Default generation = base-pair (FNS), marginalized per-base sampling

app.py

@@ -1,480 +1,480 @@
-"""
-DaisyChain — interactive routing demo (HuggingFace Space).
-
-Paste DNA; the learned router reads how *surprised* each ~74M specialist is (bits/base)
-plus its hidden state and hands the sequence to its home specialist — then that specialist
-streams a continuation live. Styled after the Modular-Mind panel: animated routing cards,
-a first-run loading notice, live token streaming. Every handler is a generator.
-"""
-import html as _h
-import os
-import json
-import math
-
-import gradio as gr
-
-# ZeroGPU: @spaces.GPU allocates a GPU only for the decorated call. Falls back to a no-op
-# decorator when `spaces` isn't installed (local / plain CPU).
-try:
-    import spaces
-    _gpu = spaces.GPU
-except Exception:
-    def _gpu(fn=None, **kw):
-        return fn if callable(fn) else (lambda f: f)
-
-from daisychain import DaisyChain
-
-HERE = os.path.dirname(os.path.abspath(__file__))
-MODEL_REPO = os.environ.get("DAISYCHAIN_REPO", "DaisyChainAI/daisychain-genomics")
-DEVICE = os.environ.get("DAISYCHAIN_DEVICE", "cpu")
-
-# code + tokenizer + router are bundled here; pull the big specialist weights from the
-# (gated) model repo on first launch using the HF_TOKEN Space secret. No silent
-# swallow — if the download fails we want a visible error, not a broken-but-running app.
-if not os.path.exists(os.path.join(HERE, "eukaryote", "model.safetensors")):
-    from huggingface_hub import snapshot_download
-    snapshot_download(MODEL_REPO, local_dir=HERE,
-                      token=os.environ.get("HF_TOKEN"),
-                      allow_patterns=["*/model.safetensors", "tokenizer.json", "router2.pt"])
-
-_DC = {"m": None}            # lazy-loaded so CUDA is never touched at import
-_WARMED = {"done": False}    # so the "loading" notice only shows on the first run
-
-EMOJI = {"eukaryote": "🧬 Eukaryote", "prokaryote": "🦠 Prokaryote",
-         "mrna": "📜 mRNA", "mrna_splice": "✂️ mRNA-splice"}
-# deeper, paper-friendly tones (vs the old neon dark-theme hues)
-COLOR = {"eukaryote": "#5b4bb0", "prokaryote": "#1f7a99",
-         "mrna": "#b03a63", "mrna_splice": "#317f3f"}
-DESC = DaisyChain.DESCRIPTIONS
-
-
-def _moe():
-    if _DC["m"] is None:
-        _DC["m"] = DaisyChain(root=HERE, device=DEVICE)
-    return _DC["m"]
-
-
-# ---- HTML rendering ----------------------------------------------------------------
-# Editorial "scientific-paper" aesthetic borrowed from Carbon's demo (cream paper, ink,
-# green accent, mono uppercase headers) but made our own: a daisy-gold second accent and
-# the chain-of-specialists motif. Tokens live in :root so the cards/bars all stay in sync.
-_CSS = """<style>
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&family=JetBrains+Mono:wght@400;500;700&display=swap');
-:root{--paper:#f7f5ee;--paper2:#f2efe2;--ink:#1f1f1d;--muted:#8c918b;--hairline:#d6d3c4;
-      --green:#317f3f;--green2:#2a5931;--gold:#c79a2e;--gold2:#9c7714}
-.dcx{font-family:"Inter","Helvetica Neue",sans-serif;font-weight:300;color:var(--ink);
-     font-size:13px;line-height:1.7;margin:4px 0}
-.dcx .mono{font-family:"JetBrains Mono",ui-monospace,monospace}
-.dcx .note{background:var(--paper2);border:1px solid var(--hairline);border-radius:4px;
-     padding:12px 14px;color:#5a5a55;font-size:13px}
-.dcx .h{font-family:"JetBrains Mono",ui-monospace,monospace;font-size:12px;font-weight:500;
-     letter-spacing:.16em;text-transform:uppercase;color:var(--ink);margin:6px 0 12px;
-     padding-bottom:8px;border-bottom:1px solid var(--hairline)}
-.dcx .p{color:var(--muted)}
-.dcx .g{color:var(--green2);font-weight:600}
-.dcx .chain{display:flex;gap:0;align-items:stretch;flex-wrap:wrap;margin:8px 0}
-.dcx .link{align-self:center;color:var(--hairline);font-size:18px;margin:0 2px 22px;font-weight:700}
-.dcx .card{flex:1;min-width:180px;background:var(--paper);border:1px solid var(--hairline);
-     border-radius:4px;padding:12px 13px;position:relative;overflow:hidden;transition:box-shadow .3s,border-color .3s}
-.dcx .card .nm{font-family:"JetBrains Mono",ui-monospace,monospace;font-weight:500;font-size:12px;
-     letter-spacing:.06em;text-transform:uppercase}
-.dcx .card .meta{color:var(--muted);font-size:11px;margin-top:4px;min-height:28px;line-height:1.45}
-.dcx .card .bar{height:6px;background:var(--paper2);border:1px solid var(--hairline);border-radius:99px;margin-top:9px;overflow:hidden}
-.dcx .card .fill{height:100%;border-radius:99px;animation:dcxw .7s ease}
-.dcx .card .pct{font-size:10px;color:var(--muted);margin-top:5px;letter-spacing:.04em;text-transform:uppercase;font-family:"JetBrains Mono",monospace}
-.dcx .badge{position:absolute;top:9px;right:10px;font-family:"JetBrains Mono",monospace;font-size:9px;
-     font-weight:700;letter-spacing:.12em;padding:3px 8px;border-radius:99px;color:var(--paper);background:var(--green)}
-@keyframes dcxw{from{width:0}}
-.dcx .gen{background:var(--paper2);border:1px solid var(--hairline);border-radius:4px;padding:13px 15px;
-     margin:12px 0;font-size:14px;line-height:1.8;font-family:"JetBrains Mono",ui-monospace,monospace;word-break:break-all}
-.dcx .caret{display:inline-block;width:8px;height:16px;border-radius:1px;background:var(--green);
-     margin-left:2px;vertical-align:text-bottom;animation:dcxb .8s steps(1) infinite}
-@keyframes dcxb{50%{opacity:0}}
-.dcx .sub{color:var(--muted);font-size:12px;line-height:1.6;margin-top:10px}
-.dcx .stats{display:flex;gap:0;flex-wrap:wrap;margin:12px 0;border:1px solid var(--hairline);border-radius:4px;overflow:hidden}
-.dcx .stat{flex:1;min-width:120px;text-align:center;background:var(--paper);padding:16px 10px;border-right:1px solid var(--hairline)}
-.dcx .stat:last-child{border-right:none}
-.dcx .stat .v{font-family:"JetBrains Mono",monospace;font-size:26px;font-weight:700;line-height:1;color:var(--green2)}
-.dcx .stat .l{font-size:10px;color:var(--muted);margin-top:8px;letter-spacing:.06em;text-transform:uppercase;line-height:1.4}
-.dcx table{border-collapse:collapse;width:100%;margin:10px 0;font-size:12.5px}
-.dcx th,.dcx td{border:1px solid var(--hairline);padding:8px 11px;text-align:left}
-.dcx th{background:var(--paper2);color:var(--ink);font-family:"JetBrains Mono",monospace;font-weight:500;
-     font-size:10px;letter-spacing:.1em;text-transform:uppercase}
-.dcx td.n{text-align:right;font-variant-numeric:tabular-nums;font-family:"JetBrains Mono",monospace}
-/* --- live 2D double-helix (our own take on a base-by-base DNA viz) --- */
-.dcx .helixwrap{background:var(--paper2);border:1px solid var(--hairline);border-radius:4px;padding:8px 12px;margin:12px 0;overflow-x:auto}
