app.py

import gradio as gr
import random
import threading
import time
import uuid
import os
import html

import pandas as pd
from huggingface_hub import hf_hub_download

from storage import VoteStorage
from stats_from_logs import load_stats_by_md5
from explorer import ALLOWED_CLASSIFIER_FILTERS, add_results_tab, build_results_data, load_more_results, on_gallery_select

DEBUG_MODE = os.getenv("DEBUG", "0").lower() in ("1", "true", "yes", "on")
RATINGS_APP_TOKEN = os.getenv("RATINGS_APP_TOKEN")
SUBMIT_KEY = os.getenv("RATINGS_SUBMIT_KEY")
assert SUBMIT_KEY, "Missing required env var: RATINGS_SUBMIT_KEY"
POOL_REPO_ID = "taigasan/e6-visual-ratings"
VOTE_STORAGE = VoteStorage(mode="void" if DEBUG_MODE else "hf", token=RATINGS_APP_TOKEN)
STATS_RELOAD_S = 30 * 60

# -- Pool dataset -----------------------------------------------------------
_pool_path = hf_hub_download(
    repo_id=POOL_REPO_ID,
    filename="pool.parquet",
    repo_type="dataset",
    token=RATINGS_APP_TOKEN
)
_pool_df = pd.read_parquet(_pool_path)
_pool_group_dfs = {g: gdf for g, gdf in _pool_df.groupby("group")}
_stats_lock = threading.Lock()
_stats_last_loaded_at = 0.0
_stats_by_key: dict[str, tuple[int, int]] = {}
_explorer_df = pd.DataFrame(columns=["group", "id", "md5", "rating", "sample_url", "image_url", "classifier", "classifier_score", "percentile"])


def _reload_stats_if_due(force: bool = False):
    global _stats_last_loaded_at, _stats_by_key, _explorer_df
    now = time.time()
    if not force and (now - _stats_last_loaded_at) < STATS_RELOAD_S:
        return
    with _stats_lock:
        now = time.time()
        if not force and (now - _stats_last_loaded_at) < STATS_RELOAD_S:
            return
        _stats_by_key = load_stats_by_md5(
            repo_id=POOL_REPO_ID,
            token=RATINGS_APP_TOKEN,
        )
        classifier_scores_path = hf_hub_download(
            repo_id=POOL_REPO_ID,
            filename="classifier_scores.parquet",
            repo_type="dataset",
            token=RATINGS_APP_TOKEN,
        )
        validation_set_path = hf_hub_download(
            repo_id=POOL_REPO_ID,
            filename="validation_set.parquet",
            repo_type="dataset",
            token=RATINGS_APP_TOKEN,
        )
        validation_df = pd.read_parquet(
            validation_set_path,
            columns=["group", "id", "md5", "rating", "sample_url", "image_url"],
        )
        classifier_scores_df = pd.read_parquet(classifier_scores_path)
        assert {"classifier", "md5", "classifier_score", "percentile"}.issubset(classifier_scores_df.columns), "classifier_scores.parquet missing expected columns"
        classifier_scores_df = classifier_scores_df[["classifier", "md5", "classifier_score", "percentile"]]
        classifier_scores_df["classifier"] = classifier_scores_df["classifier"].astype(str)
        classifier_scores_df["md5"] = classifier_scores_df["md5"].astype(str)
        validation_df["md5"] = validation_df["md5"].astype(str)
        _explorer_df = validation_df.merge(classifier_scores_df, on="md5", how="left", validate="one_to_many")
        _stats_last_loaded_at = now


_reload_stats_if_due(force=True)

def _pool_fetch_pair(group_name: str) -> tuple:
    gdf = _pool_group_dfs[group_name]
    assert len(gdf) >= 2, f"Not enough rows for group: {group_name}"
    md5_keys = gdf["md5"].astype(str)
    wins = md5_keys.map(lambda k: _stats_by_key.get(k, (0, 0))[0])
    losses = md5_keys.map(lambda k: _stats_by_key.get(k, (0, 0))[1])

    def _pick_from_mask(mask: pd.Series):
        candidate_df = gdf[mask]
        if len(candidate_df) < 2:
            return None
        sample = candidate_df.sample(2, replace=False)
        return sample.iloc[0], sample.iloc[1]
    
