"""The analyzer's brain. Pure functions over plain data (no database, no Telegram), so every number
is unit-tested. The loading of the data lives in app/db/repos/analytics.py.

Honesty rules built in: small samples are labelled, a correlation is never called a cause, and every
insight shows the numbers it is based on."""
import html
import re
from collections import Counter
from dataclasses import dataclass, field
from datetime import date, datetime, timedelta, timezone
from statistics import median
from typing import Optional

ET = timezone(timedelta(hours=3))
SPARSE = 3  # a course with fewer files than this is "short of material"


# ------------------------------------------------------------------ plain data
@dataclass(frozen=True)
class UserRow:
    id: int
    created: datetime  # UTC
    joined: date  # Ethiopian date of joining
    source: str
    verified: bool
    onboarded: bool
    active_days: int
    stream: Optional[str] = None
    missing: Optional[str] = None  # join-gate places last missing, e.g. "dbucentralforum"


@dataclass(frozen=True)
class EventRow:
    kind: str  # download | search | inline | find
    ref: str
    n: int
    day: date
    user_id: int


@dataclass(frozen=True)
class MaterialRow:
    id: int
    course_id: Optional[int]
    title: str
    downloads: int
    status: str
    kind: str


@dataclass(frozen=True)
class SubRow:
    status: str
    user_id: int
    created: datetime
    reviewed: Optional[datetime]


@dataclass(frozen=True)
class PostRow:
    message_id: int
    posted: datetime  # UTC
    preview: str
    tags: list[str]
    reactions: Optional[int]


@dataclass
class Snapshot:
    now: datetime
    users: list[UserRow]
    checkins: dict[int, set[date]]
    events: list[EventRow]
    materials: list[MaterialRow]
    courses: dict[int, str]
    subs: list[SubRow]
    posts: list[PostRow] = field(default_factory=list)

    @property
    def today(self) -> date:
        return self.now.astimezone(ET).date()


# ------------------------------------------------------------------ small helpers
def pct(part: int, whole: int) -> int:
    return round(100 * part / whole) if whole else 0


def bar(percent: float, width: int = 10) -> str:
    filled = max(0, min(width, round(percent / 100 * width)))
    return "▓" * filled + "░" * (width - filled)


def esc(text: str) -> str:
    return html.escape(text or "", quote=False)


def extract_tags(text: str) -> list[str]:
    """?start= tags inside a post: 'ad1', 'dorm5' (numbers are personal invite links, ignored)."""
    tags = re.findall(r"[?&]start=([A-Za-z0-9_]{1,20})", text or "")
    return sorted({t.lower() for t in tags if not t.isdigit()})


# ------------------------------------------------------------------ growth + funnel
def per_day(users: list[UserRow], today: date, days: int = 14) -> list[tuple[date, int]]:
    counts = Counter(u.joined for u in users)
    return [(today - timedelta(days=i), counts.get(today - timedelta(days=i), 0)) for i in range(days - 1, -1, -1)]


def funnel(users: list[UserRow]) -> dict:
    n = len(users)
    verified = sum(u.verified for u in users)
    onboarded = sum(u.onboarded for u in users)
    active3 = sum(u.active_days >= 3 for u in users)
    return dict(joined=n, verified=verified, onboarded=onboarded, active3=active3)


def biggest_leak(f: dict) -> Optional[tuple[str, str, int, int]]:
    """The stage with the largest loss between consecutive steps: (from, to, people lost, % lost)."""
    steps = [("joined", "in channel + forum", f["joined"], f["verified"]),
             ("in channel + forum", "finished setup", f["verified"], f["onboarded"])]
    steps = [(a, b, x - y, pct(x - y, x)) for a, b, x, y in steps if x > 0]
    return max(steps, key=lambda s: s[2]) if steps and max(s[2] for s in steps) > 0 else None


def source_table(users: list[UserRow]) -> list[dict]:
    groups: dict[str, list[UserRow]] = {}
    for u in users:
        groups.setdefault(u.source, []).append(u)
    rows = []
    for source, members in groups.items():
        f = funnel(members)
        rows.append(dict(source=source, **f, setup_pct=pct(f["onboarded"], f["joined"])))
    return sorted(rows, key=lambda r: -r["joined"])


def stuck_by_missing(users: list[UserRow]) -> list[tuple[str, int]]:
    c = Counter((u.missing or "not recorded yet") for u in users if not u.verified)
    return c.most_common()