-.dcx .helix-svg{display:block;margin:1px 0}
-.dcx .hx-strand{fill:none;stroke-width:1.6;stroke-linecap:round;stroke-linejoin:round;opacity:.5}
-.dcx .hx-strand.back{opacity:.24}
-.dcx .hx-rung{stroke:var(--hairline);stroke-width:1}
-.dcx .hx-rung.user{stroke:#e6e3d6}
-.dcx .hx-base{font-family:"JetBrains Mono",monospace;font-size:9px;font-weight:700;text-anchor:middle;dominant-baseline:central}
-.dcx .hx-legend{display:flex;gap:13px;flex-wrap:wrap;font-family:"JetBrains Mono",monospace;font-size:9px;letter-spacing:.06em;text-transform:uppercase;color:var(--muted);margin:2px 0 4px}
-.dcx .hx-dot{display:inline-block;width:8px;height:8px;border-radius:2px;margin-right:5px;vertical-align:middle}
-/* --- tokenization tracks (our take on carbon-tokenization-02): per-base + 6-mer --- */
-.dcx .seqtrack{font-family:"JetBrains Mono",monospace;font-size:11px;background:var(--paper);border:1px solid var(--hairline);border-radius:4px;padding:8px 10px;display:flex;flex-wrap:wrap;gap:1px;margin:8px 0 2px}
-.dcx .seqb{display:inline-flex;align-items:center;justify-content:center;width:18px;height:20px;border-radius:2px;color:#fff;font-weight:500}
-.dcx .seqb.A{background:#1A7A40}.dcx .seqb.T{background:#b00020}.dcx .seqb.C{background:#2c5aa0}.dcx .seqb.G{background:#b8862c}
-.dcx .tkrow{display:flex;gap:14px;align-items:baseline;margin:12px 0 2px;flex-wrap:wrap}
-.dcx .tklabel{font-family:"JetBrains Mono",monospace;font-size:9.5px;font-weight:500;color:#5b5b56;text-transform:uppercase;letter-spacing:1.6px}
-.dcx .tkstat{font-family:"JetBrains Mono",monospace;font-size:10px;color:var(--muted);letter-spacing:.5px}
-.dcx .tkstat .n{font-weight:600;color:var(--green2)}
-.dcx .tokrow{display:flex;flex-wrap:wrap;align-items:center;gap:0}
-.dcx .tok{display:inline-flex;align-items:center;font-family:"JetBrains Mono",monospace;font-size:11px;letter-spacing:.5px;padding:4px 8px;margin:2px;border:1px solid #ccc;border-radius:3px;background:#fff;color:var(--ink)}
-.dcx .tok.kmer{background:rgba(49,127,63,.10);border-color:rgba(49,127,63,.5);color:var(--green2);font-weight:500}
-/* --- editorial hero banner (dotted texture + faint green edge stripes) --- */
-.dc-banner{position:relative;overflow:hidden;border:1px solid var(--hairline);border-radius:6px;margin:2px 0 6px;
-  background:radial-gradient(circle at 22% 32%,rgba(0,0,0,.06),transparent 1px),
-            radial-gradient(circle at 78% 64%,rgba(0,0,0,.055),transparent 1px),
-            linear-gradient(90deg,rgba(49,127,63,.04),transparent 32%,transparent 68%,rgba(199,154,46,.05)),
-            var(--paper);
-  background-size:7px 7px,11px 11px,auto,auto}
-.dc-binner{position:relative;padding:22px 26px;font-family:"JetBrains Mono",ui-monospace,monospace}
-.dc-ident{display:flex;align-items:center;gap:11px;margin-bottom:16px}
-.dc-mark{font-size:30px;line-height:1}
-.dc-title{font-size:13px;font-weight:700;letter-spacing:.2em;text-transform:uppercase;color:var(--ink)}
-.dc-path{font-size:10px;font-weight:400;letter-spacing:.18em;text-transform:uppercase;color:var(--muted);margin-top:2px}
-.dc-word{font-family:"JetBrains Mono",monospace;font-size:40px;font-weight:700;letter-spacing:-.01em;color:var(--ink);line-height:1.05;margin:4px 0 6px}
-.dc-word .dot{color:var(--gold)}
-.dc-tag{font-family:"Inter",sans-serif;font-weight:300;font-size:13px;color:#5a5a55;max-width:560px;line-height:1.6}
-.dc-motif{display:flex;align-items:center;gap:4px;flex-wrap:wrap;margin-top:18px}
-.dc-chip{font-family:"JetBrains Mono",monospace;font-size:10px;letter-spacing:.06em;text-transform:uppercase;
-  background:var(--paper2);border:1px solid var(--hairline);border-radius:99px;padding:5px 11px;color:var(--ink)}
-.dc-tie{color:var(--green);font-size:13px;font-weight:700}
-.dc-rule{height:1px;background:var(--hairline);margin:14px 0}
-</style>"""
-
-
-def _wrap(body):
-    return _CSS + "<div class='dcx'>" + body + "</div>"
-
-
-def _esc(s):
-    return _h.escape(s or "").replace("\n", "<br>")
-
-
-def _notice(action="Routing"):
-    if not _WARMED["done"]:
-        try:
-            gr.Info("First run — loading the four ~74M specialists (~20–40s on CPU). After this it's quick.")
-        except Exception:
-            pass
-        return _wrap(f"<div class='note'>⏳ Loading the four ~74M specialists + {action.lower()}… "
-                     "first run can take ~20–40s on CPU; every run after is fast.</div>")
-    return _wrap(f"<div class='note'>⏳ {action}…</div>")
-
-
-def _msg(title, body):
-    return _wrap(f"<div class='note'><b>{title}</b><br>{body}</div>")
-
-
-def _cards(bpb, winner=None):
-    """One animated card per specialist: surprise (bits/base), confidence bar, winner badge + glow.
-    bpb values may be None (not computed yet). Lower bits/base = more 'at home' = fuller bar."""
-    cells = []
-    doms = list(bpb.keys())
-    for i, n in enumerate(doms):
-        c = COLOR.get(n, "#9b59b6")
-        v = bpb[n]
-        win = (n == winner)
-        conf = max(0.0, min(1.0, (2.02 - v) / 0.5)) if v is not None else 0.0  # ~1.52..2.02 -> 1..0
-        style = f"border-color:{c};box-shadow:0 0 16px {c}40" if win else ""
-        badge = f"<span class='badge' style='background:{c}'>ROUTED ✓</span>" if win else ""
-        meta = (f"{DESC.get(n,'')}<br>{v:.3f} bits/base (lower = more at home)"
-                if v is not None else f"{DESC.get(n,'')}<br>…")
-        bar = (f"<div class='bar'><div class='fill' style='width:{conf*100:.1f}%;background:{c}'></div></div>"
-               f"<div class='pct'>confidence {conf*100:.0f}%</div>") if v is not None else \
-              "<div class='bar'></div><div class='pct'>…</div>"
-        cells.append(
-            f"<div class='card' style='{style}'>{badge}"
-            f"<div class='nm' style='color:{c}'>{EMOJI.get(n, n)}</div>"
-            f"<div class='meta'>{meta}</div>{bar}</div>")
-        if i < len(doms) - 1:
-            cells.append("<div class='link'>⬭</div>")
-    return "<div class='chain'>" + "".join(cells) + "</div>"
-
-
-def _gen_box(prompt, gen, live=False):
-    caret = "<span class='caret'></span>" if live else ""
-    return (f"<div class='gen'><span class='p'>{_esc(prompt)}</span>"
-            f"<span class='g'>{_esc(gen)}</span>{caret}</div>")
-
-
-# ---- live 2D double-helix --------------------------------------------------------
-# A base-by-base SVG double-helix (two sine-wave backbones + per-base rungs, each base
-# letter on the top strand with its Watson–Crick complement on the bottom). Built in
-# Python so it streams inside our existing generator; ours = cream palette, our own
-# nucleotide colors, strands tinted by the routed specialist's accent.
