# Machine learning apps [Gradio](https://www.gradio.app/), a fast and easy library for building and sharing machine learning apps, is integrated with [`Pipeline`] to quickly create a simple interface for inference. Before you begin, make sure Gradio is installed. ```py !pip install gradio ``` Create a pipeline for your task, and then pass it to Gradio's [Interface.from_pipeline](https://www.gradio.app/docs/gradio/interface#interface-from_pipeline) function to create the interface. Gradio automatically determines the appropriate input and output components for a [`Pipeline`]. Add [launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch) to create a web server and start up the app. ```py from transformers import pipeline import gradio as gr pipeline = pipeline("image-classification", model="google/vit-base-patch16-224") gr.Interface.from_pipeline(pipeline).launch() ``` The web app runs on a local server by default. To share the app with other users, set `share=True` in [launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch) to generate a temporary public link. For a more permanent solution, host the app on Hugging Face [Spaces](https://hf.co/spaces). ```py gr.Interface.from_pipeline(pipeline).launch(share=True) ``` The Space below is created with the code above and hosted on Spaces.