mr-drews-assignment-creator/README.md

✎ Mr. Drew's Assignment Creator

A local, private, accuracy-first generator for quizzes, tests, worksheets, discussion questions, and case studies — for any grade level from kindergarten to college.

Everything runs in a single Docker container on your own machine. With a local AI (Ollama or LM Studio), nothing you type ever leaves your computer.

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Why it's accurate

Most AI assignment tools fire off one giant "make me a quiz" request and hope for the best. This app runs a three-stage pipeline instead:

1. Analyze — the AI reads your source material and maps its key concepts, facts, and vocabulary.
2. Generate — questions are written strictly from your source. The AI is forbidden from adding outside facts, and every question carries a Source: quote proving where its answer comes from.
3. Verify — a second, skeptical AI pass re-checks every question and answer against the source. Each is stamped ✓ Verified, or flagged ⚠ Check this with a note telling you exactly what to look at.

You stay in control: every question can be edited, regenerated with a steering note ("make it harder", "focus on causes"), or replaced — and you can re-run the accuracy check any time.

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Screenshots

Create — paste, upload, or pull from a URL, then set parameters and generate.

Library — every assignment you've made, stored locally on your machine.

Editor — per-question editing with ✓ Verified / ⚠ Check this accuracy badges and source quotes.

Settings — pick your AI provider (local or cloud) and set your school profile.

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What you need

• Docker — Docker Desktop on macOS/Windows, or Docker Engine on Linux. That's the only thing you install.
• One AI provider (pick any one):
  • Ollama (free, private, local) — https://ollama.com
  • LM Studio (free, private, local) — https://lmstudio.ai
  • An Anthropic, OpenAI, or Google AI API key (cloud, pay-per-use) — paste it on the Settings page, no networking setup needed.

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Quick start

macOS / Windows (Docker Desktop)

git clone https://git.bizzle.lol/bizzle/mr-drews-assignment-creator.git
cd mr-drews-assignment-creator/docker
docker compose up -d

Open http://localhost:3000. Done.

The container automatically reaches the Ollama / LM Studio running on your machine (via host.docker.internal). Just make sure one of them is running:

• Ollama — have the Ollama app open (or run ollama serve).
• LM Studio — open the Developer tab and start the local server.

Linux

git clone https://git.bizzle.lol/bizzle/mr-drews-assignment-creator.git
cd mr-drews-assignment-creator/docker
docker compose -f docker-compose.linux.yml up -d

Open http://localhost:3000. This variant uses host networking, so the container sees Ollama / LM Studio on plain localhost — no extra setup, even if Ollama is bound to 127.0.0.1 (its default).

The first docker compose up builds the image from source — expect it to take a couple of minutes. Subsequent starts are instant.

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First run: pick your AI

1. Open http://localhost:3000 and go to Settings.
2. Choose a provider:
   • Ollama / LM Studio — confirm the server address, then click Test connection.
   • Anthropic / OpenAI / Google — paste your API key.
3. Save. Head back to the home page and generate your first assignment.

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Where is my data?

Everything — assignments, settings, API keys, your school logo — lives in a single JSON file inside the assignment-data Docker volume. It survives restarts, rebuilds, and image upgrades.

Back it up any time:

cd docker
docker compose cp assignment-creator:/app/data/db.json ./db-backup.json

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Ollama / LM Studio on a different machine

Common setup: the app runs in Docker on a server or NAS, while Ollama runs on your desktop with the GPU. Two steps:

1. On the machine running the LLM, allow network connections:
   • Ollama — set OLLAMA_HOST=0.0.0.0 (Ollama app → Settings → "Expose Ollama to the network", or the env var) and restart Ollama.
   • LM Studio — Developer tab → server settings → enable "Serve on Local Network".
2. In the app, open Settings, select Ollama or LM Studio, and enter that machine's address in "Server address (base URL)" — e.g. http://192.168.1.50:11434. Click Test connection, then Save.

The saved address always wins over the compose defaults. You can also pre-set defaults for fresh installs via the OLLAMA_BASE_URL / LMSTUDIO_BASE_URL environment variables in docker/docker-compose.yml.

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Updating

cd docker
docker compose up -d --build

Your data volume is untouched by rebuilds.

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Troubleshooting

• "Could not reach Ollama" on macOS/Windows — confirm Ollama is running on your machine (ollama list in a terminal). The default compose file already points the app at your machine, not at the container.
• "Could not reach Ollama" on Linux with the default compose file — use docker-compose.linux.yml instead, or set OLLAMA_HOST=0.0.0.0 so Ollama accepts connections from containers.
• Port 3000 already in use — change the first number in ports: in docker/docker-compose.yml (e.g. "8080:3000") and open http://localhost:8080.
• Stop the app — cd docker && docker compose down (your data volume is kept).