"""AgAdvisor — Gradio frontend for Hugging Face Spaces.

HF retired the Streamlit SDK, and Gradio/Docker Spaces on cpu-basic now require PRO,
so the free path is a Gradio Space on ZeroGPU hardware. This module is a view layer
only: it drives the SAME pipeline as src/streamlit_app_conversational.py
(parse_query -> ToolMatcher -> ToolExecutor) and the SAME AccountsService (bcrypt
auth, per-user daily quota, history synced to a private HF Dataset), so abstention
and page-level citations behave identically. The Streamlit app remains the local /
EC2 entrypoint; keep the two in sync at the pipeline boundary, not the UI.
"""

# HF Spaces' free tier for Gradio is ZeroGPU. Its launcher aborts at startup ("No
# @spaces.GPU function detected") unless (a) `spaces` is imported BEFORE gradio/torch so
# it can install its hooks, and (b) the source contains a literal @spaces.GPU decorator.
# This app is CPU-only (all inference is via the OpenAI API), so the probe below only
# needs to EXIST — it is never called. `spaces` isn't a local dev dependency (HF injects
# it in the ZeroGPU build), so synthesize a no-op stub when absent, keeping the literal
# @spaces.GPU decorator so ZeroGPU's source scan still detects it on the Space.
try:
    import spaces  # provided by the HF ZeroGPU runtime; MUST precede `import gradio`
except Exception:  # local / non-ZeroGPU: synthesize a no-op `spaces` so import still works
    import sys as _sys
    import types as _types

    spaces = _types.ModuleType("spaces")
    spaces.GPU = lambda fn=None, **_kw: (fn if callable(fn) else (lambda f: f))
    _sys.modules["spaces"] = spaces


@spaces.GPU
def _zero_gpu_probe():
    """Exists solely so ZeroGPU detects a @spaces.GPU function at startup; unused."""
    return None


import logging

import gradio as gr

from src.accounts.service import AccountsService
from src.cdms.product_catalog import get_catalog
from src.parser import parse_query
from src.tools.tool_executor import ToolExecutor
from src.tools.tool_matcher import ToolMatcher

logger = logging.getLogger(__name__)


# Built once per process, not per session — these load models and are the cold-start cost.
accounts = AccountsService()
tool_matcher = ToolMatcher()
tool_executor = ToolExecutor()

try:
    PRODUCTS = sorted(get_catalog().available_products())
except Exception:
    logger.exception("Could not load the product catalog")
    PRODUCTS = []

CONTEXT_TURNS = 5  # matches the Streamlit app's last-5-messages window

_TOOL_LABELS = {
    "cdms_label": "CDMS label", "cdms": "CDMS label", "pesticide_label": "CDMS label",
    "rag": "CDMS label", "documentation": "CDMS label",
    "weather": "weather", "soil": "soil", "agriculture_web": "agriculture web", "ag_web": "agriculture web",
}


def _confidence_badge(tool: str, confidence: float) -> str:
    """Answer confidence/star badge (ISA feedback #3, ported from the Streamlit UI).

    Plain-markdown so it renders cleanly on mobile — the Streamlit version's badge
    was CSS-clipped on phones. `confidence` is the tool-router's match score (0-1).
    """
    stars = max(1, min(5, round((confidence or 0.0) * 5)))
    bar = "★" * stars + "☆" * (5 - stars)
    label = _TOOL_LABELS.get(tool, tool)
    return f"\n\n---\n`{bar}` · **{confidence:.0%} confidence** · answered via *{label}*"


# --- Response cache (ISA scalability/cost: LLM credits as the user base grows) ---
# Agronomists ask the same product questions repeatedly. A cache hit returns the
# stored answer and skips the WHOLE pipeline — query embedding, retrieval, and the
# OpenAI generation call — so repeats cost nothing. Keyed by the normalized
# question; only first-turn questions are cached so follow-ups (which depend on
# conversation context) are never wrongly reused. Process-wide + TTL + size cap.
import hashlib as _hashlib
import time as _time

