Mindespace Backend (Portfolio Edition)

A production-oriented Django backend for a mental-health web platform focused on secure user management, modular feature delivery, and containerized deployment.

This repository demonstrates my backend engineering workflow: API design, environment-driven configuration, app modularization, and Docker-based runtime setup.

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Project Snapshot

• Project Type: Backend API / service layer
• Domain: Health & wellbeing platform
• Primary Language: Python (Django)
• Deployment Style: Dockerized application runtime
• Architecture Style: Modular Django apps under modules/

What this project demonstrates

• Building and organizing a scalable Django backend
• Structuring business features into reusable Django apps
• Managing environments for local/dev deployment
• Container-first development workflow with Docker
• Maintaining a clean project layout for team collaboration

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Tech Stack

• Framework: Django
• Language: Python
• Containerization: Docker
• Process Entrypoint: Shell-based startup script (entrypoint.sh)
• Dependency Management: requirements.txt

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Repository Structure

mindespace-backend/
├── backend/               # Django project configuration (settings, urls, wsgi/asgi)
├── modules/               # Domain-specific Django apps / feature modules
├── manage.py              # Django management entrypoint
├── requirements.txt       # Python dependencies
├── Dockerfile             # Container build definition
├── entrypoint.sh          # Runtime initialization/start script
└── README.md

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Local Development Setup

1) Clone the repository

git clone https://github.com/Da-code135/mindespace-backend.git
cd mindespace-backend

2) Create and activate virtual environment

python -m venv .venv
source .venv/bin/activate        # macOS/Linux
# .venv\Scripts\activate         # Windows PowerShell

3) Install dependencies

pip install -r requirements.txt

4) Configure environment variables

Create a .env file (or your preferred env source) and add required variables for:

• Django settings module / environment mode
• Database connection
• Secret key
• Allowed hosts / CORS (if applicable)
• Third-party service credentials (if used)

Keep secrets out of version control.

5) Run migrations and start server

python manage.py migrate
python manage.py runserver

Default local URL:

• http://127.0.0.1:8000/

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Docker Workflow

Build image

docker build -t mindespace-backend .

Run container

docker run --env-file .env -p 8000:8000 mindespace-backend

The container startup is orchestrated via:

• Dockerfile
• entrypoint.sh

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Portfolio Highlights

Engineering decisions

• Modular app design: Keeps feature boundaries clear and maintainable.
• Containerized runtime: Improves consistency across machines/environments.
• Conventional Django project layout: Fast onboarding for collaborators.

Backend capabilities (high level)

• API endpoints for platform functionality
• Centralized project settings and URL routing
• Extensible module structure for new features

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How I’d present this in interviews

This project represents how I build backend systems with a focus on:

1. Maintainability through modular architecture
2. Reliability via repeatable environment setup
3. Scalability by separating core config from feature modules
4. Deployment readiness using Dockerized execution

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Suggested Next Enhancements

• Add API schema docs (Swagger/OpenAPI)
• Add CI pipeline for linting/tests
• Add test coverage badge and quality gates
• Add production deployment notes (e.g., Gunicorn + reverse proxy)
• Add architecture diagram and request-flow documentation

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Contact

If you’re reviewing this as part of my portfolio, I’m happy to walk through:

• architecture decisions,
• module boundaries,
• deployment strategy,
• and trade-offs made during implementation.