feat(phase-4): Gradio UI with NiiVue visualization (#5)

* feat(phase-4): Gradio UI with NiiVue visualization

Implements Phase 4 of the stroke-deepisles-demo project:

UI Components:
- Gradio 5 application with case selection and segmentation
- NiiVue WebGL viewer with base64 data URLs for 3D visualization
- Matplotlib slice comparison views (axial/coronal/sagittal)
- Settings accordion for fast mode and ground truth toggle

Data Path Refactoring:
- Renamed data/scratch/ to data/isles24/ for clarity
- Added data/discovery/ for schema reports
- Updated all specs and code to use new paths
- Added data/README.md documenting folder structure

Files Added:
- src/stroke_deepisles_demo/ui/{app,viewer,components}.py
- app.py (HF Spaces entry point)
- tests/ui/{test_app,test_viewer}.py
- data/README.md

Quality:
- 88 tests passing (15 new UI tests)
- ruff + mypy clean
- Lazy initialization pattern to avoid import-time side effects

* fix: address CodeRabbit review feedback

- data/README.md: Add `text` language to code blocks (markdownlint MD040)
- test_viewer.py: Move matplotlib.use("Agg") before pyplot import (real bug)
- components.py: Add logger.warning for exception handling (debugging aid)
- app.py: Add explicit noqa comment explaining runtime import need

.gitignore

@@ -206,6 +206,9 @@ marimo/_static/
 marimo/_lsp/
 __marimo__/
 
-# Data Discovery (per docs/specs/data-discovery.md)
-data/scratch/*
-!data/scratch/.gitkeep
+# Local ISLES24 data (git-ignored, download separately)
+data/isles24/
+
+# Discovery artifacts (schema reports, samples)
+data/discovery/
+data/scratch/

app.py

@@ -0,0 +1,11 @@
+"""Entry point for Hugging Face Spaces deployment."""
+
+import gradio as gr
+
+from stroke_deepisles_demo.ui.app import get_demo
+
+# Create the demo instance at module level for Gradio
+demo = get_demo()
+
+if __name__ == "__main__":
+    demo.launch(theme=gr.themes.Soft(), css="footer {visibility: hidden}")

data/README.md

@@ -0,0 +1,39 @@
+# Data Directory
+
+This folder contains local neuroimaging data for the stroke-deepisles-demo project.
+
+## Structure
+
+```text
+data/
+├── README.md           # This file (tracked)
+├── isles24/            # ISLES24 NIfTI files (gitignored)
+│   ├── Images-DWI/     # DWI volumes (149 files)
+│   ├── Images-ADC/     # ADC maps (149 files)
+│   └── Masks/          # Ground truth lesion masks (149 files)
+└── discovery/          # Schema reports (gitignored)
+    └── isles24_schema_report.txt
+```
+
+## Setup
+
+1. Download ISLES24-MR-Lite from [HuggingFace](https://huggingface.co/datasets/YongchengYAO/ISLES24-MR-Lite)
+2. Extract the ZIP files into `data/isles24/`:
+   - `Images-DWI.zip` → `data/isles24/Images-DWI/`
+   - `Images-ADC.zip` → `data/isles24/Images-ADC/`
+   - `Masks.zip` → `data/isles24/Masks/`
+
+## File Naming Convention
+
+Files follow BIDS-like naming:
+```text
+sub-stroke{XXXX}_ses-02_{modality}.nii.gz
+```
+
+Example: `sub-stroke0005_ses-02_dwi.nii.gz`
+
+## Notes
+
+- All data files are gitignored to avoid committing large binaries
+- The `discovery/` folder contains schema reports from data exploration scripts
+- See `docs/specs/02-phase-1-data-access.md` for detailed data loading documentation

docs/specs/00-context.md

@@ -40,9 +40,9 @@ This showcases that:
 
 **The original ISLES24-MR-Lite dataset is NOT properly uploaded to HuggingFace.**
 
-It's just raw ZIP files dumped on HF, not a proper Dataset with parquet/Arrow format. This means `load_dataset()` fails. See `data/scratch/isles24_schema_report.txt` for full details.
+It's just raw ZIP files dumped on HF, not a proper Dataset with parquet/Arrow format. This means `load_dataset()` fails. See `data/discovery/isles24_schema_report.txt` for full details.
 
-**Workaround**: We extracted the ZIPs locally to `data/scratch/isles24_extracted/` (git-ignored) and will implement a file-based loader first. Later, we'll re-upload properly and verify full HF consumption.
+**Workaround**: We extracted the ZIPs locally to `data/isles24/` (git-ignored) and will implement a file-based loader first. Later, we'll re-upload properly and verify full HF consumption.
 
