"""Generate a standalone HTML visualization for an agent run."""

from __future__ import annotations

import html
import json
from pathlib import Path
from typing import Any

import markdown as _markdown_lib

from supply_agent.logging.parser import load_run_events, summarize_run

_MARKDOWN_EXTENSIONS = ["fenced_code", "tables", "sane_lists"]


def _render_markdown(text: Any, *, soft_breaks: bool = False, boxed: bool = True) -> str:
    """Render text as Markdown; ``boxed`` wraps it in a bordered, scrollable box."""
    if not text:
        return ""
    extensions = [*_MARKDOWN_EXTENSIONS, "nl2br"] if soft_breaks else _MARKDOWN_EXTENSIONS
    body = _markdown_lib.markdown(str(text), extensions=extensions)
    if not boxed:
        return f'<div class="markdown-body">{body}</div>'
    return f'<div class="markdown-box"><div class="markdown-body">{body}</div></div>'


def _esc(text: Any) -> str:
    if text is None:
        return ""
    return html.escape(str(text))


def _pretty(obj: Any) -> str:
    if obj is None:
        return ""
    if isinstance(obj, str):
        try:
            obj = json.loads(obj)
        except (json.JSONDecodeError, TypeError):
            return obj
    return json.dumps(obj, ensure_ascii=False, indent=2)


def _pre(text: Any, *, klass: str = "code") -> str:
    if text is None:
        content = ""
    else:
        content = _pretty(text)
    return f'<pre class="{klass}">{_esc(content)}</pre>'


def _message_fingerprint(msg: dict[str, Any]) -> str:
    return json.dumps(msg, ensure_ascii=False, sort_keys=True, default=str)


def _common_prefix_len(a: list[dict[str, Any]], b: list[dict[str, Any]]) -> int:
    n = 0
    for left, right in zip(a, b):
        if _message_fingerprint(left) != _message_fingerprint(right):
            break
        n += 1
    return n


def _split_history_and_latest(
    messages: list[dict[str, Any]],
    prev_messages: list[dict[str, Any]] | None,
) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
    """
    Split request messages into (history, latest).

    ``latest`` is only what is newly added since the previous LLM input;
    ``history`` is everything else (hidden by default in the UI).
    """
    if not messages:
        return [], []

    if not prev_messages:
        if len(messages) > 1 and messages[0].get("role") == "system":
            return messages[:1], messages[1:]
        return [], list(messages)

    curr_has_sys = messages[0].get("role") == "system"
    prev_has_sys = prev_messages[0].get("role") == "system"
    curr_sys = messages[0] if curr_has_sys else None
    prev_sys = prev_messages[0] if prev_has_sys else None
    curr_rest = messages[1:] if curr_has_sys else messages
    prev_rest = prev_messages[1:] if prev_has_sys else prev_messages

    k = _common_prefix_len(curr_rest, prev_rest)
    history_rest = curr_rest[:k]
    latest = list(curr_rest[k:])

    history: list[dict[str, Any]] = []
    if curr_sys is not None:
        history.append(curr_sys)
    history.extend(history_rest)

    # Surface an updated system prompt in the latest section
    if (
        curr_sys is not None
        and prev_sys is not None
        and _message_fingerprint(curr_sys) != _message_fingerprint(prev_sys)
    ):
        latest = [curr_sys, *latest]
        history = list(history_rest)

    if not latest:
        return messages[:-1], messages[-1:]

    return history, latest


def _role_badge(role: str) -> str:
    return f'<span class="badge role-{_esc(role)}">{_esc(role)}</span>'


def _render_messages(
    messages: list[dict[str, Any]] | None,
    *,
    default_open: bool | None = None,
) -> str:
    if not messages:
        return '<p class="muted">无消息</p>'
    parts: list[str] = []
    for msg in messages:
        role = msg.get("role", "?")
        body_parts: list[str] = []
        if msg.get("content"):
            body_parts.append(_pre(msg["content"], klass="code prose"))

        if role in ("user", "tool"):
            open_attr = ""
        elif default_open is None:
            open_attr = "open" if role != "system" else ""
        else:
            open_attr = "open" if default_open else ""

