - Environment Variables Guide
|
*Options**: `"True"`, `"False"`
- **Example**:
```sh
export GRADIO_MCP_SERVER="True"
```
|
Key Environment Variables
|
https://gradio.app/guides/environment-variables
|
Additional Features - Environment Variables Guide
|
To set environment variables in your terminal, use the `export` command followed by the variable name and its value. For example:
```sh
export GRADIO_SERVER_PORT=8000
```
If you're using a `.env` file to manage your environment variables, you can add them like this:
```sh
GRADIO_SERVER_PORT=8000
GRADIO_SERVER_NAME="localhost"
```
Then, use a tool like `dotenv` to load these variables when running your application.
|
How to Set Environment Variables
|
https://gradio.app/guides/environment-variables
|
Additional Features - Environment Variables Guide
|
By default, Gradio automatically generates a navigation bar for multipage apps that displays all your pages with "Home" as the title for the main page. You can customize the navbar behavior using the `gr.Navbar` component.
Per-Page Navbar Configuration
You can have different navbar configurations for each page of your app:
```python
import gradio as gr
with gr.Blocks() as demo:
Navbar for the main page
navbar = gr.Navbar(
visible=True,
main_page_name="Dashboard",
value=[("About", "https://example.com/about")]
)
gr.Textbox(label="Main page content")
with demo.route("Settings"):
Different navbar for the Settings page
navbar = gr.Navbar(
visible=True,
main_page_name="Home",
value=[("Documentation", "https://docs.example.com")]
)
gr.Textbox(label="Settings page")
demo.launch()
```
**Important Notes:**
- You can have one `gr.Navbar` component per page. Each page's navbar configuration is independent.
- The `main_page_name` parameter customizes the title of the home page link in the navbar.
- The `value` parameter allows you to add additional links to the navbar, which can be internal pages or external URLs.
- If no `gr.Navbar` component is present on a page, the default navbar behavior is used (visible with "Home" as the home page title).
- You can update the navbar properties using standard Gradio event handling, just like with any other component.
Here's an example that demonstrates the last point:
$code_navbar_customization
|
Customizing the Navbar
|
https://gradio.app/guides/multipage-apps
|
Additional Features - Multipage Apps Guide
|
**Prerequisite**: Gradio requires [Python 3.10 or higher](https://www.python.org/downloads/).
We recommend installing Gradio using `pip`, which is included by default in Python. Run this in your terminal or command prompt:
```bash
pip install --upgrade gradio
```
Tip: It is best to install Gradio in a virtual environment. Detailed installation instructions for all common operating systems <a href="https://www.gradio.app/main/guides/installing-gradio-in-a-virtual-environment">are provided here</a>.
|
Installation
|
https://gradio.app/guides/quickstart
|
Getting Started - Quickstart Guide
|
You can run Gradio in your favorite code editor, Jupyter notebook, Google Colab, or anywhere else you write Python. Let's write your first Gradio app:
$code_hello_world_4
Tip: We shorten the imported name from <code>gradio</code> to <code>gr</code>. This is a widely adopted convention for better readability of code.
Now, run your code. If you've written the Python code in a file named `app.py`, then you would run `python app.py` from the terminal.
The demo below will open in a browser on [http://localhost:7860](http://localhost:7860) if running from a file. If you are running within a notebook, the demo will appear embedded within the notebook.
$demo_hello_world_4
Type your name in the textbox on the left, drag the slider, and then press the Submit button. You should see a friendly greeting on the right.
Tip: When developing locally, you can run your Gradio app in <strong>hot reload mode</strong>, which automatically reloads the Gradio app whenever you make changes to the file. To do this, simply type in <code>gradio</code> before the name of the file instead of <code>python</code>. In the example above, you would type: `gradio app.py` in your terminal. You can also enable <strong>vibe mode</strong> by using the <code>--vibe</code> flag, e.g. <code>gradio --vibe app.py</code>, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. Learn more in the <a href="https://www.gradio.app/guides/developing-faster-with-reload-mode">Hot Reloading Guide</a>.
**Understanding the `Interface` Class**
You'll notice that in order to make your first demo, you created an instance of the `gr.Interface` class. The `Interface` class is designed to create demos for machine learning models which accept one or more inputs, and return one or more outputs.
The `Interface` class has three core arguments:
- `fn`: the function to wrap a user interface (UI) around
- `inputs`: the Gradio component(s) to use for the input. The num
|
Building Your First Demo
|
https://gradio.app/guides/quickstart
|
Getting Started - Quickstart Guide
|
turn one or more outputs.
The `Interface` class has three core arguments:
- `fn`: the function to wrap a user interface (UI) around
- `inputs`: the Gradio component(s) to use for the input. The number of components should match the number of arguments in your function.
- `outputs`: the Gradio component(s) to use for the output. The number of components should match the number of return values from your function.
The `fn` argument is very flexible -- you can pass *any* Python function that you want to wrap with a UI. In the example above, we saw a relatively simple function, but the function could be anything from a music generator to a tax calculator to the prediction function of a pretrained machine learning model.
The `inputs` and `outputs` arguments take one or more Gradio components. As we'll see, Gradio includes more than [30 built-in components](https://www.gradio.app/docs/gradio/introduction) (such as the `gr.Textbox()`, `gr.Image()`, and `gr.HTML()` components) that are designed for machine learning applications.
Tip: For the `inputs` and `outputs` arguments, you can pass in the name of these components as a string (`"textbox"`) or an instance of the class (`gr.Textbox()`).