-_COMP = {"A": "T", "T": "A", "C": "G", "G": "C", "N": "N"}
-# shared nucleotide palette (matches our tokenization track): A green, T red, C blue, G amber
-_BASE_COL = {"A": "#1A7A40", "T": "#b00020", "C": "#2c5aa0", "G": "#b8862c"}
-_USER_COL = "#bdbaa9"
-_HX_SP, _HX_AMP, _HX_YC, _HX_ROWH, _HX_TURN, _HX_PERROW = 14, 12, 22, 48, 10.5, 46
-
-
-def _hx_strand(n, sign):
-    pts = []
-    for s in range(n * 4 + 1):
-        t = s / 4
-        x = t * _HX_SP + _HX_SP / 2
-        ang = (t + 0.5) * 2 * math.pi / _HX_TURN
-        y = _HX_YC + sign * _HX_AMP * math.sin(ang)
-        pts.append(f"{x:.1f},{y:.1f}")
-    return " ".join(pts)
-
-
-def _hx_row(bases, start, user_len, accent):
-    n = len(bases)
-    w = n * _HX_SP + 6
-    out = [f"<svg class='helix-svg' width='{w}' height='{_HX_ROWH}' "
-           f"viewBox='0 0 {w} {_HX_ROWH}' xmlns='http://www.w3.org/2000/svg'>",
-           f"<polyline class='hx-strand' style='stroke:{accent}' points='{_hx_strand(n,1)}'/>",
-           f"<polyline class='hx-strand back' style='stroke:{accent}' points='{_hx_strand(n,-1)}'/>"]
-    for i in range(n):
-        x = i * _HX_SP + _HX_SP / 2
-        ang = (i + 0.5) * 2 * math.pi / _HX_TURN
-        yt = _HX_YC + _HX_AMP * math.sin(ang); yb = _HX_YC - _HX_AMP * math.sin(ang)
-        kind = "user" if start + i < user_len else "gen"
-        out.append(f"<line class='hx-rung {kind}' x1='{x:.1f}' y1='{yt:.1f}' x2='{x:.1f}' y2='{yb:.1f}'/>")
-    for i in range(n):
-        x = i * _HX_SP + _HX_SP / 2
-        ang = (i + 0.5) * 2 * math.pi / _HX_TURN
-        yt = _HX_YC + _HX_AMP * math.sin(ang); yb = _HX_YC - _HX_AMP * math.sin(ang)
-        b = bases[i]; comp = _COMP.get(b, "N")
-        gen = start + i >= user_len
-        col = _BASE_COL.get(b, "#999") if gen else _USER_COL
-        ccol = _BASE_COL.get(comp, "#999") if gen else _USER_COL
-        op = "1" if gen else ".6"
-        out.append(f"<text class='hx-base' x='{x:.1f}' y='{yt:.1f}' style='fill:{col};opacity:{op}'>{b}</text>")
-        out.append(f"<text class='hx-base' x='{x:.1f}' y='{yb:.1f}' style='fill:{ccol};opacity:{op}'>{comp}</text>")
-    out.append("</svg>")
-    return "".join(out)
-
-
-def _helix(prompt, gen, accent, live=False):
-    bases = [c for c in (prompt + gen) if c in "ACGTN"]
-    user_len = len([c for c in prompt if c in "ACGTN"])
-    rows, i = [], 0
-    while i < len(bases):
-        rows.append(_hx_row(bases[i:i + _HX_PERROW], i, user_len, accent))
-        i += _HX_PERROW
-    caret = "<span class='caret'></span>" if live else ""
-    legend = ("<div class='hx-legend'>"
-              + "".join(f"<span><span class='hx-dot' style='background:{_BASE_COL[b]}'></span>{b}</span>" for b in "ACGT")
-              + f"<span><span class='hx-dot' style='background:{_USER_COL}'></span>your input</span></div>")
-    return f"<div class='helixwrap'>{''.join(rows) or '&nbsp;'}{caret}</div>{legend}"
-
-
-def _seq_track(gen):
-    """Per-base colored track of the generated bases (our take on the tokenization demo)."""
-    cells = "".join(f"<span class='seqb {c}'>{c}</span>" for c in gen if c in "ACGT")
-    return f"<div class='seqtrack'>{cells or '&nbsp;'}</div>"
-
-
-def _kmer_strip(gen):
-    """The generated sequence cut into our model's actual non-overlapping 6-mer tokens."""
-    s = "".join(c for c in gen if c in "ACGTN")
-    toks = [s[i:i + 6] for i in range(0, len(s) - len(s) % 6, 6)]
-    chips = "".join(f"<span class='tok kmer'>{t}</span>" for t in toks)
-    head = ("<div class='tkrow'><span class='tklabel'>6-mer tokens</span>"
-            f"<span class='tkstat'>tokens <span class='n'>{len(toks)}</span></span>"
-            f"<span class='tkstat'>bases <span class='n'>{len(s)}</span></span>"
-            "<span class='tkstat'>6 bases / token</span></div>")
-    return head + f"<div class='tokrow'>{chips}</div>"
-
-
-FOOTER = ("Four ~74M DNA/RNA specialists (≈295M total, <b>under Carbon-500M</b>), each distilled "
-          "per-domain from Carbon-500M. A learned router reads every specialist's surprise + hidden "
-          "state and routes to the home specialist — held-out routing accuracy <b>99.8%</b>. Only one "
-          "specialist runs per query (~7× cheaper than the 500M monolith).")
-
-
-# ---- handler ----------------------------------------------------------------------
-@_gpu(duration=120)
-def route_run(seq, n_bases, do_gen, decode="auto"):
-    yield _notice("Routing & generating")
-    seq = (seq or "").strip()
-    if len(seq) < 18:
-        yield _msg("🧬 Enter a DNA sequence", "Paste at least 18 bases (A/C/G/T) — try an example below.")
-        return
-    dc = _moe()
-    doms = dc.domains
-    bpb = {d: None for d in doms}
-    # progressively reveal each specialist's surprise (the chain lighting up)
-    sc, hd = dc._scores_hidden(seq)
-    for d in doms:
-        bpb[d] = sc[d] / 6 / 0.6931
-        yield _wrap("<div class='h'>🔗 Sending the sequence down the chain…</div>" + _cards(bpb))
-    home, _ = dc.route(seq)
-    c = COLOR.get(home, "#9b59b6")
-    head = (f"<div class='h'>🧭 Routed to <span style='color:{c}'>{EMOJI.get(home, home)}</span>"
-            f" — the specialist most at home with your sequence</div>" + _cards(bpb, winner=home))
-    if do_gen:
-        # decoding: explicit override, else "auto" = argmax for the repeat-structured domains,
-        # sampled for mrna (a coding domain that stutters into homopolymers under argmax).
-        dl = (decode or "").lower()
-        if "sampl" in dl:
-            greedy = False
-        elif "argmax" in dl:
-            greedy = True
-        else:
-            greedy = (home != "mrna")
-        feed_ctx = seq                       # FULL context to the specialist (it frame-aligns + caps)
-        ctx = seq[-48:]                      # short context shown in the helix / text box
-        mode = "argmax · best guess" if greedy else "sampled · exploratory"
-        hxhead = (f"<div class='h'>🧬 {EMOJI.get(home, home)} — building the strand base-by-base "
-                  f"<span style='color:var(--muted)'>({mode})</span></div>")
-        rawhdr = "<div class='h'>raw sequence — select to copy</div>"
-        for gen in dc.generate_stream(home, length=int(n_bases), temperature=1.0, top_k=40,
-                                      repetition_penalty=1.3, prompt=feed_ctx, greedy=greedy):
-            yield _wrap(head + hxhead + _helix(ctx, gen, c, live=True)
-                        + _seq_track(gen) + _kmer_strip(gen)
-                        + rawhdr + _gen_box(ctx, gen, live=True))
-        _WARMED["done"] = True
-        gennote = ("<div class='sub'>🧪 <b>Generation is exploratory</b> — these ~74M specialists are "
-                   "trained on a slice of the corpus, so sampled DNA is low-complexity (and splice / "
-                   "bacterial domains are genuinely AT-rich). The <b>routing</b> and per-base likelihood "
-                   "are the result here, not Carbon-level generation.</div>")
-        yield _wrap(head + hxhead + _helix(ctx, gen, c, live=False)
-                    + _seq_track(gen) + _kmer_strip(gen)
-                    + rawhdr + _gen_box(ctx, gen, live=False)
-                    + gennote + f"<div class='sub'>{FOOTER}</div>")
-    else:
-        _WARMED["done"] = True
-        yield _wrap(head + f"<div class='sub'>{FOOTER}</div>")
-
-
-STATS_HTML = _wrap(
-    "<div class='h'>📊 DaisyChain vs Carbon-500M — the fair baseline</div>"
-    "<div class='stats'>"
-    "<div class='stat'><div class='v' style='color:#37b24d'>99.8%</div>"
-    "<div class='l'>routing accuracy<br>(held-out)</div></div>"
-    "<div class='stat'><div class='v' style='color:#7c5cff'>≈295M</div>"
-    "<div class='l'>total params<br>(4 × ~74M) &lt; Carbon-500M</div></div>"
-    "<div class='stat'><div class='v' style='color:#22b8cf'>~7×</div>"
-    "<div class='l'>cheaper per query<br>(one 74M specialist active)</div></div>"
-    "</div>"
-    "<table><tr><th>metric</th><th>DaisyChain</th><th>Carbon-500M</th></tr>"
-    "<tr><td>Likelihood — bits/base (↓ better)</td><td class='n'>1.79</td><td class='n'>1.75</td></tr>"
-    "<tr><td>Seq-recovery, eukaryote (↑ better)</td><td class='n'>31.0%</td><td class='n'>42.2%</td></tr>"
-    "<tr><td>Seq-recovery, bacteria (↑ better)</td><td class='n'>36.5%</td><td class='n'>49.5%</td></tr>"
-    "</table>"
-    "<div class='sub'>Four ~74M specialists (≈295M total, <b>under Carbon-500M</b>); only one runs per "
-    "query, so it's ~7× cheaper per token. Behind the 500M / 1T-token monolith but within striking "
-    "distance — the gap is concentrated in the structured domains (mRNA, bacteria) and keeps closing "
-    "with more per-domain training. Same protocols as Carbon's eval suite (sequence recovery; per-base "
-    "likelihood). Carbon-500M is the right yardstick for a sub-500M modular set, not the 3B flagship.</div>")
-
-
-BANNER = _CSS + """
-<div class='dcx'><div class='dc-banner'><div class='dc-binner'>
-  <div class='dc-ident'>
-    <span class='dc-mark'>🌼</span>
-    <div>
-      <div class='dc-title'>DAISYCHAIN</div>
-      <div class='dc-path'>DAISYCHAINAI / GENOMICS · ROUTED DNA SPECIALISTS</div>
-    </div>
-  </div>
-  <div class='dc-word'>DaisyChain<span class='dot'>.</span></div>
-  <div class='dc-tag'>A modular genomic mind. Four dense ~74M DNA/RNA specialists
-    (&approx;295M total, <b>under Carbon-500M</b>), each distilled per-domain from Carbon-500M.