        if role == "tool":
            tool_name = msg.get("name") or "?"
            summary_html = (
                f'{_role_badge(role)} <span class="msg-preview">{_esc(tool_name)}</span>'
            )
        else:
            preview = _preview(msg.get("content"))
            if not preview and msg.get("tool_calls"):
                names = []
                for tc in msg["tool_calls"]:
                    name, _, _ = _normalize_tool_call(tc)
                    names.append(name)
                preview = "调用: " + ", ".join(names)
            summary_html = (
                f'{_role_badge(role)} <span class="msg-preview">{_esc(preview)}</span>'
            )

        parts.append(
            f"""
            <details class="msg" {open_attr}>
              <summary>{summary_html}</summary>
              <div class="msg-body">{"".join(body_parts)}</div>
            </details>
            """
        )
    return '<div class="msg-list">' + "".join(parts) + "</div>"


def _preview(text: Any, limit: int = 80) -> str:
    if not text:
        return ""
    s = " ".join(str(text).split())
    return s if len(s) <= limit else s[: limit - 1] + "…"


def _tool_names(tools: list[dict[str, Any]] | None) -> list[str]:
    if not tools:
        return []
    names: list[str] = []
    for t in tools:
        fn = t.get("function") or t
        names.append(fn.get("name", "?"))
    return names


def _extract_system_prompt_and_tools(
    events: list[dict[str, Any]],
) -> tuple[str | None, list[str]]:
    """Pull the system prompt and tool list from the first llm_input event."""
    for ev in events:
        if ev.get("event") != "llm_input":
            continue
        data = ev.get("data") or {}
        messages = data.get("messages") or []
        sys_msg = next((m for m in messages if m.get("role") == "system"), None)
        content = sys_msg.get("content") if sys_msg else None
        return content, _tool_names(data.get("tools"))
    return None, []


def _render_llm_input_section(
    data: dict[str, Any],
    *,
    prev_messages: list[dict[str, Any]] | None = None,
) -> tuple[str, list[dict[str, Any]]]:
    """Render the LLM-input body; returns (html, messages) so callers can track history."""
    messages = list(data.get("messages") or [])
    _history, latest = _split_history_and_latest(messages, prev_messages)
    latest_count = len(latest)

    html_out = f"""
    <section class="substep">
      <div class="substep-header">
        <span class="substep-title">LLM 输入</span>
        <span class="tag">新增 {latest_count}</span>
      </div>
      <div class="substep-body">
        {_render_messages(latest, default_open=True)}
      </div>
    </section>
    """
    return html_out, messages


def _render_reasoning(reasoning: Any) -> str:
    if not reasoning:
        return ""
    return f"""
    <div class="reasoning">
      <div class="sublabel">思考过程</div>
      {_render_markdown(reasoning, soft_breaks=True, boxed=False)}
    </div>
    """


def _parse_tool_arguments(arguments: Any) -> Any:
    if isinstance(arguments, str):
        try:
            return json.loads(arguments)
        except (json.JSONDecodeError, TypeError):
            return arguments
    return arguments


def _normalize_tool_call(tc: dict[str, Any]) -> tuple[str, Any, str]:
    """Return (name, parsed_args, id) from flat or OpenAI function-wrapped tool_call."""
    name = tc.get("name") or (tc.get("function") or {}).get("name") or "?"
    raw_args = tc.get("arguments")
    if raw_args is None and isinstance(tc.get("function"), dict):
        raw_args = tc["function"].get("arguments")
    return name, _parse_tool_arguments(raw_args), str(tc.get("id") or "")


def _render_output_tool_calls(tool_calls: list[dict[str, Any]]) -> str:
    """Show each tool the model decided to call, collapsed by default; expand for args."""
    if not tool_calls:
        return ""
    parts: list[str] = []
    for tc in tool_calls:
        name, args, _ = _normalize_tool_call(tc)
        parts.append(
            f"""
            <details class="msg">
              <summary><span class="badge role-tool">tool</span> <span class="msg-preview">{_esc(name)}</span></summary>
              <div class="msg-body">{_pre(args)}</div>
            </details>
            """
        )
    return '<div class="msg-list">' + "".join(parts) + "</div>"