# ------------------------------------------------------------------ activity + retention
def dau_wau(checkins: dict[int, set[date]], today: date) -> dict:
    week = [today - timedelta(days=i) for i in range(6, -1, -1)]
    dau = [(d, sum(1 for days in checkins.values() if d in days)) for d in week]
    wau = sum(1 for days in checkins.values() if any(d in days for d in week))
    last_full = dau[-2][1] if len(dau) >= 2 else 0  # yesterday is complete, today is not
    return dict(dau=dau, wau=wau, stickiness=pct(last_full, wau))


def retention(users: list[UserRow], checkins: dict[int, set[date]], today: date, cohorts: int = 7) -> dict:
    """Share of each day's new students who were active exactly N days later. Check-ins only exist from the day
    activity tracking started, so earlier cohorts are 'n/a' instead of a misleading 0. A day that is not over
    yet (today) is never counted."""
    all_days = [d for days in checkins.values() for d in days]
    start = min(all_days) if all_days else None
    rows = []
    for offset in range(cohorts, 0, -1):
        d = today - timedelta(days=offset)
        members = [u for u in users if u.joined == d]
        if not members:
            continue
        cells = {}
        for n in (1, 3, 7):
            target = d + timedelta(days=n)
            if target >= today or start is None or target < start:
                cells[n] = None  # not reached yet, or before we tracked activity
            else:
                cells[n] = pct(sum(1 for u in members if target in checkins.get(u.id, set())), len(members))
        rows.append(dict(day=d, size=len(members), **{f"d{n}": v for n, v in cells.items()}))
    eligible = [u for u in users if u.joined < today]
    came_back = sum(1 for u in eligible if any(day > u.joined for day in checkins.get(u.id, set())))
    return dict(rows=rows, tracking_start=start, came_back=came_back, eligible=len(eligible))


# ------------------------------------------------------------------ library + searches
def library(snap: Snapshot) -> dict:
    ok = [m for m in snap.materials if m.status == "ok"]
    per_course = Counter(m.course_id for m in ok if m.course_id)
    empty = [name for cid, name in snap.courses.items() if per_course.get(cid, 0) == 0]
    sparse = [name for cid, name in snap.courses.items() if 0 < per_course.get(cid, 0) < SPARSE]
    week_start = snap.today - timedelta(days=6)
    downloads_week = [e for e in snap.events if e.kind == "download" and e.day >= week_start]
    since = snap.today - timedelta(days=13)
    searches = [e for e in snap.events if e.kind in ("search", "inline", "find") and e.day >= since and len(e.ref) >= 2]
    missed = Counter(e.ref for e in searches if e.n == 0)
    found = Counter(e.ref for e in searches if e.n > 0)
    top_files = sorted(ok, key=lambda m: -m.downloads)[:5]
    return dict(
        files=len(ok), kinds=Counter(m.kind for m in ok), courses=len(snap.courses), empty=empty, sparse=sparse,
        downloads_total=sum(m.downloads for m in ok), downloads_week=len(downloads_week),
        downloaders_week=len({e.user_id for e in downloads_week}), top_files=top_files,
        by_course=[(snap.courses.get(cid, "?"), n) for cid, n in per_course.most_common(5)],
        searches=len(searches), missed_total=sum(missed.values()), missed=missed.most_common(8), found=found.most_common(5),
    )


