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title: Math Tutor Response Annotation Demo
sdk: docker
app_port: 7860
pinned: false
---
# Math Tutor Response Annotation Demo
This folder is a self-contained Potato annotation demo for:
`annotated_data/math_annotator_train&test_sets_simple_2q_train_2q_test.xlsx`
It follows the same broad structure as Kseniia's Hugging Face Space: a Potato
`config.yaml`, prepared CSV data with an HTML display field, and Docker files
for Hugging Face Spaces.
## Files
- `config.yaml`: Potato task configuration and the 10 tutor-evaluation dimensions.
- `layouts/task_layout.html`: Potato-generated annotation form layout for the 10 dimensions.
- `scripts/prepare_potato_data.py`: Converts the Excel workbook into Potato CSV/JSON.
- `scripts/check_data.py`: Validates generated CSV/JSON files.
- `my-annotation-task/data/math_annotator_demo_all_with_id_text2show.csv`: Combined train/test demo data.
- `my-annotation-task/data/math_annotator_training_set_with_id_text2show.csv`: Training split only.
- `my-annotation-task/data/math_annotator_testing_set_with_id_text2show.csv`: Testing split only.
- `my-annotation-task/data/training_questions.json`: Known-label training items generated from the train sheet.
- `my-annotation-task/data/gold_standards.json`: Known-label gold items generated from the test sheet.
- `Dockerfile`, `requirements.txt`, `hf_start.py`: Hugging Face Spaces runtime.
## Regenerate Data
From the repository root:
```bash
python3 math_potato_annotation_demo/scripts/prepare_potato_data.py
python3 math_potato_annotation_demo/scripts/check_data.py
```
Expected generated counts:
- 14 train items
- 14 test items
- 28 combined demo items
## Run Locally
```bash
cd math_potato_annotation_demo
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python hf_start.py
```
Then open:
`http://localhost:7860`
## Deploy to Hugging Face Spaces
1. Create a new Hugging Face Space with SDK type `Docker`.
2. Upload or push the contents of this folder to the Space repository.
3. Let the Space build from `Dockerfile`.
4. For persistent annotation backups, set an `HF_TOKEN` Space secret and enable a
backup/export workflow before collecting real annotations.
## Notes
- The first demo uses `math_annotator_demo_all_with_id_text2show.csv`, so both
train and test examples are visible in one annotation queue.
- `training_questions.json` and `gold_standards.json` are generated and ready
for a later qualification/QC pass, but the blocks in `config.yaml` are kept
commented for the first UI demo to reduce startup risk.
- The UI itself is Potato's built-in UI. The custom part here is only CSS/JS in
`custom_footer_html` to show one dimension at a time, similar to the screenshot.
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