Spaces:
Running
Running
| title: AI Evaluation Dashboard | |
| emoji: 📊 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: docker | |
| pinned: false | |
| app_port: 3000 | |
| # AI Evaluation Dashboard | |
| This repository is a Next.js application for viewing and authoring AI evaluations. It includes demo evaluation fixtures under `public/evaluations/` and a dynamic details page that performs server-side rendering and route-handler based inference. | |
| ## Run locally | |
| Install dependencies and run the dev server: | |
| ```bash | |
| npm ci | |
| npm run dev | |
| ``` | |
| Build for production and run: | |
| ```bash | |
| npm ci | |
| npm run build | |
| NODE_ENV=production PORT=3000 npm run start | |
| ``` | |
| ## Docker (recommended for Hugging Face Spaces) | |
| A `Dockerfile` is included for deploying this app as a dynamic service on Hugging Face Spaces (Docker runtime). | |
| Build the image locally: | |
| ```bash | |
| docker build -t ai-eval-dashboard . | |
| ``` | |
| Run the container (expose port 3000): | |
| ```bash | |
| docker run -p 3000:3000 -e HF_TOKEN="$HF_TOKEN" ai-eval-dashboard | |
| ``` | |
| Visit `http://localhost:3000` to verify. | |
| ### Deploy to Hugging Face Spaces | |
| 1. Create a new Space at https://huggingface.co/new-space and choose **Docker** as the runtime. | |
| 2. Add a secret named `HF_TOKEN` (if you plan to access private or gated models or the Inference API) in the Space settings. | |
| 3. Push this repository to the Space Git (or upload files through the UI). The Space will build the Docker image using the included `Dockerfile` and serve your app on port 3000. | |
| Notes: | |
| - The app's server may attempt to construct ML pipelines server-side if you use Transformers.js and large models; prefer small/quantized models or use the Hugging Face Inference API instead (see below). | |
| - If your build needs native dependencies (e.g. `sharp`), the Docker image may require extra apt packages; update the Dockerfile accordingly. | |
| ## Alternative: Use Hugging Face Inference API (avoid hosting model weights) | |
| If downloading and running model weights inside the Space is impractical (memory/disk limits), modify the server route to proxy requests to the Hugging Face Inference API. | |
| Example server-side call (Route Handler): | |
| ```js | |
| const resp = await fetch('https://api-inference.huggingface.co/models/<model-id>', { | |
| method: 'POST', | |
| headers: { Authorization: `Bearer ${process.env.HF_TOKEN}`, 'Content-Type': 'application/json' }, | |
| body: JSON.stringify({ inputs: text }) | |
| }) | |
| const json = await resp.json() | |
| ``` | |
| Store `HF_TOKEN` in the Space secrets and your route will be able to call the API. | |
| ## Troubleshooting | |
| - Build fails in Spaces: check the build logs; you may need extra apt packages or to pin Node version. | |
| - Runtime OOM / killed: model is too large for Spaces; use Inference API or smaller models. | |
| ## What I added | |
| - `Dockerfile` — multi-stage build for production | |
| - `.dockerignore` — to reduce image size | |
| - Updated `README.md` with Spaces frontmatter and deployment instructions | |
| If you want, I can: | |
| - Modify the Dockerfile to use Next.js standalone mode for a smaller runtime image. | |
| - Add a small health-check route and a simple `docker-compose.yml` for local testing. | |
| Which of those would you like next? | |
| npm run build | |
| Send the contents of the "out" folder to https://huggingface.co/spaces/evaleval/general-eval-card | |