"""Weekly digest assembly and Resend delivery."""
from __future__ import annotations

import json
import urllib.error
import urllib.request
from collections import Counter, defaultdict
from pathlib import Path
from typing import Any

from scripts.lib.chat_eval import analytics
from scripts.lib.chat_eval.report import build_report
from scripts.lib.chat_eval.turn_log import iter_turns


_RESEND_EMAILS_URL = "https://api.resend.com/emails"

# A chat is classified as probe traffic (regression-suite) if its opener
# appears this many times or more across the window. Deterministic test
# batteries fire the same opener repeatedly; real students don't.
# This is more robust than a turn-count threshold — a real student whose
# bot crashed on turn 1 still gets counted as a real chat.
_PROBE_OPENER_REPEATS = 3

# Topics that are catalog scaffolding, not real economics concepts.
# Skip them from "what students struggled with" since their inclusion
# would be operationally meaningful but pedagogically meaningless.
_META_TOPICS = {"m_g_r_e_c_o_n780__fall1", "not_topic", "t_e_m_p_l_a_t_e", ""}

# Dimension labels for human-facing output. snake_case is fine for tables,
# but the TL;DR sentence needs friendlier copy.
_DIMENSION_LABELS = {
    "substance": "substance",
    "voice": "faculty voice",
    "rigor": "rigor",
    "pedagogy_fit": "pedagogy fit",
    "citation_discipline": "citation discipline",
}

_EMAIL_CSS = """
body { font-family: -apple-system, "Segoe UI", Helvetica, Arial, sans-serif;
       color: #1d1d1f; line-height: 1.55; max-width: 720px; margin: 0 auto;
       padding: 1.5rem; }
h1 { font-size: 1.6rem; margin-bottom: 0.5rem; }
h2 { font-size: 1.25rem; margin-top: 2rem; padding-bottom: 0.3rem;
     border-bottom: 1px solid #e1e1e1; }
h3 { font-size: 1.05rem; margin-top: 1.3rem; }
table { border-collapse: collapse; margin: 0.75rem 0; width: 100%; }
th, td { text-align: left; border-bottom: 1px solid #e1e1e1;
         padding: 0.4rem 0.6rem; font-size: 0.92em; vertical-align: top; }
th { color: #555; font-weight: 600; }
code { background: #f4f4f6; padding: 0.1em 0.35em; border-radius: 3px;
       font-family: ui-monospace, Menlo, Consolas, monospace; font-size: 0.92em; }
blockquote { border-left: 3px solid #00539b; color: #555; margin: 0.75rem 0;
             padding: 0.1rem 0 0.1rem 1rem; }
"""


def render_html(markdown_str: str) -> str:
    """Render a markdown document to a self-contained HTML email body."""
    import markdown as _markdown

    body = _markdown.markdown(markdown_str, extensions=["tables"])
    return (
        "<!DOCTYPE html>\n"
        "<html><head><meta charset=\"utf-8\">"
        f"<style>{_EMAIL_CSS}</style></head>"
        f"<body>{body}</body></html>"
    )


def send_via_resend(
    *,
    api_key: str,
    sender: str,
    recipients: list[str],
    subject: str,
    html_body: str,
) -> None:
    """POST one email through the Resend API. Raises on non-2xx."""
    payload = json.dumps({
        "from": sender,
        "to": recipients,
        "subject": subject,
        "html": html_body,
    }).encode("utf-8")

    req = urllib.request.Request(
        _RESEND_EMAILS_URL,
        data=payload,
        method="POST",
        headers={
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json",
            "User-Agent": "me-hub-weekly-digest/1.0",
        },
    )
    try:
        with urllib.request.urlopen(req, timeout=30) as resp:
            if resp.status >= 300:
                body = resp.read().decode("utf-8", "replace")
                raise RuntimeError(f"Resend returned {resp.status}: {body}")
    except urllib.error.HTTPError as e:
        try:
            body = e.read().decode("utf-8", "replace")
        except Exception:
            body = ""
        raise RuntimeError(f"Resend returned {e.code}: {body}") from e


def faculty_digest(
    turns_log_path: Path,
    tuning_log_path: Path,
    *,
    since: str,
    today: str,
    window_days: int = 7,
) -> str:
    """Build the full faculty-forward weekly digest markdown body.

