"""The PRIMO pages, rendered as ``pm-*`` markup for ``gr.HTML`` blocks.

Gradio has no card or rich-table component, so every page here is a string of
HTML styled entirely by ``primo.css`` -- the rail, the board grid, the
leaderboard tables and the tasks table. The functions are pure: they take the
data the app already fetched (``Board`` objects, the results ``DataFrame``, the
registry) and return a string, so they unit-test without Gradio or a network.

Two rules hold everywhere:

* Every string that comes from the registry or from a submission goes through
  ``html.escape`` -- board names, blurbs, disease names and, above all, the
  user-chosen model names that become table cells and column headers.
* The private ``hf_username`` is never rendered. It is a name-ownership lock in
  the results dataset, not a public credit, so no table here carries a
  "submitted by" column.

Navigation is reload-based: the rail is a column of ``<a href="?board=slug">``
and ``<a href="?tab=name">`` links, which costs a page load per click but buys
shareable per-board URLs and needs no JavaScript, matching how the board cards
have always worked.
"""

import math
import numbers
from html import escape

import pandas as pd
from boards import (
    AREA_GROUP,
    CATEGORY_GROUP,
    GROUP_NOTE,
    MODALITY_GROUP,
    Board,
    OpenBoard,
    in_group,
    metric_label,
    modality_label,
    open_in_group,
)
from leaderboard import per_task_table, ranked_table, tasks_table, top_models

SECTIONS = (MODALITY_GROUP, AREA_GROUP, CATEGORY_GROUP)
N_TOP_MODELS = 3

BRAND = (
    '<a class="pm-brand" target="_self" href="?tab=boards">'
    '<img src="/gradio_api/file=assets/primo-mark.webp" alt="">'
    "<b>PRIMO</b><span>benchmark</span></a>"
)

FOOT_LINKS = (
    ("tasks", "Tasks"),
    ("submit", "Submit a model"),
    ("contribute", "Contribute"),
    ("method", "Method"),
)

NAME_COLUMNS = frozenset({"Model", "Best"})
STRONG_COLUMNS = frozenset({"Task"})
METRIC_COLUMNS = frozenset({"Metric"})
RIGHT_COLUMNS = frozenset({"Rank", "Patients"})


# --------------------------------------------------------------------- rail
def rail_html(
    boards: list[Board], active_slug: str | None, active_tab: str | None
) -> str:
    """The left navigation: brand, one link per board grouped by facet, foot links.

    ``active_slug`` highlights the board a visitor is on; ``active_tab`` highlights
    a foot link. Open boards link to Contribute, mirroring the overview cards.
    """
    parts = ['<div class="pm-rail-inner">', BRAND]
    for group in SECTIONS:
        cards, opens = in_group(boards, group), open_in_group(boards, group)
        if not cards and not opens:
            continue
        parts.append(
            f'<div class="pm-rail-group"><p class="pm-rail-label">{escape(group)}</p>'
        )
        for board in cards:
            cls = "pm-link pm-active" if board.slug == active_slug else "pm-link"
            parts.append(
                f'<a class="{cls}" target="_self" href="?board={escape(board.slug)}">'
                f'<span class="pm-label">{escape(board.name)}</span>'
                f'<span class="pm-count">{board.n_tasks}</span></a>'
            )
        for board in opens:
            parts.append(
                '<a class="pm-link pm-link--open" target="_self" href="?tab=contribute">'
                f'<span class="pm-label">{escape(board.name)}</span>'
                '<span class="pm-badge pm-badge--quiet">open</span></a>'
            )
        parts.append("</div>")
    parts.append('<div class="pm-rail-foot">')
    for tab, label in FOOT_LINKS:
        cls = "pm-foot-link pm-active" if tab == active_tab else "pm-foot-link"
        parts.append(
            f'<a class="{cls}" target="_self" href="?tab={tab}">{escape(label)}</a>'
        )
    parts.append("</div></div>")
    return "".join(parts)


# ------------------------------------------------------------------ boards
def _leading(top) -> str:
    """The card's mini-ranking, with the same empty/baseline states as before.

