🚀 Embed ML models in Web Frameworks with Streamlit 🚀

There are many ways to make web interfaces to allow interaction with Machine Learning models and we will cover two of them.

Introduction

Now that you know how to build some Machine Learning models, it is the time to discover how to embeded it into a web app with a user-friendly interface. You have already seen, of course, some ML tools and you maybe played with it. If so, you know that it is much more interesting to interact with a ML model through a beautiful interface than using a notebook, especially when you are not an expert of this domain.

In this project, we aim to help you to discover how to embed a ML model into a web app to interact with it much easier, by inputing the required information, making predictions and showing the result.

Description

You will have a minimal interface demo with Streamlit, this will just serve you to make sure that everything works correctly. Then, you will have to make your own interfaces, those allowing you to interact with a Machine Learning model, that is to say:

• Pass values through the interface;
• Recover these values in backend;
• Apply the necessary processing;
• Submit the previously processed values to the ML model to make the predictions;
• Process the predictions obtained and display them on the interface.

Instructions

Your task is to understand the frameworks and build your app integrating a ML model. Your work should follow these next steps.

1. Build a Streamlit app to embed the classification model you built a few weeks ago.
2. Create a dashboard to display visualization of data available in your database.
3. Write an anrticle on the project and publish it on Medium

Rubrics

Streamlit:

• Excellent: Have an that works correctly with a nice and personalized interface.

• Good: Have an app that launches, makes prediction and shows result.

• Fair: Have an app that launches but having bugs regarding prediction or interface.

Installation

You have two ways in order to setup and run this project.

• Windows:

    python -m venv venv; venv\Scripts\activate; python -m pip install -q --upgrade pip; python -m pip install -qr requirements.txt  
  

• Linux & MacOs:

    python3 -m venv venv; source venv/bin/activate; python -m pip install -q --upgrade pip; python -m pip install -qr requirements.txt  
  

Both long command-lines have a same structure, they pipe multiple commands using the symbol ; but you may manually execute them one after another.

1. Create the Python's virtual environment that isolates the required libraries of the project to avoid conflicts;
2. Activate the Python's virtual environment so that the Python kernel & libraries will be those of the isolated environment;
3. Upgrade Pip, the installed libraries/packages manager to have the up-to-date version that will work correctly;
4. Install the required libraries/packages listed in the requirements.txt file so that it will be allow to import them into the python's scripts and notebooks without any issue.

NB: For MacOs users, please install Xcode if you have an issue.

• Run the demo apps (being at the repository root):

  Streamlit:

  streamlit run streamlit_project/basic_demo/app.py
  

  • Go to your browser at the following address :

    http://localhost:8501

Screenshots

Streamlit Basic Demo

Resources

Here are some ressources you would read to have a good understanding of Streamlit :

• Get started with Streamlit

Contributing

Feel free to make a PR or report an issue 😃.

Oh, one more thing, please do not forget to put a description when you make your PR 🙂.

Author

• Emmanuel KOUPOH
•