Project Template for AI Projects

A starter structure for AI / ML / Data-Science projects. Two patterns are supported: a single-service layout for one focused product, and a multi-service monorepo for projects that may grow into several backend services behind a unified frontend.

This repo is a GitHub template. Click "Use this template" to create a new repo from it, then run one of the bootstrap scripts.

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How to use this template

1. Click "Use this template" → "Create a new repository" at the top of this page.

2. Clone your new repo locally.

3. Go into the Project. Pick a pattern and run the matching bootstrap script:

   cd AI-Project-Template
   
   # Single-service: one focused product, one Python package.
   chmod +x bootstrap_single.sh
   bash bootstrap_single.sh
   
   # Multi-service: monorepo of independent backend services.
   chmod +x bootstrap_multi.sh
   bash bootstrap_multi.sh

   Each script is idempotent — it lays down folders, stub files, .gitignore, .env.example, and a runnable Flask + gunicorn skeleton.

4. Rename the placeholder (your_pkg for single-service, service_a for multi-service) to your real package or service name. Use a valid Python identifier — lowercase, no hyphens.

5. Set up the venv and verify it runs:

   cd backend
   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   
   # Dev server (single-service):
   flask --app your_pkg.api.main run --host 0.0.0.0 --port 8000
   # OR more production-ready
   gunicorn --bind 127.0.0.1:8000 your_pkg.api.main:app
   
   # Dev server (multi-service):
   flask --app service_a.api.main run --host 0.0.0.0 --port 8000
   # OR more production-ready
   gunicorn --bind 127.0.0.1:8000 service_a.api.main:app

   Then in an other terminal verify response:

   # Multi architecture
   curl http://localhost:8000/service_a/health
   
   # Single architecture
   curl http://localhost:8000/your_pkg/health

   You should get {"status": "ok"}.

6. Replace this README with one for your actual project.

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What's inside

The full layout, conventions, and rationale (why services are siblings, why models/ is split from ml_models/, where databases fit, etc.) are documented in docs/:

• docs/single-service-project-structure.md
• docs/multi-service-project-structure.md
• *-toml.md variants of each, for projects that prefer a minimal pyproject.toml.

Defaults baked into the bootstraps: Flask + gunicorn, requirements.txt (no pyproject.toml), flat layout (no src/ folder), one venv per area.

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License

MIT — see LICENSE.