# Creating a Segmentation App Consuming a MONAI Bundle

This tutorial shows how to create an organ segmentation application for a PyTorch model that has been trained with MONAI and packaged in the [MONAI Bundle](https://monai.readthedocs.io/en/stable/bundle_intro.html) format.

## Setup

```bash
# Create a virtual environment with Python 3.10.
# Skip if you are already in a virtual environment.
conda create -n monai python=3.10 pytorch torchvision jupyterlab cudatoolkit=12.2 -c pytorch -c conda-forge
conda activate monai

# Launch JupyterLab if you want to work on Jupyter Notebook
jupyter-lab
```

## Executing from Jupyter Notebook

```{toctree}
:maxdepth: 4

../../notebooks/tutorials/04_monai_bundle_app.ipynb
```

```{raw} html
<div style="text-align: center;">
    <iframe width="560" height="315" src="https://www.youtube.com/embed/cqDVxzYt9lY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</div>
```

```{raw} html
<div style="text-align: center;">
    <iframe width="560" height="315" src="https://www.youtube.com/embed/nivgfD4pwWE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

    <p>Video may show the use of previous SDK version.</p>
</div>
```

```{raw} html
<p style="text-align: center;">
    <a class="sphinx-bs btn text-wrap btn-outline-primary col-md-6 reference external" href="../../_static/notebooks/tutorials/04_monai_bundle_app.ipynb">
        <span>Download 04_monai_bundle_app.ipynb</span>
    </a>
</p>
```

## Executing from Shell

**_Note:_** Data files are now access controlled. Please first request permission to access the [shared folder on Google Drive](https://drive.google.com/drive/folders/1EONJsrwbGsS30td0hs8zl4WKjihew1Z3?usp=sharing). Please download zip file, `mednist_classifieai_spleen_seg_bundle_data.zip` in the `ai_spleen_seg_app` folder, to the same folder as the notebook example.

```bash
# Clone the github project (the latest version of main branch only)
git clone --branch main --depth 1 https://github.com/Project-MONAI/monai-deploy-app-sdk.git

cd monai-deploy-app-sdk

# Install monai-deploy-app-sdk package
pip install --upgrade monai-deploy-app-sdk

# Download/Extract ai_spleen_bundle_data zip file from https://drive.google.com/file/d/1cJq0iQh_yzYIxVElSlVa141aEmHZADJh/view?usp=sharing

# Download the zip file containing both the model and test data.
# After downloading it using gdown, unzip the zip file saved by gdown and
# copy the model file into a folder structure that is required by CLI Packager
rm -rf dcm
unzip -o ai_spleen_seg_bundle_data.zip
rm -rf spleen_model && mkdir -p spleen_model && mv model.ts spleen_model && ls spleen_model

# Install necessary packages from the app; note that numpy-stl and trimesh are only
# needed if the application uses the STL Conversion Operator
pip install monai torch pydicom highdicom SimpleITK Pillow nibabel scikit-image numpy-stl trimesh

# Use env variables for input, output, and model paths for local running of Python application
export HOLOSCAN_INPUT_PATH=dcm
export HOLOSCAN_MODEL_PATH=spleen_model/model.ts
export HOLOSCAN_OUTPUT_PATH="output"
export HOLOSCAN_LOG_LEVEL=TRACE

# Local execution of the app directly or using MONAI Deploy CLI
python examples/apps/ai_spleen_seg_app/app.py

# Package app (creating MAP docker image) using `-l DEBUG` option to see progress.
# This assumes that nvidia docker is installed in the local machine.
# Please see https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker to install nvidia-docker2.
monai-deploy package examples/apps/ai_spleen_seg_app \
    --config examples/apps/ai_spleen_seg_app/app.yaml \
    --tag seg_app:latest \
    --models spleen_model/model.ts \
    --platform x86_64 \
    -l DEBUG

# Note: for AMD GPUs, nvidia-docker is not required, but the dependency of the App SDK, namely Holoscan SDK
#       has not been tested to work with a ROCM base image.

# Run the app with docker image and input file locally
rm -rf output
monai-deploy run seg_app-x64-workstation-dgpu-linux-amd64:latest -i dcm -o output
ls -R output
```