"""Evolving SciPaths workflow graph (Neo4j-backed).

Visual formatting follows Mina Brain's node chrome (filled circle + white
inner stroke + colored ring, 25-char labels, write-pulse rings, #fafbfd
stage, click detail card) but lays nodes out as a strict top-down n-ary
tree: Target → Citing → Theme → Claim → Ingredient → Prior study.
"""

from __future__ import annotations

import json
import re
from pathlib import Path
from typing import Any, Optional

import streamlit as st

try:
    import neo4j_workflow as neo4j_store
except Exception:  # pragma: no cover
    neo4j_store = None  # type: ignore

# Short labels shared by Steps panel + workflow graph captions.
STEP_COPY: dict[int, str] = {
    1: "Load the paper",
    2: "Find where it’s cited",
    3: "Collect citation contexts",
    4: "Classify how it’s used",
    5: "Keep real reuse (uses / extends)",
    6: "Pull the citing passages",
    7: "Group similar reuse themes and summarize target contributions",
    8: "Find enabling contributions and corresponding prior studies",
}

WORK_PANEL_STEPS: list[tuple[int, str]] = [(n, STEP_COPY[n]) for n in range(1, 9)]

CANVAS_LABEL_MAX = 25

# Mina Brain NODE_COLORS mapped onto SciPaths kinds (fill / ring / text).
KIND_META = {
    "target": {
        "fill": "#4F6EF7",
        "ring": "#A5B4FC",
        "text": "#1E3A8A",
        "r": 22,
        "type_label": "Target paper",
        "color": "#4F6EF7",
        "size": 22,
    },
    "citing": {
        "fill": "#60A5FA",
        "ring": "#BFDBFE",
        "text": "#1D4ED8",
        "r": 14,
        "type_label": "Citing paper",
        "color": "#60A5FA",
        "size": 14,
    },
    "cluster": {
        "fill": "#8B5CF6",
        "ring": "#DDD6FE",
        "text": "#5B21B6",
        "r": 16,
        "type_label": "Reuse theme",
        "color": "#8B5CF6",
        "size": 16,
    },
    "claim": {
        "fill": "#FBBF24",
        "ring": "#FDE68A",
        "text": "#B45309",
        "r": 15,
        "type_label": "Target contribution",
        "color": "#FBBF24",
        "size": 15,
    },
    "ingredient": {
        "fill": "#94A3B8",
        "ring": "#E2E8F0",
        "text": "#475569",
        "r": 12,
        "type_label": "Enabling contribution",
        "color": "#94A3B8",
        "size": 12,
    },
    "study": {
        "fill": "#34D399",
        "ring": "#A7F3D0",
        "text": "#047857",
        "r": 13,
        "type_label": "Prior study",
        "color": "#34D399",
        "size": 13,
    },
}

# Category order (top-down tree levels) + palette the canvas shell / rail consume.
_KIND_ORDER = ["target", "citing", "cluster", "claim", "ingredient", "study"]
_KIND_META_JS = {
    k: {"fill": v["fill"], "ring": v["ring"], "text": v["text"], "label": v["type_label"]}
    for k, v in KIND_META.items()
}


def _load_json(path: Path) -> Any | None:
    if not path.exists():
        return None
    try:
        return json.loads(path.read_text(encoding="utf-8"))
    except Exception:
        return None


def _clean(text: str) -> str:
    return re.sub(r"\s+", " ", (text or "").strip())


def canvas_label(text: str, *, limit: int = CANVAS_LABEL_MAX) -> str:
    cleaned = _clean(text)
    if len(cleaned) <= limit:
        return cleaned
    return cleaned[:limit].rstrip() + "…"


def visible_step_from_events(events: list[str]) -> int:
    started = 0
    for raw in events or []:
        text = str(raw)
        m = re.search(r"Step\s+(\d+)\s*(?:/|\s+complete)", text, re.IGNORECASE)
        if m:
            started = max(started, int(m.group(1)))
            continue
        lower = text.lower()
        if "[annotation]" in lower or "annotate" in lower:
            started = max(started, 8)
    return min(8, started)


def pulse_step_from_events(events: list[str]) -> int:
    started: set[int] = set()
    completed: set[int] = set()
    for raw in events or []:
        text = str(raw)
        m_done = re.search(r"Step\s+(\d+)\s+complete", text, re.IGNORECASE)
        if m_done:
            completed.add(int(m_done.group(1)))
            continue
        m_start = re.search(r"Step\s+(\d+)\s*/", text)
        if m_start:
            started.add(int(m_start.group(1)))
            continue
        lower = text.lower()
        if "[annotation]" in lower or "annotate" in lower:
            if "complete" in lower or "skipped" in lower:
                completed.add(8)
            else:
                started.add(8)
    active = [n for n in sorted(started) if n not in completed]
    return active[-1] if active else 0


def _paper_title_from_dir(paper_dir: Optional[Path]) -> str:
    if not paper_dir:
        return "Target paper"
    data = _load_json(paper_dir / "paper_metadata.json")
    record = None
    if isinstance(data, list) and data and isinstance(data[0], dict):
        record = data[0]
    elif isinstance(data, dict):
        record = data
    return _clean(str((record or {}).get("title") or "")) or "Target paper"


def _paper_abstract_from_dir(paper_dir: Optional[Path]) -> str:
    if not paper_dir:
        return ""
    data = _load_json(paper_dir / "paper_metadata.json")
    record = None
    if isinstance(data, list) and data and isinstance(data[0], dict):
        record = data[0]
    elif isinstance(data, dict):
        record = data
    abstract = _clean(str((record or {}).get("abstract") or ""))
    abstract = re.sub(r"^(abstract)\s*[:.]?\s*", "", abstract, flags=re.IGNORECASE)
    abstract = re.sub(r"(?<=[.!?\)\]\"'”’])\d+$", "", abstract)
    return abstract


def _node(
    *,
    nid: str,
    kind: str,
    short: str,
    title: str,
    detail: str,
    step_added: int,
    pulse: bool,
) -> dict[str, Any]:
    meta = KIND_META.get(kind, KIND_META["citing"])
    return {
        "id": nid,
        "kind": kind,
        "label": canvas_label(short),
        "title": _clean(title) or short,
        "detail": _clean(detail),
        "step_added": step_added,
        "pulse": pulse,
        "color": meta["fill"],
        "fill": meta["fill"],
        "ring": meta["ring"],
        "text": meta["text"],
        "r": meta["r"],
        "size": meta["r"],
        "type_label": meta["type_label"],
    }