def _render_llm_output_section(data: dict[str, Any]) -> str:
    tool_calls = data.get("tool_calls") or []
    reasoning = data.get("reasoning")
    content = data.get("content")
    usage = data.get("usage")

    usage_html = ""
    if usage:
        usage_html = f"""
        <div class="usage">
          <span>prompt: {_esc(usage.get("prompt_tokens", "—"))}</span>
          <span>completion: {_esc(usage.get("completion_tokens", "—"))}</span>
          <span>total: {_esc(usage.get("total_tokens", "—"))}</span>
          <span>cost: {_esc(usage.get("cost", "—"))}</span>
        </div>
        """

    content_html = (
        f"<h4>模型输出文本</h4>{_render_markdown(content, soft_breaks=True)}"
        if content
        else ""
    )

    tags: list[str] = []
    if reasoning:
        tags.append('<span class="tag">有思考</span>')
    if tool_calls:
        tags.append(f'<span class="tag">{len(tool_calls)} tool calls</span>')

    return f"""
    <section class="substep">
      <div class="substep-header">
        <span class="substep-title">LLM 输出</span>
        {"".join(tags)}
      </div>
      <div class="substep-body">
        {_render_reasoning(reasoning)}
        {content_html}
        {_render_output_tool_calls(tool_calls)}
        {usage_html}
      </div>
    </section>
    """


def _render_step_card(
    step_no: int,
    iteration: Any,
    title: str,
    inner_html: str,
) -> str:
    return f"""
    <article class="card card-step" id="step-{step_no}">
      <header class="card-header">
        <div class="step-num">Step {step_no}</div>
        <div class="card-title">{title}</div>
        <div class="card-tags">
          <span class="tag">iteration {iteration}</span>
        </div>
      </header>
      <div class="card-body step-body">
        {inner_html}
      </div>
    </article>
    """


def _render_skill(data: dict[str, Any], step_no: int, iteration: Any) -> str:
    return f"""
    <article class="card card-skill" id="step-{step_no}">
      <header class="card-header">
        <div class="step-num">Step {step_no}</div>
        <div class="card-title">技能加载</div>
        <div class="card-tags">
          <span class="tag">iteration {iteration}</span>
          <span class="tag">{_esc(data.get("skill", ""))}</span>
        </div>
      </header>
    </article>
    """


_HIDDEN_EVENT_TYPES = ("run_start", "run_end", "tool_call")


def _group_visible_events(
    events: list[dict[str, Any]],
) -> list[list[dict[str, Any]]]:
    """
    Group renderable events into steps.

    A consecutive ``llm_input`` immediately followed by ``llm_output`` forms a
    single step (one round-trip); everything else is its own step.
    """
    visible = [ev for ev in events if ev.get("event") not in _HIDDEN_EVENT_TYPES]
    groups: list[list[dict[str, Any]]] = []
    i = 0
    n = len(visible)
    while i < n:
        ev = visible[i]
        if (
            ev.get("event") == "llm_input"
            and i + 1 < n
            and visible[i + 1].get("event") == "llm_output"
        ):
            groups.append([ev, visible[i + 1]])
            i += 2
        else:
            groups.append([ev])
            i += 1
    return groups


def _event_iteration(ev: dict[str, Any]) -> Any:
    data = ev.get("data") or {}
    return ev.get("iteration", data.get("iteration", "—"))


def _render_timeline(events: list[dict[str, Any]]) -> str:
    parts: list[str] = []
    prev_llm_messages: list[dict[str, Any]] | None = None
    for step_no, group in enumerate(_group_visible_events(events), start=1):
        if len(group) == 2:
            input_ev, output_ev = group
            input_data = input_ev.get("data") or {}
            output_data = output_ev.get("data") or {}
            input_html, messages = _render_llm_input_section(
                input_data, prev_messages=prev_llm_messages
            )
            prev_llm_messages = messages
            output_html = _render_llm_output_section(output_data)
            parts.append(
                _render_step_card(
                    step_no,
                    _event_iteration(input_ev),
                    "LLM 输入 · 输出",
                    input_html + output_html,
                )
            )
            continue

        ev = group[0]
        etype = ev.get("event")
        data = ev.get("data") or {}
        iteration = _event_iteration(ev)