    Sections in priority order: TL;DR, what students struggled with, did the
    bot help, one thing to change, cost, operational. Filters probe traffic
    out of student-facing sections; surfaces it under operational signals.
    """
    all_turns = list(iter_turns(turns_log_path, since=since))
    chats = _split_into_chats(all_turns)
    real_chats, probe_chats = _classify_chats(chats)
    real_turns = [t for c in real_chats for t in c]
    real_judged = [t for t in real_turns if (t.get("judge") or {}).get("scores")]

    parts: list[str] = []
    parts.append(f"# Weekly briefing — MGRECON780 chat tutor")
    parts.append(f"_{today} · last {window_days} days_")
    parts.append("")
    parts.extend(_section_tldr(real_chats, real_turns, real_judged, probe_chats))
    parts.extend(_section_struggles(real_turns, real_judged))
    parts.extend(_section_bot_value(real_judged, real_turns))
    parts.extend(_section_one_change(real_judged, real_turns, turns_log_path, since))
    parts.extend(_section_cost(turns_log_path, since))
    parts.extend(_section_operational(
        all_turns, real_chats, probe_chats, tuning_log_path, since,
    ))
    parts.extend(_section_full_detail(
        turns_log_path, tuning_log_path, since=since, window_days=window_days,
    ))
    return "\n".join(parts)


def _section_full_detail(
    turns_log_path: Path,
    tuning_log_path: Path,
    *,
    since: str,
    window_days: int,
) -> list[str]:
    """Append the full v1 report below the faculty briefing. Same content as
    the pre-redesign email — tuning loop, all analytics tables, chat sessions —
    for operators or faculty who want to drill into the underlying data.
    """
    parts = ["", "---", "", "# Full report", ""]
    parts.append(_tuning_loop_summary(tuning_log_path, since=since))
    parts.append(build_report(
        turns_log_path,
        since=since[:10] if since else None,
        title=f"Chat feedback detail (last {window_days} days)",
        heading_level=2,
    ))
    return parts


def _classify_chats(
    chats: list[list[dict]],
) -> tuple[list[list[dict]], list[list[dict]]]:
    """Split chats into (real, probe).

    A chat is probe traffic if its opener (first user_message) appears
    `_PROBE_OPENER_REPEATS` or more times across all chats. Real students
    type unique openers; regression suites fire the same battery repeatedly.
    """
    opener_freq: Counter[str] = Counter()
    for chat in chats:
        if chat:
            opener_freq[chat[0].get("user_message", "")] += 1
    real, probe = [], []
    for chat in chats:
        if not chat:
            continue
        opener = chat[0].get("user_message", "")
        if opener_freq[opener] >= _PROBE_OPENER_REPEATS:
            probe.append(chat)
        else:
            real.append(chat)
    return real, probe


def _split_into_chats(turns: list[dict]) -> list[list[dict]]:
    """Group turns into chats. New chat when session_id changes or
    turn_index_in_session resets to 0."""
    chats: list[list[dict]] = []
    current: list[dict] = []
    last_sid = None
    for turn in sorted(turns, key=lambda t: t.get("ts", "")):
        sid = turn.get("session_id", "")
        idx = turn.get("turn_index_in_session", 0)
        if not current or sid != last_sid or idx == 0:
            if current:
                chats.append(current)
            current = [turn]
        else:
            current.append(turn)
        last_sid = sid
    if current:
        chats.append(current)
    return chats


def _section_tldr(
    real_chats: list[list[dict]],
    real_turns: list[dict],
    real_judged: list[dict],
    probe_chats: list[list[dict]],
) -> list[str]:
    parts = ["## TL;DR", ""]
    if not real_turns:
        parts.append(
            "No real student traffic this week. "
            f"{len(probe_chats)} regression-suite probe(s) ran against the bot."
        )
        parts.append("")
        return parts

