Project-MONAI
diff --git a/‎.github/workflows/pr.yml‎
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diff --git a/‎monai/deploy/operators/monai_bundle_inference_operator.py‎
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diff --git a/‎monai/deploy/operators/nii_data_loader_operator.py‎
Lines changed: 41 additions & 1 deletion b/‎monai/deploy/operators/nii_data_loader_operator.py‎
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diff --git a/‎tools/pipeline-generator/.gitignore‎
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diff --git a/‎tools/pipeline-generator/README.md‎
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@@ -55,3 +55,22 @@ jobs:
         with:
           fail_ci_if_error: false
           files: ./coverage.xml
+
+  test-pipeline-generator:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v2
+        with:
+          python-version: "3.10"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+      - name: Install dependencies
+        working-directory: tools/pipeline-generator
+        run: |
+          uv sync
+      - name: Run tests
+        working-directory: tools/pipeline-generator
+        run: |
+          uv run pytest
@@ -23,6 +23,13 @@
 
 # @md.env(pip_packages=["SimpleITK>=2.0.2"])
 class NiftiDataLoader(Operator):
+    # NOTE:
+    # This loader has very limited use because it does not capture or embed the metadata of a NIfTI file.
+    # Future improvements are needed to retrieve the metadata and return an Image object with the metadata,
+    # or use monai image transform to load the image and return a meta tensor.
+    # Also SITK GetArrayFromImage() returns an NDArray, and the newly added transpose here is to change the
+    # index ordering to match the index ordering of the DCOM pixel array. Existing client of this operator,
+    # if any, will likely have issues.
     """
     This operator reads a nifti image, extracts the numpy array and forwards it to the next operator
 
@@ -80,7 +87,40 @@ def convert_and_save(self, nii_path):
         image_reader = SimpleITK.ImageFileReader()
         image_reader.SetFileName(str(nii_path))
         image = image_reader.Execute()
-        image_np = np.transpose(SimpleITK.GetArrayFromImage(image), [2, 1, 0])
+        image_np = SimpleITK.GetArrayFromImage(image)
+
+        # Get image metadata to properly distinguish between different image types
+        spatial_dims = image.GetDimension()  # Actual spatial dimensions (2D, 3D, etc.)
+        num_components = image.GetNumberOfComponentsPerPixel()  # Components/channels per pixel
+
+        self._logger.debug(
+            f"Image spatial dimensions: {spatial_dims}, components per pixel: {num_components}, array shape: {image_np.shape}"
+        )
+
+        # Handle different dimensionalities properly using SimpleITK metadata
+        if spatial_dims == 2:
+            if num_components == 1:
+                # 2D grayscale: transpose from (y, x) to (x, y)
+                image_np = np.transpose(image_np, [1, 0])
+            else:
+                # 2D with multiple components/channels: transpose from (y, x, c) to (x, y, c)
+                # SimpleITK stores multi-component 2D images as (y, x, c)
+                image_np = np.transpose(image_np, [1, 0, 2])
+        elif spatial_dims == 3:
+            if num_components == 1:
+                # 3D grayscale volume: transpose from (z, y, x) to (x, y, z)
+                image_np = np.transpose(image_np, [2, 1, 0])
+            else:
+                # 3D volume with multiple components: transpose from (z, y, x, c) to (x, y, z, c)
+                # SimpleITK stores multi-component 3D images as (z, y, x, c)
+                image_np = np.transpose(image_np, [2, 1, 0, 3])
+        else:
+            # For other spatial dimensions, log a warning and return as-is
+            self._logger.warning(
+                f"Unexpected {spatial_dims}D spatial image with {num_components} components per pixel, "
+                f"array shape {image_np.shape} from {nii_path}, returning without transpose"
+            )
+
         return image_np
 
 
 