def _collect_citing(paper_dir: Optional[Path], limit: int = 6) -> list[dict[str, str]]:
    if not paper_dir:
        return []
    out: list[dict[str, str]] = []
    seen: set[str] = set()
    contrib = _load_json(paper_dir / "usage_contributions.json") or {}
    items = contrib.get("contributions") if isinstance(contrib, dict) else None
    if isinstance(items, list):
        for item in items:
            if not isinstance(item, dict):
                continue
            cid = str(item.get("citing_paper_id") or "").strip()
            title = _clean(str(item.get("citing_title") or "Citing paper"))
            if not cid or cid in seen:
                continue
            seen.add(cid)
            out.append(
                {
                    "id": f"citing:{cid}",
                    "title": title,
                    "detail": _clean(
                        str(item.get("paper_claim") or item.get("claim") or item.get("evidence_span") or "")
                    ),
                    "label_tag": item.get("label") or "",
                }
            )
            if len(out) >= limit:
                return out
    return out


def _collect_clusters(paper_dir: Optional[Path], limit: int = 4) -> list[dict[str, str]]:
    if not paper_dir:
        return []
    discovery = _load_json(paper_dir / "usage_discovery_from_contributions.json") or {}
    clusters = discovery.get("clusters") if isinstance(discovery, dict) else None
    out: list[dict[str, str]] = []
    if not isinstance(clusters, list):
        return out
    for item in clusters[:limit]:
        if not isinstance(item, dict):
            continue
        cid = str(item.get("cluster_id") or f"C{len(out) + 1}")
        title = _clean(
            str(item.get("representative_claim") or item.get("cluster_title") or f"Theme {cid}")
        )
        out.append(
            {
                "id": f"cluster:{cid}",
                "cluster_id": cid,
                "title": title,
                "detail": _clean(str(item.get("merge_rationale") or f"{item.get('count', '')} contribution instances")),
            }
        )
    return out


def _collect_annotation(payload: Optional[dict]) -> tuple[list[dict], list[dict], list[dict]]:
    claims: list[dict] = []
    ingredients: list[dict] = []
    studies: list[dict] = []
    if not isinstance(payload, dict):
        return claims, ingredients, studies
    for claim in (payload.get("claims") or [])[:4]:
        if not isinstance(claim, dict):
            continue
        claim_id = str(claim.get("claim_id") or f"C{len(claims) + 1}")
        title = _clean(str(claim.get("rewritten_claim") or claim.get("text") or claim_id))
        claims.append(
            {
                "id": f"claim:{claim_id}",
                "claim_id": claim_id,
                "cluster_id": str(claim.get("cluster_id") or ""),
                "title": title,
                "detail": _clean(str(claim.get("decision") or "")),
            }
        )
        for ing in (claim.get("ingredients") or [])[:3]:
            if not isinstance(ing, dict):
                continue
            iid = str(ing.get("ingredient_id") or f"{claim_id}.I{len(ingredients) + 1}")
            ann = ing.get("canonical_annotation") if isinstance(ing.get("canonical_annotation"), dict) else {}
            ingredients.append(
                {
                    "id": f"ing:{iid}",
                    "ingredient_id": iid,
                    "claim_id": claim_id,
                    "title": _clean(str(ing.get("ingredient") or iid)),
                    "detail": _clean(
                        str((ann or {}).get("contribution") or (ann or {}).get("rationale") or "")
                    ),
                    "role": _clean(str((ann or {}).get("role") or "")),
                }
            )
            g = ing.get("canonical_grounding") if isinstance(ing.get("canonical_grounding"), dict) else None
            if g:
                sid = str(g.get("paper_id") or g.get("ref_id") or g.get("ref_title") or iid)
                studies.append(
                    {
                        "id": f"study:{sid}",
                        "ingredient_id": iid,
                        "title": _clean(str(g.get("ref_title") or g.get("ref_id") or "Prior study")),
                        "detail": _clean(str(g.get("ref_authors") or g.get("ref_year") or "")),
                    }
                )
    seen: set[str] = set()
    uniq: list[dict] = []
    for s in studies:
        if s["id"] in seen:
            continue
        seen.add(s["id"])
        uniq.append(s)
    return claims, ingredients, uniq[:6]


def build_graph_model(
    *,
    paper_dir: Optional[Path],
    payload: Optional[dict],
    visible_step: int,
    pulse_step: int = 0,
    caption: str = "",
) -> dict[str, Any]:
    step = max(0, min(8, int(visible_step or 0)))
    nodes: list[dict[str, Any]] = []
    edges: list[dict[str, Any]] = []

    if step >= 1:
        title = _paper_title_from_dir(paper_dir)
        abstract = _paper_abstract_from_dir(paper_dir)
        nodes.append(
            _node(
                nid="target",
                kind="target",
                short="Target",
                title=title,
                detail=abstract[:500],
                step_added=1,
                pulse=pulse_step == 1,
            )
        )

    citing = _collect_citing(paper_dir) if step >= 2 else []
    if step >= 2:
        rows = citing or [
            {"id": f"citing:placeholder:{i}", "title": f"Citing paper {i+1}", "detail": "Waiting for citation artifacts…", "label_tag": ""}
            for i in range(3)
        ]
        for i, item in enumerate(rows):
            nodes.append(
                _node(
                    nid=item["id"],
                    kind="citing",
                    short=f"Citing {i + 1}",
                    title=item["title"],
                    detail=item.get("detail") or item.get("label_tag") or "",
                    step_added=2,
                    pulse=pulse_step in {2, 3, 4, 5, 6},
                )
            )
            edges.append(
                {
                    "id": f"e-target-{item['id']}",
                    "source": "target",
                    "target": item["id"],
                    "kind": "cite",
                    "muted": step < 5,
                    "pulse": pulse_step in {2, 3, 4, 5},
                    "step_added": 2,
                }
            )

    # Step 7: reuse themes + target contributions arrive together.
    clusters = _collect_clusters(paper_dir) if step >= 7 else []
    claims, ingredients, studies = (
        _collect_annotation(payload) if step >= 7 else ([], [], [])
    )
    if step >= 7 and clusters:
        for item in clusters:
            cid = item.get("cluster_id") or "?"
            nodes.append(
                _node(
                    nid=item["id"],
                    kind="cluster",
                    short=f"Theme {cid}",
                    title=item["title"],
                    detail=item.get("detail") or "",
                    step_added=7,
                    pulse=pulse_step == 7,
                )
            )
            edges.append(
                {
                    "id": f"e-cluster-{item['id']}",
                    "source": "target",
                    "target": item["id"],
                    "kind": "theme",
                    "pulse": pulse_step == 7,
                    "muted": False,
                    "step_added": 7,
                }
            )

    if step >= 7:
        for item in claims:
            cid = item.get("claim_id") or "?"
            nodes.append(
                _node(
                    nid=item["id"],
                    kind="claim",
                    short=f"Claim {cid}",
                    title=item["title"],
                    detail=item.get("detail") or "",
                    step_added=7,
                    pulse=pulse_step == 7,
                )
            )
            src = f"cluster:{item.get('cluster_id')}" if item.get("cluster_id") else "target"
            if not any(n["id"] == src for n in nodes):
                src = "target"
            edges.append(
                {
                    "id": f"e-claim-{item['id']}",
                    "source": src,
                    "target": item["id"],
                    "kind": "derive",
                    "pulse": pulse_step == 7,
                    "muted": False,
                    "step_added": 7,
                }
            )