    score_map = _mean_scores(real_judged)
    lowest = (
        min(score_map.items(), key=lambda kv: kv[1])
        if score_map else (None, None)
    )
    highest = (
        max(score_map.items(), key=lambda kv: kv[1])
        if score_map else (None, None)
    )
    missed_concepts = _top_misconceptions_missed(real_judged, top_n=1)
    miss_phrase = (
        f"`{missed_concepts[0][0]}` slipped past the bot {missed_concepts[0][1]['missed']}x"
        if missed_concepts else "no misconceptions slipped past the bot"
    )
    no_response = _no_response_turns(real_turns)
    no_response_note = (
        f" ⚠ **{len(no_response)} student turn(s) got no response from the bot.**"
        if no_response else ""
    )
    parts.append(
        f"**{len(real_chats)} student chats** ({len(real_turns)} turns, "
        f"{len(real_judged)} graded). "
        f"Bot was strongest on **{_DIMENSION_LABELS.get(highest[0], highest[0])}** "
        f"({highest[1]:.1f}/5) and weakest on "
        f"**{_DIMENSION_LABELS.get(lowest[0], lowest[0])}** ({lowest[1]:.1f}/5); "
        f"{miss_phrase}.{no_response_note}"
    )
    parts.append("")
    return parts


def _section_struggles(
    real_turns: list[dict],
    real_judged: list[dict],
) -> list[str]:
    parts = ["## 1. Where students struggled", ""]
    if not real_turns:
        parts.append("_No real student traffic this week._")
        parts.append("")
        return parts

    # Top stuck concepts: syntopicon topics that appeared in a turn whose
    # learning_trajectory was "clarification" (judge flagged that the
    # student needed the bot to re-explain). Each (topic, turn) pair
    # counts once — so a single chat doesn't inflate the count via 10
    # retrievals to the same topic.
    stuck_topic_counts: Counter[str] = Counter()
    for turn in real_judged:
        judge = turn.get("judge") or {}
        if judge.get("learning_trajectory") != "clarification":
            continue
        seen_in_turn: set[str] = set()
        for r in turn.get("retrieved", []):
            topic = r.get("topic") or ""
            if not topic or topic in _META_TOPICS or topic.isdigit():
                continue
            if topic in seen_in_turn:
                continue
            stuck_topic_counts[topic] += 1
            seen_in_turn.add(topic)
    if stuck_topic_counts:
        parts.append("**Concepts where students needed re-explanation** "
                     "(turns flagged as `clarification` by the judge):")
        parts.append("")
        parts.append("| Topic | Re-explained in turns |")
        parts.append("|---|---|")
        for topic, n in stuck_topic_counts.most_common(5):
            parts.append(f"| {topic.replace('_', ' ')} | {n} |")
        parts.append("")

    # Misconceptions slipping through (bot didn't correct what student showed).
    missed = _top_misconceptions_missed(real_judged, top_n=5)
    if missed:
        parts.append("**Misconceptions students exhibited that the bot didn't address:**")
        parts.append("")
        parts.append("| Misconception | Times missed | Times handled |")
        parts.append("|---|---|---|")
        for mid, stats in missed:
            parts.append(f"| `{mid}` | {stats['missed']} | {stats['handled']} |")
        parts.append("")

    # Representative confused-student questions: real-student turns with
    # is_follow_up=True or rigor<=2. Show 3.
    confused = _representative_confused_questions(real_turns, real_judged, top_n=3)
    if confused:
        parts.append("**Where students got stuck (in their own words):**")
        parts.append("")
        for q in confused:
            parts.append(f"> {q}")
            parts.append("")

    if not stuck_topic_counts and not missed and not confused:
        parts.append("_No clear struggle patterns this week — students mostly "
                     "got what they asked for on the first try._")
        parts.append("")
    return parts


def _section_bot_value(
    real_judged: list[dict],
    real_turns: list[dict],
) -> list[str]:
    parts = ["## 2. Did the bot help?", ""]
    if not real_judged:
        parts.append("_No judged turns yet for real student chats._")
        parts.append("")
        return parts

