"""Helpers for lightweight, API-only image search across multiple sources.

The app is a pure remote client: every source is queried over HTTP and only a
small list of image URLs + metadata is kept in memory. No local indexes,
dataset downloads, or persistent storage.
"""

from __future__ import annotations

import base64
import csv
import enum
import html
import json
import os
import tempfile
import zipfile
from pathlib import Path
from typing import Any
from urllib.parse import urlparse

import requests

# --- shared config ---------------------------------------------------------
API_URL = "https://knn.laion.ai/knn-service"
OPENVERSE_URL = "https://api.openverse.org/v1/images/"
WIKIMEDIA_URL = "https://commons.wikimedia.org/w/api.php"
DATASETS_SERVER = "https://datasets-server.huggingface.co"
RELAION_DATASET = "laion/relaion-high-resolution"

DEFAULT_INDEX = "laion5B-L-14"
COMMON_INDICES = ["laion5B-L-14", "laion5B-H-14", "laion_400m"]
MAX_RESULTS = 50
MAX_DOWNLOADS = 20
REQUEST_TIMEOUT = 20

HTTP_HEADERS = {
    "User-Agent": "laion-safe-search/1.0 (https://huggingface.co/spaces/zackdorsey/img)"
}

# --- source registry -------------------------------------------------------
# Each source declares which controls are meaningful for it so the UI can adapt.
DEFAULT_SOURCE = "openverse"
SOURCES: dict[str, dict[str, Any]] = {
    "openverse": {
        "label": "Openverse — CC / public-domain images (no key)",
        "supports_nsfw": True,     # honours a mature/sensitive toggle
        "supports_punsafe": False, # no per-result CLIP safety score
        "supports_image": False,   # text query only
    },
    "wikimedia": {
        "label": "Wikimedia Commons — freely-licensed media (no key)",
        "supports_nsfw": False,
        "supports_punsafe": False,
        "supports_image": False,
    },
    "relaion": {
        "label": "reLAION-HR — LAION safety re-release, high-res (needs HF token)",
        "supports_nsfw": True,      # via the continuous punsafe range
        "supports_punsafe": True,   # server-side punsafe filtering over 166M rows
        "supports_image": False,    # keyword caption search, not CLIP-semantic
    },
    "laion": {
        "label": "LAION-5B — clip-retrieval kNN (often offline)",
        "supports_nsfw": True,     # via the p_unsafe range
        "supports_punsafe": True,
        "supports_image": True,
    },
}


class SourceUnavailable(RuntimeError):
    """Raised when a remote source cannot be reached or refuses the request."""


class Modality(str, enum.Enum):
    """Query modality accepted by the clip-retrieval knn-service backend."""

    TEXT = "text"
    IMAGE = "image"


# --- LAION clip-retrieval client (vendored minimal HTTP surface) -----------
class ClipClient:
    """Minimal, dependency-free client for the public clip-retrieval knn-service.

    Vendors the small HTTP surface of ``clip_retrieval.clip_client.ClipClient``
    so the app does not need the heavy ``clip-retrieval`` package. The backend
    computes CLIP embeddings server-side; we only send JSON.
    """

    def __init__(
        self,
        url: str,
        indice_name: str,
        aesthetic_score: int = 9,
        aesthetic_weight: float = 0.5,
        modality: Modality = Modality.IMAGE,
        num_images: int = 40,
        deduplicate: bool = True,
        use_safety_model: bool = False,
        use_violence_detector: bool = False,
    ) -> None:
        self.url = url
        self.indice_name = indice_name
        self.aesthetic_score = int(aesthetic_score)
        self.aesthetic_weight = float(aesthetic_weight)
        self.modality = modality.value if isinstance(modality, Modality) else str(modality)
        self.num_images = int(num_images)
        self.deduplicate = deduplicate
        self.use_safety_model = use_safety_model
        self.use_violence_detector = use_violence_detector

    def query(
        self,
        text: str | None = None,
        image: str | None = None,
        embedding_input: list[float] | None = None,
    ) -> Any:
        """Search by text, image URL/path, or a raw embedding."""
        if text and image:
            raise ValueError("Only one of text or image may be provided per query.")
        if text:
            return self.__search_knn_api__(text=text)
        if image:
            if image.startswith("http://") or image.startswith("https://"):
                return self.__search_knn_api__(image_url=image)
            return self.__search_knn_api__(image=self._encode_image(image))
        if embedding_input is not None:
            return self.__search_knn_api__(embedding_input=embedding_input)
        raise ValueError("Provide a text prompt, an image, or an embedding.")