    An empty board says "be the first"; a board held only by our baselines says
    "beat the baseline" instead, because "be the first" misleads once a PCA
    already holds a score.
    """
    parts = ['<p class="pm-over" style="margin-top:14px">Leading</p>']
    if not top:
        parts.append(
            '<p class="pm-leader pm-leader--2">No ranked model yet. Be the first.</p>'
        )
        return "".join(parts)
    for row in top:
        badge = (
            ' <span class="pm-badge pm-badge--marine">baseline</span>'
            if row.is_baseline
            else ""
        )
        score = "n/a" if row.score is None else f"{row.score:.3f}"
        parts.append(
            f'<p class="pm-leader">{escape(row.name)}{badge}'
            f' <span class="pm-num--dim">{score}</span></p>'
        )
    return "".join(parts)


def _live_card(board: Board, df: pd.DataFrame, by_id: dict[str, dict]) -> str:
    top = top_models(df, by_id, board, N_TOP_MODELS)
    return (
        f'<a class="pm-card pm-card--live" target="_self" href="?board={escape(board.slug)}">'
        f"<h3>{escape(board.name)}</h3>"
        f'<p class="pm-meta">{board.n_tasks} tasks · {board.n_cohorts} cohorts<br>'
        f"{board.n_patients:,} patients · {board.n_diseases} diseases</p>"
        f'<p class="pm-asks">{escape(board.blurb)}</p>'
        f"{_leading(top)}</a>"
    )


def _open_card(board: OpenBoard) -> str:
    return (
        '<a class="pm-card pm-card--open" target="_self" href="?tab=contribute">'
        f"<h3>{escape(board.name)}</h3>"
        '<p class="pm-meta"><span class="pm-badge pm-badge--quiet">OPEN · no cohort yet</span></p>'
        f'<p class="pm-asks">{escape(board.blurb)}</p>'
        '<p class="pm-cta">Propose a cohort →</p></a>'
    )


def render_boards(boards: list[Board], df: pd.DataFrame, by_id: dict[str, dict]) -> str:
    """The Boards overview: every board as a card, grouped by facet.

    Live cards link to their board and teaser their leaders; open cards state a
    gap and link to Contribute. A section counts its open slices so the page
    reads as showing its own gaps, not the registry as the whole territory.
    """
    if not boards:
        return (
            '<div class="pm-body"><p class="pm-caption">The task registry is '
            "unavailable right now. Please retry in a moment.</p></div>"
        )
    out = [
        '<div class="pm-head"><div><h1>Leaderboards</h1>',
        "<p>PRIMO evaluates representations of omics samples through "
        "drug-development-related tasks. Benchmarks are organized by data "
        "modality, therapeutic area, or task category.</p></div></div>",
        '<div class="pm-body">',
    ]
    for group in SECTIONS:
        cards, opens = in_group(boards, group), open_in_group(boards, group)
        if not cards and not opens:
            continue
        note = escape(GROUP_NOTE.get(group, ""))
        counter = f" · +{len(opens)} open" if opens else ""
        out.append(
            '<div class="pm-group"><div class="pm-group-head">'
            f'<p class="pm-over">{escape(group)}</p>'
            f'<p class="pm-note">{note}{counter}</p></div><div class="pm-grid">'
        )
        out += [_live_card(b, df, by_id) for b in cards]
        out += [_open_card(b) for b in opens]
        out.append("</div></div>")
    out.append("</div>")
    return "".join(out)


# ------------------------------------------------------------- html tables
def _fmt_value(value, is_score: bool) -> str | None:
    """Display text for one cell; ``None`` marks a blank (``n/a``) cell."""
    try:
        if pd.isna(value):
            return None
    except (TypeError, ValueError):
        pass
    if isinstance(value, str):
        return value
    if isinstance(value, numbers.Integral):
        return str(int(value))
    if isinstance(value, numbers.Real):
        if not math.isfinite(float(value)):
            return None
        return f"{float(value):.3f}" if is_score else str(value)
    return str(value)


def _score_columns(df: pd.DataFrame) -> list[str]:
    """Numeric columns to format and bold -- ``Rank`` is an index, not a score."""
    return [c for c in df.select_dtypes("number").columns if c not in RIGHT_COLUMNS]


def _bold_cells(df: pd.DataFrame, score_cols: list[str], axis: int) -> set:
    """Which ``(row, col)`` cells hold the best value.

    ``axis=0`` bolds the best model per column (the ranked table, read down);
    ``axis=1`` bolds the best model per row (the per-task table, read across).
    """
    bold = set()
    if not score_cols:
        return bold
    if axis == 0:
        for col in score_cols:
            best = df[col].max(skipna=True)
            if pd.notna(best):
                for idx, value in df[col].items():
                    if pd.notna(value) and value == best:
                        bold.add((idx, col))
    else:
        for idx, row in df.iterrows():
            present = {c: row[c] for c in score_cols if pd.notna(row[c])}
            if present:
                best = max(present.values())
                bold.update((idx, c) for c, v in present.items() if v == best)
    return bold