    
    # 1) Repeat the lowest-margin edge participating in a cycle. (To prevent deadlock, stop if all margins are 4+.)
    #   a) If deadlocked on a cycle with 4+ images and no inner cycles, sample a random missing edge inside the cycle.
    # 2) Pair images that both have wins only . (One of them will lose/tie. Stop when there is only one left.)
    # 3) Pair images that both have losses only. (One of them will win/tie. Stop when there is only one left.)
    # 4) Pair images with only 2 edges.
    # 5) X% chance, re-sample an existing edge, inversely proportional to existing number of samples.
    # 6) Y% chance, sample a random missing edge between images already sampled.
    # 7) Pair an unsampled image with a random sampled image. 

    # 2) Pair images that currently have wins-only records.
    picked = _pick_from_mask((wins > 0) & (losses == 0))
    if picked is not None:
        return picked[0], picked[1], "wins-only"

    # 3) Pair images that currently have losses-only records.
    picked = _pick_from_mask((wins == 0) & (losses > 0))
    if picked is not None:
        return picked[0], picked[1], "losses-only"

    # 4) Pair images that currently have exactly 2 total edges.
    vote_totals = wins + losses
    picked = _pick_from_mask(vote_totals == 2)
    if picked is not None:
        return picked[0], picked[1], "total_votes=2"

    # 7) Prefer pairing an unsampled image with a random previously sampled image.
    unsampled_mask = vote_totals == 0
    if unsampled_mask.any():
        unsampled_row = gdf[unsampled_mask].sample(1).iloc[0]
        sampled_df = gdf[~unsampled_mask]
        if len(sampled_df) >= 1:
            sampled_row = sampled_df.sample(1).iloc[0]
        else:
            sampled_row = gdf.drop(index=unsampled_row.name).sample(1).iloc[0]
        return unsampled_row, sampled_row, "unsampled+sampled"

    # 8) Safety fall back to low-vote weighted sampling.
    sample_weights = 1.0 / (vote_totals + 1.0)
    sample = gdf.sample(2, weights=sample_weights, replace=False)
    return sample.iloc[0], sample.iloc[1], "low-vote"


def _row_image_url(row) -> str:
    sample_url = row.get("sample_url")
    if isinstance(sample_url, str) and sample_url:
        return sample_url
    image_url = row.get("image_url")
    if isinstance(image_url, str) and image_url:
        return image_url
    return ''

DATASETS: dict[str, dict] = {
    "pool": {
        "fetch_pair": _pool_fetch_pair,
        "get_id": lambda row: row["md5"],
        "get_image": _row_image_url,
        "groups": {g: g for g in sorted(_pool_df["group"].unique())},
    },
}
DEFAULT_DATASET = list(DATASETS.keys())[0]

def _select_groups(cfg: dict, rating_pref: str) -> list[str]:
    groups = list(cfg["groups"].keys())
    if rating_pref == "all":
        return groups
    return [g for g in groups if g.endswith(f"_{rating_pref}")]

def _commit_oldest_pending(state: dict):
    pending = state.setdefault("pending", [])
    if len(pending) <= 1:
        return
    oldest = pending.pop(0)
    if oldest.get("winner") in ("A", "B"):
        _apply_local_stats_update(oldest["winner"], oldest["key_a"], oldest["key_b"])
    threading.Thread(target=VOTE_STORAGE.append_vote_row, args=(oldest.copy(), oldest.get("winner")), daemon=True).start()


def _apply_local_stats_update(winner: str, key_a: str, key_b: str):
    assert winner in ("A", "B")
    with _stats_lock:
        wins_a, losses_a = _stats_by_key.get(str(key_a), (0, 0))
        wins_b, losses_b = _stats_by_key.get(str(key_b), (0, 0))
        if winner == "A":
            _stats_by_key[str(key_a)] = (wins_a + 1, losses_a)
            _stats_by_key[str(key_b)] = (wins_b, losses_b + 1)
        else:
            _stats_by_key[str(key_a)] = (wins_a, losses_a + 1)
            _stats_by_key[str(key_b)] = (wins_b + 1, losses_b)


def _format_rating_post_row(post_id: int, wins: int, losses: int, label: str | None = None) -> str:
    total_votes = wins + losses
    url = f"https://e621.net/posts/{post_id}"
    row = f"{url} | Times rated: {total_votes}"
    return f"{label}: {row}" if label else row