    # Mean scores overall + bar visualization.
    score_map = _mean_scores(real_judged)
    parts.append("**Quality scores** (1–5, averaged over judged turns):")
    parts.append("")
    parts.append("| Dimension | Score | |")
    parts.append("|---|---|---|")
    for dim in ["substance", "voice", "rigor", "pedagogy_fit", "citation_discipline"]:
        if dim not in score_map:
            continue
        v = score_map[dim]
        bar = _score_bar(v)
        label = _DIMENSION_LABELS.get(dim, dim)
        parts.append(f"| {label} | {v:.2f} | {bar} |")
    parts.append("")

    # Score by intent — does the bot answer "explain" well but flunk "debug"?
    # Drop `<error>` (planner failure) since it's a system event, not an intent.
    by_intent = {
        i: s for i, s in _scores_by_intent(real_judged).items()
        if i != "<error>"
    }
    if by_intent:
        parts.append("**By student intent** (mean across all 5 dimensions, n=count of judged turns):")
        parts.append("")
        parts.append("| Intent | n | Mean |")
        parts.append("|---|---|---|")
        for intent, stats in sorted(by_intent.items(), key=lambda kv: -kv[1]["n"]):
            parts.append(f"| {intent} | {stats['n']} | {stats['mean']:.2f} |")
        parts.append("")

    # Pedagogical opportunities taken.
    taken = sum(1 for t in real_judged
                if (t.get("judge") or {}).get("pedagogical_opportunity_taken") is True)
    missed_opps = sum(1 for t in real_judged
                      if (t.get("judge") or {}).get("pedagogical_opportunity_taken") is False)
    total_opps = taken + missed_opps
    if total_opps:
        pct = 100.0 * taken / total_opps
        parts.append(f"**Teachable moments:** the bot acted on **{taken}/{total_opps}** "
                     f"({pct:.0f}%) — i.e., {missed_opps} times the judge flagged a "
                     "moment where the bot could have pushed the student further but didn't.")
        parts.append("")

    # Bot-side production bug: empty response. Surface separately from voice
    # failures since this isn't a quality issue but a "the bot crashed" issue.
    no_response = _no_response_turns(real_turns)
    if no_response:
        parts.append(
            f"**⚠ No response generated** for {len(no_response)} student turn(s) — "
            "the chat handler returned an empty reply. Investigate the chat "
            "orchestrator logs for these turn IDs:"
        )
        parts.append("")
        for t in no_response[:5]:
            opener = _flatten(t.get("user_message") or "", 80)
            parts.append(f"- `{t.get('turn_id', '?')}` — \"{opener}\"")
        parts.append("")

    # Voice failures — show up to 3 with full diagnosis. Newlines in
    # response/diagnosis are flattened so the markdown blockquote stays
    # intact (otherwise embedded bullets break out of the quote).
    vf = _voice_failures_from_turns(real_turns)
    if vf:
        parts.append(f"**Voice failures** ({len(vf)} turns scored ≤2 on voice — "
                     "these read as generic AI, not as your faculty):")
        parts.append("")
        parts.append("| Student asked | Bot replied | Judge diagnosis |")
        parts.append("|---|---|---|")
        for v in vf[:3]:
            q = _flatten(v["user_message"], 80)
            r = _flatten(v["response"], 140) or "_(no response generated)_"
            d = _flatten(v["diagnosis"], 200)
            parts.append(f"| {q} | {r} | {d} |")
        parts.append("")
    return parts


def _section_one_change(
    real_judged: list[dict],
    real_turns: list[dict],
    turns_log_path: Path,
    since: str,
) -> list[str]:
    """Rank-ordered, opinionated recommendations. Top 3 max."""
    parts = ["## 3. If you change one thing this week", ""]
    recs: list[tuple[int, str]] = []  # (priority, markdown line)

    # No-response bug always wins — students typed something and got nothing.
    no_response = _no_response_turns(real_turns)
    if no_response:
        recs.append((
            200,
            f"**Fix the no-response bug.** {len(no_response)} student turn(s) got "
            "an empty reply this week — students walked away with nothing. "
            "This is a production bug, not a quality issue; check the chat "
            "orchestrator's error path.",
        ))