    # Step 8: enabling contributions + prior studies.
    if step >= 8:
        for i, item in enumerate(ingredients):
            nodes.append(
                _node(
                    nid=item["id"],
                    kind="ingredient",
                    short=f"Enable {i + 1}",
                    title=item["title"],
                    detail=" · ".join(x for x in [item.get("role") or "", item.get("detail") or ""] if x),
                    step_added=8,
                    pulse=pulse_step == 8,
                )
            )
            src = f"claim:{item.get('claim_id')}"
            if any(n["id"] == src for n in nodes):
                edges.append(
                    {
                        "id": f"e-ing-{item['id']}",
                        "source": src,
                        "target": item["id"],
                        "kind": "enable",
                        "pulse": pulse_step == 8,
                        "muted": False,
                        "step_added": 8,
                    }
                )
        for i, item in enumerate(studies):
            nodes.append(
                _node(
                    nid=item["id"],
                    kind="study",
                    short=f"Prior {i + 1}",
                    title=item["title"],
                    detail=item.get("detail") or "",
                    step_added=8,
                    pulse=pulse_step == 8,
                )
            )
            src = f"ing:{item.get('ingredient_id')}"
            if any(n["id"] == src for n in nodes):
                edges.append(
                    {
                        "id": f"e-study-{item['id']}",
                        "source": src,
                        "target": item["id"],
                        "kind": "ground",
                        "pulse": pulse_step == 8,
                        "muted": False,
                        "step_added": 8,
                    }
                )

    if step <= 0:
        phase_caption = "Waiting to run"
    elif pulse_step:
        phase_caption = STEP_COPY.get(pulse_step, "")
    elif step >= 8:
        phase_caption = "Workflow complete"
    else:
        phase_caption = caption or STEP_COPY.get(step, "")

    return {
        "step": step,
        "pulse_step": pulse_step,
        "caption": phase_caption,
        "nodes": nodes,
        "edges": edges,
        "backend": "memory",
    }


def _nodes_edges_payload(
    nodes_in: list[dict[str, Any]], edges_in: list[dict[str, Any]]
) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
    """Serialize nodes/edges for the canvas shell."""
    nodes_js: list[dict[str, Any]] = []
    for n in nodes_in:
        kind = str(n.get("kind") or "citing")
        meta = KIND_META.get(kind, KIND_META["citing"])
        nodes_js.append(
            {
                "id": n["id"],
                "label": n.get("label") or "",
                "kind": kind,
                "fill": n.get("fill") or meta["fill"],
                "ring": n.get("ring") or meta["ring"],
                "text": n.get("text") or meta["text"],
                "r": float(n.get("r") or meta["r"]),
                "pulse": bool(n.get("pulse")),
                "fullTitle": n.get("title") or "",
                "detail": n.get("detail") or "",
                "typeLabel": n.get("type_label") or meta["type_label"],
            }
        )
    edges_js: list[dict[str, Any]] = []
    for e in edges_in:
        edges_js.append(
            {
                "id": e.get("id"),
                "source": e.get("source"),
                "target": e.get("target"),
                "pulse": bool(e.get("pulse")),
                "muted": bool(e.get("muted")),
            }
        )
    return nodes_js, edges_js


def _graph_payload(model: dict[str, Any]) -> dict[str, Any]:
    """Serialize a graph model into the payload the canvas shell consumes."""
    nodes_js, edges_js = _nodes_edges_payload(
        list(model.get("nodes") or []), list(model.get("edges") or [])
    )
    out: dict[str, Any] = {
        "nodes": nodes_js,
        "edges": edges_js,
        "kindMeta": _KIND_META_JS,
        "kindOrder": _KIND_ORDER,
        "frameKey": str(model.get("frame_key") or ""),
    }
    frame = model.get("frame")
    if isinstance(frame, dict) and frame.get("nodes"):
        f_nodes, f_edges = _nodes_edges_payload(
            list(frame.get("nodes") or []), list(frame.get("edges") or [])
        )
        out["frame"] = {"nodes": f_nodes, "edges": f_edges}
    return out


def courier_html(payload: dict[str, Any]) -> str:
    """Tiny invisible frame that posts a graph model into the persistent shell.

    Runs in its own Streamlit component iframe, reaches the parent document, and
    postMessages the model to the graph iframe — so the graph updates in place
    instead of the whole component being re-emitted (which reloads it).
    """
    model_str = json.dumps(payload)
    embed = json.dumps(model_str).replace("</", "<\\/")
    return (
        "<!DOCTYPE html><html><head><meta charset=\"utf-8\" /></head><body>"
        "<script>"
        "(function(){"
        "var model=JSON.parse(" + embed + ");"
        "function post(){try{var f=window.parent.document.querySelectorAll('iframe');"
        "for(var i=0;i<f.length;i++){try{f[i].contentWindow.postMessage({type:'scipaths-graph',model:model},'*');}catch(e){}}}catch(e){}}"
        "post();var n=0;var t=setInterval(function(){n++;post();if(n>10)clearInterval(t);},160);"
        "})();"
        "</script></body></html>"
    )


# Bump when canvas JS changes so Streamlit remounts the component iframe.
_GRAPH_SHELL_VERSION = "tree-v4"


def graph_shell_html(height: int = 520) -> str:
    """Model-free canvas shell. Data arrives via postMessage (incremental).