_RESP_CACHE: dict[str, tuple[str, float]] = {}
_RESP_CACHE_TTL = 6 * 3600   # 6 hours
_RESP_CACHE_MAX = 500


def _cache_key(question: str) -> str:
    norm = " ".join(question.lower().split())
    return _hashlib.sha256(norm.encode()).hexdigest()


def _cache_put(key: str, reply: str) -> None:
    if len(_RESP_CACHE) >= _RESP_CACHE_MAX:  # evict oldest
        _RESP_CACHE.pop(min(_RESP_CACHE, key=lambda k: _RESP_CACHE[k][1]), None)
    _RESP_CACHE[key] = (reply, _time.time())


def answer(question: str, history: list[dict], on_step=None) -> tuple[str, dict]:
    """Run one question through the tool pipeline. Mirrors the Streamlit flow.

    Retrieval is auto-mode (decided in the CDMS tool): serve from the committed
    index when the label is present, else live-fetch + index + cache it.
    `on_step(str)` is called at each stage so the UI can show the real process
    (and make first-time label fetches legible as the slow step).

    Returns (reply, debug) where debug holds routing/source details for the
    optional Debug panel.
    """
    def _step(msg: str) -> None:
        if on_step:
            try:
                on_step(msg)
            except Exception:
                pass

    # Cache only first-turn questions (no context to depend on).
    cacheable = not history
    if cacheable:
        _ck = _cache_key(question)
        _hit = _RESP_CACHE.get(_ck)
        if _hit and (_time.time() - _hit[1]) < _RESP_CACHE_TTL:
            _step("⚡ Served from a recent cached answer.")
            return _hit[0], {"cached": True}
    context = [
        {"role": m["role"], "content": m["content"]}
        for m in (history or [])[-CONTEXT_TURNS * 2:]
    ]

    _step("Understanding your question…")
    try:
        keywords = parse_query(question).get("extracted_keywords", [])
    except Exception:
        keywords = []

    confidence = 0.3
    _step("Choosing the right tool…")
    try:
        match = tool_matcher.match_tool(keywords, question, conversation_context=context)
        tool = match["tool_name"]
        confidence = float(match.get("confidence", 0.3) or 0.3)
    except Exception:
        logger.exception("Tool matching failed; falling back to the label tool")
        tool = "cdms_label"

    debug = {"tool": tool, "confidence": confidence, "keywords": keywords}
    try:
        result = tool_executor.execute(
            tool_name=tool, user_question=question, conversation_context=context,
            on_step=on_step,
        )
    except Exception:
        # Never surface tracebacks to end users; log server-side.
        logger.exception("Tool execution failed")
        return "I ran into an unexpected problem answering that. Please try rephrasing.", debug

    raw = result.get("raw_data", {}) if isinstance(result, dict) else {}
    debug.update({
        "tool_used": result.get("tool_used", tool),
        "source": raw.get("source", "index"),
        "chunks": raw.get("total_chunks_found", raw.get("pdfs_indexed")),
        "success": result.get("success", False),
    })

    if not result.get("success", False):
        return result.get(
            "llm_response", "I couldn't find that in my label set."
        ), debug
    reply = result.get("llm_response", "I couldn't process that request.")
    # Show a confidence/star rating with real answers. Not on an abstention — a
    # confidence score on "I don't have that label" would be misleading.
    if "don't have the label" not in reply:
        reply += _confidence_badge(tool, confidence)
        # Cache only real answers (never abstentions) so a later retry can still
        # succeed — and only first-turn questions (cacheable).
        if cacheable:
            _cache_put(_ck, reply)
    return reply, debug


import queue as _queue
import threading as _threading


def _format_steps(steps: list[str]) -> str:
    """Render the live pipeline steps as a small checklist for the accordion."""
    if not steps:
        return "_working…_"
    return "\n".join(f"- {s}" for s in steps)