def _render_current(state: dict, submit_status: str = "") -> tuple:
    _reload_stats_if_due()
    wins_a, losses_a = _stats_by_key.get(str(state["key_a"]), (0, 0))
    wins_b, losses_b = _stats_by_key.get(str(state["key_b"]), (0, 0))
    title_a = "Image A"
    title_b = "Image B"
    img_a_html = f"<div class=\"rating-card\"><div class=\"rating-card-title\"><strong>{html.escape(title_a)}</strong></div><div class=\"rating-image-frame\"><img src=\"{html.escape(state['url_a'])}\" class=\"rating-image\" loading=\"eager\" referrerpolicy=\"no-referrer\"></div></div>"
    img_b_html = f"<div class=\"rating-card\"><div class=\"rating-card-title\"><strong>{html.escape(title_b)}</strong></div><div class=\"rating-image-frame\"><img src=\"{html.escape(state['url_b'])}\" class=\"rating-image\" loading=\"eager\" referrerpolicy=\"no-referrer\"></div></div>"
    link_a = _format_rating_post_row(state["id_a"], wins_a, losses_a, label="Image A")
    link_b = _format_rating_post_row(state["id_b"], wins_b, losses_b, label="Image B")
    can_go_back = bool(state.get("can_go_back"))
    back_md = "[Undo Rating (Ctrl+z)](#back)" if can_go_back else "<span class='subtle-back-link-disabled'>Undo Rating (Ctrl+z)</span>"
    group_md = f"<span class='subtle-note'>Group: {state['group']}</span>"
    pair_reason = state.get("pair_reason", "")
    pair_reason_md = f"<span class='subtle-note'>Pair: {html.escape(pair_reason)}</span>" if pair_reason else ""
    status_md = f"<span class='submit-status-msg'>{html.escape(submit_status)}</span>" if submit_status else ""
    return img_a_html, img_b_html, link_a, link_b, back_md, group_md, pair_reason_md, status_md, state



def _normalize_rating_pref(pref: str | None) -> str:
    return pref if pref in ("safe", "all") else "safe"


def _initial_load(state: dict, pref: str | None, submit_key: str | None):
    rating_pref = _normalize_rating_pref(pref)
    submit_key = _normalize_submit_key(submit_key)
    return rating_pref, submit_key, *new_round(DEFAULT_DATASET, rating_pref, state)


def _on_rating_change(rating_pref: str, state: dict):
    rating_pref = _normalize_rating_pref(rating_pref)
    return *new_round(DEFAULT_DATASET, rating_pref, state), rating_pref


def _normalize_submit_key(submit_key: str | None) -> str:
    return submit_key or ""


def _filtered_explorer_df(rating_pref: str) -> pd.DataFrame:
    return _filtered_explorer_df_by_classifier(rating_pref, ALLOWED_CLASSIFIER_FILTERS[0])


def _filtered_explorer_df_by_classifier(rating_pref: str, classifier_name: str) -> pd.DataFrame:
    if rating_pref == "all":
        rating_filtered = _explorer_df
    else:
        assert rating_pref == "safe", f"Unsupported rating preference: {rating_pref}"
        rating_filtered = _explorer_df[_explorer_df["rating"] == "s"]
    assert classifier_name in ALLOWED_CLASSIFIER_FILTERS, f"Unsupported classifier filter: {classifier_name}"
    return rating_filtered[rating_filtered["classifier"] == classifier_name]


def _load_results(rating_pref_value: str, sort_mode_value: str, classifier_filter_value: str):
    rating_pref = _normalize_rating_pref(rating_pref_value)
    sort_mode = _normalize_sort_mode(sort_mode_value)
    classifier_name = _normalize_classifier_filter(classifier_filter_value)
    _reload_stats_if_due()
    filtered_explorer_df = _filtered_explorer_df_by_classifier(rating_pref, classifier_name)
    summary, score_distribution_plot, distribution_data, gallery_items, page_meta, next_offset, btn_update = build_results_data(
        filtered_explorer_df,
        _explorer_df,
        rating_pref,
        sort_mode,
        classifier_name,
    )
    return summary, score_distribution_plot, distribution_data, gallery_items, btn_update, "Click an image to reveal its ID and link.", page_meta, next_offset


def _normalize_sort_mode(sort_mode: str | None) -> str:
    if sort_mode in ("Default", "Rating: Low to High", "Rating: High to Low"):
        return sort_mode
    return "Default"


def _normalize_classifier_filter(classifier_name: str | None) -> str:
    if classifier_name in ALLOWED_CLASSIFIER_FILTERS:
        return str(classifier_name)
    return ALLOWED_CLASSIFIER_FILTERS[0]