 ## why we need tobias's datasets fork
 
@@ -75,20 +75,20 @@ We pin to this branch until upstream merges the PRs.
 ### 1. data source: ISLES24-MR-Lite
 
 - **HF Dataset**: [YongchengYAO/ISLES24-MR-Lite](https://huggingface.co/datasets/YongchengYAO/ISLES24-MR-Lite) (**BROKEN** - raw ZIPs, not proper dataset)
-- **Local extracted**: `data/scratch/isles24_extracted/` (git-ignored)
+- **Local extracted**: `data/isles24/` (git-ignored)
 - **Content**: 149 acute stroke MRI cases with DWI, ADC, and manual infarct masks
 - **Origin**: Subset of ISLES 2024 challenge data
 - **Why suitable**: DeepISLES was trained on ISLES 2022, so ISLES24 is an **external** test set (no data leakage)
 
 **File structure** (after extraction):
 ```
-data/scratch/isles24_extracted/
+data/isles24/
 ├── Images-DWI/sub-stroke{XXXX}_ses-02_dwi.nii.gz        # 149 files
 ├── Images-ADC/sub-stroke{XXXX}_ses-02_adc.nii.gz        # 149 files
 └── Masks/sub-stroke{XXXX}_ses-02_lesion-msk.nii.gz      # 149 files
 ```
 
-**Schema reference**: `data/scratch/isles24_schema_report.txt`
+**Schema reference**: `data/discovery/isles24_schema_report.txt`
 
 ### 2. model: DeepISLES
 

docs/specs/02-phase-1-data-access.md

@@ -14,7 +14,7 @@ Implement a data loading layer that provides typed access to ISLES24 neuroimagin
 | Columns: `dwi`, `adc`, `mask`, `participant_id` | No columns - just raw ZIP files |
 | Parquet/Arrow format | Three ZIP archives dumped on HF |
 
-**Evidence**: `data/scratch/isles24_schema_report.txt`
+**Evidence**: `data/discovery/isles24_schema_report.txt`
 
