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# Gradio: Build Machine Learning Web Apps — in Python
Gradio is an open-source Python library that is used to build machine learning and data science demos and web applications.
With Gradio, you can quickly create a beautiful user interface around your machine learning models or data science workflow and let people "try it out" by dragging-and-dropping in their own images,
pasting text, recording their own voice, and interacting with your demo, all through the browser.
One of the *best ways to share* your machine learning model, API, or data science workflow with others is to create an **interactive app** that allows your users or colleagues to try out the demo in their browsers.
2\. Run the code below as a Python script or in a Jupyter Notebook (or [Google Colab](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)):
3\. The demo below will appear automatically within the Jupyter Notebook, or pop in a browser on [http://localhost:7860](http://localhost:7860) if running from a script:
You'll notice that in order to make the demo, we created a `gradio.Interface`. This `Interface` class can wrap any Python function with a user interface. In the example above, we saw a simple text-based function, but the function could be anything from music generator to a tax calculator to the prediction function of a pretrained machine learning model.
Let's say you want to customize the input text field — for example, you wanted it to be larger and have a text placeholder. If we use the actual class for `Textbox` instead of using the string shortcut, you have access to much more customizability through component attributes.
Suppose you had a more complex function, with multiple inputs and outputs. In the example below, we define a function that takes a string, boolean, and number, and returns a string and number. Take a look how you pass a list of input and output components.
You simply wrap the components in a list. 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, again in order.
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!
When using the `Image` component as input, your function will receive a NumPy array with the shape `(width, height, 3)`, where the last dimension represents the RGB values. We'll return an image as well in the form of a NumPy array.
You can also set the datatype used by the component 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:
Also note that our input `Image` component comes with an edit button 🖉, which allows for cropping and zooming into images. Manipulating images in this way can help reveal biases or hidden flaws in a machine learning model!
2\. **Blocks**, a low-level API for designing web apps with more flexible layouts and data flows. Blocks allows you to do things like feature multiple data flows and demos, control where components appear on the page, handle complex data flows (e.g. outputs can serve as inputs to other functions), and update properties/visibility of components based on user interaction — still all in Python. If this customizability is what you need, try `Blocks` instead!
- A `Button` was created, and then a `click` event-listener was added to this button. The API for this should look familiar! Like an `Interface`, the `click` method takes a Python function, input components, and output components.
A lot more going on here! We'll cover how to create complex `Blocks` apps like this in the [building with blocks](https://github.com/gradio-app/gradio/tree/main/guides/3\)building_with_blocks) section for you.
Congrats, you're now familiar with the basics of Gradio! 🥳 Go to our [next guide](https://gradio.app/key_features) to learn more about the key features of Gradio.
Also check out the paper *[Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://arxiv.org/abs/1906.02569), ICML HILL 2019*, and please cite it if you use Gradio in your work.
