README.md

metadata

title: Takeoff AI Annotator
emoji: 📐
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false

Takeoff AI Annotator

A self-improving annotation tool for detecting facade frame (curtain wall panel) units on architectural construction drawings. Every human correction becomes a training signal that feeds back into the YOLO model over time.

Deployed as a Hugging Face Space: Infin8-AI/Annotation

Tabs

Detect

Upload a construction plan image (PNG/JPG) or a PDF page and click Detect Panels. The detected bounding boxes are drawn on the image and a JSON payload is shown for review.

Review & Edit

Upload a PDF and use the HTML5 canvas to inspect detections page by page:

Action	Effect	Training signal logged
Detect This Page	Run YOLO/OpenCV on the current page	—
Approve All	Mark all boxes green	N × accepted events
Reject All	Mark all boxes red	N × rejected events
Approve Selected	Click a table row, then approve	1 × accepted
Delete Selected	Remove a single box	1 × rejected
Add Box Manually	Draw a new ground-truth box	1 × created
Export Events (JSON)	Download events.jsonl with full before/after coords	—
Export Dataset (YOLO)	Download a zip of 640×640 tiles + YOLO labels	—

Box colours: orange = pending, green = approved, red = rejected.

Send to Backend

POST approved detections to a running tenant-core-api instance using the from-annotation endpoint.

Endpoint: POST /api/v1/tenants/documents/{document_id}/frame-instances/from-annotation

Continuous learning loop

Gradio Review UI
   ↓  every approve / reject / add / delete
events.jsonl  (exported from the Review tab)
   ↓
python scripts/build_dataset_from_events.py --events events.jsonl --pdf source.pdf
   ↓
python scripts/train_facade_detector.py --version v2
   ↓
cp models/yolo_facade_v2/best.pt tools/annotator/best.pt
   ↓
Restart Gradio — model version shown in header

Detection engine

The app loads best.pt (YOLO11n weights) from the same directory at startup. If not found, it falls back to OpenCV contour detection automatically. The status bar shows which engine is active.

To point to a custom model path:

FACADE_MODEL_PATH=/path/to/best.pt python app.py

Local development

cd tools/annotator
pip install -r requirements.txt
python app.py

PDF rendering uses PyMuPDF — no Poppler required locally (packages.txt installs it on HF Spaces / Linux).

Deploying to HF Spaces

The Space (Infin8-AI/Annotation) expects the root of the repo to contain app.py. Use git subtree to push just this subdirectory:

# First time — add the remote
git remote add hf https://YOUR_HF_TOKEN@huggingface.co/spaces/Infin8-AI/Annotation

# Push (maps this subdirectory to the Space root)
git subtree push --prefix=tools/annotator hf main