def _format_debug(debug: dict | None) -> str:
    """Render the last answer's routing details for the Debug panel."""
    if not debug:
        return "_No debug info yet — ask a question._"
    if debug.get("cached"):
        return "**Debug:** served from the in-process response cache (no pipeline run)."
    rows = [
        f"- **Tool:** `{debug.get('tool_used', debug.get('tool', '?'))}`",
        f"- **Confidence:** {float(debug.get('confidence', 0) or 0):.0%}",
        f"- **Retrieval source:** `{debug.get('source', '?')}` "
        f"({'live-fetched + cached' if debug.get('source') == 'live' else 'served from index'})",
        f"- **Chunks used:** {debug.get('chunks', '?')}",
        f"- **Keywords:** {', '.join(debug.get('keywords') or []) or '—'}",
    ]
    return "**Debug — last answer**\n" + "\n".join(rows)


def on_submit(question: str, history: list[dict], user: dict | None, last_mid):
    """Quota-gate, persist, answer, persist — as a streaming generator.

    Yields progressive updates so the UI shows the ACTUAL pipeline steps live
    (retrieval / live-fetch / indexing / writing), then collapses them into the
    "Process steps" accordion once the answer is in. The synchronous pipeline
    runs in a worker thread that pushes step strings onto a queue; this generator
    drains the queue and re-yields. Outputs:
        (chatbot, question, quota, steps_md, steps_accordion, last_msg_id, debug_md)
    """
    history = history or []
    if not user:
        yield history, "", "Please sign in first.", "", gr.update(), last_mid, gr.update()
        return
    if not question or not question.strip():
        yield history, "", "", "", gr.update(), last_mid, gr.update()
        return

    uid, chat_id = user["id"], user["chat_id"]

    if not accounts.check_quota(uid):
        history = history + [
            {"role": "user", "content": question},
            {"role": "assistant",
             "content": "You've reached today's question limit. Please come back tomorrow."},
        ]
        yield history, "", _quota_label(uid), "", gr.update(open=False), last_mid, gr.update()
        return

    accounts.add_message(chat_id, uid, "user", question)
    accounts.record_query(uid)

    # Show the user's turn immediately; stream steps into the open accordion.
    working_history = history + [{"role": "user", "content": question}]
    steps: list[str] = []
    step_q: _queue.Queue = _queue.Queue()
    box: dict = {}

    def _run():
        try:
            box["reply"], box["debug"] = answer(question, history, on_step=lambda m: step_q.put(m))
        except Exception as e:  # logged in answer(); keep a generic user-facing reply
            logger.exception("answer() failed in worker thread")
            box["error"] = e
        finally:
            step_q.put(None)  # sentinel: pipeline finished

    worker = _threading.Thread(target=_run, daemon=True)
    worker.start()

    yield working_history, "", _quota_label(uid), _format_steps(steps), gr.update(open=True), last_mid, gr.update()

    while True:
        item = step_q.get()
        if item is None:
            break
        steps.append(item)
        yield working_history, "", _quota_label(uid), _format_steps(steps), gr.update(open=True), last_mid, gr.update()

    worker.join()

    if box.get("error") is not None:
        reply = "I ran into an unexpected problem answering that. Please try rephrasing."
    else:
        reply = box.get("reply", "I couldn't process that request.")

    # Persist the assistant turn; keep its id so ratings/comments can reference it.
    mid = accounts.add_message(chat_id, uid, "assistant", reply, metadata={"tool": "cdms_label"})

    final_history = working_history + [{"role": "assistant", "content": reply}]
    steps.append("Done.")
    yield (final_history, "", _quota_label(uid), _format_steps(steps),
           gr.update(open=False), mid, gr.update(value=_format_debug(box.get("debug"))))


def _quota_label(uid: int) -> str:
    try:
        return f"{accounts.remaining_quota(uid)} questions left today"
    except Exception:
        return ""