        if etype == "llm_input":
            input_html, messages = _render_llm_input_section(
                data, prev_messages=prev_llm_messages
            )
            prev_llm_messages = messages
            parts.append(_render_step_card(step_no, iteration, "LLM 输入", input_html))
        elif etype == "llm_output":
            output_html = _render_llm_output_section(data)
            parts.append(_render_step_card(step_no, iteration, "LLM 输出", output_html))
        elif etype == "skill_loaded":
            parts.append(_render_skill(data, step_no, iteration))
        else:
            parts.append(
                f"""
                <article class="card" id="step-{step_no}">
                  <header class="card-header">
                    <div class="step-num">Step {step_no}</div>
                    <div class="card-title">{_esc(etype)}</div>
                  </header>
                  <div class="card-body">{_pre(data)}</div>
                </article>
                """
            )
    return "\n".join(parts)


def _nav_items(events: list[dict[str, Any]]) -> str:
    items: list[str] = []
    labels = {
        "llm_input": "LLM 输入",
        "llm_output": "LLM 输出",
        "skill_loaded": "技能",
    }
    for step_no, group in enumerate(_group_visible_events(events), start=1):
        if len(group) == 2:
            ev = group[0]
            label = "LLM 输入 · 输出"
            nav_class = "nav-step-pair"
            detail = f" · iter {_event_iteration(ev)}"
        else:
            ev = group[0]
            etype = ev.get("event")
            label = labels.get(etype, etype or "?")
            nav_class = f"nav-{_esc(etype or '')}"
            detail = ""
            if etype in ("llm_input", "llm_output"):
                detail = f" · iter {_event_iteration(ev)}"
        items.append(
            f'<a class="nav-item {nav_class}" href="#step-{step_no}">'
            f'<span class="nav-seq">{step_no}</span>{label}{detail}</a>'
        )
    return "\n".join(items)


_CSS = """
:root {
  --bg: #f4f6f9;
  --bg-elev: #ffffff;
  --bg-card: #ffffff;
  --border: #d8dee8;
  --text: #1a2332;
  --muted: #6b7c93;
  --accent: #2563eb;
  --input: #2563eb;
  --output: #059669;
  --skill: #7c3aed;
  --code-bg: #f8fafc;
  --mono: "JetBrains Mono", "SF Mono", "Fira Code", ui-monospace, monospace;
  --sans: "IBM Plex Sans", "Segoe UI", system-ui, sans-serif;
}
* { box-sizing: border-box; }
html { scroll-behavior: smooth; }
body {
  margin: 0;
  font-family: var(--sans);
  background: var(--bg);
  color: var(--text);
  line-height: 1.55;
}
a { color: var(--accent); text-decoration: none; }
a:hover { text-decoration: underline; }
.layout {
  display: grid;
  grid-template-columns: 260px 1fr;
  min-height: 100vh;
}
.sidebar {
  position: sticky;
  top: 0;
  height: 100vh;
  overflow: auto;
  background: var(--bg-elev);
  border-right: 1px solid var(--border);
  padding: 1.25rem 1rem;
  box-shadow: 1px 0 0 rgba(26, 35, 50, 0.02);
}
.sidebar h1 {
  font-size: 0.95rem;
  margin: 0 0 0.25rem;
  letter-spacing: 0.02em;
  color: var(--text);
}
.sidebar .run-id {
  font-family: var(--mono);
  font-size: 0.7rem;
  color: var(--muted);
  word-break: break-all;
  margin-bottom: 1rem;
}
.nav-item {
  display: flex;
  align-items: center;
  gap: 0.5rem;
  padding: 0.4rem 0.55rem;
  border-radius: 6px;
  color: var(--text);
  font-size: 0.8rem;
  margin-bottom: 2px;
}
.nav-item:hover { background: #eef2f7; text-decoration: none; }
.nav-seq {
  font-family: var(--mono);
  font-size: 0.7rem;
  color: var(--muted);
  min-width: 1.4rem;
}
.nav-llm_input { border-left: 3px solid var(--input); }
.nav-llm_output { border-left: 3px solid var(--output); }
.nav-step-pair { border-left: 3px solid var(--input); }
.nav-skill_loaded { border-left: 3px solid var(--skill); }

