Skip to main content
In this lesson we’ll build the backend for a small image-optimization service. The objective is to create a Flask-based HTTP endpoint that accepts image uploads, compresses them using OpenCV, and returns the optimized image — with robust validation, error handling, and a reproducible local development workflow. Below is a slide that frames the development phase and highlights how AI-assisted code completion can help during implementation.
A presentation slide from KodeKloud titled "Harnessing AI Code Completion" with the subtitle "Development Phase - Backend" and the KodeKloud logo at the top. The slide has a dark blue background.
What you will build (high-level plan)
  • Set up a Python virtual environment for isolated dependency management.
  • Create a clear, modular project structure for the Flask app and utilities.
  • Implement a Flask API endpoint to accept and serve images.
  • Use OpenCV to perform image manipulation and compression.
  • Introduce and then debug a deliberate issue to practice troubleshooting.
  • Validate incoming files and add robust error handling.
  • Test the endpoint manually using Postman and automate basic checks where appropriate.
The slide below summarizes the development tasks and provides a quick checklist for the steps above.
A presentation slide titled "Game Plan." It lists development tasks such as configuring a virtual environment, setting up project structure and a Flask API, implementing OpenCV, debugging, validating images, adding error handling, and testing with Postman.
Tools and references used in this lesson This module demonstrates workflows using paid tiers of these tools; similar steps can often be followed with free tiers or alternative extensions. Expected outcome
  • A local Flask application with a POST endpoint that accepts an image file, compresses it (reducing quality/size via OpenCV), and returns the compressed image in the HTTP response.
  • A reproducible local setup: virtual environment, dependency installation, and instructions to run and test using Postman.
  • Source code for the complete project is available at https://github.com/JeremyMorgan/super-image-optimizer.
Follow the steps in this lesson to reproduce the backend locally: set up a virtual environment, install dependencies, run the Flask app, and test image upload and retrieval using Postman.
Links and references

Watch Video