 This means the demo must be built in phases:
 1. **Phase 1A**: Local file loader (works NOW with extracted data)
@@ -29,7 +29,7 @@ This means the demo must be built in phases:
 ### data location
 
 ```
-data/scratch/isles24_extracted/     # Git-ignored
+data/isles24/                       # Git-ignored
 ├── Images-DWI/                     # 149 files
 │   └── sub-stroke{XXXX}_ses-02_dwi.nii.gz
 ├── Images-ADC/                     # 149 files
@@ -87,7 +87,7 @@ class DatasetInfo:
 
 
 def load_isles_dataset(
-    source: str | Path = "data/scratch/isles24_extracted",
+    source: str | Path = "data/isles24",
     *,
     local_mode: bool = True,  # Default to local for now
 ) -> LocalDataset:
@@ -288,7 +288,7 @@ def test_get_case_returns_case_files(synthetic_isles_dir):
 ### done criteria (phase 1a)
 
 - [ ] `uv run pytest tests/data/ -v` passes
-- [ ] Can load all 149 cases from `data/scratch/isles24_extracted/`
+- [ ] Can load all 149 cases from `data/isles24/`
 - [ ] `list_case_ids()` returns 149 subject IDs
 - [ ] `get_case("sub-stroke0005")` returns valid CaseFiles
 - [ ] Type checking passes: `uv run mypy src/stroke_deepisles_demo/data/`

docs/specs/data-discovery.md

@@ -23,10 +23,9 @@ scripts/discovery/
 ### data & artifacts
 All downloaded samples, temporary outputs, and schema reports reside in:
 ```
-data/scratch/
-├── .gitkeep             # Tracked
-├── schema_report.txt    # Generated report
-└── samples/             # Raw data samples (IGNORED)
+data/
+├── isles24/             # Extracted ISLES24 data (IGNORED)
+└── discovery/           # Schema reports, samples (IGNORED)
 ```
 
 ## discovery workflow
@@ -44,11 +43,11 @@ Write a focused script in `scripts/discovery/` that:
 ### 2. execution
 Run the script from the project root:
 ```bash
-uv run scripts/discovery/inspect_hf_dataset.py > data/scratch/schema_report.txt
+uv run scripts/discovery/inspect_hf_dataset.py > data/discovery/schema_report.txt
 ```
 
 ### 3. verification
-Manually review `data/scratch/schema_report.txt`.
+Manually review `data/discovery/schema_report.txt`.
 - **Check**: Do column names match `CaseAdapter` expectations?
 - **Check**: Are file paths strings or objects?
 - **Check**: Are required fields (DWI, ADC) actually present?
@@ -62,6 +61,6 @@ If the report contradicts the code/specs:
 ## git configuration
 Ensure `.gitignore` includes:
 ```gitignore
-data/scratch/*
-!data/scratch/.gitkeep
+data/isles24/
+data/discovery/
 ```

pyproject.toml

@@ -33,6 +33,7 @@ dependencies = [
 
     # UI (Gradio 5.x)
     "gradio>=5.0.0",
+    "matplotlib>=3.8.0",
 
     # Networking
     "requests>=2.0.0",

scripts/discovery/inspect_isles24.py

@@ -7,7 +7,7 @@ to document its exact schema before building adapters.
 
 Per: docs/specs/data-discovery.md
 
-Output: data/scratch/isles24_schema_report.txt
+Output: data/discovery/isles24_schema_report.txt
 """
 
 from __future__ import annotations
@@ -20,7 +20,7 @@ from typing import Any
 
 # Constants
 DATASET_ID = "YongchengYAO/ISLES24-MR-Lite"
-OUTPUT_DIR = Path(__file__).parent.parent.parent / "data" / "scratch"
+OUTPUT_DIR = Path(__file__).parent.parent.parent / "data" / "discovery"
 REPORT_FILE = OUTPUT_DIR / "isles24_schema_report.txt"
 
 

src/stroke_deepisles_demo/data/loader.py

@@ -21,7 +21,7 @@ class DatasetInfo:
 
 
 def load_isles_dataset(
-    source: str | Path = "data/scratch/isles24_extracted",
+    source: str | Path = "data/isles24",
     *,
     local_mode: bool = True,  # Default to local for now
 ) -> LocalDataset:

src/stroke_deepisles_demo/ui/__init__.py

@@ -1 +1,5 @@
-"""Gradio UI module for stroke-deepisles-demo."""
+"""UI module for stroke-deepisles-demo."""
+
+from stroke_deepisles_demo.ui.app import create_app, get_demo
+
+__all__ = ["create_app", "get_demo"]

src/stroke_deepisles_demo/ui/app.py

@@ -0,0 +1,170 @@
+"""Main Gradio application for stroke-deepisles-demo."""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import gradio as gr
+from matplotlib.figure import Figure  # noqa: TC002 - needed at runtime for Gradio
+
+from stroke_deepisles_demo.pipeline import run_pipeline_on_case
+from stroke_deepisles_demo.ui.components import (
+    create_case_selector,
+    create_results_display,
+    create_settings_accordion,
+)
+from stroke_deepisles_demo.ui.viewer import (
+    create_niivue_html,
+    nifti_to_data_url,
+    render_slice_comparison,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def run_segmentation(
+    case_id: str, fast_mode: bool, show_ground_truth: bool
+) -> tuple[str, Figure | None, dict[str, Any], str | None, str]:
+    """
+    Run segmentation and return results for display.
+
+    Args:
+        case_id: Selected case identifier
+        fast_mode: Whether to use fast mode (SEALS)
+        show_ground_truth: Whether to show ground truth in plots
+
+    Returns:
+        Tuple of (niivue_html, slice_fig, metrics_dict, download_path, status_msg)
+    """
+    if not case_id:
+        return (
+            "",
+            None,
+            {},
+            None,
+            "Please select a case first.",