.main { padding: 1.5rem 2rem 3rem; max-width: 1100px; }
.hero {
  background: linear-gradient(145deg, #ffffff 0%, #f0f5ff 55%, #f3faf6 100%);
  border: 1px solid var(--border);
  border-radius: 12px;
  padding: 1.5rem;
  margin-bottom: 1.5rem;
  box-shadow: 0 1px 2px rgba(26, 35, 50, 0.04);
}
.hero h2 { margin: 0 0 0.75rem; font-size: 1.35rem; }
.stats {
  display: flex;
  flex-wrap: wrap;
  gap: 0.75rem;
  margin: 1rem 0;
}
.stat {
  background: #ffffff;
  border: 1px solid var(--border);
  border-radius: 8px;
  padding: 0.55rem 0.85rem;
  min-width: 110px;
  box-shadow: 0 1px 1px rgba(26, 35, 50, 0.03);
}
.stat .label { font-size: 0.7rem; color: var(--muted); text-transform: uppercase; letter-spacing: 0.04em; }
.stat .value { font-family: var(--mono); font-size: 1rem; margin-top: 0.15rem; }
.user-prompt {
  background: #eff6ff;
  border: 1px solid #bfdbfe;
  border-radius: 8px;
  padding: 0.85rem 1rem;
  white-space: pre-wrap;
}
.final {
  margin-top: 1rem;
  background: #ecfdf5;
  border: 1px solid #a7f3d0;
  border-radius: 8px;
  padding: 0.85rem 1rem;
}
.final h3 { margin: 0 0 0.5rem; font-size: 0.85rem; color: var(--output); }