    # Misconceptions slipping through — highest priority if missed >= 2
    missed = _top_misconceptions_missed(real_judged, top_n=3)
    for mid, stats in missed:
        if stats["missed"] >= 2:
            recs.append((
                100 + stats["missed"],
                f"**Tighten the prompt's `{mid}` correction.** Students "
                f"showed this misconception {stats['missed']} time(s) and the "
                f"bot let it pass. Add an explicit displacement rule in the "
                "prompt's misconception section.",
            ))

    # Intent-classifier mismatches: turns where the judge flagged
    # debug-intent answers being treated as Socratic explanation. Use the
    # judge's own diagnosis text rather than guessing from message length.
    debug_voice_misses = [
        t for t in real_judged
        if t.get("intent") == "debug"
        and ((t.get("judge") or {}).get("scores") or {}).get("voice", 5) <= 2
    ]
    if len(debug_voice_misses) >= 2:
        recs.append((
            90,
            f"**Fix the debug-mode behavior.** {len(debug_voice_misses)} debug-intent "
            "turn(s) drew voice ≤2 — students typed an answer (e.g., `C,M`, `68`) "
            "and the bot ran Socratic exploration instead of confirming or "
            "correcting. Tighten the debug-mode section of the chat prompt to "
            "default to direct confirmation when the student is submitting an answer.",
        ))

    # Doc-pruning opportunity — high retrieval, zero util_pct on real-student turns
    util = analytics.doc_utilization(real_turns) if real_turns else {}
    dead_docs = [
        (doc_id, s) for doc_id, s in util.items()
        if s["retrieved_count"] >= 20 and s["util_pct"] == 0.0
    ]
    if dead_docs:
        top_dead = sorted(dead_docs, key=lambda kv: -kv[1]["retrieved_count"])[:3]
        names = ", ".join(f"`{d[0]}`" for d in top_dead)
        recs.append((
            70,
            f"**Prune dead retrievals.** {len(dead_docs)} doc(s) were retrieved "
            "20+ times in real student chats but the bot used 0% of them. "
            f"Top offenders: {names}. These dilute the prompt and burn tokens.",
        ))

    # Faculty framing fidelity if mostly 1-2
    fid = [
        v for v in (
            (t.get("judge") or {}).get("faculty_framing_fidelity")
            for t in real_judged
        )
        if isinstance(v, int)
    ]
    if fid and sum(1 for v in fid if v <= 2) / len(fid) >= 0.30:
        recs.append((
            60,
            f"**Lopomo/Vaidya voice isn't carrying.** {sum(1 for v in fid if v <= 2)}/"
            f"{len(fid)} graded turns scored faculty-framing fidelity ≤2/5. "
            "Faculty may want to add 1-2 signature phrasings to the voice "
            "section of the chat-tutor prompt.",
        ))

    if not recs:
        parts.append(
            "_No high-confidence recommendation this week — sample is small "
            "or quality is steady. Re-check next Monday._"
        )
        parts.append("")
        return parts

    for i, (_, line) in enumerate(sorted(recs, key=lambda r: -r[0])[:3], 1):
        parts.append(f"{i}. {line}")
        parts.append("")
    return parts


def _section_cost(turns_log_path: Path, since: str) -> list[str]:
    """Compact cost section. Aggregate spend + the cache-prefix savings
    opportunity if it's non-trivial."""
    parts = ["## 4. Cost", ""]
    cost = analytics.cost_summary(turns_log_path, since=since)
    if cost["n_turns"] == 0:
        parts.append("_No turns this window._")
        parts.append("")
        return parts
    parts.append(
        f"**${cost['total_cost_usd']:.2f}** across **{cost['n_turns']} turns** "
        f"(mean **${cost['mean_cost_usd']:.3f}**/turn)."
    )
    parts.append("")

    cache = analytics.cache_summary(turns_log_path, since=since)
    if cache.get("n_turns_with_data", 0) > 0:
        hit = cache["cache_hit_rate"] * 100
        savings = cache["estimated_savings_usd"]
        if savings < 0:
            parts.append(
                f"_Caching is currently a **net loss of ${-savings:.2f}** "
                f"at {hit:.1f}% hit rate — the cache writes (1.25x premium) "
                "are outweighing the reads. Configure a permanent prefix "
                "to flip this number._"
            )
            parts.append("")
        else:
            parts.append(f"_Cache savings vs no-cache: **${savings:.2f}** "
                         f"at {hit:.1f}% hit rate._")
            parts.append("")

