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‎.gitignore

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.zed
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venv
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node_modules
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.next
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python-dotenv
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node_modules
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.env
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.env.local
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__pycache__
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.DS_Store

‎LICENSE

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Apache License
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Version 2.0, January 2004
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http://www.apache.org/licenses/
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‎README.md

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# LivePaint
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This is an example project demonstrating how to build a realtime data app using LiveKit.
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In this example, we build a realtime drawing game where players compete to complete a drawing prompt as fast as possible, while being judged by an AI agent that oversees the whole game.
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## Architecture
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### Rooms & Participants
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Each game is hosted in a single [LiveKit room](https://docs.livekit.io/home/client/connect) where each player is a standard participant. The room is reused between games, so the same group of players can complete multiple games back-to-back.
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Each room also contains a single [LiveKit agent](https://docs.livekit.io/agents) that handles game logic and serves as the realtime backend for the game.
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### Game State
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Game state is stored in the [room metadata](https://docs.livekit.io/home/client/data/room-metadata/). Each player observes it and updates local UI accordingly, but only the agent is responsible for updating it.
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- `game_state.started`: Whether a game is currently in progress
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- `game_state.prompt`: The current drawing prompt (must be non-null if the game has started)
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- `game_state.difficulty`: The difficulty of the current round
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- `game_state.winners`: List of players (by identity) who won the most recent game (cleared when a new game starts)
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### Drawings
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Drawings are stored as an unordered set of line segments, each represented by a start point and end point. When a player clears their drawing, or a new game starts, the set is emptied.
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Drawing updates are published using realtime [data messages](https://docs.livekit.io/home/client/data/messages) with the topics `player.draw_line` and `player.clear_drawing`. Drawings are stored in unit coordinates, which each point between 0 and 1 to be size-agnostic. However, when sent as a data message each segment is serialized as four 16-bit integers (representing `x1`, `y1`, `x2`, `y2`) between 0 and 65535 to minimize the message size. Each player, and the agent, reconsitutes the full drawing from these messages.
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When a player joins an inprogress game, they retrieve the current drawing state from each player with [RPC](https://docs.livekit.io/home/client/data/rpc) by calling `player.get_drawing`.
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### Game Control
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Players have access to a simple API for game control, built on top of [RPC](https://docs.livekit.io/home/client/data/rpc) to the agent.
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- `host.start_game`: Starts a new game with the given string prompt (leave blank for a random prompt)
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- `host.update_difficulty`: Updates the difficulty setting for the next game
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- `host.end_game`: Ends the current game without a winner
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### Judgement
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The agent is responsible for judging each player's drawing. It runs a single loop that wakes up every few seconds. On a judgement loop, the agent will:
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1. Convert each player's drawing from a set of line segments to a 512x512 PNG image.
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2. Send each drawing to a GPT-4o chat which is configured to "guess" what the drawing is meant to be. (Note: The actual target "prompt" is not included in this request, to avoid polluting its context.)
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3. Collects all guesses and sends them to a different GPT-4o chat which is configured to return a list of all players whose guesses are "correct" (i.e. similar enough to the target prompt).
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4. All guesses are published as data messages to all players, using the topic `host.guess`.
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5. If any winners were found, the agent updates the game state to end the game and list the winners. Otherwise it sleeps for a few seconds and checks again.
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### Running Locally
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Run the agent:
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```
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cd agent
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python -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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python main.py dev
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```
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Run the site:
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```
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cd web
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pnpm install
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pnpm dev
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```

‎agent/.dockerignore

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‎agent/Dockerfile

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# This is an example Dockerfile that builds a minimal container for running LK Agents
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# syntax=docker/dockerfile:1
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ARG PYTHON_VERSION=3.11.6
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FROM python:${PYTHON_VERSION}-slim
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# Prevents Python from writing pyc files.
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ENV PYTHONDONTWRITEBYTECODE=1
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# Keeps Python from buffering stdout and stderr to avoid situations where
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# the application crashes without emitting any logs due to buffering.
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ENV PYTHONUNBUFFERED=1
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# Create a non-privileged user that the app will run under.
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# See https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#user
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ARG UID=10001
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RUN adduser \
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--disabled-password \
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--gecos "" \
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--home "/home/appuser" \
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--shell "/sbin/nologin" \
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--uid "${UID}" \
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appuser
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# Install gcc and other build dependencies.
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RUN apt-get update && \
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apt-get install -y \
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gcc \
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python3-dev \
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&& rm -rf /var/lib/apt/lists/*
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USER appuser
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RUN mkdir -p /home/appuser/.cache
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RUN chown -R appuser /home/appuser/.cache
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WORKDIR /home/appuser
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COPY requirements.txt .
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RUN python -m pip install --user --no-cache-dir -r requirements.txt
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COPY . .
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# ensure that any dependent models are downloaded at build-time
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RUN python main.py download-files
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# Run the application.
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CMD ["python", "main.py", "start"]

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