# -- Gradio callbacks -------------------------------------------------------

def new_round(dataset_name: str, rating_pref: str, state: dict) -> tuple:
    cfg = DATASETS[dataset_name]
    groups = _select_groups(cfg, rating_pref)
    assert groups, f"No groups for rating preference: {rating_pref}"
    group = random.choice(groups)
    pair_data = cfg["fetch_pair"](cfg["groups"][group])
    if len(pair_data) == 3:
        row_a, row_b, pair_reason = pair_data
    else:
        row_a, row_b = pair_data
        pair_reason = ""
    state.setdefault("session_id", uuid.uuid4().hex)
    key_a = cfg["get_id"](row_a)
    key_b = cfg["get_id"](row_b)
    id_a = int(row_a["id"])
    id_b = int(row_b["id"])
    state.update(dataset=dataset_name, rating_pref=rating_pref, key_a=key_a, key_b=key_b, id_a=id_a, id_b=id_b, group=group, pair_reason=pair_reason)
    url_a = cfg["get_image"](row_a)
    url_b = cfg["get_image"](row_b)
    state["url_a"] = url_a
    state["url_b"] = url_b
    return _render_current(state)

def _queue_decision(winner: str | None, state: dict):
    assert state.get("session_id"), "Missing session_id: refusing to record vote"
    state.setdefault("pending", [])
    decision = {
        "winner": winner,
        "key_a": state["key_a"],
        "key_b": state["key_b"],
        "id_a": state["id_a"],
        "id_b": state["id_b"],
        "url_a": state["url_a"],
        "url_b": state["url_b"],
        "dataset": state["dataset"],
        "rating_pref": state["rating_pref"],
        "group": state["group"],
        "pair_reason": state.get("pair_reason", ""),
        "session_id": state["session_id"],
    }
    state["pending"].append(decision)
    state["last_decision"] = decision
    state["can_go_back"] = True
    _commit_oldest_pending(state)

def vote(winner: str | None, state: dict, submit_key: str | None) -> tuple:
    assert winner in ("A", "B", None)
    if _normalize_submit_key(submit_key) != SUBMIT_KEY:
        return _render_current(state, "Wrong submission key.")
    _queue_decision(winner, state)
    return new_round(state["dataset"], state["rating_pref"], state)

def go_back(state: dict) -> tuple:
    pending = state.setdefault("pending", [])
    if not state.get("can_go_back"):
        return _render_current(state)
    last = state.get("last_decision")
    if not last:
        state["can_go_back"] = False
        return _render_current(state)
    if pending and pending[-1] == last:
        pending.pop()
    state["can_go_back"] = False
    state["last_decision"] = None
    state.update(
        dataset=last["dataset"],
        rating_pref=last["rating_pref"],
        key_a=last["key_a"],
        key_b=last["key_b"],
        id_a=last["id_a"],
        id_b=last["id_b"],
        url_a=last["url_a"],
        url_b=last["url_b"],
        group=last["group"],
        pair_reason=last.get("pair_reason", ""),
    )
    return _render_current(state)

# -- UI ---------------------------------------------------------------------

with gr.Blocks(
    title="Image Rater",
    head="""
    <script>
    const VOTE_COOLDOWN_MS = 1500;
    let lastVoteAtMs = 0;
    let voteToastTimer = null;

