"""Hero / landing page — NV-Generate masthead + three equal-height datasheet cards."""
from __future__ import annotations
import gradio as gr
# SVG modality glyphs — each is a recognizable anatomical silhouette of the modality.
# CT: axial body cross-section with spine and ribs.
# MR: a k-space spiral trajectory.
# MR-Brain: an axial brain outline with hemispheres.
SVG_CT = """
"""
SVG_MR = """
"""
SVG_MRB = """
"""
# Each card has exactly 5 spec rows, each value short enough to render on one line.
CARDS = [
{
"key": "ct",
"index": "01",
"title": "NV-Generate · CT",
"subtitle": "Whole-body synthetic CT with paired anatomy masks.",
"icon": SVG_CT,
"icon_caption": "Body region · paired anatomy",
# Consistent category: USE CASES across all three cards.
"uses": ["Segmentation data", "Pathology augmentation", "Privacy-preserving sharing"],
"license": "NVIDIA Open Model",
"license_tone": "ok",
"spec": [
("MODALITY", "Computed Tomography"),
("ARCHITECTURE", "MAISI-v2 · Rectified Flow"),
("MAX VOLUME", "512 × 512 × 768 vox"),
("SEGMENTATION", "132 classes · paired"),
("LICENSE", "NVIDIA Open Model"),
],
"accent_class": "ct",
},
{
"key": "mr",
"index": "02",
"title": "NV-Generate · MR",
"subtitle": "Multi-contrast, multi-anatomy synthetic MRI.",
"icon": SVG_MR,
"icon_caption": "Multi-contrast · multi-region",
"uses": ["Modality augmentation", "Cross-region training", "Fine-tune base"],
"license": "NVIDIA Non-Commercial",
"license_tone": "warn",
"spec": [
("MODALITY", "Magnetic Resonance"),
("ARCHITECTURE", "MAISI-v2 · Rectified Flow"),
("MAX VOLUME", "512 × 512 × 128 vox"),
("CONTRASTS", "T1 · T2 · FLAIR · multi-region"),
("LICENSE", "NVIDIA Non-Commercial"),
],
"accent_class": "mr",
},
{
"key": "mr_brain",
"index": "03",
"title": "NV-Generate · MR Brain",
"subtitle": "High-resolution multi-contrast brain MRI.",
"icon": SVG_MRB,
"icon_caption": "Brain · multi-sequence",
"uses": ["Brain segmentation data", "Lesion augmentation", "Sequence translation"],
"license": "NVIDIA Open Model",
"license_tone": "ok",
"spec": [
("MODALITY", "MR · Brain"),
("ARCHITECTURE", "MAISI-v2 · Rectified Flow"),
("MAX VOLUME", "512 × 512 × 256 vox"),
("SEQUENCES", "T1 · T2 · FLAIR · SWI"),
("LICENSE", "NVIDIA Open Model"),
],
"accent_class": "mrb",
},
]
def _spec_rows(spec: list[tuple[str, str]]) -> str:
rows = []
for k, v in spec:
rows.append(
f'
{k}'
f''
f'{v}
'
)
return "".join(rows)
def render_hero() -> tuple[gr.Group, dict[str, gr.Button]]:
"""Build the hero. Returns the wrapping Group and a dict of {key: button}."""
buttons: dict[str, gr.Button] = {}
with gr.Group(elem_classes=["hero"]) as group:
# Masthead: brand on the left, real links on the right. No decorative pills.
gr.HTML(
"""
"""
)
# Datasheet cards — three equal-height subsystems
with gr.Row(elem_classes=["hero-row"], equal_height=True):
for card in CARDS:
with gr.Column(scale=1, min_width=300, elem_classes=["hero-card-col"]):
uses_html = "".join(
f'{u}' for u in card.get("uses", [])
)
license_tone_cls = "ds-license-warn" if card.get("license_tone") == "warn" else "ds-license-ok"
gr.HTML(
f"""