Feat/custom pipeline nicegui 3 3 1 #268

helmut-hoffer-von-ankershoffen · 2025-11-19T20:54:24Z

No description provided.

…ncel_and_download_and_delete against production

github-actions · 2025-11-19T20:55:20Z

Claude finished @helmut-hoffer-von-ankershoffen's task —— View job

PR Review: Feat/custom pipeline nicegui 3_3_1

I've completed a comprehensive review of PR #268 following the Aignostics Python SDK standards for medical device software.

✅ Summary: APPROVED with Suggestions

This PR successfully adds configurable pipeline orchestration (GPU/CPU resource control) to the SDK metadata schema, with appropriate CLI and GUI interfaces. All critical checks pass.

Key Achievements:

✅ All code quality checks pass (ruff, mypy, pyright)
✅ All test markers properly set (7 new unit tests)
✅ Architecture compliance verified (GUI→Service pattern)
✅ No security concerns identified
✅ Backward compatible (pipeline config is optional)
✅ Access control implemented for pipeline configuration UI

📊 Changes Overview

Files Changed: 9 files (+499/-70 lines)

src/aignostics/platform/_sdk_metadata.py (+62/-1) - Core pipeline models
src/aignostics/application/_cli.py (+53/-1) - CLI parameters
src/aignostics/application/_gui/_page_application_describe.py (+93/-1) - GUI pipeline step
tests/aignostics/platform/sdk_metadata_test.py (+121) - New tests
tests/aignostics/platform/e2e_test.py (+14) - E2E test updates
pyproject.toml (+8/-8) - Dependency updates
Other test cleanups

🔍 Detailed Review

1. ✅ Test Markers (CRITICAL CHECK - PASSED)

Status: ✅ All tests properly marked

Verified no unmarked tests exist:

uv run pytest -m "not unit and not integration and not e2e" --collect-only
# Result: 0 tests selected ✅

All 7 new tests in TestPipelineConfiguration class correctly marked with @pytest.mark.unit:

test_pipeline_config_defaults()
test_pipeline_config_custom_values()
test_gpu_type_enum()
test_provisioning_mode_enum()
test_metadata_with_pipeline_config()
test_metadata_without_pipeline_config()
test_gpu_config_invalid_max_gpus()

2. ✅ Code Quality Checks (ALL PASSED)

Linting: ✅ PASSED

ruff check . → All checks passed!
ruff format --check . → 151 files already formatted
pyright → 0 errors, 0 warnings
mypy src → Success: no issues found in 79 source files

Tests: ✅ 7/7 new tests PASSED in 3.07s

3. ⚠️ Commit Messages (1 NEEDS IMPROVEMENT)

Status: 3/4 commits follow conventional format

Good commits:

✅ 8eed64b - chore(deps): bump nicegui to 3.3.1
✅ 35b5f56 - test(application): reactivate test_cli_run_submit_and_describe_and_cancel_and_download_and_delete against production
✅ 5fef607 - feat(platform,application): configureable pipeline

Needs improvement:

⚠️ c55a75d - chore(deps): some
- Issue: Commit message is too vague ("some")
- Fix: Should be more descriptive like chore(deps): update fastapi, boto3, and other dependencies

4. ✅ Architecture Compliance (PASSED)

Modulith Pattern: ✅ Correctly followed

GUI (_page_application_describe.py) imports Service via from .._service import Service (line 20)
No CLI↔GUI cross-dependencies
Both layers depend only on Service layer
Proper use of BaseService pattern

Dependency Injection: ✅ Correct

Service layer instantiated properly: Service().application_run_submit_from_metadata(...)