.timeline { display: flex; flex-direction: column; gap: 1rem; }
.card {
  background: var(--bg-card);
  border: 1px solid var(--border);
  border-radius: 10px;
  overflow: hidden;
  box-shadow: 0 1px 2px rgba(26, 35, 50, 0.04);
}
.card-step { border-top: 3px solid var(--input); }
.card-skill { border-top: 3px solid var(--skill); }
.card-header {
  display: flex;
  flex-wrap: wrap;
  align-items: center;
  gap: 0.6rem;
  padding: 0.75rem 1rem;
  background: #f8fafc;
  border-bottom: 1px solid var(--border);
}
.step-num {
  font-family: var(--mono);
  font-size: 0.75rem;
  color: var(--muted);
  background: #eef2f7;
  padding: 0.15rem 0.45rem;
  border-radius: 4px;
}
.card-title { font-weight: 600; font-size: 0.95rem; }
.card-tags { display: flex; flex-wrap: wrap; gap: 0.35rem; margin-left: auto; }
.tag {
  font-size: 0.7rem;
  font-family: var(--mono);
  background: #ffffff;
  border: 1px solid var(--border);
  border-radius: 999px;
  padding: 0.12rem 0.5rem;
  color: var(--muted);
}
.card-body { padding: 1rem; }
.card-body h4 {
  margin: 1rem 0 0.5rem;
  font-size: 0.8rem;
  color: var(--muted);
  text-transform: uppercase;
  letter-spacing: 0.05em;
}
.card-body h4:first-child { margin-top: 0; }
.card-body.step-body { padding: 0; }
.substep { padding: 1rem; }
.substep + .substep { border-top: 1px solid var(--border); }
.substep-header {
  display: flex;
  flex-wrap: wrap;
  align-items: center;
  gap: 0.5rem;
  margin-bottom: 0.65rem;
}
.substep-title {
  font-weight: 600;
  font-size: 0.8rem;
  color: var(--muted);
  text-transform: uppercase;
  letter-spacing: 0.05em;
}
.sublabel {
  font-size: 0.72rem;
  color: var(--muted);
  text-transform: uppercase;
  letter-spacing: 0.05em;
  margin-bottom: 0.35rem;
}
.muted { color: var(--muted); font-size: 0.85rem; }
.code {
  font-family: var(--mono);
  font-size: 0.78rem;
  background: var(--code-bg);
  border: 1px solid var(--border);
  border-radius: 6px;
  padding: 0.75rem;
  overflow: auto;
  max-height: 420px;
  white-space: pre-wrap;
  word-break: break-word;
  margin: 0;
  color: #334155;
}
.prose { line-height: 1.6; }
.reasoning {
  background: #fffbeb;
  border: 1px solid #fde68a;
  border-radius: 8px;
  padding: 0.75rem;
  margin-bottom: 1rem;
}
.msg-list { display: flex; flex-direction: column; gap: 0.4rem; }
.msg {
  border: 1px solid var(--border);
  border-radius: 6px;
  background: #f8fafc;
}
.msg summary {
  cursor: pointer;
  padding: 0.45rem 0.65rem;
  display: flex;
  align-items: center;
  gap: 0.5rem;
  list-style: none;
}
.msg summary::-webkit-details-marker { display: none; }
.msg-body { padding: 0 0.65rem 0.65rem; }
.msg-preview { color: var(--muted); font-size: 0.78rem; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; flex: 1; }
.badge {
  font-size: 0.65rem;
  font-weight: 600;
  text-transform: uppercase;
  padding: 0.12rem 0.4rem;
  border-radius: 4px;
  font-family: var(--mono);
  color: #fff;
}
.role-system { background: #475569; }
.role-user { background: #2563eb; }
.role-assistant { background: #059669; }
.role-tool { background: #d97706; }
.chip-row { display: flex; flex-wrap: wrap; gap: 0.35rem; margin-bottom: 0.5rem; }
.chip {
  font-family: var(--mono);
  font-size: 0.72rem;
  background: #fff7ed;
  border: 1px solid #fdba74;
  color: #c2410c;
  padding: 0.15rem 0.5rem;
  border-radius: 999px;
}
.usage {
  display: flex;
  flex-wrap: wrap;
  gap: 0.75rem;
  margin-top: 0.75rem;
  font-family: var(--mono);
  font-size: 0.72rem;
  color: var(--muted);
}
.sysprompt-block { margin: 0.85rem 0; }
.markdown-box {
  background: var(--code-bg);
  border: 1px solid var(--border);
  border-radius: 6px;
  padding: 0.75rem 1rem;
  max-height: 420px;
  overflow: auto;
}
.markdown-body { font-size: 0.88rem; line-height: 1.65; color: var(--text); }
.markdown-body > *:first-child { margin-top: 0; }
.markdown-body > *:last-child { margin-bottom: 0; }
.markdown-body p { margin: 0.5rem 0; }
.markdown-body h1,
.markdown-body h2,
.markdown-body h3,
.markdown-body h4,
.markdown-body h5,
.markdown-body h6 {
  margin: 1rem 0 0.5rem;
  line-height: 1.35;
  color: var(--text);
}
.markdown-body h1 { font-size: 1.15rem; }
.markdown-body h2 { font-size: 1.05rem; }
.markdown-body h3 { font-size: 0.98rem; }
.markdown-body h4 { font-size: 0.9rem; }
.markdown-body ul,
.markdown-body ol { margin: 0.4rem 0; padding-left: 1.4rem; }
.markdown-body li { margin: 0.2rem 0; }
.markdown-body li > p { margin: 0.2rem 0; }
.markdown-body code {
  background: #eef2f7;
  border-radius: 4px;
  padding: 0.1rem 0.35rem;
}
.markdown-body pre {
  background: #0f172a;
  color: #e2e8f0;
  border-radius: 6px;
  padding: 0.65rem 0.85rem;
  overflow: auto;
  margin: 0.5rem 0;
}
.markdown-body pre code { background: transparent; padding: 0; color: inherit; }
.markdown-body blockquote {
  border-left: 3px solid var(--border);
  margin: 0.5rem 0;
  padding: 0.1rem 0.75rem;
  color: var(--muted);
}
.markdown-body table { border-collapse: collapse; margin: 0.5rem 0; font-size: 0.82rem; }
.markdown-body th,
.markdown-body td { border: 1px solid var(--border); padding: 0.3rem 0.55rem; }
.markdown-body a { text-decoration: underline; }
.markdown-body strong { font-weight: 600; }
.markdown-body hr { border: none; border-top: 1px solid var(--border); margin: 0.75rem 0; }
code { font-family: var(--mono); font-size: 0.85em; }