def _cell_class(col: str, is_score: bool, blank: bool) -> str:
    if col in NAME_COLUMNS:
        return "pm-name"
    if col in STRONG_COLUMNS:
        return "pm-strong"
    if col in METRIC_COLUMNS:
        return "pm-metric"
    if is_score or col in RIGHT_COLUMNS:
        return "pm-num pm-num--dim" if blank else "pm-num"
    return ""


def _df_to_table(df: pd.DataFrame, bold_axis: int | None, empty: str) -> str:
    """Render a DataFrame as a ``pm-table``, escaping every header and cell."""
    if df.empty:
        return f'<p class="pm-caption">{escape(empty)}</p>'
    score_cols = _score_columns(df)
    bold = _bold_cells(df, score_cols, bold_axis) if bold_axis is not None else set()

    head = []
    for index, col in enumerate(df.columns):
        align = (
            ' style="text-align:right"'
            if col in score_cols or col in RIGHT_COLUMNS
            else ""
        )
        head.append(
            f'<th class="pm-sort" data-sort-index="{index}" tabindex="0" '
            f'role="button" aria-sort="none" title="Sort by {escape(str(col))}"'
            f"{align}>{escape(str(col))}</th>"
        )
    body = []
    for idx, row in df.iterrows():
        cells = []
        for col in df.columns:
            is_score = col in score_cols
            text = _fmt_value(row[col], is_score)
            cls = _cell_class(col, is_score, text is None)
            weight = ' style="font-weight:700"' if (idx, col) in bold else ""
            shown = "n/a" if text is None else escape(text)
            cells.append(f'<td class="{cls}"{weight}>{shown}</td>')
        body.append(f"<tr>{''.join(cells)}</tr>")
    return (
        '<div class="pm-table-wrap"><table class="pm-table" style="table-layout:auto">'
        f"<thead><tr>{''.join(head)}</tr></thead>"
        f"<tbody>{''.join(body)}</tbody></table></div>"
    )


# ------------------------------------------------------------------- board
def _board_meta(board: Board) -> str:
    metrics = " · ".join(metric_label(m) for m in board.metrics)
    tail = f" · {metrics}" if metrics else ""
    return (
        f"{escape(board.blurb)} · {board.n_tasks} tasks · {board.n_cohorts} cohorts · "
        f"{board.n_patients:,} patients · {escape(modality_label(board.modality))}{tail}"
    )


def render_board(board: Board | None, df: pd.DataFrame, by_id: dict[str, dict]) -> str:
    """One board: title strip, the ranked leaderboard, then the per-task table."""
    if board is None:
        return (
            '<div class="pm-head"><div><h1>No board available</h1>'
            "<p>The task registry could not be loaded. Please retry shortly.</p>"
            "</div></div>"
        )
    ranked = _df_to_table(
        ranked_table(df, by_id, board),
        bold_axis=0,
        empty="No model has covered every task of this board yet. Be the first to submit.",
    )
    per_task = _df_to_table(
        per_task_table(df, by_id, board),
        bold_axis=1,
        empty="No submission has scored on this board yet.",
    )
    return (
        '<div class="pm-head"><div><p class="pm-over">Board</p>'
        f"<h1>{escape(board.name)}</h1><p>{_board_meta(board)}</p></div></div>"
        '<div class="pm-body">'
        '<div class="pm-group"><div class="pm-group-head">'
        '<p class="pm-over pm-over--marine">Ranked</p>'
        f'<p class="pm-note">Only models that covered all {board.n_tasks} tasks are '
        "ranked. Mean averages the family columns and mixes metrics; a tie-break, "
        "not a score.</p>"
        f"</div>{ranked}</div>"
        '<div class="pm-group"><div class="pm-group-head">'
        '<p class="pm-over pm-over--marine">Per task</p>'
        '<p class="pm-note">Every submission, partial ones included. Read across a row.</p>'
        f"</div>{per_task}</div>"
        "</div>"
    )


# ------------------------------------------------------------------- tasks
def render_tasks(by_id: dict[str, dict]) -> str:
    """The Tasks page: one scannable row per hidden target. Provenance is never shown."""
    df = tasks_table(list(by_id.values()))
    table = _df_to_table(
        df, bold_axis=None, empty="The task registry is unavailable right now."
    )
    return (
        '<div class="pm-head"><div><h1>Tasks</h1>'
        f"<p>{len(df)} hidden clinical targets. One fixed linear probe reads each "
        "one out of your embedding; the cohorts stay anonymous, the biology does "
        "not.</p></div></div>"
        f'<div class="pm-body">{table}</div>'
    )