    HTML includes ``_GRAPH_SHELL_VERSION`` so layout/code updates remount the
    iframe. The graph is populated / grown by ``courier_html`` messages.
    """
    payload = json.dumps(
        {"nodes": [], "edges": [], "kindMeta": _KIND_META_JS, "kindOrder": _KIND_ORDER}
    )
    row_h = max(300, height - 30)
    ver = _GRAPH_SHELL_VERSION
    return f"""<!DOCTYPE html>
<html data-scipaths-shell="{ver}">
<head>
  <meta charset="utf-8" />
  <link rel="preconnect" href="https://fonts.googleapis.com" />
  <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet" />
  <style>
    html, body {{ margin:0; padding:0; background:transparent; font-family: Inter, system-ui, sans-serif; color:#13201d; }}
    * {{ box-sizing:border-box; }}
    .wrap {{ border-top:1px solid rgba(19,32,29,0.12); padding-top:0.5rem; }}
    .kicker {{ font-size:0.68rem; font-weight:700; letter-spacing:0.12em; text-transform:uppercase; color:#6a7a74; margin-bottom:0.4rem; }}
    .row {{ display:flex; gap:0.6rem; align-items:stretch; height:{row_h}px; }}
    /* ── Control rail ── */
    .rail {{
      width:210px; flex:0 0 210px; display:flex; flex-direction:column; overflow:hidden;
      border:1px solid rgba(15,23,42,0.10); border-radius:11px; background:#ffffff;
    }}
    .rail-head {{ padding:0.55rem 0.75rem; border-bottom:1px solid rgba(15,23,42,0.08);
      font-size:0.66rem; font-weight:700; letter-spacing:0.09em; text-transform:uppercase; color:#6a7a74; }}
    .rail-body {{ flex:1; overflow-y:auto; padding:0.6rem 0.65rem; }}
    .rail-search {{ position:relative; margin-bottom:0.7rem; }}
    .rail-search input {{
      width:100%; height:30px; padding:0 1.6rem 0 0.6rem; font-size:0.76rem; font-family:inherit;
      border:1px solid rgba(15,23,42,0.16); border-radius:7px; outline:none; color:#13201d;
    }}
    .rail-search input:focus {{ border-color:#4F6EF7; box-shadow:0 0 0 3px rgba(79,110,247,0.16); }}
    .rail-search .clr {{ position:absolute; right:6px; top:50%; transform:translateY(-50%);
      border:0; background:transparent; color:#94a3b8; cursor:pointer; font-size:0.9rem; line-height:1; }}
    .rail-actions {{ display:grid; grid-template-columns:1fr 1fr; gap:0.35rem; margin-bottom:0.75rem; }}
    .rail-actions button {{
      font-size:0.66rem; font-weight:600; font-family:inherit; padding:0.34rem 0.3rem; cursor:pointer;
      border:1px solid rgba(15,23,42,0.16); border-radius:7px; background:#fff; color:#13201d;
    }}
    .rail-actions button:hover {{ background:#f4f6fb; }}
    .rail-actions button.on {{ border-color:#4F6EF7; background:#eef1fe; color:#1E3A8A; }}
    .sec-head {{ display:flex; align-items:center; justify-content:space-between; margin:0 0 0.35rem 0.15rem; }}
    .sec-head .lbl {{ font-size:0.63rem; font-weight:700; letter-spacing:0.07em; text-transform:uppercase; color:#94a3b8; }}
    .sec-head .acts {{ display:flex; gap:0.45rem; }}
    .sec-head .acts button {{ border:0; background:transparent; font-size:0.62rem; font-weight:600; color:#4F6EF7; cursor:pointer; padding:0; }}
    .cat {{
      width:100%; display:flex; align-items:center; gap:0.5rem; padding:0.28rem 0.35rem; cursor:pointer;
      border:0; background:transparent; border-radius:6px; text-align:left; font-family:inherit;
    }}
    .cat:hover {{ background:#f4f6fb; }}
    .cat .box {{ width:13px; height:13px; flex:0 0 13px; border-radius:3px; border:1.5px solid rgba(15,23,42,0.28);
      display:flex; align-items:center; justify-content:center; color:#fff; font-size:9px; }}
    .cat .box.on {{ background:#4F6EF7; border-color:#4F6EF7; }}
    .cat .dot {{ width:12px; height:12px; flex:0 0 12px; border-radius:50%; }}
    .cat .name {{ flex:1; font-size:0.74rem; color:#334155; overflow:hidden; text-overflow:ellipsis; white-space:nowrap; }}
    .cat .cnt {{ font-size:0.68rem; color:#94a3b8; font-variant-numeric:tabular-nums; }}
    .cat.off .name {{ color:#b6c0cc; text-decoration:line-through; }}
    .cat.off .dot {{ opacity:0.35; }}
    /* ── Stage ── */
    .stage {{
      position:relative; flex:1; min-width:0; border-radius:11px; overflow:hidden;
      background:#fafbfd; border:1px solid rgba(15,23,42,0.08);
    }}
    canvas {{ display:block; width:100%; height:100%; cursor:grab; }}
    canvas.dragging {{ cursor:grabbing; }}
    .card {{
      position:absolute; left:12px; top:12px; width:250px; max-height:calc(100% - 24px);
      overflow:auto; background:rgba(255,255,255,0.97); border:1px solid rgba(15,23,42,0.10);
      border-radius:10px; padding:0.7rem 0.75rem; box-shadow:0 10px 30px rgba(15,23,42,0.10);
      display:none; z-index:2;
    }}
    .card.open {{ display:block; }}
    .card-type {{ font-size:0.66rem; font-weight:700; letter-spacing:0.08em; text-transform:uppercase; color:#4F6EF7; margin-bottom:0.25rem; }}
    .card-title {{ font-size:0.9rem; font-weight:700; line-height:1.3; margin:0 0 0.4rem 0; color:#13201d; }}
    .card-detail {{ font-size:0.78rem; line-height:1.45; color:#3d4f4a; margin:0; white-space:pre-wrap; }}
    .card-close {{ position:absolute; right:8px; top:6px; border:0; background:transparent; color:#6a7a74; font-size:1rem; cursor:pointer; }}
    .hint {{ position:absolute; left:10px; bottom:8px; font-size:0.66rem; color:#94a3b8; pointer-events:none;
      background:rgba(255,255,255,0.9); padding:0.2rem 0.45rem; border-radius:6px; }}
  </style>
</head>
<body>
  <div class="wrap">
    <div class="kicker">Workflow tree</div>
    <div class="row">
      <aside class="rail">
        <div class="rail-head">Graph controls</div>
        <div class="rail-body">
          <div class="rail-search">
            <input id="q" type="text" placeholder="Highlight nodes" autocomplete="off" />
            <button class="clr" id="qClr" title="Clear" style="display:none">×</button>
          </div>
          <div class="rail-actions">
            <button id="fitBtn">Fit view</button>
            <button id="resetBtn">Reset</button>
          </div>
          <div class="sec-head">
            <span class="lbl">Node categories</span>
            <span class="acts">
              <button id="allBtn">All</button>
              <button id="noneBtn">None</button>
            </span>
          </div>
          <div id="cats"></div>
        </div>
      </aside>
      <div class="stage" id="stage">
        <canvas id="cv"></canvas>
        <div id="card" class="card">
          <button class="card-close" id="closeBtn" aria-label="Close">×</button>
          <div class="card-type" id="cardType"></div>
          <div class="card-title" id="cardTitle"></div>
          <p class="card-detail" id="cardDetail"></p>
        </div>
        <div class="hint">Top-down tree · scroll to zoom · drag canvas to pan · click a node</div>
      </div>
    </div>
  </div>
  <script>
    const payload = {payload};
    const KM = payload.kindMeta || {{}};
    const stage = document.getElementById('stage');
    const canvas = document.getElementById('cv');
    const ctx = canvas.getContext('2d');
    const card = document.getElementById('card');
    const cardType = document.getElementById('cardType');
    const cardTitle = document.getElementById('cardTitle');
    const cardDetail = document.getElementById('cardDetail');
    document.getElementById('closeBtn').onclick = () => {{ selected = null; card.classList.remove('open'); }};

    let W = 0, H = 0, dpr = 1;
    function resize() {{
      dpr = window.devicePixelRatio || 1;
      W = stage.clientWidth; H = stage.clientHeight;
      canvas.width = Math.floor(W * dpr);
      canvas.height = Math.floor(H * dpr);
      canvas.style.width = W + 'px';
      canvas.style.height = H + 'px';
    }}
    resize();
    window.addEventListener('resize', () => {{ resize(); }});