    function showVoteToast(message) {
        let toast = document.getElementById('vote-cooldown-toast');
        if (!toast) {
            toast = document.createElement('div');
            toast.id = 'vote-cooldown-toast';
            toast.style.position = 'fixed';
            toast.style.left = '50%';
            toast.style.bottom = '20px';
            toast.style.transform = 'translateX(-50%)';
            toast.style.background = 'rgba(20, 20, 20, 0.92)';
            toast.style.color = '#fff';
            toast.style.padding = '8px 12px';
            toast.style.borderRadius = '8px';
            toast.style.fontSize = '0.92rem';
            toast.style.zIndex = '9999';
            toast.style.pointerEvents = 'none';
            toast.style.opacity = '0';
            toast.style.transition = 'opacity 120ms ease';
            document.body.appendChild(toast);
        }
        toast.textContent = message;
        toast.style.opacity = '1';
        if (voteToastTimer) clearTimeout(voteToastTimer);
        voteToastTimer = setTimeout(function () {
            toast.style.opacity = '0';
        }, 1400);
    }

    function showVoteBlockedMessage(remainingMs) {
        const remainingS = Math.max(0.1, remainingMs / 1000).toFixed(1);
        showVoteToast(`Please wait ${remainingS}s before submitting again.`);
    }

    function findVoteButtonTarget(target) {
        return target?.closest('#btn-vote-a button, button#btn-vote-a, #btn-vote-b button, button#btn-vote-b, #btn-skip button, button#btn-skip');
    }

    function clearImageContainers() {
        const leftImg = document.querySelector('#img-a img');
        const rightImg = document.querySelector('#img-b img');
        if (leftImg) {
            leftImg.src = '';
            leftImg.removeAttribute('srcset');
        }
        if (rightImg) {
            rightImg.src = '';
            rightImg.removeAttribute('srcset');
        }
    }

    function isVisible(el) {
        return !!(el && el.offsetParent !== null);
    }

    window.addEventListener('keydown', function (e) {
        const t = e.target;
        const voteAButton = document.querySelector('#btn-vote-a button, button#btn-vote-a');
        const voteBButton = document.querySelector('#btn-vote-b button, button#btn-vote-b');
        const skipButton = document.querySelector('#btn-skip button, button#btn-skip');
        const backButton = document.querySelector('#btn-back-action button, button#btn-back-action');
        const resultsLoadMoreButton = document.querySelector('#btn-results-load-more button, button#btn-results-load-more');
        const ratingTabActive = isVisible(voteAButton) || isVisible(voteBButton) || isVisible(skipButton);
        const resultsTabActive = isVisible(resultsLoadMoreButton);
        if (t && (t.tagName === 'INPUT' || t.tagName === 'TEXTAREA' || t.isContentEditable)) return;
        if (e.key === 'ArrowLeft' && ratingTabActive) {
            e.preventDefault();
            voteAButton?.click();
        } else if (e.key === 'ArrowRight' && ratingTabActive) {
            e.preventDefault();
            voteBButton?.click();
        } else if (e.key === 'ArrowUp' && ratingTabActive) {
            e.preventDefault();
            skipButton?.click();
        } else if ((e.key === 'z' || e.key === 'Z') && (e.ctrlKey || e.metaKey) && ratingTabActive) {
            e.preventDefault();
            backButton?.click();
        } else if (e.key === 'ArrowDown' && resultsTabActive) {
            e.preventDefault();
            resultsLoadMoreButton?.click();
        }
    });
    document.addEventListener('click', function (e) {
        const voteBtn = findVoteButtonTarget(e.target);
        if (voteBtn) {
            const nowMs = Date.now();
            const elapsedMs = nowMs - lastVoteAtMs;
            if (elapsedMs < VOTE_COOLDOWN_MS) {
                e.preventDefault();
                e.stopPropagation();
                if (typeof e.stopImmediatePropagation === 'function') e.stopImmediatePropagation();
                showVoteBlockedMessage(VOTE_COOLDOWN_MS - elapsedMs);
                return;
            }
            lastVoteAtMs = nowMs;
            clearImageContainers();
            return;
        }
        const a = e.target.closest('a[href="#back"]');
        if (!a) return;
        e.preventDefault();
        document.querySelector('#btn-back-action button, button#btn-back-action')?.click();
    }, true);
    </script>
    """,
    css="""
    .subtle-link button {
        background: none !important;
        border: none !important;
        box-shadow: none !important;
        color: #7a7a7a !important;
        text-decoration: underline !important;
        padding: 0 !important;
        min-height: 0 !important;
        font-size: 0.9em !important;
        justify-content: flex-start !important;
    }
    .subtle-link button:hover {
        color: #5a5a5a !important;
    }
    .subtle-link {
        width: fit-content !important;
    }
    .subtle-link button {
        width: fit-content !important;
    }
    .subtle-note {
        color: #888;
        font-size: 0.9em;
    }
    .rating-card {
        width: 100%;
    }
    .rating-card-title {
        min-height: 24px;
        margin-bottom: 8px;
    }
    .rating-image-frame {
        width: 100%;
        height: 512px;
        border: 1px solid #e6e6e6;
        border-radius: 8px;
        background: #333;
        display: flex;
        align-items: center;
        justify-content: center;
        overflow: hidden;
    }
    .rating-image {
        width: auto !important;
        height: auto !important;
        max-width: 100% !important;
        max-height: 100% !important;
        object-fit: contain !important;
        object-position: center center !important;
        display: block;
    }
    .subtle-back-link-wrap a {
        color: #7a7a7a !important;
        text-decoration: underline;
    }
    .subtle-back-link-wrap a:hover {
        color: #5a5a5a !important;
    }
    .subtle-back-link-disabled {
        color: #b8b8b8 !important;
        pointer-events: none;
        text-decoration: none;
    }
    .hidden-action-btn {
        display: none !important;
    }
    #submit-status {
        position: fixed;
        left: 50%;
        bottom: 20px;
        transform: translateX(-50%);
        z-index: 9998;
        pointer-events: none;
        min-height: 1.2em;
    }
    .submit-status-msg {
        background: rgba(20, 20, 20, 0.92);
        color: #fff;
        padding: 8px 12px;
        border-radius: 8px;
        font-size: 0.92rem;
    }
    #results-gallery {
        --explorer-thumb-ratio: 1 / 1;
    }
    #results-gallery button,
    #results-gallery .thumbnail-item {
        aspect-ratio: var(--explorer-thumb-ratio) !important;
    }
    #results-gallery img {
        width: 100% !important;
        height: 100% !important;
        object-fit: contain !important;
        background: #1f2937;
    }
    """,
) as demo:
    state = gr.State({})
    rating_pref_store = gr.BrowserState(default_value="safe", storage_key="rating_pref")
    submit_key_store = gr.BrowserState(default_value="", storage_key="submit_key")
    results_sort_store = gr.BrowserState(default_value="Default", storage_key="results_sort_mode")
    results_classifier_store = gr.BrowserState(default_value=ALLOWED_CLASSIFIER_FILTERS[0], storage_key="results_classifier")

