Streamlit: The fastest way to create and share data applications!
Tags: datascience, computerscience, python, machinelearning
Published: 2021-12-22 22:28:01

Transform data scripts into shareable web applications in minutes, all in Python, and for free with no front-end experience required.

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1639865707513/6LBN01HIW.png)

**Installation:**

It is necessary to have Python 3.6 or higher, and the installation will be done via PIP:


```Python
pip install streamlit
streamlit hello
``` 

Right after that, it is necessary to configure some environment variables, depending on the operating system you are using:

* [Linux](https://github.com/streamlit/streamlit/wiki/Installing-in-a-virtual-environment#on-mac--linux)

* [Mac](https://github.com/streamlit/streamlit/wiki/Installing-in-a-virtual-environment#on-mac--linux)

* [Windows](https://github.com/streamlit/streamlit/wiki/Installing-in-a-virtual-environment#on-windows)

Streamlit is compatible with many libraries and frameworks such as Keras, Scikit learn, Altair, bokeh, Latex, plotly, OpenCV, Vega-Lite, PyTorch, NumPy, seaborn, Deck.GL Tensorflow, matplotlib, pandas, and many others

**Components:**

With the popularization of Streamlit, several components appeared with the most diverse functionalities, such as:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1639865892137/oZ1AqImw0.png)

**HiPlot**

A lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways of representing information.

`pip install hiplot`

**Gallery:**

There are many examples of its use and configurations of different apps; thinking about demonstrating its usability by the community, a gallery was created with several demonstrations of its use. 

https://www.streamlit.io/gallery

Happy example :)

This demo project below allows you to browse the entire Udacity autonomous car dataset and perform inference in real-time using the YOLO object detection network.


![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1639865983344/BzGlsDicM.png)

**Deploy Apps:**

One of its great differentials is the possibility of deploying apps in a very automated way using GitOps, that is, through continuous integration, the developer, from a given push on a branch in a code repository like GitHub, triggers an automation that does to deploy the app.

https://docs.streamlit.io/en/stable/deploy_streamlit_app.html#

https://towardsdatascience.com/deploying-your-machine-learning-apps-in-2021-a3471c049507

This framework is straightforward and easy to use, the deployment of new apps is very intuitive. As a negative point, I missed a more visible layer of security, and I think I will have to implement an interface between the user and the apps to ensure more security….

**References:**

[https://www.streamlit.io/](https://www.streamlit.io/)

{% medium https://rknagao.medium.com/streamlit-101-o-b%C3%A1sico-para-colocar-seu-projeto-no-ar-38a71bd641eb %}

{% medium https://medium.com/spatial-data-science/build-and-deploy-full-web-applications-quickly-with-python-e196771f48df %}

[https://towardsdatascience.com/diabetes-prediction-application-using-streamlit-fed6120124a5](https://towardsdatascience.com/diabetes-prediction-application-using-streamlit-fed6120124a5)

[https://towardsdatascience.com/image-processing-using-streamlit-d650fb0ccf8](https://towardsdatascience.com/image-processing-using-streamlit-d650fb0ccf8)

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