    // Semantic depth bands (visual tree levels — not edge hop count).
    const KIND_LEVEL = {{ target: 0, citing: 1, cluster: 2, claim: 3, ingredient: 4, study: 5 }};
    const KIND_SIBLING_ORDER = {{ citing: 0, cluster: 1, claim: 2, ingredient: 3, study: 4, target: -1 }};
    const ROW_GAP = 108;
    const TOP_Y = 52;
    const MIN_GAP = 78;
    const levelOf = (n) => (KIND_LEVEL[n.kind] != null ? KIND_LEVEL[n.kind] : 1);

    // Position memory for spawn animation only (tree slots are recomputed).
    const memKey = 'scipaths-tree-pos-v1';
    let saved = {{}};
    try {{ saved = JSON.parse(sessionStorage.getItem(memKey) || '{{}}'); }} catch (e) {{ saved = {{}}; }}

    // Mutable graph state — grown in place by incremental postMessage updates.
    let nodes = [];
    const byId = new Map();
    let edges = [];
    let target = null;
    let fitPending = false;
    // Camera locked to the final tree frame so zoom stays fixed while the graph grows.
    let cameraLocked = false;
    let lockedFrameKey = '';

    // ── Filter / search state ──
    const hiddenKinds = new Set();
    let query = '';
    const isVisible = (n) => !hiddenKinds.has(n.kind);

    // ── Write pulse — Mina: staggered one-shot expanding rings + travelling dashes.
    const PULSE_RING_MS = 1200;
    const PULSE_RING_OFFSETS = [0, 0.35];
    const PULSE_LIFETIME_MS = PULSE_RING_MS * (1 + Math.max(...PULSE_RING_OFFSETS));
    const PULSE_DASH = [7, 5];
    let pulseNodeIds = new Set();
    let pulseEdgeIds = new Set();
    let pulseStart = 0;

    // ── View transform (zoom + pan) ──
    let zoom = 0.95;
    let panX = (W / 2) * (1 - zoom);
    let panY = 18;
    const toWorld = (px, py) => ({{ x: (px - panX) / zoom, y: (py - panY) / zoom }});

    let dragNode = null, panning = false, moved = false;
    let last = {{ x: 0, y: 0 }};
    let selected = null;

    function persist() {{
      const out = {{}};
      nodes.forEach(n => {{ out[n.id] = {{ x: n.x, y: n.y }}; }});
      try {{ sessionStorage.setItem(memKey, JSON.stringify(out)); }} catch (e) {{}}
    }}

    function parentNode(n, edgeList) {{
      const list = edgeList || edges;
      let best = null, bestLvl = -Infinity;
      for (const e of list) {{
        if (e.target !== n.id) continue;
        const p = byId.get(e.source);
        if (!p) continue;
        const pl = levelOf(p);
        if (pl < levelOf(n) && pl >= bestLvl) {{ best = p; bestLvl = pl; }}
      }}
      if (!best && n.id !== 'target') best = byId.get('target') || null;
      return best;
    }}

    function layoutTree() {{
      if (!nodes.length) return;
      // Always assign depth; pack only visible nodes so filters reflow cleanly.
      nodes.forEach(n => {{ n.ty = TOP_Y + levelOf(n) * ROW_GAP; }});
      const vis = nodes.filter(isVisible);
      if (!vis.length) return;
      const children = new Map();
      const visibleParent = (n) => {{
        let p = parentNode(n);
        while (p && !isVisible(p)) p = parentNode(p);
        return p;
      }};
      vis.forEach(n => {{
        const p = visibleParent(n);
        n._parentId = p ? p.id : null;
        if (!p) return;
        if (!children.has(p.id)) children.set(p.id, []);
        children.get(p.id).push(n);
      }});
      for (const kids of children.values()) {{
        kids.sort((a, b) => {{
          const oa = KIND_SIBLING_ORDER[a.kind] != null ? KIND_SIBLING_ORDER[a.kind] : 9;
          const ob = KIND_SIBLING_ORDER[b.kind] != null ? KIND_SIBLING_ORDER[b.kind] : 9;
          if (oa !== ob) return oa - ob;
          return String(a.id).localeCompare(String(b.id));
        }});
      }}
      const leafGap = (n) => Math.max(MIN_GAP, (n.r || 14) * 2 + 36);
      const subtreeWidth = (n) => {{
        const kids = children.get(n.id) || [];
        if (!kids.length) return leafGap(n);
        let w = 0;
        kids.forEach(k => {{ w += subtreeWidth(k); }});
        return Math.max(leafGap(n), w);
      }};
      const place = (n, centerX) => {{
        n.tx = centerX;
        n.ty = TOP_Y + levelOf(n) * ROW_GAP;
        const kids = children.get(n.id) || [];
        if (!kids.length) return;
        const widths = kids.map(subtreeWidth);
        const total = widths.reduce((s, w) => s + w, 0);
        let x = centerX - total / 2;
        kids.forEach((k, i) => {{
          const w = widths[i];
          place(k, x + w / 2);
          x += w;
        }});
      }};
      const roots = vis.filter(n => !n._parentId);
      const orderedRoots = roots.length ? roots : [vis[0]];
      orderedRoots.sort((a, b) => String(a.id).localeCompare(String(b.id)));
      const widths = orderedRoots.map(subtreeWidth);
      const total = widths.reduce((s, w) => s + w, 0);
      let x = -total / 2;
      orderedRoots.forEach((r, i) => {{
        const w = widths[i];
        place(r, x + w / 2);
        x += w;
      }});
    }}

    function nodeAt(px, py) {{
      const w = toWorld(px, py);
      for (let i = nodes.length - 1; i >= 0; i--) {{
        const n = nodes[i];
        if (!isVisible(n)) continue;
        const dx = n.x - w.x, dy = n.y - w.y;
        if (dx * dx + dy * dy <= (n.r + 5) * (n.r + 5)) return n;
      }}
      return null;
    }}
    const getPos = (ev) => {{ const r = canvas.getBoundingClientRect(); return {{ x: ev.clientX - r.left, y: ev.clientY - r.top }}; }};

