README.md

metadata

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:

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

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.