Flask Docker Skeleton Project

Overview

This skeleton project provides a foundation for building a RESTful API using Flask and containerizing it with Docker. It's designed to test your skills in web development, API design, machine learning integration, and containerization.

Tech Stack

• Python 3.11
• Flask: A lightweight WSGI web application framework
• Gunicorn: A Python WSGI HTTP Server for UNIX
• Docker: A platform for developing, shipping, and running applications

Prerequisites

• Docker installed on your local machine

Project Structure

project_root/
├── Dockerfile
├── gunicorn.conf.py
├── requirements.txt
├── app.py
└── README.md

Your Task

1. Implement the API in app.py, including an inference endpoint for a machine learning model
2. Build and run the Docker container
3. Make successful requests to your API

Detailed Requirements

• Create an inference endpoint that accepts input data and returns predictions using a machine learning model of your choice
• Implement proper error handling and input validation
• Ensure the API follows RESTful principles

Getting Started

1. API Implementation

• Open app.py and implement the required endpoints and logic
• Ensure your code follows PEP 8 style guidelines
• Add any necessary dependencies to requirements.txt

2. Building the Docker Image

docker build -t <image-name>:<version> .

3. Running the Container

docker run -p 50505:50505 <image-name>:<version>

4. Testing Your API

Use curl or any API testing tool to make requests to your endpoints. For example:

curl http://localhost:50505

Evaluation Criteria

Your submission will be evaluated based on:

• Correctness of API implementation, including the ML inference endpoint
• Code quality, organization, and adherence to best practices
• Proper use of Docker
• API functionality and adherence to RESTful principles
• Error handling and input validation

Bonus Features

While not required, implementing any of the following will be viewed favorably:

• JWT authentication
• Comprehensive logging
• API documentation (e.g., using Swagger/OpenAPI)
• Unit tests
• Data validation and sanitization
• Rate limiting
• Caching mechanisms

Submission and Review Process

1. When ready, we will schedule a meeting with you to review your implementation.

2. During this meeting, you will have the opportunity to walk us through your code live. Be prepared to:

   • Explain your design decisions
   • Demonstrate the functionality of your API
   • Discuss any challenges you faced and how you overcame them
   • Answer questions about your implementation

3. This live review allows us to better understand your thought process and gives you a chance to showcase your communication skills and technical knowledge.

We look forward to seeing your implementation and discussing it with you!