    @staticmethod
    def _encode_image(path: str) -> str:
        with open(path, "rb") as handle:
            return base64.b64encode(handle.read()).decode("utf-8")

    def __search_knn_api__(
        self,
        text: str | None = None,
        image: str | None = None,
        image_url: str | None = None,
        embedding_input: list[float] | None = None,
    ) -> Any:
        payload = {
            "text": text,
            "image": image,
            "image_url": image_url,
            "embedding_input": embedding_input,
            "deduplicate": self.deduplicate,
            "use_safety_model": self.use_safety_model,
            "use_violence_detector": self.use_violence_detector,
            "indice_name": self.indice_name,
            "use_mclip": False,
            "aesthetic_score": self.aesthetic_score,
            "aesthetic_weight": self.aesthetic_weight,
            "modality": self.modality,
            "num_images": self.num_images,
            "num_result_ids": self.num_images,
        }
        response = requests.post(
            self.url, data=json.dumps(payload), headers=HTTP_HEADERS, timeout=REQUEST_TIMEOUT
        )
        response.raise_for_status()
        return response.json()


def make_client(
    index_name: str = DEFAULT_INDEX,
    num_results: int = 20,
    aesthetic_score: int | None = None,
    aesthetic_weight: float | None = None,
    deduplicate: bool = True,
) -> ClipClient:
    """Create a lightweight remote-only clip-retrieval client."""
    client_options: dict[str, Any] = {
        "url": API_URL,
        "indice_name": index_name,
        "modality": Modality.IMAGE,
        "num_images": max(1, min(int(num_results), MAX_RESULTS)),
        "deduplicate": deduplicate,
    }
    if aesthetic_score is not None:
        client_options["aesthetic_score"] = aesthetic_score
    if aesthetic_weight is not None:
        client_options["aesthetic_weight"] = aesthetic_weight
    return ClipClient(**client_options)


def query_laion(
    text: str | None,
    image: str | None,
    index_name: str,
    num_results: int,
    aesthetic_score: int | None = None,
    aesthetic_weight: float | None = None,
    deduplicate: bool = True,
) -> list[dict[str, Any]]:
    """Query the public clip-retrieval API without local indexes or datasets."""
    if not text and not image:
        raise ValueError("Enter a text prompt, image URL, or upload a small image.")

    client = make_client(
        index_name=index_name,
        num_results=num_results,
        aesthetic_score=aesthetic_score,
        aesthetic_weight=aesthetic_weight,
        deduplicate=deduplicate,
    )
    try:
        raw = client.query(text=text or None, image=image or None)
    except requests.exceptions.RequestException as exc:  # offline / reset / timeout
        raise SourceUnavailable(
            "LAION's public clip-retrieval backend (knn.laion.ai) is not responding — "
            "it is frequently offline. Switch the Source to Openverse or Wikimedia Commons."
        ) from exc

    if isinstance(raw, dict) and raw.get("message"):
        raise SourceUnavailable(f"clip-retrieval API returned: {raw['message']}")
    if not isinstance(raw, list):
        raise SourceUnavailable("Unexpected LAION API response format.")
    return [normalize_laion(item, rank) for rank, item in enumerate(raw, start=1)]


# --- Openverse -------------------------------------------------------------
# Anonymous Openverse requests are capped at 20 results per page.
OPENVERSE_ANON_PAGE_MAX = 20

# Openverse per-result sensitivity reasons (the ``unstable__sensitivity`` array).
SENSITIVITY_TEXT = "sensitive_text"              # automated text-content detection
SENSITIVITY_USER = "user_reported_sensitivity"   # flagged by Openverse user reports
SENSITIVITY_PROVIDER = "provider_supplied_sensitivity"  # upstream provider marked it mature
SENSITIVITY_LABELS = {
    SENSITIVITY_TEXT: "auto-flagged text",
    SENSITIVITY_USER: "user-reported",
    SENSITIVITY_PROVIDER: "provider-marked",
}

# Graded content-sensitivity levels used across sources.
SENSITIVITY_LEVELS = ("safe", "moderate", "unrestricted")


def search_openverse(
    query: str,
    num_results: int,
    sensitivity: str = "safe",
) -> list[dict[str, Any]]:
    """Search Openverse (CC / public-domain images). No API key required.