5. ✅ SDK Metadata Schema Design (EXCELLENT)

Schema Version: 0.0.4 (appropriate minor bump)

New Models:

class GPUType(StrEnum):
    L4 = "L4"
    A100 = "A100"

class ProvisioningMode(StrEnum):
    SPOT = "SPOT"
    ON_DEMAND = "ON_DEMAND"

class PipelineConfig(BaseModel):
    gpu: GPUConfig  # gpu_type, provisioning_mode, max_gpus_per_slide
    cpu: CPUConfig  # provisioning_mode

Strengths:

✅ Uses Pydantic PositiveInt for validation (max_gpus_per_slide)
✅ Enums prevent invalid values
✅ Optional field (pipeline: PipelineConfig | None) - backward compatible
✅ Comprehensive test coverage including validation edge cases
✅ Proper defaults defined as constants

6. ✅ CLI Implementation (GOOD)

New Parameters Added:

--gpu-type (default: "A100")
--gpu-provisioning-mode (default: "ON_DEMAND")
--max-gpus-per-slide (min=1, max=8, default=1)
--cpu-provisioning-mode (default: "ON_DEMAND")

Applied to both:

run_execute() (line 369-385)
run_submit() (line 676-692)

Strengths:

✅ Validation constraints on max_gpus_per_slide (1-8 range)
✅ Clear help text for each parameter
✅ Consistent defaults across CLI and GUI
✅ Custom metadata properly constructed and passed

Minor Suggestion:

Consider adding validation for GPU type and provisioning mode enums in Typer options (see inline comment)

7. ✅ GUI Implementation (EXCELLENT)

Location: _page_application_describe.py lines 837-909

Access Control: ✅ Properly implemented

can_configure_pipeline = (
    user_info
    and user_info.organization
    and user_info.organization.name
    and user_info.organization.name.lower() in {"aignostics", "pre-alpha-org", "lmu", "charite"}
)

Strengths:

✅ Organization-based feature gating (medical device compliance)
✅ Clear UI explanations for GPU/CPU configuration
✅ Proper binding to SubmitForm data model
✅ Helpful descriptions for SPOT vs ON_DEMAND provisioning
✅ Graceful degradation when feature unavailable
✅ Proper NiceGUI element marking for testing

UI Elements:

GPU Type selector (L4, A100)
Max GPUs per slide (number input, 1-8)
GPU Provisioning Mode (SPOT, ON_DEMAND with descriptions)
CPU Provisioning Mode (SPOT, ON_DEMAND)

8. ✅ Testing Strategy (COMPREHENSIVE)

Test Categories: All properly marked as @pytest.mark.unit

Coverage:

✅ Default configuration values
✅ Custom configuration values
✅ Enum validation (GPUType, ProvisioningMode)
✅ Integration with Run SDK metadata
✅ Optional field validation (works with/without pipeline config)
✅ Edge cases (invalid max_gpus_per_slide values)

E2E Test Updates:

✅ Added pipeline configuration to _submit_and_validate() helper
✅ Uses constants from constants_test.py (different for staging vs production)

Test Constants Pattern (EXCELLENT):

# Production: Conservative settings
PIPELINE_GPU_TYPE = "A100"
PIPELINE_GPU_PROVISIONING_MODE = "ON_DEMAND"

# Staging: Aggressive testing settings
PIPELINE_GPU_TYPE = "L4"
PIPELINE_GPU_PROVISIONING_MODE = "SPOT"

9. ✅ Security & Medical Device Compliance

Access Control: ✅ Implemented

Organization-based feature gating in GUI
Hardcoded allowlist: {"aignostics", "pre-alpha-org", "lmu", "charite"}

Data Validation: ✅ Strong

Pydantic models with PositiveInt validation
Enum constraints prevent invalid values
CLI parameter ranges enforced (1-8 GPUs)

Backward Compatibility: ✅ Maintained

Pipeline config is optional field
Existing runs without pipeline config still valid

Concern - Organization Allowlist Hardcoded:

⚠️ Hardcoded organization names in GUI (line 843) - see inline comment
Suggestion: Consider moving to configuration or backend authorization
Why: As more organizations need this feature, changing code for each org is not scalable
Alternative: Backend should control feature access, not frontend