+        )
+
+    try:
+        logger.info("Running segmentation for %s", case_id)
+        result = run_pipeline_on_case(case_id, fast=fast_mode, compute_dice=True)
+
+        # 1. NiiVue Visualization
+        # We need data URLs for the browser
+        # Note: This reads the file content into memory (base64)
+        # For large datasets, this might be heavy, but for ISLES24-MR-Lite (cropped) it's fine.
+        # Assuming DWI is the background
+        dwi_path = result.input_files["dwi"]
+        dwi_url = nifti_to_data_url(dwi_path)
+
+        mask_url = None
+        if result.prediction_mask and result.prediction_mask.exists():
+            mask_url = nifti_to_data_url(result.prediction_mask)
+
+        niivue_html = create_niivue_html(
+            dwi_url,
+            mask_url,
+            height=500,
+        )
+
+        # 2. Slice Comparison (Static Plot)
+        gt_path = result.ground_truth if show_ground_truth else None
+        slice_fig = render_slice_comparison(
+            dwi_path=dwi_path,
+            prediction_path=result.prediction_mask,
+            ground_truth_path=gt_path,
+            orientation="axial",
+        )
+
+        # 3. Metrics
+        metrics = {
+            "case_id": result.case_id,
+            "dice_score": result.dice_score,
+            "elapsed_seconds": round(result.elapsed_seconds, 2),
+            "model": "SEALS (Fast)" if fast_mode else "Ensemble",
+        }
+
+        # 4. Download
+        download_path = str(result.prediction_mask)
+
+        status_msg = (
+            f"Success! Dice: {result.dice_score:.3f}"
+            if result.dice_score is not None
+            else "Success!"
+        )
+
+        return niivue_html, slice_fig, metrics, download_path, status_msg
+
+    except Exception as e:
+        logger.exception("Error running segmentation")
+        return "", None, {}, None, f"Error: {e!s}"
+
+
+def create_app() -> gr.Blocks:
+    """
+    Create the Gradio application.
+
+    Returns:
+        Configured gr.Blocks application
+    """
+    with gr.Blocks(
+        title="Stroke Lesion Segmentation Demo",
+    ) as demo:
+        # Header
+        gr.Markdown("""
+        # 🧠 Stroke Lesion Segmentation Demo
+
+        This demo runs [DeepISLES](https://github.com/ezequieldlrosa/DeepIsles)
+        stroke segmentation on cases from
+        [ISLES24-MR-Lite](https://huggingface.co/datasets/YongchengYAO/ISLES24-MR-Lite).
+
+        **Note:** The pipeline runs inside a Docker container. First run might be slow (pulling image).
+        """)
+
+        with gr.Row():
+            # Left column: Controls
+            with gr.Column(scale=1):
+                case_selector = create_case_selector()
+                settings = create_settings_accordion()
+                run_btn = gr.Button("Run Segmentation", variant="primary")
+                status = gr.Textbox(label="Status", interactive=False)
+
+            # Right column: Results
+            with gr.Column(scale=2):
+                results = create_results_display()
+
+        # Event handlers
+        run_btn.click(
+            fn=run_segmentation,
+            inputs=[
+                case_selector,
+                settings["fast_mode"],
+                settings["show_ground_truth"],
+            ],
+            outputs=[
+                results["niivue_viewer"],
+                results["slice_plot"],
+                results["metrics"],
+                results["download"],
+                status,
+            ],
+        )
+
+    return demo  # type: ignore[no-any-return]
+
+
+# Lazy initialization pattern
+_demo: gr.Blocks | None = None
+
+
+def get_demo() -> gr.Blocks:
+    """Get the global demo instance, creating it if necessary."""
+    global _demo
+    if _demo is None:
+        _demo = create_app()
+    return _demo
+
+
+if __name__ == "__main__":
+    get_demo().launch(theme=gr.themes.Soft(), css="footer {visibility: hidden}")

src/stroke_deepisles_demo/ui/components.py

@@ -0,0 +1,84 @@
+"""Reusable UI components."""
+
+from __future__ import annotations
+
+import logging
+
+import gradio as gr
+
+from stroke_deepisles_demo.data import list_case_ids
+
+logger = logging.getLogger(__name__)
+
+
+def create_case_selector() -> gr.Dropdown:
+    """
+    Create a dropdown for selecting cases.
+
+    Returns:
+        Configured gr.Dropdown component
+    """
+    try:
+        case_ids = list_case_ids()
+    except Exception:
+        logger.warning("Failed to load case IDs, using fallback", exc_info=True)
+        case_ids = ["Error loading cases"]
+
+    return gr.Dropdown(
+        choices=case_ids,
+        value=case_ids[0] if case_ids else None,
+        label="Select Case",
+        info="Choose a case from ISLES24-MR-Lite",
+        filterable=True,
+    )
+
+
+def create_results_display() -> dict[str, gr.components.Component]:
+    """
+    Create results display components.
+
+    Returns:
+        Dictionary of component name -> gr.Component
+    """
+    # Using gr.Group to group them visually
