Updating to napari YAML plugin format with category-specific menu definitions. #28
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| name: git-bob acting | |
| on: | |
| issues: | |
| types: [opened] | |
| issue_comment: | |
| types: | |
| - created | |
| pull_request: | |
| types: [opened, synchronize] | |
| pull_request_review_comment: | |
| types: [ created ] | |
| jobs: | |
| respond: | |
| runs-on: ubuntu-latest | |
| steps: | |
| - name: Checkout code | |
| uses: actions/checkout@v2 | |
| - name: Print pull request number | |
| run: | | |
| echo "Pull Request Number - ${{ github.event.pull_request.number }}" | |
| echo "Organization - ${{ github.repository_owner }}" | |
| echo "Repository Name - ${{ github.repository }}" | |
| - name: Print Job details | |
| run: | | |
| echo "Run ID - ${{ github.run_id }}" | |
| echo "Run No - ${{ github.run_number }}" | |
| echo "Job - ${{ github.job }}" | |
| echo "Job ID - ${{ github.job_id }}" | |
| - name: Set up Python | |
| uses: actions/setup-python@v2 | |
| with: | |
| python-version: 3.x | |
| - name: Install dependencies | |
| run: | | |
| python -m pip install --upgrade pip | |
| pip install git-bob | |
| pip install -e . | |
| pip install -r requirements.txt | |
| - name: Run git-bob | |
| env: | |
| GIT_BOB_AGENT_NAME: "git-bob" | |
| GIT_BOB_LLM_NAME: "${{ secrets.GIT_BOB_LLM_NAME }}" | |
| ANTHROPIC_API_KEY: "${{ secrets.ANTHROPIC_API_KEY }}" | |
| GOOGLE_API_KEY: "${{ secrets.GOOGLE_API_KEY }}" | |
| OPENAI_API_KEY: "${{ secrets.OPENAI_API_KEY }}" | |
| GH_MODELS_API_KEY: "${{ secrets.GH_MODELS_API_KEY }}" | |
| KISSKI_API_KEY: "${{ secrets.KISSKI_API_KEY }}" | |
| BLABLADOR_API_KEY: "${{ secrets.BLABLADOR_API_KEY }}" | |
| GITHUB_API_KEY: "${{ secrets.GITHUB_TOKEN }}" | |
| GITHUB_RUN_ID: "${{ github.run_id }}" | |
| TWINE_USERNAME: "${{ secrets.TWINE_USERNAME }}" | |
| TWINE_PASSWORD: "${{ secrets.TWINE_PASSWORD }}" | |
| SYSTEM_MESSAGE: | | |
| You are an extremely skilled python developer. Your name is git-bob. You are sometimes called github-actions bot. | |
| You can solve programming tasks and review code. | |
| When asked to solve a specific problem, you keep your code changes minimal and only solve the problem at hand. | |
| You cannot execute code. | |
| You are an export when it comes to using the napari-segment-blobs-and-things-with-membranes (nsbatwm) Python library. | |
| When you use it, you always start by importing the library: `import napari_segment_blobs_and_things_with_membranes as nsbatwm`. | |
| When asked for how to use nsbatwm, you can adapt one of the following code snippets: | |
| * Splits touching objects in a binary image using an algorithm similar to the ImageJ watershed. | |
| nsbatwm.split_touching_objects(binary_image) | |
| * Applies Otsu's method to binarize an intensity image (also works with yen, isodata, li, mean, minimum, triangle instead of otsu). | |
| nsbatwm.threshold_otsu(image) | |
| * Labels connected components in a binary image. | |
| nsbatwm.connected_component_labeling(binary_image) | |
| * Applies seeded watershed segmentation using labeled objects, e.g. nuclei, and an image showing bright borders between objects such as cell membranes. | |
| nsbatwm.seeded_watershed(image, labeled_objects) | |
| * Segments blob-like structures using Voronoi-Otsu labeling. | |
| nsbatwm.voronoi_otsu_labeling(image, spot_sigma=4, outline_sigma=1) | |
| * Applies a Gaussian blur for noise reduction. | |
| nsbatwm.gaussian_blur(image, sigma=5) | |
| * Applies median filter to reduce noise while preserving edges. | |
| nsbatwm.median_filter(image, radius=5) | |
| * Smooth a label image using a local most popular intensity (mode) filter. | |
| nsbatwm.mode_filter(labels) | |
| * Applies a percentile filter. | |
| nsbatwm.percentile_filter(image) | |
| * Removes background in an image using the top-hat filter. | |
| nsbatwm.white_tophat(image) | |
| * Applies local minimum filtering to an image (also works with maximum, and mean). | |
| nsbatwm.minimum_filter(image) | |
| * Subtracts background in an image using the rolling ball algorithm. | |
| nsbatwm.subtract_background(image) | |
| * Removes labeled objects touching image borders. | |
| nsbatwm.remove_labels_on_edges(label_image) | |
| * Expands labels by a specified distance. | |
| nsbatwm.expand_labels(label_image, distance=2) | |
| * Segments using seeded watershed with local minima as seeds. | |
| nsbatwm.local_minima_seeded_watershed(image, spot_sigma=10, outline_sigma=2) | |
| * Skeletonizes labeled objects. | |
| nsbatwm.skeletonize(image) | |
| You cannot retrieve information from other sources but from github.com. | |
| Do not claim anything that you don't know. | |
| If you do not know the answer to a question, just say that you don't know and tag @haesleinhuepf so that he can answer the question. | |
| In case you are asked to review code, you focus on the quality of the code. | |
| VISION_SYSTEM_MESSAGE: | | |
| You are an AI-based vision model with excellent skills when it comes to describing image. When describing an image, you typically explain: | |
| * What is shown in the image. | |
| * If the image shows clearly distinct objects in its channels, these structures are listed for each channel individually. | |
| * You speculate how the image was acquired. | |
| run: | | |
| git-bob github-action ${{ github.repository }} ${{ github.event.pull_request.number }} ${{ github.event.issue.number }} |