| """EyeQC clinical theme + custom CSS.""" |
|
|
| import gradio as gr |
|
|
| THEME = gr.themes.Soft( |
| primary_hue=gr.themes.colors.teal, |
| secondary_hue=gr.themes.colors.cyan, |
| neutral_hue=gr.themes.colors.slate, |
| font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"], |
| font_mono=[gr.themes.GoogleFont("IBM Plex Mono"), "monospace"], |
| ).set( |
| body_background_fill="#070d11", |
| block_background_fill="#0e1a20", |
| block_border_width="1px", |
| block_border_color="#1b2c34", |
| block_radius="16px", |
| block_label_text_color="#7fb8c4", |
| block_title_text_color="#d7e2e6", |
| button_primary_background_fill="linear-gradient(92deg,#0b6b7d,#22a5b8)", |
| button_primary_background_fill_hover="linear-gradient(92deg,#0b7c90,#2bbad0)", |
| button_primary_text_color="#ffffff", |
| body_text_color="#cdd9dd", |
| ) |
|
|
| CSS = """ |
| :root { --ink:#eaf2f4; --teal:#2aa5b8; --deep:#0b3c49; --muted:#7f97a0; |
| --line:#1b2c34; --panel:#0e1a20; } |
| .gradio-container { max-width: 1440px !important; margin: 0 auto !important; } |
| #hero { |
| background: |
| radial-gradient(900px 240px at 88% -30%, rgba(42,165,184,.22), transparent 60%), |
| linear-gradient(120deg, #0f4a58 0%, #0a3a45 40%, #082730 75%, #061a20 100%); |
| border:1px solid #14343d; border-radius:22px; padding:28px 32px; margin-bottom:14px; |
| box-shadow:0 14px 50px rgba(0,0,0,.45); |
| } |
| #hero .brand { display:flex; align-items:center; gap:14px; } |
| #hero .logo { width:44px; height:44px; border-radius:12px; flex:none; |
| background:linear-gradient(135deg,#22a5b8,#0b6b7d); |
| box-shadow:0 6px 18px rgba(34,165,184,.4); |
| display:flex; align-items:center; justify-content:center; font-size:24px; } |
| #hero h1 { font-size:32px; font-weight:800; letter-spacing:-.6px; margin:0; color:#fff; } |
| #hero .tag { font-size:12px; color:#8fd0da; letter-spacing:2px; text-transform:uppercase; |
| margin:3px 0 0 0; font-weight:600; } |
| #hero .sub { color:#9fc2c8; font-size:14px; margin:12px 0 0 0; max-width:980px; line-height:1.55; } |
| #hero .pill { display:inline-block; background:rgba(42,165,184,.10); |
| border:1px solid rgba(42,165,184,.28); color:#bfe4ea; font-size:11.5px; |
| padding:5px 12px; border-radius:999px; margin:12px 8px 0 0; } |
| .section-title { font-size:12px; font-weight:700; text-transform:uppercase; |
| letter-spacing:1.4px; color:var(--teal); margin:8px 0 4px 2px; } |
| .verdict-card { border-radius:16px; padding:18px 20px; color:#fff; font-weight:700; |
| text-align:center; box-shadow:0 8px 24px rgba(0,0,0,.3); } |
| .verdict-PASS { background:linear-gradient(135deg,#1f9d61,#0f6b43); } |
| .verdict-ACCEPTABLE { background:linear-gradient(135deg,#d69e2e,#9c7012); } |
| .verdict-FAIL { background:linear-gradient(135deg,#e05252,#a82f2f); } |
| .verdict-card .big { font-size:36px; line-height:1; } |
| .verdict-card .sm { font-size:12px; font-weight:500; opacity:.92; } |
| .chip { display:inline-block; padding:6px 12px; border-radius:999px; font-size:12px; |
| font-weight:600; margin:6px 6px 0 0; border:1px solid var(--line); } |
| .chip-g { background:rgba(40,192,127,.14); color:#7fe3b3; border-color:rgba(40,192,127,.4);} |
| .chip-u { background:rgba(224,82,82,.14); color:#f2a3a3; border-color:rgba(224,82,82,.4);} |
| .chip-q { background:rgba(224,178,60,.14); color:#f0d38a; border-color:rgba(224,178,60,.4);} |
| .reason { background:#0c161b; border:1px solid var(--line); border-left:4px solid var(--teal); |
| border-radius:12px; padding:14px 16px; color:var(--ink); font-size:13.5px; line-height:1.55; } |
| .reason table { width:100%; border-collapse:collapse; margin-top:6px; } |
| .reason th { color:var(--muted); text-align:left; font-weight:600; padding:2px 6px; } |
| .reason td { padding:2px 6px; } |
| .footer-note { color:var(--muted); font-size:12px; text-align:center; margin-top:16px; |
| line-height:1.6; } |
| .md-note { color:var(--muted); font-size:12.5px; } |
| table td, table th { font-size:12.5px !important; } |
| """ |
|
|
|
|
| def hero_html(): |
| return """ |
| <div id="hero"> |
| <div class="brand"> |
| <div class="logo">👁</div> |
| <div> |
| <h1>EyeQC</h1> |
| <div class="tag">Retinal image quality & foundation-model bench</div> |
| </div> |
| </div> |
| <p class="sub">EyeQC helps you trust what you're looking at. It checks whether a |
| retinal photo is clear enough to read, shows you exactly where and why an image |
| falls short, and evens out the differences between cameras and sites so a cohort |
| is comparable. It also asks a harder question of AI models: is a diagnosis |
| picking up real disease, or just reacting to a blurry, poorly-lit image?</p> |
| <span class="pill">Vessel-aware gradability</span> |
| <span class="pill">Conformal verdicts</span> |
| <span class="pill">Failure localisation</span> |
| <span class="pill">ComBat harmonisation</span> |
| <span class="pill">Degradation-sensitivity probe</span> |
| <span class="pill">Spatial disentanglement</span> |
| <span class="pill">FLAIR VQA</span> |
| </div> |
| """ |
|
|
|
|
| def verdict_card_html(summary): |
| v = summary["verdict"] |
| return f""" |
| <div class="verdict-card verdict-{v}"> |
| <div class="big">{summary['composite']:.0f}<span style="font-size:16px">/100</span></div> |
| <div style="font-size:19px;margin-top:2px">{v}</div> |
| <div class="sm">clinical band: {summary['band']}</div> |
| </div> |
| """ |
|
|
|
|
| def conformal_chip_html(pred): |
| lab = pred["label"] |
| cls = {"gradable": "chip-g", "ungradable": "chip-u"}.get(lab, "chip-q") |
| cov = pred.get("coverage") |
| cov_txt = f" · {int(cov*100)}% coverage" if cov else "" |
| src = pred.get("source", "") |
| return (f'<span class="chip {cls}">conformal: {lab}{cov_txt}</span>' |
| f'<span class="md-note"> ({src})</span>') |
|
|