-    A learned router reads how <i>surprised</i> each specialist is by your sequence
-    (bits/base) plus its hidden state, then hands the work to its home specialist —
-    held-out routing accuracy <b>99.8%</b>. Watch it route in real time.</div>
-  <div class='dc-motif'>
-    <span class='dc-chip'>🧬 Eukaryote</span><span class='dc-tie'>—</span>
-    <span class='dc-chip'>🦠 Prokaryote</span><span class='dc-tie'>—</span>
-    <span class='dc-chip'>📜 mRNA</span><span class='dc-tie'>—</span>
-    <span class='dc-chip'>✂️ mRNA-splice</span>
-  </div>
-</div></div></div>"""
-
-# Light editorial chrome for the Gradio shell so the cream paper extends to the whole page.
-# We pin Gradio's theme CSS variables (for BOTH light and .dark) to the paper palette so the
-# text never renders white-on-cream when a visitor's browser/Space defaults to dark mode.
-_PAGE_CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&family=JetBrains+Mono:wght@400;500;700&display=swap');
-.gradio-container, .gradio-container.dark, .dark, body, body.dark{
-  --body-background-fill:#f7f5ee!important;
-  --background-fill-primary:#f7f5ee!important;
-  --background-fill-secondary:#f2efe2!important;
-  --block-background-fill:#fbfaf4!important;
-  --block-label-background-fill:#f2efe2!important;
-  --input-background-fill:#fffdf6!important;
-  --body-text-color:#1f1f1d!important;
-  --body-text-color-subdued:#5a5a55!important;
-  --block-title-text-color:#1f1f1d!important;
-  --block-label-text-color:#5a5a55!important;
-  --block-info-text-color:#5a5a55!important;
-  --border-color-primary:#d6d3c4!important;
-  --neutral-50:#f7f5ee!important;
-  --table-even-background-fill:#fbfaf4!important;
-  --table-odd-background-fill:#f2efe2!important;
-  --table-row-focus:#eef3e9!important;
-  --table-text-color:#1f1f1d!important;
-  background:#f7f5ee!important;
-  color:#1f1f1d!important;
-}
-/* Examples dataset table rows (were rendering black in dark mode) */
-.gradio-container .gr-samples-table, .gradio-container [class*='dataset'] table,
-.gradio-container [class*='dataset'] td, .gradio-container [class*='dataset'] tr,
-.gradio-container [class*='dataset'] tbody{background:#fbfaf4!important;color:#1f1f1d!important}
-.gradio-container [class*='dataset'] tr:nth-child(even) td{background:#f2efe2!important}
-/* Radio / checkbox options (were rendering black in dark mode) */
-.gradio-container [class*='radio'] label, .gradio-container fieldset label,
-.gradio-container [class*='checkbox'] label, .gradio-container .wrap label{
-  background:#fbfaf4!important;color:#1f1f1d!important;border:1px solid #d6d3c4!important}
-.gradio-container [class*='radio'] label *, .gradio-container fieldset label *,
-.gradio-container [class*='checkbox'] label *{color:#1f1f1d!important}
-.gradio-container input[type=radio],.gradio-container input[type=checkbox]{accent-color:#317f3f!important}
-.gradio-container{font-family:"Inter","Helvetica Neue",sans-serif!important;max-width:1080px!important}
-/* force any Gradio-rendered label / markdown / example text to ink, never white */
-.gradio-container label, .gradio-container .prose, .gradio-container p,
-.gradio-container span, .gradio-container td, .gradio-container th,
-.gradio-container .gr-text-input, .gradio-container input, .gradio-container textarea{color:#1f1f1d!important}
-.gradio-container input::placeholder, .gradio-container textarea::placeholder{color:#9c9989!important}
-footer{display:none!important}
-.gr-button-primary, button.primary{background:#317f3f!important;border:1px solid #2a5931!important;color:#f7f5ee!important;
-  font-family:"JetBrains Mono",monospace!important;letter-spacing:.08em!important;text-transform:uppercase!important;font-size:12px!important}
-.gr-button-primary:hover, button.primary:hover{background:#2a5931!important}
-.gr-button-primary *, button.primary *{color:#f7f5ee!important}
-"""
-
-
-def build():
-    theme = gr.themes.Default(primary_hue="green", neutral_hue="stone",
-                              font=[gr.themes.GoogleFont("Inter"), "sans-serif"],
-                              font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "monospace"])
-    # force the light palette so text is never white-on-cream if a visitor defaults to dark mode
-    _force_light = ("() => { const u = new URL(window.location.href);"
-                    " if (u.searchParams.get('__theme') !== 'light') {"
-                    " u.searchParams.set('__theme','light'); window.location.replace(u.href); } }")
-    with gr.Blocks(title="DaisyChain — modular genomic mind", theme=theme, css=_PAGE_CSS,
-                   js=_force_light) as demo:
-        gr.HTML(BANNER)
-        with gr.Row():
-            seq = gr.Textbox(label="DNA SEQUENCE", lines=3, scale=4,
-                             placeholder="ACGT… (eukaryotic, bacterial, mRNA, or splice-site DNA)")
-            n = gr.Slider(60, 300, value=90, step=30, label="GENERATE BASES", scale=1)
-        with gr.Row():
-            gen_ck = gr.Checkbox(value=True, label="stream a continuation from the routed specialist")
-            decode = gr.Radio(["sampled (recommended)", "greedy (argmax)"], value="sampled (recommended)",
-                              label="DECODING", scale=1,
-                              info="sampled = varied output (the realistic best for these small models); argmax = deterministic, accurate for the very next bases but collapses into repeats over a full generation")
-            btn = gr.Button("🔗 Route through the DaisyChain", variant="primary")
-        out = gr.HTML(_wrap("<div class='h'>The chain · paste a sequence to light it up</div>"
-                            + _cards({d: None for d in DaisyChain.DESCRIPTIONS})))
-        btn.click(route_run, [seq, n, gen_ck, decode], out)
-        try:
-            ex = json.load(open(os.path.join(HERE, "examples.json")))
-            gr.Examples([[v, 90, True, "sampled (recommended)"] for v in ex.values()],
-                        inputs=[seq, n, gen_ck, decode],
-                        label="Example sequences (one per domain)")
-        except Exception:
-            pass
-        gr.HTML(STATS_HTML)
-    return demo
-
-
-if __name__ == "__main__":
-    build().launch()
+"""
+DaisyChain — interactive routing demo (HuggingFace Space).