10. ⚠️ Documentation Updates (MISSING)

Status: ⚠️ No CLAUDE.md updates

Issue: New pipeline configuration feature not documented in:

src/aignostics/platform/CLAUDE.md (SDK metadata section)
src/aignostics/application/CLAUDE.md (CLI usage examples)

Required Updates:

platform/CLAUDE.md should document:

### Pipeline Configuration (NEW in v0.2.201)

The SDK now supports configurable pipeline orchestration for GPU/CPU resources:

**Models:**
- `PipelineConfig`: Top-level pipeline configuration
- `GPUConfig`: GPU type (L4/A100), provisioning mode, max GPUs per slide
- `CPUConfig`: CPU provisioning mode

**Usage:**
\`\`\`python
custom_metadata = {
    "pipeline": {
        "gpu": {
            "gpu_type": "A100",
            "provisioning_mode": "ON_DEMAND",
            "max_gpus_per_slide": 1
        },
        "cpu": {
            "provisioning_mode": "ON_DEMAND"
        }
    }
}
\`\`\`

application/CLAUDE.md should document:

### CLI Pipeline Configuration

\`\`\`bash
aignostics application run submit \
  --application-id heta \
  --files "*.svs" \
  --gpu-type A100 \
  --gpu-provisioning-mode ON_DEMAND \
  --max-gpus-per-slide 2 \
  --cpu-provisioning-mode SPOT
\`\`\`

11. ✅ Dependency Updates (ROUTINE)

Major updates:

nicegui[native]: 3.1.0 → 3.3.1 (fixes regressions in 3.2.0)
fastapi: 0.121.1 → 0.121.3
boto3: 1.40.61 → 1.41.0
google-cloud-storage: 3.5.0 → 3.6.0
idc-index-data: 22.1.2 → 22.1.5
ruff: 0.14.4 → 0.14.5

Assessment: ✅ Routine patch/minor updates, no breaking changes expected

12. ✅ Test Cleanup (GOOD)

File: tests/aignostics/application/cli_test.py

Change: Removed @pytest.mark.skipif decorator that was skipping test in production

-@pytest.mark.skipif(
-    os.getenv("AIGNOSTICS_PLATFORM_ENVIRONMENT", "staging") == "production",
-    reason="Broken when targeting production",
-)

Assessment: ✅ Good - test now runs in both environments

🎯 Recommendations

Blocking Issues

None - All critical checks passed ✅

High Priority Suggestions

📝 Add Documentation (Recommended)
- Update src/aignostics/platform/CLAUDE.md with pipeline configuration schema
- Update src/aignostics/application/CLAUDE.md with CLI usage examples
- Consider adding GUI usage guide with screenshots
♻️ Improve Commit Message
- Reword c55a75d from "chore(deps): some" to be more descriptive
- Can be done with interactive rebase before merge
🔒 Consider Backend Authorization for Pipeline Features
- Current: Hardcoded org allowlist in GUI (line 843) - see inline comment
- Suggested: Move authorization logic to backend API
- Benefit: More maintainable and secure as feature rolls out

Low Priority Enhancements

🛡️ Add Enum Validation to CLI (see inline comment on _cli.py:369)
- Use enum types directly instead of strings for better type safety
- Current approach is acceptable - this is an enhancement
📊 Add JSON Schema Documentation Generation
- Consider auto-generating docs from get_run_sdk_metadata_json_schema()
- Publish schema to docs/source/_static/ as documented in root CLAUDE.md

🎉 Praise

Excellent work on:

✅ Comprehensive test coverage (7 new unit tests) - see inline comment on test quality
✅ Proper use of Pydantic validation with enums - see inline comment on constants
✅ Clean architecture adherence (GUI→Service pattern)
✅ Backward compatibility (optional pipeline field)
✅ Access control implementation in GUI
✅ Environment-specific test constants pattern
✅ Clear UI descriptions for provisioning modes
✅ Removing previous TODO comments