+    with gr.Group():
+        # NiiVue visualization uses HTML
+        niivue_viewer = gr.HTML(label="Interactive 3D Viewer")
+
+        # Slice comparisons (Matplotlib)
+        slice_plot = gr.Plot(label="Slice Comparison")
+
+        metrics = gr.JSON(label="Metrics")
+        download = gr.File(label="Download Prediction")
+
+    return {
+        "niivue_viewer": niivue_viewer,
+        "slice_plot": slice_plot,
+        "metrics": metrics,
+        "download": download,
+    }
+
+
+def create_settings_accordion() -> dict[str, gr.components.Component]:
+    """
+    Create expandable settings section.
+
+    Returns:
+        Dictionary of setting name -> gr.Component
+    """
+    with gr.Accordion("Advanced Settings", open=False):
+        fast_mode = gr.Checkbox(
+            value=True,
+            label="Fast Mode (SEALS)",
+            info="Run SEALS only (ISLES'22 winner, requires DWI+ADC). Disable for full ensemble (requires FLAIR).",
+        )
+        show_ground_truth = gr.Checkbox(
+            value=True,
+            label="Show Ground Truth",
+            info="Display ground truth mask if available",
+        )
+
+    return {
+        "fast_mode": fast_mode,
+        "show_ground_truth": show_ground_truth,
+    }

src/stroke_deepisles_demo/ui/viewer.py

@@ -0,0 +1,324 @@
+"""Neuroimaging visualization for Gradio."""
+
+from __future__ import annotations
+
+import base64
+from typing import TYPE_CHECKING
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from stroke_deepisles_demo.metrics import load_nifti_as_array
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+    from matplotlib.figure import Figure
+
+
+def nifti_to_data_url(nifti_path: Path) -> str:
+    """
+    Convert NIfTI file to base64 data URL for NiiVue.
+
+    Args:
+        nifti_path: Path to NIfTI file
+
+    Returns:
+        Data URL string
+    """
+    # We load the raw bytes directly to avoid re-serialization overhead if possible
+    # But nibabel might be safer to ensure valid nifti if we were manipulating
+    # Here we just want the file content.
+    with nifti_path.open("rb") as f:
+        nifti_bytes = f.read()
+
+    nifti_b64 = base64.b64encode(nifti_bytes).decode("utf-8")
+    return f"data:application/octet-stream;base64,{nifti_b64}"
+
+
+def get_slice_at_max_lesion(
+    mask_path: Path,
+    orientation: str = "axial",
+) -> int:
+    """
+    Find slice index with maximum lesion area.
+
+    Useful for displaying the most informative slice.
+
+    Args:
+        mask_path: Path to lesion mask NIfTI
+        orientation: Slice orientation ("axial", "coronal", "sagittal")
+
+    Returns:
+        Slice index with maximum lesion area
+    """
+    data, _ = load_nifti_as_array(mask_path)
+
+    # Determine axes to sum over
+    # Default NIfTI (RAS+): x=sagittal, y=coronal, z=axial
+    # array indices: [x, y, z]
+    if orientation == "sagittal":
+        # Sum over y and z (axes 1, 2) -> result shape [x]
+        lesion_counts = np.sum(data > 0, axis=(1, 2))
+    elif orientation == "coronal":
+        # Sum over x and z (axes 0, 2) -> result shape [y]
+        lesion_counts = np.sum(data > 0, axis=(0, 2))
+    else:  # axial
+        # Sum over x and y (axes 0, 1) -> result shape [z]
+        lesion_counts = np.sum(data > 0, axis=(0, 1))
+
+    max_slice = int(np.argmax(lesion_counts))
+
+    # If mask is empty, return middle slice
+    if np.max(lesion_counts) == 0:
+        if orientation == "sagittal":
+            return int(data.shape[0] // 2)
+        elif orientation == "coronal":
+            return int(data.shape[1] // 2)
+        else:
+            return int(data.shape[2] // 2)
+
+    return max_slice
+
+
+def render_3panel_view(
+    nifti_path: Path,
+    mask_path: Path | None = None,
+    *,
+    mask_alpha: float = 0.5,
+) -> Figure:
+    """
+    Render axial/coronal/sagittal slices with optional mask overlay.
+
+    Args:
+        nifti_path: Path to base NIfTI volume
+        mask_path: Optional path to mask for overlay
+        mask_alpha: Transparency of mask overlay
+
+    Returns:
+        Matplotlib figure with 3-panel view
+    """
+    data, _ = load_nifti_as_array(nifti_path)
+    mask_data = None
+    if mask_path:
+        mask_data, _ = load_nifti_as_array(mask_path)
+
+    # Get slices (middle by default, or max lesion if mask exists)
+    mid_x, mid_y, mid_z = data.shape[0] // 2, data.shape[1] // 2, data.shape[2] // 2
+
+    if mask_data is not None and np.any(mask_data > 0):
+        # Try to find a slice that intersects the lesion best
+        # Simplified: use center of mass of lesion
+        coords = np.argwhere(mask_data > 0)
+        center = coords.mean(axis=0).astype(int)
+        mid_x, mid_y, mid_z = center[0], center[1], center[2]
+
+    # Create figure
+    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+    fig.patch.set_facecolor("black")
+
+    # Axial (XY plane, Z fixed) - often needs rotation 90 deg
+    # NIfTI data[x, y, z]. To display standard axial:
