Gradio Customer Churn App 💻

This project uses a trained machine learning classification model to build a web interface that allows users to predict whether a customer will churn(leave) or not churn (stay).

Summary

Deployed App	Article
Huggingface Space	Medium Article

Introduction

After building a machine learning model, how do we enable other people to use it? We can't expect people to understand the code and interact with the model through a jupyter notebook or python file, so we must build a graphical user interface that is user friendly.

In this project, a classification ML model was embedded into a web app with Gradio. The user will interact with it by inputing the required information for the model to predict and classify if a customer will churn or not. This repo contains the project notebook showing the process of training the model.

Process

• Research the documentation on Gradio

• Build a basic interface with inputs and outputs

• Import the model and the requirements(Scaler, Encoder, featurenengineering functions, etc.)

• Retrieve the input values and process them

• Pass the processed data through the ML model to predict

• Format the prediction output and send it to the interface to be displayed

Setting up the Environment 🍀

For manual installation, you need to have Python 3 on your system. Then you can clone this repo and be at the repo's root https://github.com/Azie88/Gradio-ML-classification-model-app-deployment, then follow the steps as outlined below;

1. Create a Python virtual environment to isolate the project's required libraries and avoid conflicts. Execute the following command in your terminal:

   python -m venv venv

2. Activate the Python virtual environment to use the isolated Python kernel and libraries. Run the appropriate command based on your operating system:

   • For Windows:

   venv\Scripts\activate

   • For Linux and MacOS:

   source venv/bin/activate

3. Upgrade Pip, the package manager, to ensure you have the latest version. Use the following command:

   python -m pip install --upgrade pip

4. Install the required libraries listed in the requirements.txt file. Run the command:

   python -m pip install -r requirements.txt

Note: If you encounter any issues on MacOS, please make sure Xcode is installed.

5. Run the app.py file (being at the repository root):

Gradio:

For development

  gradio app.py

For normal deployment/execution

  python app.py  

• Go to your browser at the following IP address :

  http://localhost:7860

Screenshots 🖼️

Author 🖊️

Andrew Obando

---

Feel free to star ⭐ this repository if you find it helpful!