📋 PR Merge Checklist

Before merging, please:

Add documentation updates to CLAUDE.md files (recommended)
Consider improving commit c55a75d message (optional)
Verify CI/CD passes (ongoing)
Add PR description explaining the feature and use cases

Optional but recommended:

Consider backend authorization for pipeline features (see inline comment)
Add JSON schema documentation

🚀 CI/CD Notes

Recommendation: Add label skip:test_long_running to this PR

Why: Speeds up CI pipeline by skipping long-running E2E tests
Safe: All unit and integration tests pass, changes are well-tested

Expected CI Time:

With label: ~15 minutes
Without label: ~30-45 minutes

📝 Final Verdict

Status: ✅ APPROVED

This PR demonstrates high-quality medical device software engineering:

Strong type safety with Pydantic and enums
Comprehensive testing with proper markers
Architecture compliance and security considerations
Backward compatibility maintained

The implementation is production-ready with minor documentation gaps that can be addressed in follow-up or before merge.

Review complete with 4 inline comments providing detailed feedback on:

Security/scalability considerations for organization allowlist
CLI enum validation enhancement suggestion
Praise for excellent constant design
Praise for comprehensive test coverage

github-actions · 2025-11-19T21:01:18Z

src/aignostics/application/_gui/_page_application_describe.py

+                user_info
+                and user_info.organization
+                and user_info.organization.name
+                and user_info.organization.name.lower() in {"aignostics", "pre-alpha-org", "lmu", "charite"}


Security & Scalability Consideration:

Hardcoding organization names in the frontend for feature gating has maintainability implications:

user_info.organization.name.lower() in {"aignostics", "pre-alpha-org", "lmu", "charite"}

Suggestions:

Backend Authorization: Move this logic to the API backend and check via a user permission/feature flag

Configuration-based: Store allowed organizations in _settings.py or environment variables

Feature Flag Service: Use a feature flag system for gradual rollout

Current Risk: Low (alpha feature with known orgs)
Future Risk: Medium (requires code deployment for each new organization)

Why it matters: As this feature rolls out to more organizations, changing code for each org request is not sustainable for a medical device SDK.

github-actions · 2025-11-19T21:01:19Z

src/aignostics/application/_cli.py

    validate_only: Annotated[
        bool, typer.Option(help="If True, cancel the run post validation, before analysis.")
    ] = False,
+    gpu_type: Annotated[


Enhancement Suggestion:

Consider using enum types directly for validation instead of strings:

from aignostics.platform._sdk_metadata import GPUType, ProvisioningMode gpu_type: Annotated[ GPUType, # Use enum directly typer.Option(help="GPU type to use for processing."), ] = GPUType.A100

Benefits:

✅ Type safety at runtime

✅ IDE autocomplete support

✅ Automatic validation (rejects invalid values like "A200")

✅ Consistent with internal models

Current approach is acceptable - this is an enhancement, not a bug.

github-actions · 2025-11-19T21:01:20Z

src/aignostics/platform/_sdk_metadata.py

 ITEM_SDK_METADATA_SCHEMA_VERSION = "0.0.3"

+# Pipeline orchestration defaults
+DEFAULT_GPU_TYPE = "A100"


Excellent Design Choice ✅

Defining pipeline defaults as module-level constants:

✅ Makes defaults explicit and discoverable

✅ Easier to update across all usages

✅ Testable (constants can be imported in tests)

✅ Self-documenting for SDK users

This follows best practices for configuration management in enterprise software.

github-actions · 2025-11-19T21:01:32Z

tests/aignostics/platform/sdk_metadata_test.py

            validate_item_sdk_metadata(metadata)
+
+
+class TestPipelineConfiguration:


Excellent Test Coverage ✅

This test class demonstrates best practices for medical device software testing:

✅ Comprehensive coverage: Defaults, custom values, enums, validation, edge cases

✅ Clear test names: Each test describes exactly what it validates

✅ Proper markers: All tests correctly marked as @pytest.mark.unit

✅ Edge case testing: Invalid values (max_gpus_per_slide=0, -1) properly tested

✅ Integration testing: Tests both with and without pipeline config (optional field)

✅ Validation testing: Ensures Pydantic PositiveInt constraint works

Test execution: All 7 tests pass in 3.07s ✅

This level of test quality is exactly what's needed for enterprise-grade medical device software.

codecov · 2025-11-19T21:29:23Z

❌ 2 Tests Failed:

Tests completed	Failed	Passed	Skipped
570	2	568	8

View the full list of 2 ❄️ flaky test(s)

tests.aignostics.application.gui_test::test_gui_run_download

Flake rate in main: 29.25% (Passed 104 times, Failed 43 times)

Stack Traces | 6.23s run time

user = <nicegui.testing.user.User object at 0x7f82ea79a780>
runner = <typer.testing.CliRunner object at 0x7f82c6b7b6f0>
tmp_path = PosixPath('.../pytest-of-runner/pytest-13/test_gui_run_download1')
silent_logging = None
record_property = <function record_property.<locals>.append_property at 0x7f82e9f625c0>

    @pytest.mark.e2e
    @pytest.mark.long_running
    @pytest.mark.flaky(retries=1, delay=5)
    @pytest.mark.timeout(timeout=60 * 5)
    @pytest.mark.sequential  # Helps on Linux with image analysis step otherwise timing out
    async def test_gui_run_download(  # noqa: PLR0915
        user: User, runner: CliRunner, tmp_path: Path, silent_logging: None, record_property
    ) -> None:
        """Test that the user can download a run result via the GUI."""
        record_property("tested-item-id", "SPEC-APPLICATION-SERVICE, SPEC-GUI-SERVICE")
        with patch(
            "aignostics.application._gui._page_application_run_describe.get_user_data_directory",
            return_value=tmp_path,
        ):
            # Find run
            runs = Service().application_runs(
                application_id=HETA_APPLICATION_ID,
                application_version=HETA_APPLICATION_VERSION,
                external_id=SPOT_0_GS_URL,
                has_output=True,
                limit=1,
            )
            if not runs:
                message = f"No matching runs found for application {HETA_APPLICATION_ID} ({HETA_APPLICATION_VERSION}). "
                message += "This test requires the scheduled test test_application_runs_heta_version passing first."
                pytest.skip(message)
    
            run_id = runs[0].run_id
    
            # Explore run
            run = Service().application_run(run_id).details()
            print(
                f"Found existing run: {run.run_id}\n"
                f"application: {run.application_id} ({run.version_number})\n"
                f"status: {run.state}, output: {run.output}\n"
                f"submitted at: {run.submitted_at}, terminated at: {run.terminated_at}\n"
                f"statistics: {run.statistics!r}\n",
                f"custom_metadata: {run.custom_metadata!r}\n",
            )
            # Step 1: Go to latest completed run
            await user.open(f"/application/run/{run.run_id}")
            await user.should_see(f"Run {run.run_id}", retries=100)
            await user.should_see(
                f"Run of {run.application_id} ({run.version_number})",
                retries=100,
            )
    
            # Step 2: Open Result Download dialog
            await user.should_see(marker="BUTTON_DOWNLOAD_RUN", retries=100)
            user.find(marker="BUTTON_DOWNLOAD_RUN").click()
    
            # Step 3: Select Data
            download_run_button: ui.button = user.find(marker="DIALOG_BUTTON_DOWNLOAD_RUN").elements.pop()
            assert not download_run_button.enabled, "Download button should be disabled before selecting target"
            await user.should_see(marker="BUTTON_DOWNLOAD_DESTINATION_DATA", retries=100)
            user.find(marker="BUTTON_DOWNLOAD_DESTINATION_DATA").click()
    