    with gr.Tabs():
        with gr.Tab("Image Quality Rater"):
            gr.Markdown("Rate relative image quality. Choose the image with better quality, or select same quality if they are comparable. Both images are drawn from the same group to avoid cross-group bias.")

            with gr.Row():
                img_a = gr.HTML(elem_id="img-a")
                img_b = gr.HTML(elem_id="img-b")

            with gr.Row():
                btn_a = gr.Button("👍 Prefer A", variant="primary", elem_id="btn-vote-a")
                btn_skip = gr.Button("Same quality", elem_id="btn-skip")
                btn_b = gr.Button("👍 Prefer B", variant="primary", elem_id="btn-vote-b")

            with gr.Accordion("Settings", open=False):
                gr.Markdown("<span style='color:#888;font-size:0.9em;'>Advanced options</span>")
                rating_dd = gr.Dropdown(
                    choices=["safe", "all"],
                    value="safe",
                    label="Rating",
                    elem_id="rating-pref",
                )
                submit_key_tb = gr.Textbox(
                    value="",
                    type="password",
                    label="Submit key",
                    elem_id="submit-key",
                )

            link_a = gr.Markdown(label="Image A link")
            link_b = gr.Markdown(label="Image B link")
            back_link = gr.Markdown(elem_classes=["subtle-back-link-wrap"])
            btn_back_action = gr.Button("Undo Rating (Ctrl+z)", elem_id="btn-back-action", elem_classes=["hidden-action-btn"])
            details_md = gr.Markdown()
            pair_reason_md = gr.Markdown()
            submit_status_md = gr.Markdown(elem_id="submit-status")
            gr.Markdown("<span class='subtle-note'>Dataset: <a href='https://huggingface.co/datasets/taigasan/e6-visual-ratings' target='_blank' rel='noopener noreferrer'>taigasan/e6-visual-ratings</a></span>")
            gr.Markdown("<span class='subtle-note'>Keyboard Shortcuts: ⬅️ vote A, ⬆️ same quality, ➡️ vote B, Ctrl+z undo rating</span>")

