# Battlesnake Python Starter Project

An official Battlesnake template written in Python. Get started at [play.battlesnake.com](https://play.battlesnake.com).

![Battlesnake Logo](https://media.battlesnake.com/social/StarterSnakeGitHubRepos_Python.png)

This project is a great starting point for anyone wanting to program their first Battlesnake in Python. It can be run locally or easily deployed to a cloud provider of your choosing. See the [Battlesnake API Docs](https://docs.battlesnake.com/api) for more detail. 

## Technologies Used

This project uses [Python 3](https://www.python.org/) and [Flask](https://flask.palletsprojects.com/). It also comes with an optional [Dockerfile](https://docs.docker.com/engine/reference/builder/) to help with deployment.

## Run Your Battlesnake

Install dependencies using pip

```sh
pip install -r requirements.txt
```

Start your Battlesnake

```sh
python main.py
```

You should see the following output once it is running

```sh
Running your Battlesnake at http://0.0.0.0:8000
 * Serving Flask app 'My Battlesnake'
 * Debug mode: off
```

Open [localhost:8000](http://localhost:8000) in your browser and you should see

```json
{"apiversion":"1","author":"","color":"#888888","head":"default","tail":"default"}
```

## Play a Game Locally

Install the [Battlesnake CLI](https://github.com/BattlesnakeOfficial/rules/tree/main/cli)
* You can [download compiled binaries here](https://github.com/BattlesnakeOfficial/rules/releases)
* or [install as a go package](https://github.com/BattlesnakeOfficial/rules/tree/main/cli#installation) (requires Go 1.18 or higher)

Command to run a local game

```sh
battlesnake play -W 11 -H 11 --name 'Python Starter Project' --url http://localhost:8000 -g solo --browser
```

## Next Steps

Continue with the [Battlesnake Quickstart Guide](https://docs.battlesnake.com/quickstart) to customize and improve your Battlesnake's behavior.

## Included Competitive Snake
This repo now includes `snakes/BestBattleSnake.py`, a stronger default snake that combines:
- collision and head-to-head risk checks
- flood-fill space evaluation to avoid traps
- food routing that gets more aggressive as health drops
- tail access checks for better long-term survival

Run it explicitly with:

```sh
SNAKE=BestBattleSnake python main.py
```

Optional duel tuning (when only 2 snakes are alive):

```sh
BATTLE_SNAKE_DUEL_STYLE=balanced python main.py
```

Allowed values: `safe`, `balanced`, `aggressive`.

## Export Training Dataset
Game saves now include a `dataset` section with labeled move samples.

Export all stored samples to JSONL:

```sh
python -m server.DatasetExporter --input data --output data/dataset/good_moves.jsonl
```

Or with `just`:

```sh
just export-dataset
```

Curate a high-quality training subset (single file):

```sh
python -m server.DatasetCurator --input good_moves-2026-04-03.jsonl --output data/dataset/best_moves.jsonl
```

Curate from multiple JSONL sources (repeat `--input`):

```sh
python -m server.DatasetCurator \
  --input good_moves-2026-04-03.jsonl \
  --input good_moves-2026-04-04.jsonl \
  --output data/dataset/best_moves.jsonl
```

Curate from folder or glob:

```sh
python -m server.DatasetCurator --input data/dataset --output data/dataset/best_moves.jsonl
python -m server.DatasetCurator --input "good_moves-*.jsonl" --output data/dataset/best_moves.jsonl
```

Append mode (keeps existing curated rows and deduplicates against them):

```sh
python -m server.DatasetCurator --input "good_moves-*.jsonl" --output data/dataset/best_moves.jsonl --append
```

Archive processed input files after curation:

```sh
python -m server.DatasetCurator --input "good_moves-*.jsonl" --output data/dataset/best_moves.jsonl --append --archive-input
python -m server.DatasetCurator --input "good_moves-*.jsonl" --output data/dataset/best_moves.jsonl --append --archive-input --archive-dir data/dataset/archive
```

Or with `just`:

```sh
just curate-dataset
just curate-dataset append=true
just curate-dataset append=true archive=true archive_dir=data/dataset/archive
```

Analyze dataset quality overall and by day (best game overall/day included):

```sh
python -m server.DatasetStats --input "good_moves-*.jsonl"
python -m server.DatasetStats --input data/dataset --output data/dataset/stats-report.json
```

The stats report now includes both:
- `best_game` (survival/length focused)
- `best_pressure_game` (high-pressure quality focused: fewer safe options + strong survival)

Or with `just`:

```sh
just analyze-dataset
just analyze-dataset input=data/dataset output=data/dataset/stats-report.json
```

To store compact dataset-only records (JSONL) and skip full per-game JSON files:

```sh
STORE_DATASET_ONLY=true DATASET_JSONL_PATH=data/dataset/good_moves.jsonl python main.py
```

Optional compact storage tuning:
- `DATASET_ROTATE_DAILY=true` creates one JSONL file per day (default: `true`)
- `DATASET_JSONL_MAX_MB=50` rotates when file reaches max size in MB (default: `50`)
- `DATASET_COMPRESS_ROTATED=true` gzip-compresses rotated/old JSONL files (default: `true`)

**Note:** To play games on [play.battlesnake.com](https://play.battlesnake.com) you'll need to deploy your Battlesnake to a live web server OR use a port forwarding tool like [ngrok](https://ngrok.com/) to access your server locally.