# How to Build from Source

This guide provides step-by-step instructions for building the Video Accuracy Evaluation service from source.

Both the backend and UI images pin pip to `26.2` in system Python and in the
Poetry-created application virtual environment. Rebuild the images and recreate
the application containers to apply this pin to an existing deployment.

If you want to build the microservices image locally, you can optionally refer to the steps in the [Building the Backend Image](#building-the-backend-image) and [Building the UI Image](#building-the-ui-image) sections. These sections provide detailed instructions on how to build the Docker images for both the backend and UI components of the `Video Accuracy Evaluation` service separately.

If you want to build the images via `docker compose`, please refer to the section [Build the Images via Docker Compose](#build-the-images-via-docker-compose).

Once all the images are built, you can proceed to start the service using the `docker compose` command as described in the [Get Started](./get-started.md) page.

## Clone the Repository

Clone the Edge AI Libraries repository and change to its root directory:

```bash
git clone --branch main https://github.com/open-edge-platform/edge-ai-libraries.git
cd edge-ai-libraries/tools/video-summary-accuracy-analyzer
```

Run the commands in this guide from the repository root unless otherwise noted.

## Configure the environment variables

The `compose.yaml` file names the images as `${REGISTRY:-intel/}video-accuracy-eval:${TAG:-latest}` and `${REGISTRY:-intel/}video-accuracy-eval-ui:${TAG:-latest}`. Export `REGISTRY` and `TAG` so that the images you build, pull, and run share the same coordinates:

```bash
export REGISTRY="intel/"
export TAG=latest
```

The `scripts/setup_env.sh` script also sets these defaults when they are not already defined.

## Building the Backend Image
To build the Docker image for the evaluation service, follow these steps:

1. Build the docker image using command:

   ```bash
   docker build -t "${REGISTRY:-intel/}video-accuracy-eval:${TAG:-latest}" -f docker/Dockerfile .
   ```

2. Verify that the Docker image has been built successfully:

   ```bash
   docker images | grep video-accuracy-eval
   ```

   You should see an entry for `video-accuracy-eval` with the `latest` tag.

## Building the UI image
To build the Docker image for the `video-accuracy-eval-ui` service, follow these steps:

1. Build the Docker image using the provided `Dockerfile`:

   ```bash
   docker build -t "${REGISTRY:-intel/}video-accuracy-eval-ui:${TAG:-latest}" -f ui/docker/Dockerfile .
   ```

2. Verify that the Docker image has been built successfully:

   ```bash
   docker images | grep video-accuracy-eval-ui
   ```


## Build the Images via Docker Compose
This guide explains how to build the images using the compose.yaml file via the `docker compose` command.

1. Set Up Environment Variables:

   ```bash
   export HUGGINGFACEHUB_API_TOKEN=<your-huggingface-token>
   source scripts/setup_env.sh
   ```

2. Build the Docker images defined in the `compose.yaml` file:

   ```bash
   docker compose -f docker/compose.yaml build
   ```

3. Verify that the Docker images have been built successfully:

   ```bash
   docker images | grep video-accuracy-eval
   ```

   You should see entries for both `video-accuracy-eval` and `video-accuracy-eval-ui`.

## Running the Application Container
After building the images for the `Video Accuracy Evaluation` application, you can run the application container using `docker compose` by following these steps:

1. Set Up Environment Variables:

   ```bash
   export HUGGINGFACEHUB_API_TOKEN=<your-huggingface-token>
   source scripts/setup_env.sh
   ```

2. Start the Docker containers with the previously built images:

   ```bash
   docker compose -f docker/compose.yaml up
   ```

3. Access the application:

   - Open your web browser and navigate to "http://< host-ip >:8101" to view the application dashboard.

## Verification

- Ensure the application is running by checking the Docker container status:

  ```bash
  docker ps
  ```

-  Access the application dashboard and verify that it is functioning as expected.

## Troubleshooting

- If you encounter any issues during the build or run process, check the Docker logs for errors:

  ```bash
  docker logs -f <container-id>
  ```