+
+Paste DNA; the learned router reads how *surprised* each ~74M specialist is (bits/base)
+plus its hidden state and hands the sequence to its home specialist — then that specialist
+streams a continuation live. Styled after the Modular-Mind panel: animated routing cards,
+a first-run loading notice, live token streaming. Every handler is a generator.
+"""
+import html as _h
+import os
+import json
+import math
+
+import gradio as gr
+
+# ZeroGPU: @spaces.GPU allocates a GPU only for the decorated call. Falls back to a no-op
+# decorator when `spaces` isn't installed (local / plain CPU).
+try:
+    import spaces
+    _gpu = spaces.GPU
+except Exception:
+    def _gpu(fn=None, **kw):
+        return fn if callable(fn) else (lambda f: f)
+
+from daisychain import DaisyChain
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+MODEL_REPO = os.environ.get("DAISYCHAIN_REPO", "DaisyChainAI/daisychain-genomics")
+DEVICE = os.environ.get("DAISYCHAIN_DEVICE", "cpu")
+
+# code + tokenizer + router are bundled here; pull the big specialist weights from the
+# (gated) model repo on first launch using the HF_TOKEN Space secret. No silent
+# swallow — if the download fails we want a visible error, not a broken-but-running app.
+if not os.path.exists(os.path.join(HERE, "eukaryote", "model.safetensors")):
+    from huggingface_hub import snapshot_download
+    snapshot_download(MODEL_REPO, local_dir=HERE,
+                      token=os.environ.get("HF_TOKEN"),
+                      allow_patterns=["*/model.safetensors", "tokenizer.json", "router2.pt"])
+
+_DC = {"m": None}            # lazy-loaded so CUDA is never touched at import
+_WARMED = {"done": False}    # so the "loading" notice only shows on the first run
+
+EMOJI = {"eukaryote": "🧬 Eukaryote", "prokaryote": "🦠 Prokaryote",
+         "mrna": "📜 mRNA", "mrna_splice": "✂️ mRNA-splice"}
+# deeper, paper-friendly tones (vs the old neon dark-theme hues)
+COLOR = {"eukaryote": "#5b4bb0", "prokaryote": "#1f7a99",
+         "mrna": "#b03a63", "mrna_splice": "#317f3f"}
+DESC = DaisyChain.DESCRIPTIONS
+
+
+def _moe():
+    if _DC["m"] is None:
+        _DC["m"] = DaisyChain(root=HERE, device=DEVICE)
+    return _DC["m"]
+
+
+# ---- HTML rendering ----------------------------------------------------------------
+# Editorial "scientific-paper" aesthetic borrowed from Carbon's demo (cream paper, ink,
+# green accent, mono uppercase headers) but made our own: a daisy-gold second accent and
+# the chain-of-specialists motif. Tokens live in :root so the cards/bars all stay in sync.
+_CSS = """<style>
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&family=JetBrains+Mono:wght@400;500;700&display=swap');
+:root{--paper:#f7f5ee;--paper2:#f2efe2;--ink:#1f1f1d;--muted:#8c918b;--hairline:#d6d3c4;
+      --green:#317f3f;--green2:#2a5931;--gold:#c79a2e;--gold2:#9c7714}
+.dcx{font-family:"Inter","Helvetica Neue",sans-serif;font-weight:300;color:var(--ink);
+     font-size:13px;line-height:1.7;margin:4px 0}
+.dcx .mono{font-family:"JetBrains Mono",ui-monospace,monospace}
+.dcx .note{background:var(--paper2);border:1px solid var(--hairline);border-radius:4px;
+     padding:12px 14px;color:#5a5a55;font-size:13px}
+.dcx .h{font-family:"JetBrains Mono",ui-monospace,monospace;font-size:12px;font-weight:500;
+     letter-spacing:.16em;text-transform:uppercase;color:var(--ink);margin:6px 0 12px;
+     padding-bottom:8px;border-bottom:1px solid var(--hairline)}
+.dcx .p{color:var(--muted)}
+.dcx .g{color:var(--green2);font-weight:600}
+.dcx .chain{display:flex;gap:0;align-items:stretch;flex-wrap:wrap;margin:8px 0}
+.dcx .link{align-self:center;color:var(--hairline);font-size:18px;margin:0 2px 22px;font-weight:700}
+.dcx .card{flex:1;min-width:180px;background:var(--paper);border:1px solid var(--hairline);
+     border-radius:4px;padding:12px 13px;position:relative;overflow:hidden;transition:box-shadow .3s,border-color .3s}
+.dcx .card .nm{font-family:"JetBrains Mono",ui-monospace,monospace;font-weight:500;font-size:12px;
+     letter-spacing:.06em;text-transform:uppercase}
+.dcx .card .meta{color:var(--muted);font-size:11px;margin-top:4px;min-height:28px;line-height:1.45}
+.dcx .card .bar{height:6px;background:var(--paper2);border:1px solid var(--hairline);border-radius:99px;margin-top:9px;overflow:hidden}
+.dcx .card .fill{height:100%;border-radius:99px;animation:dcxw .7s ease}
+.dcx .card .pct{font-size:10px;color:var(--muted);margin-top:5px;letter-spacing:.04em;text-transform:uppercase;font-family:"JetBrains Mono",monospace}
+.dcx .badge{position:absolute;top:9px;right:10px;font-family:"JetBrains Mono",monospace;font-size:9px;
+     font-weight:700;letter-spacing:.12em;padding:3px 8px;border-radius:99px;color:var(--paper);background:var(--green)}
+@keyframes dcxw{from{width:0}}
+.dcx .gen{background:var(--paper2);border:1px solid var(--hairline);border-radius:4px;padding:13px 15px;
+     margin:12px 0;font-size:14px;line-height:1.8;font-family:"JetBrains Mono",ui-monospace,monospace;word-break:break-all}
+.dcx .caret{display:inline-block;width:8px;height:16px;border-radius:1px;background:var(--green);
+     margin-left:2px;vertical-align:text-bottom;animation:dcxb .8s steps(1) infinite}
+@keyframes dcxb{50%{opacity:0}}
+.dcx .sub{color:var(--muted);font-size:12px;line-height:1.6;margin-top:10px}
+.dcx .stats{display:flex;gap:0;flex-wrap:wrap;margin:12px 0;border:1px solid var(--hairline);border-radius:4px;overflow:hidden}
+.dcx .stat{flex:1;min-width:120px;text-align:center;background:var(--paper);padding:16px 10px;border-right:1px solid var(--hairline)}
+.dcx .stat:last-child{border-right:none}
+.dcx .stat .v{font-family:"JetBrains Mono",monospace;font-size:26px;font-weight:700;line-height:1;color:var(--green2)}
+.dcx .stat .l{font-size:10px;color:var(--muted);margin-top:8px;letter-spacing:.06em;text-transform:uppercase;line-height:1.4}
+.dcx table{border-collapse:collapse;width:100%;margin:10px 0;font-size:12.5px}
+.dcx th,.dcx td{border:1px solid var(--hairline);padding:8px 11px;text-align:left}
+.dcx th{background:var(--paper2);color:var(--ink);font-family:"JetBrains Mono",monospace;font-weight:500;
+     font-size:10px;letter-spacing:.1em;text-transform:uppercase}
+.dcx td.n{text-align:right;font-variant-numeric:tabular-nums;font-family:"JetBrains Mono",monospace}
+/* --- live 2D double-helix (our own take on a base-by-base DNA viz) --- */
+.dcx .helixwrap{background:var(--paper2);border:1px solid var(--hairline);border-radius:4px;padding:8px 12px;margin:12px 0;overflow-x:auto}
+.dcx .helix-svg{display:block;margin:1px 0}