        (
            results_summary_md,
            results_sort_dd,
            results_classifier_dd,
            results_score_distribution_plot,
            results_distribution_state,
            results_gallery,
            results_load_more_btn,
            selected_image_md,
            results_page_meta_state,
            results_page_offset_state,
        ) = add_results_tab(_pool_df)

    outputs = [img_a, img_b, link_a, link_b, back_link, details_md, pair_reason_md, submit_status_md, state]
    results_outputs = [
        results_summary_md,
        results_score_distribution_plot,
        results_distribution_state,
        results_gallery,
        results_load_more_btn,
        selected_image_md,
        results_page_meta_state,
        results_page_offset_state,
    ]

    btn_a.click(fn=lambda s, k: vote("A", s, k), inputs=[state, submit_key_store], outputs=outputs, queue=False, show_progress="hidden")
    btn_b.click(fn=lambda s, k: vote("B", s, k), inputs=[state, submit_key_store], outputs=outputs, queue=False, show_progress="hidden")
    btn_skip.click(fn=lambda s, k: vote(None, s, k), inputs=[state, submit_key_store], outputs=outputs, queue=False, show_progress="hidden")
    btn_back_action.click(fn=go_back, inputs=[state], outputs=outputs, queue=False, show_progress="hidden")
    rating_dd.change(fn=_on_rating_change, inputs=[rating_dd, state], outputs=[*outputs, rating_pref_store], queue=False, show_progress="hidden")
    submit_key_tb.input(fn=_normalize_submit_key, inputs=[submit_key_tb], outputs=[submit_key_store], queue=False, show_progress="hidden")
    submit_key_tb.change(fn=_normalize_submit_key, inputs=[submit_key_tb], outputs=[submit_key_store], queue=False, show_progress="hidden")
    rating_dd.change(fn=_load_results, inputs=[rating_dd, results_sort_store, results_classifier_store], outputs=results_outputs, queue=False, show_progress="hidden")
    results_sort_dd.change(fn=_normalize_sort_mode, inputs=[results_sort_dd], outputs=[results_sort_store], queue=False, show_progress="hidden")
    results_sort_dd.change(fn=_load_results, inputs=[rating_pref_store, results_sort_dd, results_classifier_store], outputs=results_outputs, queue=False, show_progress="hidden")
    results_classifier_dd.change(fn=_normalize_classifier_filter, inputs=[results_classifier_dd], outputs=[results_classifier_store], queue=False, show_progress="hidden")
    results_classifier_dd.change(fn=_load_results, inputs=[rating_pref_store, results_sort_store, results_classifier_dd], outputs=results_outputs, queue=False, show_progress="hidden")
    demo.load(fn=_initial_load, inputs=[state, rating_pref_store, submit_key_store], outputs=[rating_dd, submit_key_tb, *outputs], queue=False, show_progress="hidden")
    demo.load(fn=_load_results, inputs=[rating_pref_store, results_sort_store, results_classifier_store], outputs=results_outputs, queue=False, show_progress="hidden")
    demo.load(fn=_normalize_sort_mode, inputs=[results_sort_store], outputs=[results_sort_dd], queue=False, show_progress="hidden")
    demo.load(fn=_normalize_classifier_filter, inputs=[results_classifier_store], outputs=[results_classifier_dd], queue=False, show_progress="hidden")
    results_load_more_btn.click(
        fn=lambda r, s, c, o: load_more_results(_filtered_explorer_df_by_classifier(_normalize_rating_pref(r), _normalize_classifier_filter(c)), _explorer_df, s, o),
        inputs=[rating_pref_store, results_sort_store, results_classifier_store, results_page_offset_state],
        outputs=[results_gallery, results_page_meta_state, results_page_offset_state, results_load_more_btn],
        queue=False,
        show_progress="hidden",
    )
    results_gallery.select(
        fn=on_gallery_select,
        inputs=[results_page_meta_state, results_distribution_state],
        outputs=[selected_image_md, results_score_distribution_plot],
        queue=False,
        show_progress="hidden",
    )

if __name__ == "__main__":
    demo.launch()