    rec = analytics.cache_recommendation(turns_log_path, since=since, ttl="1h", top_n=15)
    if rec.get("recommended"):
        parts.append(
            f"**Caching opportunity:** pinning the top {len(rec['recommended'])} "
            f"retrieved docs into a 1-hour prefix would save approximately "
            f"**${rec['total_recommended_savings_usd']:.2f}** at current usage. "
            "(One-line operator task.)"
        )
        parts.append("")
    return parts


def _section_operational(
    all_turns: list[dict],
    real_chats: list[list[dict]],
    probe_chats: list[list[dict]],
    tuning_log_path: Path,
    since: str,
) -> list[str]:
    parts = ["## 5. Operational", ""]
    parts.append(
        f"- **Traffic:** {len(real_chats)} student chats "
        f"({sum(len(c) for c in real_chats)} turns) + "
        f"{len(probe_chats)} probe / regression-test chats "
        f"({sum(len(c) for c in probe_chats)} turns). "
        "Student-facing sections above exclude probe traffic."
    )

    tuning_entries = list(iter_turns(tuning_log_path, since=since))
    if tuning_entries:
        accepted = sum(1 for e in tuning_entries if e.get("outcome") == "accepted")
        frozen = sum(1 for e in tuning_entries if e.get("outcome") == "rolled_back")
        parts.append(
            f"- **Tuning loop:** {len(tuning_entries)} daily run(s), "
            f"{accepted} penalty applied, {frozen} freeze(s)."
        )
    else:
        parts.append(
            "- **Tuning loop:** no daily-tuning entries in this window. "
            "If digest is running from the cron (which calls `fetch-state` first), "
            "investigate the `tune-retrieval` workflow."
        )

    error_intents = sum(1 for t in all_turns if t.get("intent") == "<error>")
    if error_intents:
        parts.append(
            f"- **Errors:** {error_intents} turn(s) recorded `<error>` intent "
            "(planner failure — investigate the planner logs)."
        )
    parts.append("")
    return parts


def _mean_scores(judged: list[dict]) -> dict[str, float]:
    sums: dict[str, float] = defaultdict(float)
    counts: dict[str, int] = defaultdict(int)
    for t in judged:
        for k, v in ((t.get("judge") or {}).get("scores") or {}).items():
            if isinstance(v, (int, float)):
                sums[k] += v
                counts[k] += 1
    return {k: sums[k] / counts[k] for k in sums if counts[k]}


def _scores_by_intent(judged: list[dict]) -> dict[str, dict[str, Any]]:
    sums: dict[str, float] = defaultdict(float)
    counts: dict[str, int] = defaultdict(int)
    for t in judged:
        intent = t.get("intent") or "unknown"
        scores = ((t.get("judge") or {}).get("scores") or {}).values()
        nums = [v for v in scores if isinstance(v, (int, float))]
        if not nums:
            continue
        sums[intent] += sum(nums) / len(nums)
        counts[intent] += 1
    return {
        i: {"n": counts[i], "mean": sums[i] / counts[i]}
        for i in counts
    }


def _top_misconceptions_missed(
    judged: list[dict], *, top_n: int = 5,
) -> list[tuple[str, dict[str, int]]]:
    counts: dict[str, dict[str, int]] = defaultdict(
        lambda: {"missed": 0, "handled": 0}
    )
    for t in judged:
        for flag in ((t.get("judge") or {}).get("misconception_flags") or []):
            mid = flag.get("id", "")
            if not mid:
                continue
            in_q = bool(flag.get("in_query"))
            handled = bool(flag.get("handled_in_response"))
            if in_q and not handled:
                counts[mid]["missed"] += 1
            if handled:
                counts[mid]["handled"] += 1
    ranked = sorted(
        counts.items(),
        key=lambda kv: (-kv[1]["missed"], -kv[1]["handled"]),
    )
    return [(mid, s) for mid, s in ranked if s["missed"] > 0][:top_n]


