Streamlit Machine Leaning app

Streamlit Machine Leaning app

Description

Streamlit: is an open-source Python library. with the aid of "Streamlit" it is simple to Creating and sharing stunning, personalized web apps for machine learning and data science. You can create and deploy effective and Powerful data apps in a matter of minutes.

Introduction

Deploying a machine learning model. has numerous options. For instance, we can deploy our models using frameworks like Django and Flask. But the issue is that in order to use these frameworks, we need to understand CSS, HTML, and Javascript.?

What should data engineers use to deploy their machine learning models into production? is a topic that emerges. Streamlit is a straightforward solution to this problem, enabling you to deploy any machine learning model with ease and without concern for the front end.?

To implement and to deploy our machine learning model, it is important to first grasp what Streamlit is and how to utilize it.

What is Streamlit?

We can create interactive apps from data science and machine learning scripts using the open-source Python framework Streamlit. It supports the core Python plotting libraries, including GraphViz, Plotly, Numpy, Pandas, Seaborn, Pyplot, and Matplotlib. In addition, Streamlit features a native graphing library that includes maps, bar charts, area charts, and line charts.

There is no multi-page application functionality in Streamlit. On the Streamlit?support forum, there are numerous workarounds available that offer implementations resembling multi-page applications.

Why Use Streamlit for Data Science Projects?

As we previously explained, using Streamlit makes creating apps as simple as writing Python scripts as opposed to using frameworks like Flask and Django. For us to employ data science and machine learning models for useful decision-making, they must be successfully deployed into production.?

The impact of the models is significantly reduced if the projects are not released as web applications, and we are unable to gain valuable insights from them. Additionally, once the models are in use, the stakeholders can play around with them to see what functions well. As a result, using Streamlit to build interactive web applications and launch them into a production environment is the greatest choice for data scientists since they simply have to:

• Be familiar with Python fundamentals.
• Be able to create scripts to carry out specified tasks.
• Create the Streamlit application by writing a few lines of code.

The following Streamlit design guidelines must be followed while developing web apps by machine learning engineers and data scientists.

• Embrace scripting; with just a few lines of code, we can create an app.
• Weave in Interaction - To interact with the Streamlit web apps, we can use widgets rather than coding backend, setting routes, and managing HTTP requests.
• Deploy Instantaneously - Our programs can be managed, shared, and deployed right from Streamlit.

How to Use Streamlit?

Let's get started building web apps with Streamlit.

Install Streamlit

We must understand how to install and configure Streamlit on our computer. Run the following line in your terminal to install Streamlit.

?? pip install streamlit        

The following command can be used to see what version of Streamlit is currently installed on our computer.

?? streamlit --version        

The command listed below can be used to test the library after Streamlit has been successfully installed.

streamlit hello        

We can see that a new tab will open in our web browser with the Hello app from Streamlit.

Run a Streamlit App

Using Streamlit commands, we can write a test Python script and run it with the command listed below.

streamlit run <test_script.py>        

Development and Data Flow:

The software provides us with two alternatives whenever we make changes to the Streamlit Python script:

• ?Rerun
• Always rerun

We can use the Always rerun option to have our web app automatically update each time we make a change. With the help of this tool, we can collaborate with Streamlit more easily and streamline our development process.

Additionally, Streamlit has a distinct data flow. Streamlit sends us the whole Python script if anything on our screen is updated.

Display the Data

Tables, dataframes, and arrays are examples of the sorts of data that can be displayed using Streamlit techniques. For example, we can write a string using the method st.write ("Our string").

Widgets and Layouts

Streamlit offers a variety of widgets, including st.slider, st.selectbox, st.checkbox, and others. By ordering our widgets, we can also choose how our app will be laid out. For instance, we can align the data in the left panel using the st.sidebar function.

How and Where to Deploy Streamlit App?

The New App option is located in the top right corner of the Streamlit website, as seen below. By choosing this option, you can create an app.

• If you have connected your Github account, a new window will appear with a list of your repositories. You can select one and enter the location of its file.
• You can click the Deploy! button once all fields have been filled in. After a few seconds, the app will be visible and operational.
• Make sure your app's dependencies and source code are on Github before deploying it, as Streamlit cloud launches apps directly from the Github repository.

Note: You can find more details about this project on my?GitHub?repository or visit my?medium?account if you’re interested in doing so.