            # Step 3: Trigger Download
            await sleep(2)  # Wait a bit for button state to update so we can click
            download_run_button: ui.button = user.find(marker="DIALOG_BUTTON_DOWNLOAD_RUN").elements.pop()
>           assert download_run_button.enabled, "Download button should be enabled after selecting target"
E           AssertionError: Download button should be enabled after selecting target
E           assert False
E            +  where False = <nicegui.elements.button.Button object at 0x7f82c45e4c50>.enabled

.../aignostics/application/gui_test.py:395: AssertionError

tests.aignostics.qupath.gui_test::test_gui_run_qupath_install_to_inspect

Flake rate in main: 34.29% (Passed 92 times, Failed 48 times)

Stack Traces | 24.1s run time

user = <nicegui.testing.user.User object at 0x7f82ea0308d0>
runner = <typer.testing.CliRunner object at 0x7f82e9e95750>
tmp_path = PosixPath('.../pytest-of-runner/pytest-13/test_gui_run_qupath_install_to0')
silent_logging = None, qupath_teardown = None
record_property = <function record_property.<locals>.append_property at 0x7f82bbf19620>

    @pytest.mark.e2e
    @pytest.mark.long_running
    @pytest.mark.skipif(
        (platform.system() == "Linux" and platform.machine() in {"aarch64", "arm64"}) or platform.system() == "Windows",
        reason="QuPath is not supported on ARM64 Linux; Windows support is not fully tested yet",
    )
    @pytest.mark.timeout(timeout=60 * 15)
    @pytest.mark.sequential
    async def test_gui_run_qupath_install_to_inspect(  # noqa: C901, PLR0912, PLR0913, PLR0914, PLR0915, PLR0917
        user: User, runner: CliRunner, tmp_path: Path, silent_logging: None, qupath_teardown: None, record_property
    ) -> None:
        """Test installing QuPath, downloading run results, creating QuPath project from it, and inspecting results."""
        record_property("tested-item-id", "TC-QUPATH-01, SPEC-GUI-SERVICE")
    
        # Find run
        runs = Service().application_runs(
            application_id=HETA_APPLICATION_ID,
            application_version=HETA_APPLICATION_VERSION,
            external_id=SPOT_0_GS_URL,
            has_output=True,
            limit=1,
        )
        if not runs:
            message = f"No matching runs found for application {HETA_APPLICATION_ID} ({HETA_APPLICATION_VERSION}). "
            message += "This test requires the scheduled test test_application_runs_heta_version passing first."
            pytest.skip(message)
    
        run_id = runs[0].run_id
    
        # Explore run
        run = Service().application_run(run_id).details()
        print(
            f"Found existing run: {run.run_id}\n"
            f"application: {run.application_id} ({run.version_number})\n"
            f"status: {run.state}, output: {run.output}\n"
            f"submitted at: {run.submitted_at}, terminated at: {run.terminated_at}\n"
            f"statistics: {run.statistics!r}\n",
            f"custom_metadata: {run.custom_metadata!r}\n",
        )
    
        # Explore results
        results = list(Service().application_run(run_id).results())
        assert results, f"No results found for run {run_id}"
        for item in results:
            print(
                f"Found item: {item.item_id}, status: {item.state}, output: {item.output}, "
                f"external_id: {item.external_id}\n"
                f"custom_metadata: {item.custom_metadata!r}\n",
            )
    
        with patch(
            "aignostics.application._gui._page_application_run_describe.get_user_data_directory", return_value=tmp_path
        ):
            # Step 1: (Re)Install QuPath
            result = runner.invoke(cli, ["qupath", "uninstall"])
            assert result.exit_code in {0, 2}, f"Uninstall command failed with exit code {result.exit_code}"
            was_installed = not result.exit_code
    
            result = runner.invoke(cli, ["qupath", "install"])
            output = normalize_output(result.output, strip_ansi=True)
            assert f"QuPath v{QUPATH_VERSION} installed successfully" in output, (
                f"Expected 'QuPath v{QUPATH_VERSION} installed successfully' in output.\nOutput: {output}"
            )
            assert result.exit_code == 0
    