+.dcx .hx-strand{fill:none;stroke-width:1.6;stroke-linecap:round;stroke-linejoin:round;opacity:.5}
+.dcx .hx-strand.back{opacity:.24}
+.dcx .hx-rung{stroke:var(--hairline);stroke-width:1}
+.dcx .hx-rung.user{stroke:#e6e3d6}
+.dcx .hx-base{font-family:"JetBrains Mono",monospace;font-size:9px;font-weight:700;text-anchor:middle;dominant-baseline:central}
+.dcx .hx-legend{display:flex;gap:13px;flex-wrap:wrap;font-family:"JetBrains Mono",monospace;font-size:9px;letter-spacing:.06em;text-transform:uppercase;color:var(--muted);margin:2px 0 4px}
+.dcx .hx-dot{display:inline-block;width:8px;height:8px;border-radius:2px;margin-right:5px;vertical-align:middle}
+/* --- tokenization tracks (our take on carbon-tokenization-02): per-base + 6-mer --- */
+.dcx .seqtrack{font-family:"JetBrains Mono",monospace;font-size:11px;background:var(--paper);border:1px solid var(--hairline);border-radius:4px;padding:8px 10px;display:flex;flex-wrap:wrap;gap:1px;margin:8px 0 2px}
+.dcx .seqb{display:inline-flex;align-items:center;justify-content:center;width:18px;height:20px;border-radius:2px;color:#fff;font-weight:500}
+.dcx .seqb.A{background:#1A7A40}.dcx .seqb.T{background:#b00020}.dcx .seqb.C{background:#2c5aa0}.dcx .seqb.G{background:#b8862c}
+.dcx .tkrow{display:flex;gap:14px;align-items:baseline;margin:12px 0 2px;flex-wrap:wrap}
+.dcx .tklabel{font-family:"JetBrains Mono",monospace;font-size:9.5px;font-weight:500;color:#5b5b56;text-transform:uppercase;letter-spacing:1.6px}
+.dcx .tkstat{font-family:"JetBrains Mono",monospace;font-size:10px;color:var(--muted);letter-spacing:.5px}
+.dcx .tkstat .n{font-weight:600;color:var(--green2)}
+.dcx .tokrow{display:flex;flex-wrap:wrap;align-items:center;gap:0}
+.dcx .tok{display:inline-flex;align-items:center;font-family:"JetBrains Mono",monospace;font-size:11px;letter-spacing:.5px;padding:4px 8px;margin:2px;border:1px solid #ccc;border-radius:3px;background:#fff;color:var(--ink)}
+.dcx .tok.kmer{background:rgba(49,127,63,.10);border-color:rgba(49,127,63,.5);color:var(--green2);font-weight:500}
+/* --- editorial hero banner (dotted texture + faint green edge stripes) --- */
+.dc-banner{position:relative;overflow:hidden;border:1px solid var(--hairline);border-radius:6px;margin:2px 0 6px;
+  background:radial-gradient(circle at 22% 32%,rgba(0,0,0,.06),transparent 1px),
+            radial-gradient(circle at 78% 64%,rgba(0,0,0,.055),transparent 1px),
+            linear-gradient(90deg,rgba(49,127,63,.04),transparent 32%,transparent 68%,rgba(199,154,46,.05)),
+            var(--paper);
+  background-size:7px 7px,11px 11px,auto,auto}
+.dc-binner{position:relative;padding:22px 26px;font-family:"JetBrains Mono",ui-monospace,monospace}
+.dc-ident{display:flex;align-items:center;gap:11px;margin-bottom:16px}
+.dc-mark{font-size:30px;line-height:1}
+.dc-title{font-size:13px;font-weight:700;letter-spacing:.2em;text-transform:uppercase;color:var(--ink)}
+.dc-path{font-size:10px;font-weight:400;letter-spacing:.18em;text-transform:uppercase;color:var(--muted);margin-top:2px}
+.dc-word{font-family:"JetBrains Mono",monospace;font-size:40px;font-weight:700;letter-spacing:-.01em;color:var(--ink);line-height:1.05;margin:4px 0 6px}
+.dc-word .dot{color:var(--gold)}
+.dc-tag{font-family:"Inter",sans-serif;font-weight:300;font-size:13px;color:#5a5a55;max-width:560px;line-height:1.6}
+.dc-motif{display:flex;align-items:center;gap:4px;flex-wrap:wrap;margin-top:18px}
+.dc-chip{font-family:"JetBrains Mono",monospace;font-size:10px;letter-spacing:.06em;text-transform:uppercase;
+  background:var(--paper2);border:1px solid var(--hairline);border-radius:99px;padding:5px 11px;color:var(--ink)}
+.dc-tie{color:var(--green);font-size:13px;font-weight:700}
+.dc-rule{height:1px;background:var(--hairline);margin:14px 0}
+</style>"""
+
+
+def _wrap(body):
+    return _CSS + "<div class='dcx'>" + body + "</div>"
+
+
+def _esc(s):
+    return _h.escape(s or "").replace("\n", "<br>")
+
+
+def _notice(action="Routing"):
+    if not _WARMED["done"]:
+        try:
+            gr.Info("First run — loading the four ~74M specialists (~20–40s on CPU). After this it's quick.")
+        except Exception:
+            pass
+        return _wrap(f"<div class='note'>⏳ Loading the four ~74M specialists + {action.lower()}… "
+                     "first run can take ~20–40s on CPU; every run after is fast.</div>")
+    return _wrap(f"<div class='note'>⏳ {action}…</div>")
+
+
+def _msg(title, body):
+    return _wrap(f"<div class='note'><b>{title}</b><br>{body}</div>")
+
+
+def _cards(bpb, winner=None):
+    """One animated card per specialist: surprise (bits/base), confidence bar, winner badge + glow.
+    bpb values may be None (not computed yet). Lower bits/base = more 'at home' = fuller bar."""
+    cells = []
+    doms = list(bpb.keys())
+    for i, n in enumerate(doms):
+        c = COLOR.get(n, "#9b59b6")
+        v = bpb[n]
+        win = (n == winner)
+        conf = max(0.0, min(1.0, (2.02 - v) / 0.5)) if v is not None else 0.0  # ~1.52..2.02 -> 1..0
+        style = f"border-color:{c};box-shadow:0 0 16px {c}40" if win else ""
+        badge = f"<span class='badge' style='background:{c}'>ROUTED ✓</span>" if win else ""
+        meta = (f"{DESC.get(n,'')}<br>{v:.3f} bits/base (lower = more at home)"
+                if v is not None else f"{DESC.get(n,'')}<br>…")
+        bar = (f"<div class='bar'><div class='fill' style='width:{conf*100:.1f}%;background:{c}'></div></div>"
+               f"<div class='pct'>confidence {conf*100:.0f}%</div>") if v is not None else \
+              "<div class='bar'></div><div class='pct'>…</div>"
+        cells.append(
+            f"<div class='card' style='{style}'>{badge}"
+            f"<div class='nm' style='color:{c}'>{EMOJI.get(n, n)}</div>"
+            f"<div class='meta'>{meta}</div>{bar}</div>")
+        if i < len(doms) - 1:
+            cells.append("<div class='link'>⬭</div>")
+    return "<div class='chain'>" + "".join(cells) + "</div>"
+
+
+def _gen_box(prompt, gen, live=False):
+    caret = "<span class='caret'></span>" if live else ""
+    return (f"<div class='gen'><span class='p'>{_esc(prompt)}</span>"
+            f"<span class='g'>{_esc(gen)}</span>{caret}</div>")
+
+
+# ---- live 2D double-helix --------------------------------------------------------
+# A base-by-base SVG double-helix (two sine-wave backbones + per-base rungs, each base
+# letter on the top strand with its Watson–Crick complement on the bottom). Built in
+# Python so it streams inside our existing generator; ours = cream palette, our own
+# nucleotide colors, strands tinted by the routed specialist's accent.