# ------------------------------------------------------------------ contributions + channel posts
def contributions(snap: Snapshot) -> dict:
    by = Counter(s.status for s in snap.subs)
    done = [s for s in snap.subs if s.reviewed]
    hours = [(s.reviewed - s.created).total_seconds() / 3600 for s in done]
    pending = [s for s in snap.subs if s.status == "pending"]
    oldest = max(((snap.now - s.created).total_seconds() / 3600 for s in pending), default=0.0)
    top = Counter(s.user_id for s in snap.subs if s.status == "approved").most_common(3)
    return dict(
        pending=by["pending"], approved=by["approved"], rejected=by["rejected"],
        approval=pct(by["approved"], by["approved"] + by["rejected"]),
        avg_hours=round(sum(hours) / len(hours), 1) if hours else None, oldest_pending_hours=round(oldest, 1), top=top,
    )


def channel_posts(snap: Snapshot, window_hours: int = 6) -> list[dict]:
    """For each post: students who joined the bot in the hours after it, and students who came through a tag
    written in the post. NOTE: a correlation, not proof; close posts share the same joiners."""
    rows = []
    for p in snap.posts:
        end = p.posted + timedelta(hours=window_hours)
        soon = sum(1 for u in snap.users if p.posted <= u.created < end)
        tagged = sum(1 for u in snap.users if u.source in p.tags)
        rows.append(dict(message_id=p.message_id, posted=p.posted, preview=p.preview, tags=p.tags,
                         reactions=p.reactions, soon=soon, tagged=tagged))
    return sorted(rows, key=lambda r: (-r["tagged"], -r["soon"]))


# ------------------------------------------------------------------ insights (rule based, numbers shown)
@dataclass
class Insight:
    priority: int  # 1 = most important
    title: str
    evidence: str
    action: str


def insights(snap: Snapshot) -> list[Insight]:
    out: list[Insight] = []
    users, today = snap.users, snap.today
    if not users:
        return out
    f = funnel(users)
    leak = biggest_leak(f)

    stuck = [u for u in users if not u.verified]
    if f["joined"] and pct(len(stuck), f["joined"]) >= 15:
        top = [(m, n) for m, n in stuck_by_missing(users)][:3]
        detail = ", ".join(f"{n} missing {m}" if m != "not recorded yet" else f"{n} not recorded yet" for m, n in top)
        out.append(Insight(1, "Students stop at the join step",
                           f"{len(stuck)} of {f['joined']} ({pct(len(stuck), f['joined'])}%) never got through: {detail}.",
                           "Check /gatecheck. If most miss the forum, consider making it optional or send one reminder."))
    elif leak and leak[3] >= 10:
        out.append(Insight(2, f"Biggest drop: {leak[0]} → {leak[1]}", f"{leak[2]} people ({leak[3]}%) are lost here.",
                           "Make this step shorter or explain why it matters."))

    verified_not_setup = f["verified"] - f["onboarded"]
    if f["verified"] and pct(verified_not_setup, f["verified"]) >= 10:
        out.append(Insight(2, "Some verified students did not finish setup",
                           f"{verified_not_setup} of {f['verified']} ({pct(verified_not_setup, f['verified'])}%) stopped at stream/year.",
                           "Check that the two setup questions are clear."))

    ret = retention(users, snap.checkins, today)
    d1 = [(r["d1"], r["size"]) for r in ret["rows"] if r["d1"] is not None]
    if d1 and sum(s for _, s in d1) >= 20:
        total = sum(s for _, s in d1)
        avg = round(sum(v * s for v, s in d1) / total)
        if avg < 20:
            out.append(Insight(1, "Few students come back the next day", f"Day-1 return is {avg}% (over {total} students).",
                               "Give a reason to return: new files weekly, a daily XP nudge, exam-week reminders."))
    elif ret["tracking_start"] is None or snap.checkins and not d1:
        since = ret["tracking_start"].isoformat() if ret["tracking_start"] else "now"
        out.append(Insight(4, "Not enough days of data for retention yet",
                           f"Activity has been tracked since {since}.", "Check again in a few days."))

