src/about.py

"""UI copy."""

TITLE = """<h1 align="center" id="space-title">🧪 PhysInOne Benchmark Leaderboard</h1>"""

INTRODUCTION_TEXT = """
**PhysInOne Benchmark** — Visual Physics Learning & Reasoning in One Suite.
Created by [vLAR Group](https://vlar-group.github.io/) / HK PolyU.

**How to Participate**

1. Create a dataset under **your own** Hugging Face account (public is recommended), and upload your scene-level ZIP files using the structure below.
2. Fill in the "Submit" tab with: display name / task / `user_dataset` (for example, `username/repo`).
3. The system will enqueue the job, and the backend Worker will evaluate it **scene by scene** asynchronously. You can track progress in the "Queue" tab.
"""

DATASET_LAYOUT_TEXT = """
### Dataset Layout

```
<user_dataset>/
└── predictions/
    └── <task_name>/
        ├── scene_001.zip
        ├── scene_002.zip
        └── ...
```

- Package each scene prediction as a ZIP file (recommended size: < 200 MB).
- You only need to prepare ZIP files for the tasks you want to submit; see each task's "Expected layout" section for details.
- Missing scenes will be recorded as `missing` and will affect the final score according to the task's aggregation policy.
"""

SUBMIT_INSTRUCTIONS = """
## 📦 Submission Guide

| Field | Description |
|---|---|
| Display name | Public team label (can be changed later); 2-40 characters, allowing letters, numbers, Chinese characters, `_`, `-`, `.`, and spaces |
| Task | Select from the dropdown |
| User dataset | Format: `username/repo`; **your identity = the dataset owner**, so it cannot be forged |

After submission, the job will appear in the "Queue" tab. Status progresses as `pending → running → done / failed`, and the UI will show how many scenes have been completed.
"""

ABOUT_TEXT = """
## About This Leaderboard

- Architecture: public Frontend Space + private Worker Space + private dataset.
- Evaluation uses **scene** as the smallest atomic unit and supports resumable execution.
- User identity is derived from the owner of `user_dataset`, so **OAuth is not required** and name collisions are avoided.
- Ground truth data is fully isolated and inaccessible to participants.
- See [DESIGN.md](https://huggingface.co/spaces/vLAR/PhysInOne-Leaderboard/blob/main/DESIGN.md) for full details.
"""

CITATION_BUTTON_LABEL = "If this work is helpful to you, please consider citing:"
CITATION_BUTTON_TEXT = r"""@article{physinone2026,
  title  = {PhysInOne: Visual Physics Learning and Reasoning in One Suite},
  author = {vLAR Group, HK PolyU},
  year   = {2026},
  eprint = {2604.09415},
  archivePrefix = {arXiv}
}
"""