    ``sensitivity`` grades how flagged content is handled:
      - ``"safe"`` (default): exclude everything Openverse flags as sensitive.
      - ``"moderate"``: include items flagged *only* by automated text detection
        (often false positives — art, medical, news), but still hide
        provider-marked mature and user-reported results.
      - ``"unrestricted"``: include all flagged results.
    """
    if not query:
        raise ValueError("Enter a text prompt to search Openverse.")
    params: dict[str, Any] = {
        "q": query,
        "page_size": max(1, min(int(num_results), OPENVERSE_ANON_PAGE_MAX)),
    }
    if sensitivity in ("moderate", "unrestricted"):
        params["unstable__include_sensitive_results"] = "true"

    try:
        response = requests.get(
            OPENVERSE_URL, params=params, headers=HTTP_HEADERS, timeout=REQUEST_TIMEOUT
        )
        response.raise_for_status()
        data = response.json()
    except requests.exceptions.RequestException as exc:
        raise SourceUnavailable(f"Openverse is not responding: {exc}") from exc

    results = [
        normalize_openverse(item, rank)
        for rank, item in enumerate(data.get("results", []), start=1)
    ]
    if sensitivity == "moderate":
        # Keep unflagged items and those flagged *only* by automated text detection.
        results = [
            item
            for item in results
            if not item.get("sensitivity") or set(item["sensitivity"]) <= {SENSITIVITY_TEXT}
        ]
        for new_rank, item in enumerate(results, start=1):
            item["rank"] = new_rank
    return results


# --- Wikimedia Commons -----------------------------------------------------
def search_wikimedia(query: str, num_results: int) -> list[dict[str, Any]]:
    """Search Wikimedia Commons freely-licensed media. No API key required."""
    if not query:
        raise ValueError("Enter a text prompt to search Wikimedia Commons.")
    params = {
        "action": "query",
        "generator": "search",
        "gsrnamespace": 6,  # File:
        "gsrsearch": query,
        "gsrlimit": max(1, min(int(num_results), MAX_RESULTS)),
        "prop": "imageinfo",
        "iiprop": "url|size|extmetadata",
        "iiurlwidth": 320,
        "format": "json",
    }
    try:
        response = requests.get(
            WIKIMEDIA_URL, params=params, headers=HTTP_HEADERS, timeout=REQUEST_TIMEOUT
        )
        response.raise_for_status()
        data = response.json()
    except requests.exceptions.RequestException as exc:
        raise SourceUnavailable(f"Wikimedia Commons is not responding: {exc}") from exc

    pages = ((data.get("query") or {}).get("pages") or {})
    ordered = sorted(pages.values(), key=lambda page: page.get("index", 1_000_000))
    results = []
    for rank, page in enumerate(ordered, start=1):
        info = (page.get("imageinfo") or [{}])[0]
        results.append(normalize_wikimedia(page, info, rank))
    return results


# --- reLAION-HR (HF datasets-server, gated) --------------------------------
def _hf_token() -> str | None:
    return os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")


def search_relaion(
    query: str,
    num_results: int,
    min_punsafe: float = 0.0,
    min_pwatermark: float = 0.0,
    languages: list[str] | None = None,
    punsafe_dir: str = "above",
) -> list[dict[str, Any]]:
    """Keyword search over ReLAION-HR captions with threshold + language filters.

    Uses the Hugging Face datasets-server ``/filter`` endpoint against
    ``laion/relaion-high-resolution`` (the safety re-release of LAION-5B). Matches
    caption substrings (``TEXT ILIKE``) and filters server-side over all 166M rows:
    ``punsafe`` and ``pwatermark`` are lower-bound thresholds (show values at or
    above), and ``languages`` restricts the ``LANGUAGE`` column when non-empty.
    The dataset is gated, so an ``HF_TOKEN`` with the gate accepted is required.
    """
    if not query:
        raise ValueError("Enter a text prompt to search reLAION-HR.")
    token = _hf_token()
    if not token:
        raise SourceUnavailable(
            "reLAION-HR is a gated dataset. Add your Hugging Face token as a Space "
            "secret named HF_TOKEN (Settings → Variables and secrets) and accept the "
            "dataset terms at huggingface.co/datasets/laion/relaion-high-resolution."
        )

    safe_q = str(query).strip().replace("\\", "")[:100].replace("'", "''")
    punsafe_op = "<=" if punsafe_dir == "below" else ">="
    clauses = [
        f"\"TEXT\" ILIKE '%{safe_q}%'",
        f"\"punsafe\" {punsafe_op} {float(min_punsafe):.4f}",
        f"\"pwatermark\" >= {float(min_pwatermark):.4f}",
    ]
    langs = [str(code) for code in (languages or []) if str(code).replace("-", "").replace("_", "").isalnum()]
    if langs:
        # datasets-server /filter rejects IN (...); OR-joined equalities are supported.
        joined = " OR ".join(f"\"LANGUAGE\" = '{code}'" for code in langs)
        clauses.append(f"({joined})")
    where = " AND ".join(clauses)
    params = {
        "dataset": RELAION_DATASET,
        "config": "default",
        "split": "train",
        "where": where,
        "offset": 0,
        "length": max(1, min(int(num_results), 100)),
    }
    try:
        response = requests.get(
            f"{DATASETS_SERVER}/filter",
            params=params,
            headers={**HTTP_HEADERS, "Authorization": f"Bearer {token}"},
            timeout=REQUEST_TIMEOUT + 15,
        )
        if response.status_code in (401, 403):
            raise SourceUnavailable(
                "reLAION-HR access was denied (HTTP %d). Ensure the Space's HF_TOKEN "
                "belongs to an account that has accepted the dataset's gated terms." % response.status_code
            )
        response.raise_for_status()
        data = response.json()
    except requests.exceptions.RequestException as exc:
        raise SourceUnavailable(f"reLAION-HR (datasets-server) is not responding: {exc}") from exc