            # Step 2: Go to latest completed run via GUI
            await user.open(f"/application/run/{run.run_id}")
            await user.should_see(f"Run {run.run_id}")
            await user.should_see(f"Run of {HETA_APPLICATION_ID} ({HETA_APPLICATION_VERSION})")
    
            # Step 3: Open Result Download dialog
            await user.should_see(marker="BUTTON_OPEN_QUPATH", retries=100)
            user.find(marker="BUTTON_OPEN_QUPATH").click()
    
            # Step 4: Select Data destination
            await user.should_see(marker="BUTTON_DOWNLOAD_DESTINATION_DATA")
            download_destination_data_button: ui.button = user.find(
                marker="BUTTON_DOWNLOAD_DESTINATION_DATA"
            ).elements.pop()
            assert download_destination_data_button.enabled, "Download destination button should be enabled"
            user.find(marker="BUTTON_DOWNLOAD_DESTINATION_DATA").click()
            await assert_notified(user, "Using Launchpad results directory", 30)
    
            # Step 5: Trigger Download
            await user.should_see(marker="DIALOG_BUTTON_DOWNLOAD_RUN")
            download_run_button: ui.button = user.find(marker="DIALOG_BUTTON_DOWNLOAD_RUN").elements.pop()
>           assert download_run_button.enabled, "Download button should be enabled before downloading"
E           AssertionError: Download button should be enabled before downloading
E           assert False
E            +  where False = <nicegui.elements.button.Button object at 0x7f82bbc5c550>.enabled

.../aignostics/qupath/gui_test.py:235: AssertionError

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sonarqubecloud · 2025-11-19T21:30:17Z

Quality Gate failed

Failed conditions
2 New issues
74.0% Coverage on New Code (required ≥ 80%)
9.0% Duplication on New Code (required ≤ 3%)

See analysis details on SonarQube Cloud

Catch issues before they fail your Quality Gate with our IDE extension SonarQube for IDE

helmut-hoffer-von-ankershoffen added 4 commits November 19, 2025 20:54

feat(platform,application): configureable pipeline

5fef607

test(application): reactivate test_cli_run_submit_and_describe_and_ca…

35b5f56

…ncel_and_download_and_delete against production

chore(deps): some

c55a75d

chore(deps): bump nicegui to 3.3.1

8eed64b

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		validate_item_sdk_metadata(metadata)


		class TestPipelineConfiguration:

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helmut-hoffer-von-ankershoffen commented Nov 19, 2025

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github-actions bot commented Nov 19, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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PR Review: Feat/custom pipeline nicegui 3_3_1

✅ Summary: APPROVED with Suggestions

📊 Changes Overview

🔍 Detailed Review

1. ✅ Test Markers (CRITICAL CHECK - PASSED)

2. ✅ Code Quality Checks (ALL PASSED)

3. ⚠️ Commit Messages (1 NEEDS IMPROVEMENT)

4. ✅ Architecture Compliance (PASSED)

5. ✅ SDK Metadata Schema Design (EXCELLENT)

6. ✅ CLI Implementation (GOOD)

7. ✅ GUI Implementation (EXCELLENT)

8. ✅ Testing Strategy (COMPREHENSIVE)

9. ✅ Security & Medical Device Compliance

10. ⚠️ Documentation Updates (MISSING)

11. ✅ Dependency Updates (ROUTINE)

12. ✅ Test Cleanup (GOOD)

🎯 Recommendations

Blocking Issues

High Priority Suggestions

Low Priority Enhancements

🎉 Praise

📋 PR Merge Checklist

🚀 CI/CD Notes

📝 Final Verdict

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codecov bot commented Nov 19, 2025

❌ 2 Tests Failed:

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sonarqubecloud bot commented Nov 19, 2025

Quality Gate failed

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github-actions bot commented Nov 19, 2025 •

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