    lib = library(snap)
    if lib["missed"] and lib["missed"][0][1] >= 2:
        q, n = lib["missed"][0]
        out.append(Insight(1, "Students search for things you do not have",
                           f"{lib['missed_total']} searches in 14 days found nothing; top: “{q}” ({n} times).",
                           "Add material for these words (see Library & searches), then they find it next time."))
    if snap.courses and (len(lib["empty"]) / len(snap.courses)) >= 0.4:
        out.append(Insight(2, "Many courses have no file", f"{len(lib['empty'])} of {len(snap.courses)} courses are empty.",
                           "Use the 🎯 wanted-courses prompt in announcements; ask seniors for past exams."))

    con = contributions(snap)
    if con["pending"] and con["oldest_pending_hours"] >= 24:
        out.append(Insight(2, "Student files are waiting", f"{con['pending']} waiting; the oldest {con['oldest_pending_hours']:.0f} h.",
                           "Run /queue. Slow reviews discourage contributors."))

    days = [n for _, n in per_day(users, today, 14)]
    nonzero = [n for n in days if n]
    if len(nonzero) >= 3 and max(days) >= 3 * max(1, median(days)):
        out.append(Insight(3, "Growth comes from single pushes",
                           f"Best day {max(days)} new students vs a typical day of {median(days):g}.",
                           "Keep a steady posting rhythm; one-off pushes fade within 2-3 days."))

    sources = [r for r in source_table(users) if r["joined"] >= 10 and r["source"] != "before tracking"]
    if len(sources) >= 2:
        best, worst = max(sources, key=lambda r: r["setup_pct"]), min(sources, key=lambda r: r["setup_pct"])
        if best["setup_pct"] - worst["setup_pct"] >= 15:
            out.append(Insight(3, "Sources differ in quality",
                               f"“{best['source']}”: {best['setup_pct']}% finish setup vs “{worst['source']}”: {worst['setup_pct']}% "
                               f"({best['joined']} and {worst['joined']} students, still small samples).",
                               "Repeat what works in the better source."))
    referral = sum(1 for u in users if u.source == "referral")
    if len(users) >= 50 and pct(referral, len(users)) < 5:
        out.append(Insight(3, "Few students join by invite", f"{referral} of {len(users)} ({pct(referral, len(users))}%) came from an invite.",
                           "Mention “Invite friends” in channel posts; the share button is one tap."))

    posts = channel_posts(snap)
    if posts and (posts[0]["tagged"] or posts[0]["soon"]):
        p = posts[0]
        out.append(Insight(4, "Your best channel post so far",
                           f"“{esc(p['preview'][:50])}” → {p['tagged']} via its tag, {p['soon']} new students within 6 h "
                           "(a correlation, not proof).", "Write more posts like this one."))
    return sorted(out, key=lambda i: i.priority)


# ------------------------------------------------------------------ text for each screen
def _fmt_day(d: date) -> str:
    return d.strftime("%m-%d")


def overview(snap: Snapshot) -> str:
    users, today = snap.users, snap.today
    f = funnel(users)
    new_today = sum(1 for u in users if u.joined == today)
    wau = dau_wau(snap.checkins, today)
    ret = retention(users, snap.checkins, today)
    lib = library(snap)
    con = contributions(snap)
    lines = [
        f"📊 <b>Analytics</b> · {snap.now.astimezone(ET).strftime('%d %b %H:%M')} (Ethiopian time)",
        f"👥 <b>{f['joined']}</b> students (+{new_today} today) · in channel+forum <b>{f['verified']}</b> ({pct(f['verified'], f['joined'])}%) "
        f"· finished setup <b>{f['onboarded']}</b> ({pct(f['onboarded'], f['joined'])}%)",
        f"🔁 Active this week <b>{wau['wau']}</b> · came back at least once <b>{ret['came_back']}</b> of {ret['eligible']} "
        f"({pct(ret['came_back'], ret['eligible'])}%)",
        f"📚 <b>{lib['files']}</b> files · {lib['downloads_total']} downloads ({lib['downloads_week']} this week) · "
        f"{len(lib['empty'])} courses empty",
        f"🔍 {lib['searches']} searches in 14 days · found nothing: {lib['missed_total']} ({pct(lib['missed_total'], lib['searches'])}%)",
        f"📤 Student files: {con['pending']} waiting · {con['approved']} approved · {con['rejected']} rejected",
    ]
    ins = insights(snap)[:3]
    if ins:
        lines.append("\n💡 <b>What matters most now</b>")
        lines += [f"{i}. <b>{esc(x.title)}</b>: {x.evidence}" for i, x in enumerate(ins, 1)]
    lines.append("\nTap a button for details. Small samples (under ~30 people) are only hints.")
    return "\n".join(lines)