    canvas.addEventListener('mousedown', (ev) => {{
      const p = getPos(ev); const n = nodeAt(p.x, p.y); moved = false;
      // Nodes snap back to tree slots — drag is for temporary peek only; prefer pan.
      if (n && ev.shiftKey) {{ dragNode = n; }} else {{ panning = true; }}
      last = p; canvas.classList.add('dragging');
    }});
    canvas.addEventListener('mousemove', (ev) => {{
      const p = getPos(ev);
      if (dragNode) {{ const w = toWorld(p.x, p.y); dragNode.x = w.x; dragNode.y = w.y; moved = true; }}
      else if (panning) {{ panX += p.x - last.x; panY += p.y - last.y; moved = true; }}
      else {{ canvas.style.cursor = nodeAt(p.x, p.y) ? 'pointer' : 'grab'; }}
      last = p;
    }});
    window.addEventListener('mouseup', (ev) => {{
      if (ev.target === canvas && !moved) {{
        const p = getPos(ev); const n = nodeAt(p.x, p.y);
        if (n) {{
          selected = n;
          cardType.textContent = n.typeLabel || n.kind || 'Node';
          cardTitle.textContent = n.fullTitle || n.label || '';
          cardDetail.textContent = n.detail || 'No additional detail for this node.';
          card.classList.add('open');
        }} else {{ selected = null; card.classList.remove('open'); }}
      }}
      dragNode = null; panning = false; canvas.classList.remove('dragging');
    }});
    canvas.addEventListener('wheel', (ev) => {{
      ev.preventDefault();
      const p = getPos(ev); const before = toWorld(p.x, p.y);
      const factor = ev.deltaY < 0 ? 1.1 : 0.9;
      zoom = Math.max(0.3, Math.min(2.5, zoom * factor));
      panX = p.x - before.x * zoom; panY = p.y - before.y * zoom;
    }}, {{ passive: false }});

    // ── Top-down tree layout: recompute slots, ease nodes into place. ──
    function tick() {{
      layoutTree();
      nodes.forEach(n => {{
        if (n === dragNode) return;
        if (n.tx == null || n.ty == null) return;
        n.x += (n.tx - n.x) * 0.22;
        n.y += (n.ty - n.y) * 0.28;
        if (Math.abs(n.tx - n.x) < 0.15) n.x = n.tx;
        if (Math.abs(n.ty - n.y) < 0.15) n.y = n.ty;
      }});
      if (fitPending && !cameraLocked) {{
        const settled = nodes.every(n => n.tx == null || (Math.abs(n.tx - n.x) < 1.5 && Math.abs(n.ty - n.y) < 1.5));
        if (settled || nodes.length <= 1) {{ fitView({{ topBias: true }}); fitPending = false; }}
      }} else if (fitPending && cameraLocked) {{
        fitPending = false;
      }}
      for (const n of nodes) saved[n.id] = {{ x: n.x, y: n.y }};
    }}

    function frameIsReady(frame) {{
      if (!frame || !frame.nodes || !frame.nodes.length) return false;
      // Prefer locking once the final annotation trunk exists; else a wide citing row.
      return frame.nodes.some(n => n.kind === 'cluster' || n.kind === 'claim' || n.kind === 'study' || n.kind === 'ingredient')
        || frame.nodes.filter(n => n.kind === 'citing').length >= 3;
    }}

    function lockCameraFromFrame(model, force) {{
      const frame = model && model.frame;
      const key = (model && model.frameKey) || '';
      if (!frameIsReady(frame)) return;
      if (!force && cameraLocked && key && key === lockedFrameKey) return;
      const prevNodes = nodes.slice();
      const prevEdges = edges.slice();
      const prevHidden = new Set(hiddenKinds);
      nodes.length = 0;
      byId.clear();
      (frame.nodes || []).forEach(n => {{
        const nd = Object.assign({{}}, n, {{ x: 0, y: TOP_Y, tx: 0, ty: TOP_Y }});
        nodes.push(nd);
        byId.set(nd.id, nd);
      }});
      edges = (frame.edges || []).filter(e => byId.get(e.source) && byId.get(e.target));
      hiddenKinds.clear();
      layoutTree();
      nodes.forEach(n => {{ if (n.tx != null) {{ n.x = n.tx; n.y = n.ty; }} }});
      fitView({{ topBias: true }});
      cameraLocked = true;
      lockedFrameKey = key;
      fitPending = false;
      // Restore the live (possibly partial) graph; camera stays put.
      nodes.length = 0;
      byId.clear();
      prevNodes.forEach(n => {{ nodes.push(n); byId.set(n.id, n); }});
      edges = prevEdges;
      hiddenKinds.clear();
      prevHidden.forEach(k => hiddenKinds.add(k));
    }}

    function draw() {{
      tick();
      ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
      ctx.clearRect(0, 0, W, H);
      ctx.fillStyle = '#fafbfd';
      ctx.fillRect(0, 0, W, H);
      ctx.translate(panX, panY);
      ctx.scale(zoom, zoom);

      const q = query.trim().toLowerCase();
      const pulseAge = performance.now() - pulseStart;
      const pulseAlive = pulseAge < PULSE_LIFETIME_MS;
      const pulseFade = pulseAlive ? 1 - pulseAge / PULSE_LIFETIME_MS : 0;

      // edges
      edges.forEach(e => {{
        const a = byId.get(e.source), b = byId.get(e.target);
        if (!a || !b || !isVisible(a) || !isVisible(b)) return;
        const pulsing = pulseAlive && pulseEdgeIds.has(e.id);
        ctx.beginPath();
        ctx.moveTo(a.x, a.y); ctx.lineTo(b.x, b.y);
        ctx.strokeStyle = e.muted ? 'rgba(148,163,184,0.30)' : 'rgba(100,116,139,0.42)';
        ctx.lineWidth = (e.muted ? 1 : 1.4) + (pulsing ? 1.2 * pulseFade : 0);
        if (pulsing) {{
          ctx.setLineDash(PULSE_DASH);
          ctx.lineDashOffset = -((pulseAge / 26) % (PULSE_DASH[0] + PULSE_DASH[1]));
        }} else {{
          ctx.setLineDash(e.muted ? [3, 3] : []);
          ctx.lineDashOffset = 0;
        }}
        ctx.stroke();
        ctx.setLineDash([]); ctx.lineDashOffset = 0;
      }});