def _load_user(user_row: dict):
    """Attach a chat (resuming the most recent) and hydrate its history."""
    uid = user_row["id"]
    chats = accounts.list_chats(uid)
    chat_id = chats[0]["id"] if chats else accounts.create_chat(uid, "Chat 1")
    messages = accounts.get_messages(chat_id, uid)
    history = [{"role": m["role"], "content": m["content"]} for m in messages]
    user = {"id": uid, "username": user_row["username"], "chat_id": chat_id}
    return user, history


def do_login(username: str, password: str):
    try:
        row = accounts.login(username, password)
    except Exception as e:
        return None, [], gr.update(visible=True), gr.update(visible=False), str(e), "", "", None
    user, history = _load_user(row)
    return (
        user, history,
        gr.update(visible=False), gr.update(visible=True),
        "", f"Signed in as **{user['username']}**", _quota_label(user["id"]), None,
    )


def do_signup(username: str, password: str):
    try:
        row = accounts.signup(username, password)
    except Exception as e:
        return None, [], gr.update(visible=True), gr.update(visible=False), str(e), "", "", None
    user, history = _load_user(row)
    return (
        user, history,
        gr.update(visible=False), gr.update(visible=True),
        "", f"Signed in as **{user['username']}**", _quota_label(user["id"]), None,
    )


def do_logout():
    return (
        None, [],
        gr.update(visible=True), gr.update(visible=False),
        "", "", "", None,
    )


def do_new_chat(user: dict | None):
    if not user:
        return [], user, None
    n = len(accounts.list_chats(user["id"])) + 1
    chat_id = accounts.create_chat(user["id"], f"Chat {n}")
    return [], {**user, "chat_id": chat_id}, None


# --- Feedback: thumbs + optional comment on the latest assistant answer -------
def on_like(user: dict | None, last_mid, evt: gr.LikeData) -> str:
    """Record a thumbs up/down for the most recent assistant answer."""
    if not user or last_mid is None:
        return ""
    rating = 1 if evt.liked else -1
    try:
        ok = accounts.add_feedback(last_mid, user["id"], rating=rating)
    except Exception:
        logger.exception("add_feedback (rating) failed")
        return "Couldn't record that rating."
    if ok is None:
        return ""
    return "Thanks — 👍 noted." if rating > 0 else "Thanks — 👎 noted."


def submit_comment(user: dict | None, last_mid, comment: str):
    """Persist an optional free-text comment against the latest answer."""
    if not user or last_mid is None:
        return comment, "Ask a question first, then comment on its answer."
    if not comment or not comment.strip():
        return comment, ""
    try:
        ok = accounts.add_feedback(last_mid, user["id"], comment=comment.strip())
    except Exception:
        logger.exception("add_feedback (comment) failed")
        return comment, "Couldn't save your comment."
    if ok is None:
        return comment, "Couldn't save your comment."
    return "", "Thanks for the feedback! 🙏"


# --- Chat-session switcher --------------------------------------------------
def _chat_choices(user: dict | None):
    """(radio choices, current value) for the chat-session switcher."""
    if not user:
        return [], None
    try:
        chats = accounts.list_chats_with_counts(user["id"])
    except Exception:
        logger.exception("list_chats_with_counts failed")
        return [], user.get("chat_id")
    choices = [(f"{c['name']} · {c['msg_count']} msgs", c["id"]) for c in chats]
    return choices, user.get("chat_id")


def load_chat(user: dict | None, chat_id: str | None):
    """Switch the active chat: load its messages, repoint the user's chat_id."""
    if not user or not chat_id:
        return gr.update(), user, None
    try:
        messages = accounts.get_messages(chat_id, user["id"])
    except Exception:
        logger.exception("get_messages failed")
        return gr.update(), user, None
    history = [{"role": m["role"], "content": m["content"]} for m in messages]
    return history, {**user, "chat_id": chat_id}, None


def delete_selected_chat(user: dict | None, chat_id: str | None):
    """Delete the selected chat, then fall back to the most recent (or a fresh one)."""
    if not user or not chat_id:
        choices, cur = _chat_choices(user)
        return gr.update(choices=choices, value=cur), gr.update(), user, None
    try:
        accounts.delete_chat(chat_id, user["id"])
    except Exception:
        logger.exception("delete_chat failed")
    chats = accounts.list_chats(user["id"])
    new_id = chats[0]["id"] if chats else accounts.create_chat(user["id"], "Chat 1")
    messages = accounts.get_messages(new_id, user["id"])
    history = [{"role": m["role"], "content": m["content"]} for m in messages]
    user2 = {**user, "chat_id": new_id}
    choices, _ = _chat_choices(user2)
    return gr.update(choices=choices, value=new_id), history, user2, None