def _representative_confused_questions(
    real_turns: list[dict],
    real_judged: list[dict],
    *,
    top_n: int = 3,
) -> list[str]:
    """Real student questions where the bot's answer scored low on rigor
    (≤3) — these are the moments the judge flagged the response as not
    rigorous enough for the question being asked. Distinct messages only."""
    candidates = [
        t for t in real_judged
        if ((t.get("judge") or {}).get("scores") or {}).get("rigor", 5) <= 3
    ]
    candidates.sort(
        key=lambda t: ((t.get("judge") or {}).get("scores") or {}).get("rigor", 5),
    )
    seen_msgs: set[str] = set()
    out: list[str] = []
    for t in candidates:
        msg = (t.get("user_message") or "").strip()
        if not msg or len(msg) <= 4 or msg in seen_msgs:
            continue
        seen_msgs.add(msg)
        out.append(msg[:160] + ("…" if len(msg) > 160 else ""))
        if len(out) >= top_n:
            break
    return out


def _no_response_turns(turns: list[dict]) -> list[dict]:
    """Turns where the bot generated no response at all — production bug.
    Detected by empty response field on a turn whose user_message was non-empty."""
    return [
        t for t in turns
        if (t.get("user_message") or "").strip()
        and not (t.get("response") or "").strip()
    ]


def _flatten(text: str, max_len: int) -> str:
    """Collapse whitespace, escape pipes (markdown table cells), truncate.
    Used to safely embed user/bot text inside markdown table cells."""
    if not text:
        return ""
    flat = " ".join(text.split()).replace("|", "\\|")
    if len(flat) > max_len:
        return flat[:max_len].rstrip() + "…"
    return flat


def _voice_failures_from_turns(
    real_turns: list[dict], *, threshold: int = 2,
) -> list[dict[str, Any]]:
    out = []
    for t in real_turns:
        judge = t.get("judge") or {}
        if not judge.get("scores"):
            continue
        if judge["scores"].get("voice", 5) > threshold:
            continue
        out.append({
            "turn_id": t.get("turn_id"),
            "user_message": t.get("user_message", ""),
            "response": t.get("response", ""),
            "voice_score": judge["scores"]["voice"],
            "diagnosis": judge.get("diagnosis", ""),
        })
    return out


def _score_bar(value: float, *, width: int = 10) -> str:
    """Inline visual bar: filled blocks for the score, empty for the rest.
    Renders fine in HTML email — Unicode block chars survive most clients."""
    filled = max(0, min(width, round(value / 5.0 * width)))
    return "█" * filled + "░" * (width - filled)


# ---------------------------------------------------------------------------
# Tuning-loop section for the appended full-detail report. Mirrors the v1
# layout — daily-run headline, freeze banner, penalty changes table,
# catalog drift footnote, identity footer.

def _tuning_loop_summary(tuning_log_path: Path, *, since: str) -> str:
    entries = list(iter_turns(tuning_log_path, since=since))
    if not entries:
        return _tuning_empty_section()
    return _tuning_render_section(entries)


def _tuning_empty_section() -> str:
    return (
        "## Tuning loop this week\n"
        "\n"
        "Tuning loop did not run this week - investigate the tune-retrieval workflow.\n"
        "\n"
    )


def _tuning_render_section(entries: list[dict]) -> str:
    entries = sorted(entries, key=lambda e: e.get("ts", ""))
    runs = len(entries)
    accepted = [e for e in entries if e.get("outcome") == "accepted"]
    freezes = [e for e in entries if e.get("outcome") == "rolled_back"]
    quiet = [
        e for e in entries
        if e.get("outcome") in ("skipped_no_eligible_doc", "skipped_insufficient_turns")
    ]
    latest = entries[-1]

    parts: list[str] = ["## Tuning loop this week", ""]
    headline_bits = [_pluralize(runs, "daily run", "daily runs")]
    if accepted:
        headline_bits.append(_pluralize(len(accepted), "penalty applied", "penalties applied"))
    if freezes:
        headline_bits.append(_pluralize(len(freezes), "freeze", "freezes"))
    if quiet:
        headline_bits.append(_pluralize(len(quiet), "quiet day", "quiet days"))
    parts.append(" | ".join(headline_bits))
    parts.append("")