      // nodes — exact Mina draw order
      nodes.forEach(n => {{
        if (!isVisible(n)) return;
        const x = n.x, y = n.y, r = n.r;
        const isSelected = selected && selected.id === n.id;
        const dimmed = q.length > 0 && !String(n.label || '').toLowerCase().includes(q);
        ctx.globalAlpha = dimmed ? 0.15 : 1;

        if (pulseAlive && pulseNodeIds.has(n.id)) {{
          for (const offset of PULSE_RING_OFFSETS) {{
            const p = pulseAge / PULSE_RING_MS - offset;
            if (p <= 0 || p >= 1) continue;
            ctx.beginPath();
            ctx.arc(x, y, r + 3 + p * 26, 0, Math.PI * 2);
            ctx.strokeStyle = n.fill;
            ctx.globalAlpha = (1 - p) * 0.55;
            ctx.lineWidth = 2.5 * (1 - p) + 0.5;
            ctx.stroke();
          }}
          ctx.globalAlpha = dimmed ? 0.15 : 1;
        }}

        if (isSelected) {{
          ctx.beginPath();
          ctx.arc(x, y, r + 7, 0, Math.PI * 2);
          ctx.fillStyle = n.fill + '22'; ctx.fill();
          ctx.beginPath();
          ctx.arc(x, y, r + 5, 0, Math.PI * 2);
          ctx.strokeStyle = n.ring; ctx.lineWidth = 1.5; ctx.stroke();
        }}

        ctx.beginPath();
        ctx.arc(x, y, r, 0, Math.PI * 2);
        ctx.fillStyle = n.fill; ctx.fill();
        ctx.lineWidth = 2; ctx.strokeStyle = '#fff'; ctx.stroke();
        ctx.beginPath();
        ctx.arc(x, y, r + 1.5, 0, Math.PI * 2);
        ctx.lineWidth = 1.25; ctx.strokeStyle = n.ring; ctx.stroke();

        const raw = String(n.label || '');
        const label = raw.length > 26 ? raw.slice(0, 25) + '…' : raw;
        const weight = n.kind === 'target' ? '700 12px' : (n.kind === 'cluster' ? '600 11px' : '500 10px');
        ctx.font = weight + ' Inter, sans-serif';
        ctx.textAlign = 'center'; ctx.textBaseline = 'alphabetic';
        ctx.lineWidth = 3; ctx.strokeStyle = 'rgba(250,251,253,0.9)';
        ctx.strokeText(label, x, y + r + 13);
        ctx.fillStyle = n.text;
        ctx.fillText(label, x, y + r + 13);
        ctx.globalAlpha = 1;
      }});

      requestAnimationFrame(draw);
    }}
    requestAnimationFrame(draw);
    setTimeout(persist, 900);

    // ── Fit / reset view ──
    function fitView(opts) {{
      const topBias = !!(opts && opts.topBias);
      const vis = nodes.filter(isVisible);
      if (!vis.length) return;
      let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity;
      vis.forEach(n => {{
        const x = n.tx != null ? n.tx : n.x, y = n.ty != null ? n.ty : n.y;
        minX = Math.min(minX, x - n.r); minY = Math.min(minY, y - n.r);
        maxX = Math.max(maxX, x + n.r + 20); maxY = Math.max(maxY, y + n.r + 28);
      }});
      const gw = Math.max(1, maxX - minX), gh = Math.max(1, maxY - minY);
      zoom = Math.max(0.28, Math.min(1.55, Math.min((W - 48) / gw, (H - 56) / gh)));
      panX = W / 2 - ((minX + maxX) / 2) * zoom;
      panY = topBias ? (28 - minY * zoom) : (H / 2 - ((minY + maxY) / 2) * zoom);
    }}
    document.getElementById('fitBtn').onclick = () => {{
      // Manual fit is allowed; keep lock so later step updates still won't auto-zoom.
      fitView({{ topBias: true }});
    }};
    document.getElementById('resetBtn').onclick = () => {{
      hiddenKinds.clear(); query = ''; document.getElementById('q').value = '';
      document.getElementById('qClr').style.display = 'none';
      selected = null; card.classList.remove('open');
      try {{ sessionStorage.removeItem(memKey); }} catch (e) {{}}
      saved = {{}};
      layoutTree();
      nodes.forEach(n => {{ if (n.tx != null) {{ n.x = n.tx; n.y = n.ty; }} }});
      // Re-apply final-frame camera when available; otherwise fit current nodes.
      if (lastFrameModel) lockCameraFromFrame(lastFrameModel, true);
      else fitView({{ topBias: true }});
      renderCats();
    }};

    // ── Search ──
    const qInput = document.getElementById('q');
    const qClr = document.getElementById('qClr');
    qInput.addEventListener('input', () => {{ query = qInput.value; qClr.style.display = query ? 'block' : 'none'; }});
    qClr.onclick = () => {{ query = ''; qInput.value = ''; qClr.style.display = 'none'; }};

    // ── Node category filter rail ──
    function counts() {{
      const m = {{}};
      nodes.forEach(n => {{ m[n.kind] = (m[n.kind] || 0) + 1; }});
      return m;
    }}
    function renderCats() {{
      const c = counts();
      const order = (payload.kindOrder || Object.keys(KM)).filter(k => (c[k] || 0) > 0);
      const host = document.getElementById('cats');
      host.innerHTML = '';
      order.forEach(k => {{
        const meta = KM[k] || {{ fill: '#94a3b8', ring: '#e2e8f0', label: k }};
        const on = !hiddenKinds.has(k);
        const btn = document.createElement('button');
        btn.className = 'cat' + (on ? '' : ' off');
        btn.innerHTML =
          '<span class="box' + (on ? ' on' : '') + '">' + (on ? '✓' : '') + '</span>' +
          '<span class="dot" style="background:' + meta.fill + ';box-shadow:0 0 0 2px ' + meta.ring + '"></span>' +
          '<span class="name">' + meta.label + '</span>' +
          '<span class="cnt">' + (c[k] || 0) + '</span>';
        btn.onclick = () => {{
          if (hiddenKinds.has(k)) hiddenKinds.delete(k); else hiddenKinds.add(k);
          if (!cameraLocked) fitPending = true;
          renderCats();
        }};
        host.appendChild(btn);
      }});
    }}
    document.getElementById('allBtn').onclick = () => {{
      hiddenKinds.clear();
      if (!cameraLocked) fitPending = true;
      renderCats();
    }};
    document.getElementById('noneBtn').onclick = () => {{
      Object.keys(counts()).forEach(k => hiddenKinds.add(k)); renderCats();
    }};
    renderCats();