# --- Data export (admin-gated) ----------------------------------------------
import csv as _csv
import os as _os
import tempfile as _tempfile


def _is_admin(user: dict | None) -> bool:
    """True if the user's name is in AGADVISOR_ADMINS (comma-separated env)."""
    if not user:
        return False
    admins = {a.strip().lower() for a in _os.getenv("AGADVISOR_ADMINS", "").split(",") if a.strip()}
    return user.get("username", "").lower() in admins


def _stats_md(user: dict | None) -> str:
    """Counts panel: all-users for admins, own activity otherwise."""
    if not user:
        return ""
    try:
        if _is_admin(user):
            s = accounts.stats(None)
            return (f"**📊 All users:** {s['feedback']} feedback · {s['queries']} queries · "
                    f"{s['users']} users  \n_(admin view — the CSV export covers everyone)_")
        s = accounts.stats(user["id"])
        return f"**📊 Your activity:** {s['feedback']} feedback · {s['queries']} queries"
    except Exception:
        logger.exception("stats failed")
        return ""


def export_csv(user: dict | None):
    """Write a feedback CSV (prompt/response/rating/comment) and return its path.
    Admins export all users; everyone else exports only their own."""
    if not user:
        return None
    scope_all = _is_admin(user)
    try:
        rows = accounts.export_feedback(None if scope_all else user["id"])
    except Exception:
        logger.exception("export_feedback failed")
        return None
    path = _tempfile.mktemp(prefix="agadvisor_feedback_", suffix=".csv")
    with open(path, "w", newline="", encoding="utf-8") as f:
        w = _csv.writer(f)
        w.writerow(["username", "timestamp", "rating", "comment", "prompt", "response"])
        for r in rows:
            ts = _time.strftime("%Y-%m-%d %H:%M:%S", _time.localtime(r.get("created_at") or 0))
            w.writerow([
                r.get("username", ""), ts, r.get("rating", ""), r.get("comment") or "",
                r.get("prompt") or "", r.get("response") or "",
            ])
    return path


def _refresh_sidebar(user: dict | None):
    """Refresh the chat switcher + stats panel (chained after auth/answer/new-chat)."""
    choices, cur = _chat_choices(user)
    return gr.update(choices=choices, value=cur), _stats_md(user)


import base64 as _base64
from pathlib import Path as _Path


def _logo_uri(name: str) -> str:
    """Inline a logo as a base64 data URI (robust on Spaces — no static-path serving)."""
    try:
        b = (_Path(__file__).parent / "assets" / "logos" / name).read_bytes()
        return "data:image/png;base64," + _base64.b64encode(b).decode()
    except Exception:
        logger.exception("Could not load logo %s", name)
        return ""


# Simple logo strip shown at the very top: the two Iowa State research-center logos
# (TrAC + AIIRA) on small white cards, centered, wrapping on mobile. No background,
# no title (the "AgAdvisor" heading below already carries the name).
_LOGO_CARD = ("flex:1 1 0;display:flex;justify-content:center;align-items:center;"
              "background:#fff;border-radius:12px;padding:12px 10px;box-shadow:0 1px 5px rgba(0,0,0,.14);")
_LOGO_IMG = "max-height:82px;max-width:100%;height:auto;width:auto;display:block;"