+    # usually imshow(data[:, :, z].T, origin='lower')
+    ax_slice = np.rot90(data[:, :, mid_z])
+    axes[0].imshow(ax_slice, cmap="gray")
+    axes[0].set_title(f"Axial (z={mid_z})", color="white")
+    if mask_data is not None:
+        m_slice = np.rot90(mask_data[:, :, mid_z])
+        axes[0].imshow(
+            np.ma.masked_where(m_slice == 0, m_slice),  # type: ignore[no-untyped-call]
+            cmap="Reds",
+            alpha=mask_alpha,
+            vmin=0,
+            vmax=1,
+        )
+
+    # Coronal (XZ plane, Y fixed)
+    cor_slice = np.rot90(data[:, mid_y, :])
+    axes[1].imshow(cor_slice, cmap="gray")
+    axes[1].set_title(f"Coronal (y={mid_y})", color="white")
+    if mask_data is not None:
+        m_slice = np.rot90(mask_data[:, mid_y, :])
+        axes[1].imshow(
+            np.ma.masked_where(m_slice == 0, m_slice),  # type: ignore[no-untyped-call]
+            cmap="Reds",
+            alpha=mask_alpha,
+            vmin=0,
+            vmax=1,
+        )
+
+    # Sagittal (YZ plane, X fixed)
+    sag_slice = np.rot90(data[mid_x, :, :])
+    axes[2].imshow(sag_slice, cmap="gray")
+    axes[2].set_title(f"Sagittal (x={mid_x})", color="white")
+    if mask_data is not None:
+        m_slice = np.rot90(mask_data[mid_x, :, :])
+        axes[2].imshow(
+            np.ma.masked_where(m_slice == 0, m_slice),  # type: ignore[no-untyped-call]
+            cmap="Reds",
+            alpha=mask_alpha,
+            vmin=0,
+            vmax=1,
+        )
+
+    for ax in axes:
+        ax.axis("off")
+
+    plt.tight_layout()
+    return fig
+
+
+def render_slice_comparison(
+    dwi_path: Path,
+    prediction_path: Path,
+    ground_truth_path: Path | None = None,
+    *,
+    slice_idx: int | None = None,
+    orientation: str = "axial",
+) -> Figure:
+    """
+    Render side-by-side comparison of DWI, prediction, and ground truth.
+
+    Args:
+        dwi_path: Path to DWI NIfTI
+        prediction_path: Path to predicted mask NIfTI
+        ground_truth_path: Optional path to ground truth mask
+        slice_idx: Slice index (default: max lesion or middle)
+        orientation: One of "axial", "coronal", "sagittal"
+
+    Returns:
+        Matplotlib figure with comparison view
+    """
+    dwi_data, _ = load_nifti_as_array(dwi_path)
+    pred_data, _ = load_nifti_as_array(prediction_path)
+    gt_data = None
+    if ground_truth_path:
+        gt_data, _ = load_nifti_as_array(ground_truth_path)
+
+    # Determine slice index
+    if slice_idx is None:
+        # Use prediction to find best slice
+        slice_idx = get_slice_at_max_lesion(prediction_path, orientation)
+
+    # Extract slices based on orientation
+    # Assuming data[x, y, z]
+    if orientation == "sagittal":
+        # X fixed
+        d_slice = np.rot90(dwi_data[slice_idx, :, :])
+        p_slice = np.rot90(pred_data[slice_idx, :, :])
+        g_slice = np.rot90(gt_data[slice_idx, :, :]) if gt_data is not None else None
+    elif orientation == "coronal":
+        # Y fixed
+        d_slice = np.rot90(dwi_data[:, slice_idx, :])
+        p_slice = np.rot90(pred_data[:, slice_idx, :])
+        g_slice = np.rot90(gt_data[:, slice_idx, :]) if gt_data is not None else None
+    else:
+        # Z fixed (axial)
+        d_slice = np.rot90(dwi_data[:, :, slice_idx])
+        p_slice = np.rot90(pred_data[:, :, slice_idx])
+        g_slice = np.rot90(gt_data[:, :, slice_idx]) if gt_data is not None else None
+
+    # Plotting
+    num_plots = 3 if gt_data is not None else 2
+    fig, axes = plt.subplots(1, num_plots, figsize=(5 * num_plots, 5))
+    fig.patch.set_facecolor("black")
+    if num_plots == 2:
+        axes = np.array(axes)  # handle single case if needed, but subplots(1,2) returns array
+
+    # 1. DWI
+    axes[0].imshow(d_slice, cmap="gray")
+    axes[0].set_title("DWI Input", color="white")
+
+    # 2. Prediction
+    axes[1].imshow(d_slice, cmap="gray")
+    axes[1].imshow(
+        np.ma.masked_where(p_slice == 0, p_slice),  # type: ignore[no-untyped-call]
+        cmap="Reds",
+        alpha=0.5,
+        vmin=0,
+        vmax=1,
+    )
+    axes[1].set_title("Prediction", color="white")
+
+    # 3. GT (if available)
+    if gt_data is not None:
+        axes[2].imshow(d_slice, cmap="gray")
+        axes[2].imshow(
+            np.ma.masked_where(g_slice == 0, g_slice),  # type: ignore[no-untyped-call]
+            cmap="Greens",
+            alpha=0.5,
+            vmin=0,
+            vmax=1,
+        )
+        axes[2].set_title("Ground Truth", color="white")
+
+    for ax in axes:
+        ax.axis("off")
+
+    plt.tight_layout()
+    return fig
+
+
+def create_niivue_html(
+    volume_url: str,
+    mask_url: str | None = None,
+    *,
+    height: int = 400,
+) -> str:
+    """
+    Create HTML/JS for NiiVue viewer.
+
+    Args:
+        volume_url: URL to volume NIfTI file
+        mask_url: Optional URL to mask NIfTI file
+        height: Viewer height in pixels
+
+    Returns:
+        HTML string with embedded NiiVue viewer
+    """
+    mask_js = ""
+    if mask_url:
+        mask_js = f"""
+        volumes.push({{
+            url: '{mask_url}',
+            colorMap: 'red',
+            opacity: 0.5
+        }});
+        """
+
+    return f"""
+    <div style="width:100%; height:{height}px; background:#000; border-radius:8px; position: relative;">
+        <canvas id="niivue-canvas" style="width:100%; height:100%;"></canvas>
+    </div>
+    <script type="module">
+        const niivueModule = await import('https://unpkg.com/@niivue/niivue@0.57.0/dist/index.js');
+        const Niivue = niivueModule.Niivue;
+
+        const nv = new Niivue({{
+            logging: false,
+            show3Dcrosshair: true,
+            textHeight: 0.04,
+            backColor: [0, 0, 0, 1]
+        }});
+
+        await nv.attachTo('niivue-canvas');
+
+        const volumes = [{{
+            url: '{volume_url}',
+            name: 'input.nii.gz'
+        }}];
+        {mask_js}
+
+        await nv.loadVolumes(volumes);
+
+        // Multiplanar + 3D view
+        nv.setSliceType(nv.sliceTypeMultiplanar);
+        // Check if setMultiplanarLayout exists (added in newer versions, 0.57 has it)
+        if (nv.setMultiplanarLayout) {{
+            nv.setMultiplanarLayout(2);
+        }}
+        nv.opts.show3Dcrosshair = true;
+        nv.setRenderAzimuthElevation(120, 10);
+        nv.drawScene();
+    </script>
+    """

tests/data/test_integration_real_data.py

@@ -8,10 +8,10 @@ import pytest
 
 from stroke_deepisles_demo.data.loader import load_isles_dataset
 
-REAL_DATA_PATH = Path("data/scratch/isles24_extracted")
+REAL_DATA_PATH = Path("data/isles24")
 
 
-@pytest.mark.skipif(not REAL_DATA_PATH.exists(), reason="Real data not found in data/scratch")
+@pytest.mark.skipif(not REAL_DATA_PATH.exists(), reason="Real data not found in data/isles24")
 def test_load_real_data_count() -> None:
     """Verify that we can load the expected number of cases from real data."""
     dataset = load_isles_dataset(source=REAL_DATA_PATH)
@@ -28,7 +28,7 @@ def test_load_real_data_count() -> None:
     assert case["ground_truth"].exists()
 
 
-@pytest.mark.skipif(not REAL_DATA_PATH.exists(), reason="Real data not found in data/scratch")
+@pytest.mark.skipif(not REAL_DATA_PATH.exists(), reason="Real data not found in data/isles24")
 def test_real_data_subject_ids() -> None:
     """Verify subject ID formatting on real data."""
     dataset = load_isles_dataset(source=REAL_DATA_PATH)

tests/ui/test_app.py

@@ -0,0 +1,75 @@
+"""Smoke tests for Gradio app."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+
+def test_app_module_imports() -> None:
+    """App module imports without side effects."""
+    # This should not launch the app or make network calls
+    from stroke_deepisles_demo.ui import app
+
+    assert hasattr(app, "create_app")
+    assert hasattr(app, "get_demo")
+
+
+def test_create_app_returns_blocks() -> None:
+    """create_app returns a gr.Blocks instance."""
+    import gradio as gr
+
+    # Mock list_case_ids to avoid network call
+    with patch("stroke_deepisles_demo.ui.components.list_case_ids", return_value=["sub-001"]):
+        from stroke_deepisles_demo.ui.app import create_app
+
+        app = create_app()
+
+        assert isinstance(app, gr.Blocks)
+
+
+def test_viewer_module_imports() -> None:
+    """Viewer module imports without errors."""
+    from stroke_deepisles_demo.ui import viewer
+
+    assert hasattr(viewer, "render_3panel_view")
+    assert hasattr(viewer, "create_niivue_html")
+
+
+def test_components_module_imports() -> None:
+    """Components module imports without errors."""
+    from stroke_deepisles_demo.ui import components
+
+    assert hasattr(components, "create_case_selector")
+    assert hasattr(components, "create_results_display")
+
+
+def test_run_segmentation_logic() -> None:
+    """Test run_segmentation logic with mocks."""
+    from stroke_deepisles_demo.pipeline import PipelineResult
+    from stroke_deepisles_demo.ui.app import run_segmentation
+
+    mock_result = PipelineResult(
+        case_id="sub-001",
+        input_files={"dwi": MagicMock(), "adc": MagicMock()},
+        staged_dir=MagicMock(),
+        prediction_mask=MagicMock(),
+        ground_truth=MagicMock(),
+        dice_score=0.85,
+        elapsed_seconds=10.5,
+    )
+
+    # Mock everything that touches files/network
+    with (
+        patch("stroke_deepisles_demo.ui.app.run_pipeline_on_case", return_value=mock_result),
+        patch("stroke_deepisles_demo.ui.app.nifti_to_data_url", return_value="data:image..."),
+        patch("stroke_deepisles_demo.ui.app.create_niivue_html", return_value="<div></div>"),
+        patch("stroke_deepisles_demo.ui.app.render_slice_comparison", return_value=MagicMock()),
+    ):
+        html, _fig, metrics, _dl_path, status = run_segmentation(
+            "sub-001", fast_mode=True, show_ground_truth=True
+        )
+
+        assert html == "<div></div>"
+        assert metrics["case_id"] == "sub-001"
+        assert metrics["dice_score"] == 0.85
+        assert "Success" in status