+_COMP = {"A": "T", "T": "A", "C": "G", "G": "C", "N": "N"}
+# shared nucleotide palette (matches our tokenization track): A green, T red, C blue, G amber
+_BASE_COL = {"A": "#1A7A40", "T": "#b00020", "C": "#2c5aa0", "G": "#b8862c"}
+_USER_COL = "#bdbaa9"
+_HX_SP, _HX_AMP, _HX_YC, _HX_ROWH, _HX_TURN, _HX_PERROW = 14, 12, 22, 48, 10.5, 46
+
+
+def _hx_strand(n, sign):
+    pts = []
+    for s in range(n * 4 + 1):
+        t = s / 4
+        x = t * _HX_SP + _HX_SP / 2
+        ang = (t + 0.5) * 2 * math.pi / _HX_TURN
+        y = _HX_YC + sign * _HX_AMP * math.sin(ang)
+        pts.append(f"{x:.1f},{y:.1f}")
+    return " ".join(pts)
+
+
+def _hx_row(bases, start, user_len, accent):
+    n = len(bases)
+    w = n * _HX_SP + 6
+    out = [f"<svg class='helix-svg' width='{w}' height='{_HX_ROWH}' "
+           f"viewBox='0 0 {w} {_HX_ROWH}' xmlns='http://www.w3.org/2000/svg'>",
+           f"<polyline class='hx-strand' style='stroke:{accent}' points='{_hx_strand(n,1)}'/>",
+           f"<polyline class='hx-strand back' style='stroke:{accent}' points='{_hx_strand(n,-1)}'/>"]
+    for i in range(n):
+        x = i * _HX_SP + _HX_SP / 2
+        ang = (i + 0.5) * 2 * math.pi / _HX_TURN
+        yt = _HX_YC + _HX_AMP * math.sin(ang); yb = _HX_YC - _HX_AMP * math.sin(ang)
+        kind = "user" if start + i < user_len else "gen"
+        out.append(f"<line class='hx-rung {kind}' x1='{x:.1f}' y1='{yt:.1f}' x2='{x:.1f}' y2='{yb:.1f}'/>")
+    for i in range(n):
+        x = i * _HX_SP + _HX_SP / 2
+        ang = (i + 0.5) * 2 * math.pi / _HX_TURN
+        yt = _HX_YC + _HX_AMP * math.sin(ang); yb = _HX_YC - _HX_AMP * math.sin(ang)
+        b = bases[i]; comp = _COMP.get(b, "N")
+        gen = start + i >= user_len
+        col = _BASE_COL.get(b, "#999") if gen else _USER_COL
+        ccol = _BASE_COL.get(comp, "#999") if gen else _USER_COL
+        op = "1" if gen else ".6"
+        out.append(f"<text class='hx-base' x='{x:.1f}' y='{yt:.1f}' style='fill:{col};opacity:{op}'>{b}</text>")
+        out.append(f"<text class='hx-base' x='{x:.1f}' y='{yb:.1f}' style='fill:{ccol};opacity:{op}'>{comp}</text>")
+    out.append("</svg>")
+    return "".join(out)
+
+
+def _helix(prompt, gen, accent, live=False):
+    bases = [c for c in (prompt + gen) if c in "ACGTN"]
+    user_len = len([c for c in prompt if c in "ACGTN"])
+    rows, i = [], 0
+    while i < len(bases):
+        rows.append(_hx_row(bases[i:i + _HX_PERROW], i, user_len, accent))
+        i += _HX_PERROW
+    caret = "<span class='caret'></span>" if live else ""
+    legend = ("<div class='hx-legend'>"
+              + "".join(f"<span><span class='hx-dot' style='background:{_BASE_COL[b]}'></span>{b}</span>" for b in "ACGT")
+              + f"<span><span class='hx-dot' style='background:{_USER_COL}'></span>your input</span></div>")
+    return f"<div class='helixwrap'>{''.join(rows) or '&nbsp;'}{caret}</div>{legend}"
+
+
+def _seq_track(gen):
+    """Per-base colored track of the generated bases (our take on the tokenization demo)."""
+    cells = "".join(f"<span class='seqb {c}'>{c}</span>" for c in gen if c in "ACGT")
+    return f"<div class='seqtrack'>{cells or '&nbsp;'}</div>"
+
+
+def _kmer_strip(gen):
+    """The generated sequence cut into our model's actual non-overlapping 6-mer tokens."""
+    s = "".join(c for c in gen if c in "ACGTN")
+    toks = [s[i:i + 6] for i in range(0, len(s) - len(s) % 6, 6)]
+    chips = "".join(f"<span class='tok kmer'>{t}</span>" for t in toks)
+    head = ("<div class='tkrow'><span class='tklabel'>6-mer tokens</span>"
+            f"<span class='tkstat'>tokens <span class='n'>{len(toks)}</span></span>"
+            f"<span class='tkstat'>bases <span class='n'>{len(s)}</span></span>"
+            "<span class='tkstat'>6 bases / token</span></div>")
+    return head + f"<div class='tokrow'>{chips}</div>"
+
+
+FOOTER = ("Four ~74M DNA/RNA specialists (≈295M total, <b>under Carbon-500M</b>), each distilled "
+          "per-domain from Carbon-500M. A learned router reads every specialist's surprise + hidden "
+          "state and routes to the home specialist — held-out routing accuracy <b>99.8%</b>. Only one "
+          "specialist runs per query (~7× cheaper than the 500M monolith).")
+
+
+# ---- handler ----------------------------------------------------------------------
+@_gpu(duration=120)
+def route_run(seq, n_bases, do_gen, decode="auto"):
+    yield _notice("Routing & generating")
+    seq = (seq or "").strip()
+    if len(seq) < 18:
+        yield _msg("🧬 Enter a DNA sequence", "Paste at least 18 bases (A/C/G/T) — try an example below.")
+        return
+    dc = _moe()
+    doms = dc.domains
+    bpb = {d: None for d in doms}
+    # progressively reveal each specialist's surprise (the chain lighting up)
+    sc, hd = dc._scores_hidden(seq)
+    for d in doms:
+        bpb[d] = sc[d] / 6 / 0.6931
+        yield _wrap("<div class='h'>🔗 Sending the sequence down the chain…</div>" + _cards(bpb))
+    home, _ = dc.route(seq)
+    c = COLOR.get(home, "#9b59b6")
+    head = (f"<div class='h'>🧭 Routed to <span style='color:{c}'>{EMOJI.get(home, home)}</span>"
+            f" — the specialist most at home with your sequence</div>" + _cards(bpb, winner=home))
+    if do_gen:
+        # decoding: default = base-pair (FNS) — marginalize the 6-mer softmax to six 4-way base
+        # distributions and sample each base, the same factorization Carbon uses. "argmax" forces
+        # the deterministic 6-mer best-guess (matches the recovery metric; collapses over long spans).
+        dl = (decode or "").lower()
+        greedy = "argmax" in dl
+        feed_ctx = seq                       # FULL context to the specialist (it frame-aligns + caps)
+        ctx = seq[-48:]                      # short context shown in the helix / text box
+        mode = "argmax · best guess" if greedy else "base-pair · FNS"
+        hxhead = (f"<div class='h'>🧬 {EMOJI.get(home, home)} — building the strand base-by-base "
+                  f"<span style='color:var(--muted)'>({mode})</span></div>")
+        rawhdr = "<div class='h'>raw sequence — select to copy</div>"
+        if greedy:
+            stream = dc.generate_stream(home, length=int(n_bases), prompt=feed_ctx, greedy=True)
+        else:
+            stream = dc.generate_baselevel_stream(home, length=int(n_bases), temperature=1.0,
+                                                  top_p=0.9, prompt=feed_ctx)
+        for gen in stream:
+            yield _wrap(head + hxhead + _helix(ctx, gen, c, live=True)
+                        + _seq_track(gen) + _kmer_strip(gen)
+                        + rawhdr + _gen_box(ctx, gen, live=True))
+        _WARMED["done"] = True
+        gennote = ("<div class='sub'>🧪 <b>Generation is exploratory</b> — these ~74M specialists are "
+                   "trained on a slice of the corpus, so sampled DNA is low-complexity (and splice / "
+                   "bacterial domains are genuinely AT-rich). The <b>routing</b> and per-base likelihood "
+                   "are the result here, not Carbon-level generation.</div>")
+        yield _wrap(head + hxhead + _helix(ctx, gen, c, live=False)
+                    + _seq_track(gen) + _kmer_strip(gen)
+                    + rawhdr + _gen_box(ctx, gen, live=False)
+                    + gennote + f"<div class='sub'>{FOOTER}</div>")
+    else:
+        _WARMED["done"] = True
+        yield _wrap(head + f"<div class='sub'>{FOOTER}</div>")
+
+
+STATS_HTML = _wrap(
+    "<div class='h'>📊 DaisyChain vs Carbon-500M — the fair baseline</div>"
+    "<div class='stats'>"
+    "<div class='stat'><div class='v' style='color:#37b24d'>99.8%</div>"
+    "<div class='l'>routing accuracy<br>(held-out)</div></div>"
+    "<div class='stat'><div class='v' style='color:#7c5cff'>≈295M</div>"
+    "<div class='l'>total params<br>(4 × ~74M) &lt; Carbon-500M</div></div>"
+    "<div class='stat'><div class='v' style='color:#22b8cf'>~7×</div>"
+    "<div class='l'>cheaper per query<br>(one 74M specialist active)</div></div>"
+    "</div>"
+    "<table><tr><th>metric</th><th>DaisyChain</th><th>Carbon-500M</th></tr>"
+    "<tr><td>Likelihood — bits/base (↓ better)</td><td class='n'>1.79</td><td class='n'>1.75</td></tr>"
+    "<tr><td>Seq-recovery, eukaryote (↑ better)</td><td class='n'>31.0%</td><td class='n'>42.2%</td></tr>"
+    "<tr><td>Seq-recovery, bacteria (↑ better)</td><td class='n'>36.5%</td><td class='n'>49.5%</td></tr>"
+    "</table>"
+    "<div class='sub'>Four ~74M specialists (≈295M total, <b>under Carbon-500M</b>); only one runs per "
+    "query, so it's ~7× cheaper per token. Behind the 500M / 1T-token monolith but within striking "
+    "distance — the gap is concentrated in the structured domains (mRNA, bacteria) and keeps closing "
+    "with more per-domain training. Same protocols as Carbon's eval suite (sequence recovery; per-base "
+    "likelihood). Carbon-500M is the right yardstick for a sub-500M modular set, not the 3B flagship.</div>")
+
+
+BANNER = _CSS + """
+<div class='dcx'><div class='dc-banner'><div class='dc-binner'>
+  <div class='dc-ident'>
+    <span class='dc-mark'>🌼</span>
+    <div>
+      <div class='dc-title'>DAISYCHAIN</div>
+      <div class='dc-path'>DAISYCHAINAI / GENOMICS · ROUTED DNA SPECIALISTS</div>
+    </div>
+  </div>
+  <div class='dc-word'>DaisyChain<span class='dot'>.</span></div>
+  <div class='dc-tag'>A modular genomic mind. Four dense ~74M DNA/RNA specialists
+    (&approx;295M total, <b>under Carbon-500M</b>), each distilled per-domain from Carbon-500M.
