README.md

---
title: T2AV Eval UI
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
---

# Video Evaluation UI

This app is configured to deploy as a Hugging Face Docker Space. The Docker
build compiles the Vite frontend, serves it from FastAPI, and stores each
annotation as a JSON file in a Hugging Face Dataset repo.

## Hugging Face Space Deployment

1. Create a new Space on Hugging Face and choose Docker as the SDK.
2. Add a Space secret named `HF_TOKEN` with a Hugging Face token that has write
   access to datasets in the `dghadiya` namespace (the token must belong to,
   or be added as a collaborator on, that account — see note below).
3. Optional: set `HF_ANNOTATIONS_DATASET_REPO` if you want to use a dataset
   repo other than `dghadiya/t2av_eval_annotations`. Optional: set
   `HF_VIDEO_DATASET_REPO` if the evaluation videos move to a different
   dataset repo (default `dghadiya/T2AV_TemporalConstraint`; this one is
   public, so no token is required just to read it).
4. Add this repository as the Space repository or push it to the Space remote:

```bash
git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
git push hf main
```

5. Hugging Face will build the root `Dockerfile` and expose the app on port
   `7860`.

The backend creates the annotation dataset repo on first save if it does not
already exist. By default it creates the dataset as private. Set
`HF_ANNOTATIONS_DATASET_PRIVATE=false` to create a public dataset instead.

Note: `HF_TOKEN` must belong to (or have write access on) whatever account
owns `HF_ANNOTATIONS_DATASET_REPO` — a token cannot create or write repos in a
namespace its owner doesn't control. To check which account a token belongs
to: `python -c "from huggingface_hub import HfApi; print(HfApi().whoami(token='hf_...'))"`.

## Backend

cd backend

pip install -r requirements.txt

uvicorn main:app --reload

Backend runs at:
http://localhost:8000

Health check:
http://localhost:8000/health

## Frontend

cd frontend

npm install

npm run dev

Frontend runs at:
http://localhost:5173