@@ -0,0 +1,2 @@
+results*/
+test_*/
@@ -0,0 +1,248 @@
+# Pipeline Generator
+
+A CLI tool for generating [MONAI Deploy](https://github.com/Project-MONAI/monai-deploy-app-sdk) application pipelines from [MONAI Bundles](https://docs.monai.io/en/stable/bundle_intro.html).
+
+## Features
+
+- List available MONAI models from HuggingFace
+- Generate complete MONAI Deploy applications from HuggingFace models
+- Support for multiple model sources through configuration
+- Automatic bundle download and analysis
+- Template-based code generation with Jinja2
+- Beautiful output formatting with Rich (Python library for rich text and beautiful formatting)
+
+## Platform Requirements
+
+- **Linux/Unix operating systems only**
+- Compatible with MONAI Deploy App SDK platform support
+- Ubuntu 22.04+ recommended (aligned with main SDK requirements)
+
+## Installation
+
+```bash
+# Clone the repository
+cd tools/pipeline-generator/
+
+# Install with uv (no virtualenv needed - uv manages it per command)
+uv pip install -e ".[dev]"
+```
+
+### Running Commands
+
+With uv, you can run commands directly without a prior "install" (pg is the Pipeline Generator command):
+
+```bash
+uv run pg --help
+uv run pg list
+uv run pg gen MONAI/spleen_ct_segmentation --output ./app
+```
+
+## Usage
+
+### Complete Workflow Example
+
+```bash
+# 1. List available models
+uv run pg list
+
+# 2. Generate an application from a model
+uv run pg gen MONAI/spleen_ct_segmentation --output my_app
+
+# 3. Run the application
+uv run pg run my_app --input /path/to/test/data --output ./results
+```
+
+> [!NOTE]  
+> Input can be NIfTI files (.nii, .nii.gz) or DICOM series directories Refer to [config.yaml](tools/pipeline-generator/pipeline_generator/config/config.yaml) for details.
+
+> [!NOTE]
+> For DICOM input types, refer to the model documentation for DICOM series processing limitations.
+
+### List Available Models
+
+List all models from configured endpoints:
+
+```bash
+uv run pg list
+```
+
+Show only MONAI Bundles:
+
+```bash
+uv run pg list --bundles-only
+```
+
+Show only tested models:
+
+```bash
+uv run pg list --tested-only
+```
+
+Combine filters:
+
+```bash
+uv run pg list --bundles-only --tested-only # Show only tested MONAI Bundles
+```
+
+Use different output formats:
+
+```bash
+uv run pg list --format simple # Simple list format
+uv run pg list --format json   # JSON output
+uv run pg list --format table  # Default table format
+```
+
+Use a custom configuration file:
+
+```bash
+uv run pg --config /path/to/config.yaml list
+```
+
+### Generate MONAI Deploy Application
+
+Generate an application from a HuggingFace model. Models are specified using the format `organization/model_name` (e.g., `MONAI/spleen_ct_segmentation`):
+
+```bash
+uv run pg gen MONAI/spleen_ct_segmentation --output my_app
+```
+
+Options:
+
+- `--output, -o`: Output directory for generated app (default: ./output)
+- `--app-name, -n`: Custom application class name (default: derived from model name)
+- `--format`: Input/output data format (optional): auto, dicom, or nifti (default: auto)
+  - For tested models, format is automatically detected from configuration
+  - For untested models, attempts detection from model metadata
+  - **DICOM Limitation**: Refer to the model documentation for multi-series support.
+- `--force, -f`: Overwrite existing output directory
+
+Generate with custom application class name:
+
+```bash
+uv run pg gen MONAI/lung_nodule_ct_detection --output lung_app --app-name LungDetectorApp
+```
+
+Force overwrite existing directory:
+
+```bash
+uv run pg gen MONAI/spleen_ct_segmentation --output test_app --force
+```
+
+Override data format (optional - auto-detected for tested models):
+
+```bash
+# Force DICOM format instead of auto-detection
+uv run pg gen MONAI/some_model --output my_app --format dicom
+```
+
+### Run Generated Application
+
+Run a generated application with automatic environment setup:
+
+```bash
+uv run pg run my_app --input /path/to/input --output /path/to/output
+```
+
+The `run` command will:
+
+1. Create a virtual environment if it doesn't exist
+1. Install dependencies from requirements.txt
+1. Run the application with the specified input/output
+
+Options:
+
+- `--input, -i`: Input data directory (required)
+- `--output, -o`: Output directory for results (default: ./output)
+- `--model, -m`: Override model/bundle path
+- `--venv-name`: Virtual environment directory name (default: .venv)
+- `--skip-install`: Skip dependency installation
+- `--gpu/--no-gpu`: Enable/disable GPU support (default: enabled)
+
+Examples:
+
+```bash
+# Skip dependency installation (if already installed)
+uv run pg run my_app --input test_data --output results --skip-install
+
+# Run without GPU
+uv run pg run my_app --input test_data --output results --no-gpu
+
+# Use custom model path
+uv run pg run my_app --input test_data --output results --model ./custom_model
+```
+
+## Configuration
+
+The tool uses a YAML configuration file to define model sources. By default, it looks for `config.yaml` in the package directory.
+
+Example configuration:
+
+```yaml
+# HuggingFace endpoints to scan for MONAI models
+endpoints:
+  - organization: "MONAI"
+    base_url: "https://huggingface.co"
+    description: "Official MONAI organization models"
+
+# Additional specific models not under the main organization
+additional_models:
+  - model_id: "Project-MONAI/exaonepath"
+    base_url: "https://huggingface.co"
+    description: "ExaOnePath model for digital pathology"
+```
+
+## Generated Application Structure
+
+When you run `pg gen`, it creates:
+
+```
+output/
+├── app.py                  # Main application code
+├── app.yaml               # Configuration for MONAI Deploy packaging
+├── requirements.txt       # Python dependencies
+├── README.md             # Documentation
+├── operators/            # Custom operators (if needed)
+│   └── nifti_operators.py
+└── model/                 # Downloaded model files
+    ├── configs/
+    ├── models/
+    └── docs/
+```
+
+## Development
+
+### Running Tests
+
+```bash
+# Run all tests
+uv run pytest
+
+# Run with coverage
+uv run pytest --cov=pipeline_generator
+
+# Run specific test file
+uv run pytest tests/test_cli.py
+```
+
+### Code Quality
+
+```bash
+# Format code
+uv run black pipeline_generator tests
+
+# Lint code
+uv run flake8 pipeline_generator tests
+
+# Type checking
+uv run mypy pipeline_generator
+```
+
+## Future Commands
+
+The CLI is designed to be extensible. Planned commands include:
+
+- `pg package <app>` - Package an application using the Holoscan CLI packaging tool
+
+## License
+
+This project is part of the MONAI Deploy App SDK and is licensed under the Apache License 2.0. See the main repository's LICENSE file for details.