    if latest.get("outcome") in ("skipped_frozen", "rolled_back"):
        reason = latest.get("frozen_reason") or _freeze_reason_from_rollback(latest)
        parts.append(
            f"> **Tuning loop is currently frozen** ({reason}). "
            "To unfreeze: `python -m scripts.lib.chat_eval.cli tune --unfreeze`."
        )
        parts.append("")

    if accepted:
        parts.extend(_tuning_accepted_table(accepted))

    if freezes:
        parts.append("### Freezes")
        parts.append("")
        for entry in freezes:
            parts.append(f"- {_freeze_line(entry)}")
        parts.append("")

    cleanup_union = sorted({
        doc_id
        for entry in entries
        for doc_id in (entry.get("cleanup_dropped_doc_ids") or [])
    })
    if cleanup_union:
        ids_str = ", ".join(f"`{doc_id}`" for doc_id in cleanup_union)
        parts.append(f"_Catalog drift: dropped stale penalties for {ids_str}._")
        parts.append("")

    parts.append("---")
    parts.append("")
    parts.append(
        f"_rubric_version: {latest.get('rubric_version', '?')} | "
        f"judge: {latest.get('judge_model', '?')} | "
        f"chat: {latest.get('chat_model', '?')} | "
        f"embed: {latest.get('embedding_model', '?')} | "
        f"catalog: {latest.get('catalog_artifact_version_id', '?')}_"
    )
    parts.append("")
    return "\n".join(parts)


def _tuning_accepted_table(accepted: list[dict]) -> list[str]:
    parts = [
        "### Penalty changes",
        "",
        "| Date | doc_id | kind | penalty before -> after | retrievals | util % | recent |",
        "|---|---|---|---|---|---|---|",
    ]
    for entry in accepted:
        action = entry.get("step_action") or {}
        date = (entry.get("ts") or "")[:10]
        doc_id = action.get("doc_id", "?")
        kind = action.get("kind", "?")
        before = action.get("penalty_before", "?")
        after = action.get("penalty_after", "?")
        retrieved = action.get("retrieved_count", "?")
        util_pct = action.get("util_pct", "?")
        history = action.get("recent_util_history") or []
        sparkline = _tuning_sparkline([h.get("util_pct", 0.0) for h in history])
        parts.append(
            f"| {date} | `{doc_id}` | {kind} | {before} -> {after} | "
            f"{retrieved} | {util_pct} | {sparkline} |"
        )
    parts.append("")
    return parts


def _pluralize(n: int, singular: str, plural: str) -> str:
    return f"{n} {singular if n == 1 else plural}"


_TUNING_SPARKLINE_BLOCKS = "▁▂▃▄▅▆▇█"


def _tuning_sparkline(values: list[float]) -> str:
    if not values:
        return ""
    out = []
    for value in values:
        idx = min(int((float(value) / 100.0) * len(_TUNING_SPARKLINE_BLOCKS)),
                  len(_TUNING_SPARKLINE_BLOCKS) - 1)
        out.append(_TUNING_SPARKLINE_BLOCKS[max(idx, 0)])
    return "".join(out)


def _drop_summary(entry: dict) -> tuple[str, object]:
    rc = entry.get("regression_check") or {}
    return rc.get("max_drop_dimension") or "?", rc.get("max_drop_value", "?")


def _freeze_reason_from_rollback(entry: dict) -> str:
    dim, drop = _drop_summary(entry)
    return f"{dim} regressed delta={drop}"


def _freeze_line(entry: dict) -> str:
    ts = (entry.get("ts") or "")[:10]
    dim, drop = _drop_summary(entry)
    confidence = (entry.get("regression_check") or {}).get("confidence") or {}
    n = confidence.get("n_after", "?")
    t_stat = confidence.get("max_drop_t_stat")
    extra = f", t={t_stat:.2f}" if isinstance(t_stat, (int, float)) else ""
    return f"{ts} - frozen on rolled_back: {dim} delta={drop} (n={n}{extra})"