_LOGO_BANNER = f"""
<div class="agadvisor-logos" style="display:flex;gap:12px;align-items:stretch;padding:10px 0 6px;">
  <div style="{_LOGO_CARD}">
    <img src="__TRAC__" alt="Translational AI Center (TrAC)" style="{_LOGO_IMG}"/>
  </div>
  <div style="{_LOGO_CARD}">
    <img src="__COALESCE__" alt="COALESCE" style="{_LOGO_IMG}"/>
  </div>
  <div style="{_LOGO_CARD}">
    <img src="__AIIRA__" alt="AI Institute for Resilient Agriculture (AIIRA)" style="{_LOGO_IMG}"/>
  </div>
</div>
""".replace("__TRAC__", _logo_uri("trac.png")) \
   .replace("__COALESCE__", _logo_uri("coalesce.png")) \
   .replace("__AIIRA__", _logo_uri("aiira.png"))


# Mobile-friendly layout: full-width chat that grows with the viewport, and
# button rows that wrap with tap-friendly targets. The logo cards stay white in
# both themes (the logos need a light backing) — in dark mode we add a soft
# border so they read as intentional cards rather than glare.
_MOBILE_CSS = """
.gradio-container {max-width: 940px !important; margin: 0 auto !important;}
#agadvisor-chat {height: 460px !important;}
.theme-btn {min-width: 120px;}
.dark .agadvisor-logos > div {box-shadow: 0 0 0 1px rgba(255,255,255,.12) !important;}
@media (max-width: 700px) {
  .gradio-container {padding: 8px !important;}
  #agadvisor-chat {height: 60vh !important; min-height: 300px !important;}
  .agadvisor-btns {flex-wrap: wrap !important; gap: 8px !important;}
  .agadvisor-btns button {flex: 1 1 46% !important; min-height: 44px !important;}
}
"""


with gr.Blocks(title="AgAdvisor", theme=gr.themes.Soft(primary_hue="green"), css=_MOBILE_CSS) as demo:
    user_state = gr.State(None)
    last_msg_state = gr.State(None)  # id of the latest assistant message (for feedback)

    gr.HTML(_LOGO_BANNER)  # two research-center logos at the very top

    gr.Markdown(
        "# 🌿 AgAdvisor\n"
        "A CDMS pesticide-label assistant with weather, soil, and agronomic tools. "
        "Answers include page-level citations.\n\n"
        "> Pesticide labels are legally binding. This is a research prototype and is "
        "**not** a substitute for reading the label. Always verify against the label of record."
    )

    # --- Auth gate -----------------------------------------------------------
    with gr.Column(visible=True) as login_view:
        gr.Markdown("### Sign in or create an account")
        username = gr.Textbox(label="Username", autofocus=True)
        password = gr.Textbox(label="Password", type="password")
        with gr.Row():
            login_btn = gr.Button("Sign in", variant="primary")
            signup_btn = gr.Button("Create account")
        auth_error = gr.Markdown("")

    # --- Chat ----------------------------------------------------------------
    with gr.Column(visible=False) as chat_view:
        with gr.Row():
            who = gr.Markdown("")
            quota = gr.Markdown("")
            # Retrieval is automatic now (index-first, live-fetch on a miss), so
            # there's no mode toggle — just a light/dark switch for outdoor use.
            theme_btn = gr.Button(
                "🌓 Light / Dark", scale=0, min_width=120, elem_classes=["theme-btn"]
            )
        # Switch between your saved chat sessions (name + message count).
        with gr.Accordion("💬 Chat sessions", open=False):
            chat_selector = gr.Radio(choices=[], label="Your chats", value=None)
            delete_chat_btn = gr.Button("🗑️ Delete selected chat", size="sm")
        # Thumbs up/down appear on each assistant message (Chatbot.like); they
        # rate the most recent answer.
        chatbot = gr.Chatbot(type="messages", height=460, label="AgAdvisor",
                             elem_id="agadvisor-chat")
        # The real pipeline steps stream here live, then collapse (open=False)
        # once the answer is in — so first-time label fetches are legible.
        with gr.Accordion("🔎 Process steps", open=False) as steps_accordion:
            steps_md = gr.Markdown("")
        question = gr.Textbox(
            placeholder="e.g. What is the application rate for Roundup on soybeans?",
            label="Your question",
            autofocus=True,
        )
        with gr.Row(elem_classes=["agadvisor-btns"]):
            send_btn = gr.Button("Ask", variant="primary")
            new_chat_btn = gr.Button("New chat")
            logout_btn = gr.Button("Log out")