    // ── Incremental model merge: spawn under parent, expand tree downward. ──
    let lastSig = '';
    let lastFrameModel = null;
    function applyModel(model) {{
      if (!model) return;
      if (model.kindMeta) Object.assign(KM, model.kindMeta);
      if (model.kindOrder) payload.kindOrder = model.kindOrder;
      if (model.frame) lastFrameModel = {{ frame: model.frame, frameKey: model.frameKey || '' }};
      // Lock zoom/pan to the final tree before merging visible nodes.
      lockCameraFromFrame(model);

      const inNodes = model.nodes || [];
      const inEdges = model.edges || [];
      const sig = inNodes.map(n => n.id + (n.pulse ? '*' : '')).join(',') + '|' + inEdges.map(e => e.id).join(',');
      if (sig === lastSig) return;
      lastSig = sig;
      const incIds = new Set(inNodes.map(n => n.id));
      const pN = new Set(), pE = new Set();
      let added = false;
      // Edges first so parent lookups work while spawning.
      const pendingEdges = inEdges.map(e => Object.assign({{}}, e));
      inNodes.forEach(n => {{
        let nd = byId.get(n.id);
        if (nd) {{
          nd.label = n.label; nd.kind = n.kind; nd.fill = n.fill; nd.ring = n.ring;
          nd.text = n.text; nd.r = n.r; nd.typeLabel = n.typeLabel; nd.fullTitle = n.fullTitle; nd.detail = n.detail;
          if (n.pulse) pN.add(n.id);
        }} else {{
          // Temporary register for parent resolution against in-flight nodes.
          nd = Object.assign({{}}, n, {{ x: 0, y: TOP_Y, tx: 0, ty: TOP_Y }});
          nodes.push(nd); byId.set(n.id, nd);
          let x = 0, y = TOP_Y;
          const prev = saved[n.id];
          const parent = (() => {{
            let best = null, bestLvl = -Infinity;
            for (const e of pendingEdges) {{
              if (e.target !== n.id) continue;
              const p = byId.get(e.source);
              if (!p) continue;
              const pl = levelOf(p);
              if (pl < levelOf(nd) && pl >= bestLvl) {{ best = p; bestLvl = pl; }}
            }}
            return best || byId.get('target');
          }})();
          if (prev) {{ x = prev.x; y = prev.y; }}
          else if (parent) {{
            x = parent.x;
            y = parent.y + Math.max(36, (levelOf(nd) - levelOf(parent)) * ROW_GAP * 0.55);
          }} else {{
            x = 0; y = TOP_Y;
          }}
          nd.x = x; nd.y = y; nd.tx = x; nd.ty = y;
          added = true; pN.add(n.id);
        }}
      }});
      for (let i = nodes.length - 1; i >= 0; i--) {{
        if (!incIds.has(nodes[i].id)) {{ byId.delete(nodes[i].id); nodes.splice(i, 1); }}
      }}
      edges = pendingEdges.filter(e => byId.get(e.source) && byId.get(e.target));
      edges.forEach(e => {{ if (e.pulse) pE.add(e.id); }});
      pulseNodeIds = pN; pulseEdgeIds = pE;
      if (pN.size || pE.size || added) {{ pulseStart = performance.now(); }}
      target = byId.get('target');
      layoutTree();
      // Only auto-fit before the final-frame camera is locked.
      if (!cameraLocked && (added || pN.size)) fitPending = true;
      renderCats();
      persist();
    }}
    window.addEventListener('message', (e) => {{
      const d = e.data;
      if (d && d.type === 'scipaths-graph') applyModel(d.model);
    }});
    if (payload.nodes && payload.nodes.length) applyModel(payload);
  </script>
</body>
</html>"""


def sync_and_load_graph(run_id: str, model: dict[str, Any]) -> dict[str, Any]:
    """Push cumulative graph to Neo4j and read it back for rendering."""
    if not run_id or neo4j_store is None:
        model = dict(model)
        model["backend"] = "memory"
        return model
    nodes = model.get("nodes") or []
    edges = model.get("edges") or []
    ok = neo4j_store.upsert_graph(run_id, nodes, edges)
    if not ok:
        model = dict(model)
        model["backend"] = "memory"
        return model
    fetched = neo4j_store.fetch_graph(run_id)
    if not fetched:
        model = dict(model)
        model["backend"] = "neo4j-write"
        return model
    # Preserve caption/step and enrich fetched nodes with Mina colors.
    out_nodes = []
    for n in fetched.get("nodes") or []:
        kind = str(n.get("kind") or "citing")
        meta = KIND_META.get(kind, KIND_META["citing"])
        out_nodes.append(
            {
                **n,
                "type_label": meta["type_label"],
                "color": n.get("color") or meta["fill"],
                "fill": n.get("fill") or meta["fill"],
                "ring": n.get("ring") or meta["ring"],
                "text": n.get("text") or meta["text"],
                "r": n.get("r") or meta["r"],
                "size": n.get("size") or meta["r"],
            }
        )
    return {
        "step": model.get("step"),
        "pulse_step": model.get("pulse_step"),
        "caption": model.get("caption"),
        "nodes": out_nodes,
        "edges": fetched.get("edges") or [],
        "backend": "neo4j",
    }


def build_synced_model(
    *,
    paper_dir: Optional[Path],
    payload: Optional[dict],
    events: list[str],
    run_id: str = "",
) -> dict[str, Any]:
    """Build the cumulative graph model for the given events and sync to Neo4j."""
    visible = visible_step_from_events(events)
    pulse = pulse_step_from_events(events)
    joined = " ".join(str(e) for e in (events or []))
    if "Pipeline completed successfully." in joined or (
        "Step 8 complete" in joined or ("annotation" in joined.lower() and "complete" in joined.lower())
    ):
        if visible >= 8:
            pulse = 0
            visible = 8

    model = build_graph_model(
        paper_dir=paper_dir,
        payload=payload,
        visible_step=visible,
        pulse_step=pulse,
    )
    # Final tree used only to lock camera zoom/pan from the first reveal.
    frame = build_graph_model(
        paper_dir=paper_dir,
        payload=payload,
        visible_step=8,
        pulse_step=0,
    )
    model["frame"] = {"nodes": frame.get("nodes") or [], "edges": frame.get("edges") or []}
    model["frame_key"] = run_id or "default"
    synced = sync_and_load_graph(run_id or "default", model)
    # Keep frame on the payload even if Neo4j sync rewrites node chrome.
    synced["frame"] = model["frame"]
    synced["frame_key"] = model["frame_key"]
    return synced


def render_graph_shell(placeholder, *, height: int = 520) -> None:
    """Mount the canvas shell. Streamlit 1.60+ embeds HTML strings via srcdoc."""
    html = graph_shell_html(height)
    if placeholder is None:
        st.iframe(html, height=height)
        return
    with placeholder.container():
        st.iframe(html, height=height)


def push_graph_update(courier_slot, model: dict[str, Any], *, height: int = 0) -> None:
    """Push a model into the mounted shell via an invisible courier frame."""
    html = courier_html(_graph_payload(model))
    if courier_slot is None:
        st.components.v1.html(html, height=height)
        return
    with courier_slot.container():
        st.components.v1.html(html, height=height)


def render_workflow_into(
    placeholder,
    *,
    paper_dir: Optional[Path],
    payload: Optional[dict],
    events: list[str],
    run_id: str = "",
    height: int = 520,
) -> None:
    """Back-compat single-slot render: mount shell then push once into it."""
    render_graph_shell(placeholder, height=height)
    model = build_synced_model(
        paper_dir=paper_dir, payload=payload, events=events, run_id=run_id
    )
    push_graph_update(None, model)