The Conclusive Face-Off: Flask vs Streamlit
Introduction
AI models are very useful and powerful, but they are not useful without any Web UI tool. Before a model can provide any value, it will almost certainly need to be deployed.
Several tool have recently emerged that Data Scientists can utilize to deploy a machine learning/deep learning model rapidly and without any hassle.
Quick Intro
Streamlit: Another popular tool for creating user interfaces is Streamlit. It’s an open-source Python toolkit for creating custom, unique data science and machine learning web apps. Streamlit is interoperable with a number of well-known libraries and frameworks. If you need to get a dashboard up and running quickly and have the ability to add a lot of components and controls, Streamlit is a good choice. In addition, it is faster than Dash or Flask for creating a web UI or dashboard.
Pros:
Anyone who is familiar with Python can use Streamlit. HTML and CSS aren’t required in any way.
Extremely easy to learn.
There’s no need to be concerned about routing.
Time frame for development to deployment is really short.
Cons:
It is not scalable.
Any of the frontend components are difficult to change.
few features still in beta.
Flask
Flask is a Python backend framework for deploying apps. Flask can be used to deploy almost any apps. You are not limited to data-related apps. You may add components to Flask, but you can also quickly develop your own on the fly.
Pros:
It is highly customizable.
Highly scalable.
Fully tested.
Cons:
Because this is only a backend framework, you’ll need to master frontend development.
The time it takes to develop an app and deploy it depends on how complicated it is.
Intermediate Python skills, as well as HTML, CSS, and JavaScript, are necessary.
Conclusion
Flask and Streamlit serve distinct objectives, Flask is a solution for creating backends and APIs for apps, while Streamlit is a solution for fast developing apps with minimal code. Flask will be more involved than Streamlit as a result. as per my experience, if you want to create more complex app with scalability then go ahead with Flask.