        # Optional free-text feedback on the latest answer (thumbs are on the
        # message itself). Both persist to the accounts DB and sync to HF.
        with gr.Row():
            comment_box = gr.Textbox(
                placeholder="Optional: tell us what was wrong or missing in the last answer",
                label="Feedback comment", scale=4,
            )
            feedback_btn = gr.Button("Submit feedback", scale=1)
        feedback_status = gr.Markdown("")

        # Data export: your own activity, or (for admins in AGADVISOR_ADMINS) all
        # users. The CSV pairs each rating/comment with its prompt + response.
        with gr.Accordion("📊 Data export", open=False):
            export_stats = gr.Markdown("")
            with gr.Row():
                refresh_stats_btn = gr.Button("Refresh stats", size="sm")
                download_btn = gr.Button("Download feedback CSV", size="sm")
            export_file = gr.File(label="Feedback export (CSV)")

        # Optional debug view of the last answer's routing (tool/source/chunks).
        with gr.Accordion("⚙️ Debug", open=False):
            debug_toggle = gr.Checkbox(label="Show debug info for the last answer", value=False)
            debug_md = gr.Markdown("", visible=False)

        if PRODUCTS:
            gr.Examples(
                examples=[
                    [f"What is the application rate for {p}?"] for p in PRODUCTS[:6]
                ],
                inputs=question,
                label=f"Labels in the index ({len(PRODUCTS)} products)",
            )

    login_btn.click(
        do_login, [username, password],
        [user_state, chatbot, login_view, chat_view, auth_error, who, quota, last_msg_state],
    ).then(_refresh_sidebar, user_state, [chat_selector, export_stats])
    signup_btn.click(
        do_signup, [username, password],
        [user_state, chatbot, login_view, chat_view, auth_error, who, quota, last_msg_state],
    ).then(_refresh_sidebar, user_state, [chat_selector, export_stats])
    logout_btn.click(
        do_logout, None,
        [user_state, chatbot, login_view, chat_view, auth_error, who, quota, last_msg_state],
    ).then(_refresh_sidebar, user_state, [chat_selector, export_stats])
    new_chat_btn.click(
        do_new_chat, user_state, [chatbot, user_state, last_msg_state]
    ).then(_refresh_sidebar, user_state, [chat_selector, export_stats])

    # Light/dark toggle: flip Gradio's `dark` class on the document body.
    theme_btn.click(None, None, None, js="() => { document.body.classList.toggle('dark'); }")

    # Chat-session switcher.
    chat_selector.change(load_chat, [user_state, chat_selector], [chatbot, user_state, last_msg_state])
    delete_chat_btn.click(
        delete_selected_chat, [user_state, chat_selector],
        [chat_selector, chatbot, user_state, last_msg_state],
    )

    # Data export + debug.
    refresh_stats_btn.click(_stats_md, user_state, export_stats)
    download_btn.click(export_csv, user_state, export_file)
    debug_toggle.change(lambda on: gr.update(visible=on), debug_toggle, debug_md)

    # Feedback wiring.
    chatbot.like(on_like, [user_state, last_msg_state], feedback_status)
    feedback_btn.click(
        submit_comment, [user_state, last_msg_state, comment_box],
        [comment_box, feedback_status],
    )

    for trigger in (send_btn.click, question.submit):
        trigger(
            on_submit,
            [question, chatbot, user_state, last_msg_state],
            [chatbot, question, quota, steps_md, steps_accordion, last_msg_state, debug_md],
        ).then(_refresh_sidebar, user_state, [chat_selector, export_stats])


if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)