If your function accepts more than one argument, as is the case above, pass a list of input components to `inputs`, with each input component corresponding to one of the arguments of the function, in order. The same holds true if your function returns more than one value: simply pass in a list of components to `outputs`. This flexibility makes the `Interface` class a very powerful way to create demos.
We'll dive deeper into the `gr.Interface` on our series on [building Interfaces](https://www.gradio.app/main/guides/the-interface-class).
|
Building Your First Demo
|
https://gradio.app/guides/quickstart
|
Getting Started - Quickstart Guide
|
What good is a beautiful demo if you can't share it? Gradio lets you easily share a machine learning demo without having to worry about the hassle of hosting on a web server. Simply set `share=True` in `launch()`, and a publicly accessible URL will be created for your demo. Let's revisit our example demo, but change the last line as follows:
```python
import gradio as gr
def greet(name):
return "Hello " + name + "!"
demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
demo.launch(share=True) Share your demo with just 1 extra parameter 🚀
```
When you run this code, a public URL will be generated for your demo in a matter of seconds, something like:
👉 `https://a23dsf231adb.gradio.live`
Now, anyone around the world can try your Gradio demo from their browser, while the machine learning model and all computation continues to run locally on your computer.
To learn more about sharing your demo, read our dedicated guide on [sharing your Gradio application](https://www.gradio.app/guides/sharing-your-app).
|
Sharing Your Demo
|
https://gradio.app/guides/quickstart
|
Getting Started - Quickstart Guide
|
So far, we've been discussing the `Interface` class, which is a high-level class that lets to build demos quickly with Gradio. But what else does Gradio include?
Custom Demos with `gr.Blocks`
Gradio offers a low-level approach for designing web apps with more customizable layouts and data flows with the `gr.Blocks` class. Blocks supports things like controlling where components appear on the page, handling multiple data flows and more complex interactions (e.g. outputs can serve as inputs to other functions), and updating properties/visibility of components based on user interaction — still all in Python.
You can build very custom and complex applications using `gr.Blocks()`. For example, the popular image generation [Automatic1111 Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) is built using Gradio Blocks. We dive deeper into the `gr.Blocks` on our series on [building with Blocks](https://www.gradio.app/guides/blocks-and-event-listeners).
Chatbots with `gr.ChatInterface`
Gradio includes another high-level class, `gr.ChatInterface`, which is specifically designed to create Chatbot UIs. Similar to `Interface`, you supply a function and Gradio creates a fully working Chatbot UI. If you're interested in creating a chatbot, you can jump straight to [our dedicated guide on `gr.ChatInterface`](https://www.gradio.app/guides/creating-a-chatbot-fast).
The Gradio Python & JavaScript Ecosystem
That's the gist of the core `gradio` Python library, but Gradio is actually so much more! It's an entire ecosystem of Python and JavaScript libraries that let you build machine learning applications, or query them programmatically, in Python or JavaScript. Here are other related parts of the Gradio ecosystem:
* [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) (`gradio_client`): query any Gradio app programmatically in Python.
* [Gradio JavaScript Client](https://www.gradio.app/guides/getting-started-with-t
|
An Overview of Gradio
|
https://gradio.app/guides/quickstart
|
Getting Started - Quickstart Guide
|
app/guides/getting-started-with-the-python-client) (`gradio_client`): query any Gradio app programmatically in Python.
* [Gradio JavaScript Client](https://www.gradio.app/guides/getting-started-with-the-js-client) (`@gradio/client`): query any Gradio app programmatically in JavaScript.
* [Hugging Face Spaces](https://huggingface.co/spaces): the most popular place to host Gradio applications — for free!
|
An Overview of Gradio
|
https://gradio.app/guides/quickstart
|
Getting Started - Quickstart Guide
|
Keep learning about Gradio sequentially using the Gradio Guides, which include explanations as well as example code and embedded interactive demos. Next up: [let's dive deeper into the Interface class](https://www.gradio.app/guides/the-interface-class).
Or, if you already know the basics and are looking for something specific, you can search the more [technical API documentation](https://www.gradio.app/docs/).
|
What's Next?
|
https://gradio.app/guides/quickstart
|
Getting Started - Quickstart Guide
|
You can also build Gradio applications without writing any code. Simply type `gradio sketch` into your terminal to open up an editor that lets you define and modify Gradio components, adjust their layouts, add events, all through a web editor. Or [use this hosted version of Gradio Sketch, running on Hugging Face Spaces](https://huggingface.co/spaces/aliabid94/Sketch).
|
Gradio Sketch
|
https://gradio.app/guides/quickstart
|
Getting Started - Quickstart Guide
|
The Model Context Protocol (MCP) standardizes how applications provide context to LLMs. It allows Claude to interact with external tools, like image generators, file systems, or APIs, etc.
|
What is MCP?
|
https://gradio.app/guides/building-an-mcp-client-with-gradio
|
Mcp - Building An Mcp Client With Gradio Guide
|
- Python 3.10+
- An Anthropic API key
- Basic understanding of Python programming
|
Prerequisites
|
https://gradio.app/guides/building-an-mcp-client-with-gradio
|
Mcp - Building An Mcp Client With Gradio Guide
|
First, install the required packages:
```bash
pip install gradio anthropic mcp
```
Create a `.env` file in your project directory and add your Anthropic API key:
```
ANTHROPIC_API_KEY=your_api_key_here
```
|
Setup
|
https://gradio.app/guides/building-an-mcp-client-with-gradio
|
Mcp - Building An Mcp Client With Gradio Guide
|
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