Spec-Driven Task App

This repository contains a small spec-driven full-stack example: a FastAPI backend and a plain HTML + JavaScript frontend for managing tasks in memory.

Project structure

backend/
	main.py
	models.py
	routes.py
	storage.py
frontend/
	index.html
tests/
	test_tasks.py
requirements.txt
README.md

Quickstart

1. Install dependencies (recommended in a virtualenv):

pip install -r requirements.txt

2. Run the backend:

python -m backend.main

The API will be available at http://127.0.0.1:8000 and the OpenAPI docs at http://127.0.0.1:8000/docs.

Spec-Driven Task Manager

This repository contains a small full-stack Task Manager implemented using a spec-driven approach.

Task spec (single source of truth):

{ "id": "uuid", "title": "string", "description": "string", "status": "pending | completed" }

Run the backend

Install dependencies into a virtualenv, then run with Uvicorn:

python -m pip install -r requirements.txt
uvicorn backend.main:app --reload --port 8000

The backend serves the API and the frontend. Open http://localhost:8000/ to use the web UI.

API endpoints:

• POST /tasks — create a task (JSON: title, description)
• GET /tasks — list tasks
• GET /tasks?status=pending|completed — filter tasks by status
• PATCH /tasks/{id}/status — update a task's status (JSON: {status: "completed"})

Test-only endpoint:

• POST /test/clear — clears in-memory storage (used by the test suite)

Frontend

Open http://localhost:8000/ in your browser. The single index.html page allows creating tasks, filtering, and toggling status.

Run tests

python -m pip install -r requirements.txt
pytest -q

How AI / Code generation tools were used

• The project was implemented following a spec-driven workflow. Pydantic models were derived from the task spec and used for validation and OpenAPI generation.
• Code generation tools were used to scaffold boilerplate (models, routes, storage) and to iterate on API shapes and tests, accelerating implementation while preserving human review and refinement.