+    A learned router reads how <i>surprised</i> each specialist is by your sequence
+    (bits/base) plus its hidden state, then hands the work to its home specialist —
+    held-out routing accuracy <b>99.8%</b>. Watch it route in real time.</div>
+  <div class='dc-motif'>
+    <span class='dc-chip'>🧬 Eukaryote</span><span class='dc-tie'>—</span>
+    <span class='dc-chip'>🦠 Prokaryote</span><span class='dc-tie'>—</span>
+    <span class='dc-chip'>📜 mRNA</span><span class='dc-tie'>—</span>
+    <span class='dc-chip'>✂️ mRNA-splice</span>
+  </div>
+</div></div></div>"""
+
+# Light editorial chrome for the Gradio shell so the cream paper extends to the whole page.
+# We pin Gradio's theme CSS variables (for BOTH light and .dark) to the paper palette so the
+# text never renders white-on-cream when a visitor's browser/Space defaults to dark mode.
+_PAGE_CSS = """
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&family=JetBrains+Mono:wght@400;500;700&display=swap');
+.gradio-container, .gradio-container.dark, .dark, body, body.dark{
+  --body-background-fill:#f7f5ee!important;
+  --background-fill-primary:#f7f5ee!important;
+  --background-fill-secondary:#f2efe2!important;
+  --block-background-fill:#fbfaf4!important;
+  --block-label-background-fill:#f2efe2!important;
+  --input-background-fill:#fffdf6!important;
+  --body-text-color:#1f1f1d!important;
+  --body-text-color-subdued:#5a5a55!important;
+  --block-title-text-color:#1f1f1d!important;
+  --block-label-text-color:#5a5a55!important;
+  --block-info-text-color:#5a5a55!important;
+  --border-color-primary:#d6d3c4!important;
+  --neutral-50:#f7f5ee!important;
+  --table-even-background-fill:#fbfaf4!important;
+  --table-odd-background-fill:#f2efe2!important;
+  --table-row-focus:#eef3e9!important;
+  --table-text-color:#1f1f1d!important;
+  background:#f7f5ee!important;
+  color:#1f1f1d!important;
+}
+/* Examples dataset table rows (were rendering black in dark mode) */
+.gradio-container .gr-samples-table, .gradio-container [class*='dataset'] table,
+.gradio-container [class*='dataset'] td, .gradio-container [class*='dataset'] tr,
+.gradio-container [class*='dataset'] tbody{background:#fbfaf4!important;color:#1f1f1d!important}
+.gradio-container [class*='dataset'] tr:nth-child(even) td{background:#f2efe2!important}
+/* Radio / checkbox options (were rendering black in dark mode) */
+.gradio-container [class*='radio'] label, .gradio-container fieldset label,
+.gradio-container [class*='checkbox'] label, .gradio-container .wrap label{
+  background:#fbfaf4!important;color:#1f1f1d!important;border:1px solid #d6d3c4!important}
+.gradio-container [class*='radio'] label *, .gradio-container fieldset label *,
+.gradio-container [class*='checkbox'] label *{color:#1f1f1d!important}
+.gradio-container input[type=radio],.gradio-container input[type=checkbox]{accent-color:#317f3f!important}
+.gradio-container{font-family:"Inter","Helvetica Neue",sans-serif!important;max-width:1080px!important}
+/* force any Gradio-rendered label / markdown / example text to ink, never white */
+.gradio-container label, .gradio-container .prose, .gradio-container p,
+.gradio-container span, .gradio-container td, .gradio-container th,
+.gradio-container .gr-text-input, .gradio-container input, .gradio-container textarea{color:#1f1f1d!important}
+.gradio-container input::placeholder, .gradio-container textarea::placeholder{color:#9c9989!important}
+footer{display:none!important}
+.gr-button-primary, button.primary{background:#317f3f!important;border:1px solid #2a5931!important;color:#f7f5ee!important;
+  font-family:"JetBrains Mono",monospace!important;letter-spacing:.08em!important;text-transform:uppercase!important;font-size:12px!important}
+.gr-button-primary:hover, button.primary:hover{background:#2a5931!important}
+.gr-button-primary *, button.primary *{color:#f7f5ee!important}
+"""
+
+
+def build():
+    theme = gr.themes.Default(primary_hue="green", neutral_hue="stone",
+                              font=[gr.themes.GoogleFont("Inter"), "sans-serif"],
+                              font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "monospace"])
+    # force the light palette so text is never white-on-cream if a visitor defaults to dark mode
+    _force_light = ("() => { const u = new URL(window.location.href);"
+                    " if (u.searchParams.get('__theme') !== 'light') {"
+                    " u.searchParams.set('__theme','light'); window.location.replace(u.href); } }")
+    with gr.Blocks(title="DaisyChain — modular genomic mind", theme=theme, css=_PAGE_CSS,
+                   js=_force_light) as demo:
+        gr.HTML(BANNER)
+        with gr.Row():
+            seq = gr.Textbox(label="DNA SEQUENCE", lines=3, scale=4,
+                             placeholder="ACGT… (eukaryotic, bacterial, mRNA, or splice-site DNA)")
+            n = gr.Slider(60, 300, value=90, step=30, label="GENERATE BASES", scale=1)
+        with gr.Row():
+            gen_ck = gr.Checkbox(value=True, label="stream a continuation from the routed specialist")
+            decode = gr.Radio(["base-pair (FNS)", "greedy (argmax)"], value="base-pair (FNS)",
+                              label="DECODING", scale=1,
+                              info="base-pair (FNS) = Carbon-style: each base sampled from the marginalized per-position distribution (base-pair control, no 6-mer repeat loops); argmax = deterministic best guess (matches recovery, collapses over long spans)")
+            btn = gr.Button("🔗 Route through the DaisyChain", variant="primary")
+        out = gr.HTML(_wrap("<div class='h'>The chain · paste a sequence to light it up</div>"
+                            + _cards({d: None for d in DaisyChain.DESCRIPTIONS})))
+        btn.click(route_run, [seq, n, gen_ck, decode], out)
+        try:
+            ex = json.load(open(os.path.join(HERE, "examples.json")))
+            gr.Examples([[v, 90, True, "base-pair (FNS)"] for v in ex.values()],
+                        inputs=[seq, n, gen_ck, decode],
+                        label="Example sequences (one per domain)")
+        except Exception:
+            pass
+        gr.HTML(STATS_HTML)
+    return demo
+
+
+if __name__ == "__main__":
+    build().launch()