def section_growth(snap: Snapshot) -> str:
    days = per_day(snap.users, snap.today, 14)
    top = max((n for _, n in days), default=0) or 1
    lines = ["👥 <b>Growth and sources</b>", "<b>New students per day</b>"]
    lines += [f"{_fmt_day(d)}  {bar(100 * n / top, 12)} {n}" for d, n in days]
    f = funnel(snap.users)
    lines.append(f"\n<b>Funnel</b> (everyone ever)\njoined <b>{f['joined']}</b> → in channel + forum <b>{f['verified']}</b> → "
                 f"finished setup <b>{f['onboarded']}</b> → used the bot on 3+ days <b>{f['active3']}</b>")
    lines.append("\n<b>By source</b> (joined → in channel+forum → finished setup → 3+ days)")
    for r in source_table(snap.users)[:10]:
        small = " ·small sample" if r["joined"] < 10 else ""
        lines.append(f"• <b>{esc(r['source'])}</b>: {r['joined']} → {r['verified']} → {r['onboarded']} → {r['active3']} "
                     f"({r['setup_pct']}% setup){small}")
    stuck = stuck_by_missing(snap.users)
    if stuck:
        lines.append("\n<b>Stuck at the join step</b>")
        lines += [f"• {n}: {('missing ' + m) if m != 'not recorded yet' else m}" for m, n in stuck[:5]]
    return "\n".join(lines)


def section_retention(snap: Snapshot) -> str:
    ret = retention(snap.users, snap.checkins, snap.today)
    w = dau_wau(snap.checkins, snap.today)
    lines = ["🔁 <b>Activity and retention</b>", "<b>Active students per day</b> (opened the bot)"]
    peak = max((n for _, n in w["dau"]), default=0) or 1
    lines += [f"{_fmt_day(d)}  {bar(100 * n / peak, 12)} {n}" for d, n in w["dau"]]
    lines.append(f"\nWeekly active: <b>{w['wau']}</b> · yesterday's active ÷ weekly active: <b>{w['stickiness']}%</b>")
    lines.append(f"Came back at least once: <b>{ret['came_back']}</b> of {ret['eligible']} ({pct(ret['came_back'], ret['eligible'])}%)")
    lines.append("\n<b>Return rate by signup day</b> (share active again on day 1 / 3 / 7)")
    cell = lambda v: "n/a" if v is None else f"{v}%"  # noqa: E731
    if ret["rows"]:
        lines += [f"{_fmt_day(r['day'])} (n={r['size']}): {cell(r['d1'])} / {cell(r['d3'])} / {cell(r['d7'])}" for r in ret["rows"]]
    else:
        lines.append("no signups in the last 7 days")
    since = ret["tracking_start"].isoformat() if ret["tracking_start"] else "not yet"
    lines.append(f"\n“n/a” = that day has not come yet, or it is before activity tracking began ({since}).")
    return "\n".join(lines)


