🚀 Multi-Container Application Development and Deployment

📄 Overview

This project demonstrates the development and deployment of a multi-container application using Docker. It involves developing a simple application with two containers: a MySQL database container and a machine learning (ML) model serving API container.

🔧 Develop a Simple Application with Two Containers

The task involves creating a custom multi-container application that consists of:

🗄️ 1. DB Container (MySQL Database)

• Database Name: Model_Logger
• Table: Log
  • Columns:
    • 🆔 ID: Unique identifier for each log entry.
    • 📅 Current_Date_Time: Timestamp of the log entry.
    • 📥 Input_Params: Input parameters for the ML model.
    • 📤 Output: The output generated by the ML model.
    • ⏱️ Response Time: Time taken to process the input and generate the output.

🤖 2. ML Serving API Container

• This container runs a pre-trained ML model as a serving API.

• The API accepts input, processes it with the ML model, generates an output, and then:

  • 📋 Logs the input parameters, output, and response time into the Log table in the MySQL database (running in a separate container).
  • 💻 Returns the result to the user.

  User Interface

  Below is a screenshot of the application's interface:

The interface allows users to input their height and weight, press the Predict button, and view the prediction result.

🛠️ Docker Compose

• A docker-compose.yml file is included to manage both containers.
• With Docker Compose, you can easily start and stop the entire application.

📂 Directory Structure

• app: Contains the code for the ML model serving API.
• db-init-scripts: Contains SQL scripts to initialize the database, create the Model_Logger database, and Log table.
• docker-compose.yml: The Docker Compose file to run and manage both containers.

📝 Notes

• Ensure that the ML model API is configured to connect to the MySQL database container for logging purposes.
• Verify that the MySQL container is properly initialized with the required database and table on startup.

⚙️ Requirements

• 🐳 Docker
• 📦 Docker Compose

▶️ How to Run

1. 📥 Clone the repository.
2. 💾 Ensure Docker and Docker Compose are installed on your machine.
3. 🚀 Run the application:

   docker-compose up
   

4. 🛑Stop the application:

   docker-compose down