@media (max-width: 900px) {
  .layout { grid-template-columns: 1fr; }
  .sidebar { position: relative; height: auto; max-height: 40vh; }
}
"""


def _render_system_prompt_block(system_prompt: str | None) -> str:
    if not system_prompt:
        return ""
    return f"""
    <div class="sysprompt-block">
      <div class="sublabel">System Prompt</div>
      {_render_markdown(system_prompt)}
    </div>
    """


def _render_available_tools_block(tool_names: list[str]) -> str:
    if not tool_names:
        return ""
    chips = "".join(f'<span class="chip">{_esc(n)}</span>' for n in tool_names)
    return f"""
    <div class="sublabel">可用工具（{len(tool_names)}）</div>
    <div class="chip-row">{chips}</div>
    """


def render_html(events: list[dict[str, Any]]) -> str:
    """Render a full standalone HTML page for the given events."""
    meta = summarize_run(events)
    skills = meta.get("skills_used") or []
    skills_str = ", ".join(skills) if skills else "—"
    system_prompt, tool_names = _extract_system_prompt_and_tools(events)

    return f"""<!DOCTYPE html>
<html lang="zh-CN">
<head>
  <meta charset="utf-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1" />
  <title>Agent Run · {_esc(meta.get("run_id") or "unknown")}</title>
  <style>{_CSS}</style>
</head>
<body>
  <div class="layout">
    <aside class="sidebar">
      <h1>Agent Trace</h1>
      <div class="run-id">{_esc(meta.get("agent_name") or "agent")}</div>
      <div class="run-id">{_esc(meta.get("run_id"))}</div>
      <nav>
        {_nav_items(events)}
      </nav>
    </aside>
    <main class="main">
      <section class="hero">
        <h2>运行概览</h2>
        <div class="stats">
          <div class="stat"><div class="label">Agent</div><div class="value">{_esc(meta.get("agent_name") or "—")}</div></div>
          <div class="stat"><div class="label">Model</div><div class="value">{_esc(meta.get("model") or "—")}</div></div>
          <div class="stat"><div class="label">Iterations</div><div class="value">{_esc(meta.get("iterations"))}</div></div>
          <div class="stat"><div class="label">Tool Calls</div><div class="value">{_esc(meta.get("tool_calls_made"))}</div></div>
          <div class="stat"><div class="label">Events</div><div class="value">{_esc(meta.get("event_count"))}</div></div>
          <div class="stat"><div class="label">Skills</div><div class="value">{_esc(skills_str)}</div></div>
        </div>
        {_render_available_tools_block(tool_names)}
        {_render_system_prompt_block(system_prompt)}
        <div class="sublabel">用户输入</div>
        <div class="user-prompt">{_esc(meta.get("user_input") or "—")}</div>
        {f'''
        <div class="final">
          <h3>最终回答</h3>
          {_render_markdown(meta.get("final_content"), soft_breaks=True, boxed=False)}
        </div>
        ''' if meta.get("final_content") else ""}
      </section>

      <section class="timeline">
        <h2 style="margin:0 0 0.5rem;font-size:1.1rem">执行时间线</h2>
        <p class="muted" style="margin:0 0 1rem">按步骤展示：LLM 输入 → 思考/输出</p>
        {_render_timeline(events)}
      </section>
    </main>
  </div>
</body>
</html>
"""


def generate_visualization(
    run_path: Path | str,
    output: Path | str | None = None,
) -> Path:
    """
    Load a run log and write an HTML visualization.

    Returns the output HTML path.
    """
    run_path = Path(run_path)
    events = load_run_events(run_path)
    if not events:
        raise ValueError(f"No events found in {run_path}")

    html_doc = render_html(events)

    if output is None:
        # Prefer placing next to the source file
        src = run_path
        if src.suffix in {".log", ".jsonl"}:
            out = src.with_suffix(".html")
        else:
            out = Path(str(src) + ".html")
    else:
        out = Path(output)

    out.parent.mkdir(parents=True, exist_ok=True)
    out.write_text(html_doc, encoding="utf-8")
    return out