def section_library(snap: Snapshot) -> str:
    lib = library(snap)
    lines = ["📚 <b>Library and searches</b>",
             f"{lib['files']} files in {lib['courses']} courses · kinds: " + ", ".join(f"{k} {n}" for k, n in lib["kinds"].most_common()),
             f"Downloads: {lib['downloads_total']} all time · {lib['downloads_week']} this week by {lib['downloaders_week']} students"]
    if lib["top_files"]:
        lines.append("\n<b>Most downloaded</b>")
        lines += [f"• {esc(m.title[:45])} ({m.downloads})" for m in lib["top_files"] if m.downloads]
    if lib["by_course"]:
        lines.append("\n<b>Courses with the most files</b>: " + ", ".join(f"{esc(n[:25])} {c}" for n, c in lib["by_course"]))
    lines.append(f"\n<b>No file at all</b> ({len(lib['empty'])}): " + (", ".join(esc(n[:28]) for n in lib["empty"][:8]) or "none")
                 + ("…" if len(lib["empty"]) > 8 else ""))
    if lib["sparse"]:
        lines.append(f"<b>Short of files</b> (under {SPARSE}): " + ", ".join(esc(n[:28]) for n in lib["sparse"][:8]))
    lines.append(f"\n🔍 <b>Searches (14 days)</b>: {lib['searches']} · found nothing {lib['missed_total']} ({pct(lib['missed_total'], lib['searches'])}%)")
    if lib["missed"]:
        lines.append("<b>Searched but NOT found</b> (add these!)")
        lines += [f"• “{esc(q)}” × {n}" for q, n in lib["missed"]]
    if lib["found"]:
        lines.append("<b>Most searched and found</b>: " + ", ".join(f"“{esc(q)}” {n}" for q, n in lib["found"]))
    return "\n".join(lines)


def section_contrib(snap: Snapshot) -> str:
    c = contributions(snap)
    lines = ["📤 <b>Student contributions</b>",
             f"Waiting <b>{c['pending']}</b> · approved <b>{c['approved']}</b> · rejected <b>{c['rejected']}</b> · approval rate <b>{c['approval']}%</b>"]
    if c["avg_hours"] is not None:
        lines.append(f"Average time to review: {c['avg_hours']} h")
    if c["pending"]:
        lines.append(f"Oldest waiting: {c['oldest_pending_hours']:.0f} h")
    if c["top"]:
        lines.append("Top contributors (user id · approved): " + ", ".join(f"{u} · {n}" for u, n in c["top"]))
    if not snap.subs:
        lines.append("No student files yet.")
    return "\n".join(lines)


def section_channel(snap: Snapshot) -> str:
    rows = channel_posts(snap)
    lines = ["📢 <b>Channel posts</b>"]
    if not rows:
        return "\n".join(lines + [
            "No posts recorded yet. The bot records new posts of the channel by itself. To add an older post, "
            "FORWARD it (text posts) to this bot as admin."])
    lines.append("Sorted by students who came through the post's own link tag, then by new students within 6 hours.")
    for r in rows[:8]:
        react = "?" if r["reactions"] is None else r["reactions"]
        tag = f" · tag {', '.join(r['tags'])}: <b>{r['tagged']}</b>" if r["tags"] else ""
        lines.append(f"• {r['posted'].astimezone(ET).strftime('%m-%d %H:%M')} “{esc(r['preview'][:40])}”\n"
                     f"   6h: <b>{r['soon']}</b> new{tag} · reactions: {react}")
    lines.append("\nA post and a sign-up close in time is a hint, not proof. Reactions are only known for posts seen live.")
    return "\n".join(lines)


def section_insights(snap: Snapshot) -> str:
    ins = insights(snap)
    if not ins:
        return "💡 <b>Insights</b>\nNothing stands out yet; there is not enough data."
    lines = ["💡 <b>Insights and what to do</b> (from your numbers, most important first)"]
    for i, x in enumerate(ins, 1):
        lines.append(f"\n<b>{i}. {esc(x.title)}</b>\n{x.evidence}\n➡️ {esc(x.action)}")
    return "\n".join(lines)