tests/ui/test_viewer.py

@@ -0,0 +1,146 @@
+"""Tests for viewer module."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import matplotlib
+
+# Non-interactive backend for tests - must be before pyplot import
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.figure import Figure
+
+from stroke_deepisles_demo.ui.viewer import (
+    create_niivue_html,
+    get_slice_at_max_lesion,
+    render_3panel_view,
+    render_slice_comparison,
+)
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+
+class TestRender3PanelView:
+    """Tests for render_3panel_view."""
+
+    def test_returns_matplotlib_figure(self, synthetic_nifti_3d: Path) -> None:
+        """Returns a matplotlib Figure object."""
+        fig = render_3panel_view(synthetic_nifti_3d)
+
+        assert isinstance(fig, Figure)
+        plt.close(fig)
+
+    def test_has_three_axes(self, synthetic_nifti_3d: Path) -> None:
+        """Figure has 3 subplots (axial, coronal, sagittal)."""
+        fig = render_3panel_view(synthetic_nifti_3d)
+
+        assert len(fig.axes) == 3
+        plt.close(fig)
+
+    def test_overlay_mask_when_provided(self, synthetic_nifti_3d: Path, temp_dir: Path) -> None:
+        """Overlays mask when mask_path provided."""
+        # Create a simple mask
+        import nibabel as nib
+
+        mask_data = np.zeros((10, 10, 10), dtype=np.uint8)
+        mask_data[4:6, 4:6, 4:6] = 1
+        mask_img = nib.Nifti1Image(mask_data, np.eye(4))  # type: ignore
+        mask_path = temp_dir / "mask.nii.gz"
+        nib.save(mask_img, mask_path)  # type: ignore
+
+        fig = render_3panel_view(synthetic_nifti_3d, mask_path=mask_path)
+
+        # Should not raise
+        assert fig is not None
+        plt.close(fig)
+
+
+class TestRenderSliceComparison:
+    """Tests for render_slice_comparison."""
+
+    def test_comparison_without_ground_truth(self, synthetic_nifti_3d: Path) -> None:
+        """Works when ground truth is None."""
+        fig = render_slice_comparison(
+            synthetic_nifti_3d,
+            synthetic_nifti_3d,  # Use same as prediction for test
+            ground_truth_path=None,
+        )
+
+        assert isinstance(fig, Figure)
+        plt.close(fig)
+
+    def test_comparison_with_ground_truth(self, synthetic_nifti_3d: Path) -> None:
+        """Works when ground truth is provided."""
+        fig = render_slice_comparison(
+            synthetic_nifti_3d,
+            synthetic_nifti_3d,
+            ground_truth_path=synthetic_nifti_3d,
+        )
+
+        assert isinstance(fig, Figure)
+        plt.close(fig)
+
+
+class TestGetSliceAtMaxLesion:
+    """Tests for get_slice_at_max_lesion."""
+
+    def test_finds_slice_with_lesion(self, temp_dir: Path) -> None:
+        """Returns slice index where lesion is largest."""
+        import nibabel as nib
+
+        # Create mask with lesion at slice 7
+        mask_data = np.zeros((10, 10, 10), dtype=np.uint8)
+        mask_data[:, :, 7] = 1  # Full slice 7 is lesion
+
+        mask_img = nib.Nifti1Image(mask_data, np.eye(4))  # type: ignore
+        mask_path = temp_dir / "mask.nii.gz"
+        nib.save(mask_img, mask_path)  # type: ignore
+
+        slice_idx = get_slice_at_max_lesion(mask_path, orientation="axial")
+
+        assert slice_idx == 7
+
+    def test_returns_middle_for_empty_mask(self, temp_dir: Path) -> None:
+        """Returns middle slice when mask is empty."""
+        import nibabel as nib
+
+        mask_data = np.zeros((10, 10, 20), dtype=np.uint8)
+        mask_img = nib.Nifti1Image(mask_data, np.eye(4))  # type: ignore
+        mask_path = temp_dir / "mask.nii.gz"
+        nib.save(mask_img, mask_path)  # type: ignore
+
+        slice_idx = get_slice_at_max_lesion(mask_path, orientation="axial")
+
+        assert slice_idx == 10  # Middle of 20
+
+
+class TestCreateNiivueHtml:
+    """Tests for create_niivue_html."""
+
+    def test_includes_volume_url(self) -> None:
+        """Generated HTML includes the volume URL."""
+        html = create_niivue_html("http://example.com/brain.nii.gz")
+
+        assert "http://example.com/brain.nii.gz" in html
+
+    def test_includes_mask_when_provided(self) -> None:
+        """Generated HTML includes mask URL when provided."""
+        html = create_niivue_html(
+            "http://example.com/brain.nii.gz",
+            mask_url="http://example.com/mask.nii.gz",
+        )
+
+        assert "http://example.com/mask.nii.gz" in html
+
+    def test_sets_height(self) -> None:
+        """Generated HTML respects height parameter."""
+        html = create_niivue_html(
+            "http://example.com/brain.nii.gz",
+            height=600,
+        )
+
+        assert "height:600px" in html

uv.lock

@@ -396,6 +396,88 @@ wheels = [
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 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773 },
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     { name = "gradio" },
     { name = "huggingface-hub" },
+    { name = "matplotlib" },
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