    return [
        normalize_relaion(entry.get("row", {}), rank)
        for rank, entry in enumerate(data.get("rows", []), start=1)
    ]


def normalize_relaion(row: dict[str, Any], rank: int) -> dict[str, Any]:
    """Normalize a ReLAION-HR datasets-server row."""
    url = str(row.get("URL") or "")
    return _normalized(
        rank=rank,
        source="relaion",
        url=url,
        thumb=url,
        caption=str(row.get("TEXT") or ""),
        similarity=_float_or_none(row.get("similarity")),
        p_unsafe=_float_or_none(row.get("punsafe")),
        pwatermark=_float_or_none(row.get("pwatermark")),
        language=row.get("LANGUAGE"),
        item_id=row.get("hash"),
        width=row.get("WIDTH"),
        height=row.get("HEIGHT"),
    )


# --- dispatch --------------------------------------------------------------
def run_search(
    source: str,
    text: str | None,
    image: str | None,
    num_results: int,
    sensitivity: str = "safe",
    index_name: str = DEFAULT_INDEX,
    aesthetic_score: int | None = None,
    aesthetic_weight: float | None = None,
    deduplicate: bool = True,
    min_punsafe: float = 0.0,
    min_pwatermark: float = 0.0,
    languages: list[str] | None = None,
    punsafe_dir: str = "above",
) -> list[dict[str, Any]]:
    """Route a query to the selected source and return normalized results."""
    if source == "openverse":
        return search_openverse(text or "", num_results, sensitivity=sensitivity)
    if source == "wikimedia":
        return search_wikimedia(text or "", num_results)
    if source == "relaion":
        return search_relaion(
            text or "", num_results,
            min_punsafe=min_punsafe, min_pwatermark=min_pwatermark,
            languages=languages, punsafe_dir=punsafe_dir,
        )
    if source == "laion":
        return query_laion(
            text=text,
            image=image,
            index_name=index_name,
            num_results=num_results,
            aesthetic_score=aesthetic_score,
            aesthetic_weight=aesthetic_weight,
            deduplicate=deduplicate,
        )
    raise ValueError(f"Unknown source: {source}")


# --- normalization ---------------------------------------------------------
def _normalized(
    rank: int,
    source: str,
    url: str,
    thumb: str | None = None,
    caption: str = "",
    similarity: float | None = None,
    p_unsafe: float | None = None,
    pwatermark: float | None = None,
    mature: bool | None = None,
    sensitivity: list[str] | None = None,
    license: str | None = None,
    landing_url: str | None = None,
    language: str | None = None,
    item_id: Any = None,
    width: Any = None,
    height: Any = None,
) -> dict[str, Any]:
    """Unified result shape used by rendering, export, and download."""
    return {
        "rank": rank,
        "source": source,
        "url": str(url or ""),
        "thumb": str(thumb or url or ""),
        "caption": str(caption or ""),
        "similarity": similarity,
        "p_unsafe": p_unsafe,
        "pwatermark": pwatermark,
        "mature": mature,
        "sensitivity": sensitivity or [],
        "license": license,
        "landing_url": landing_url or (url or ""),
        "language": language,
        "id": item_id,
        "width": width,
        "height": height,
    }


def normalize_laion(item: dict[str, Any], rank: int) -> dict[str, Any]:
    """Normalize a clip-retrieval result."""
    metadata = item.get("metadata") if isinstance(item.get("metadata"), dict) else {}
    merged = {**metadata, **item}
    url = str(merged.get("url") or merged.get("image_url") or "")
    return _normalized(
        rank=rank,
        source="laion",
        url=url,
        thumb=url,
        caption=str(merged.get("caption") or merged.get("text") or ""),
        similarity=_float_or_none(merged.get("similarity") or merged.get("score")),
        p_unsafe=_float_or_none(
            merged.get("p_unsafe", merged.get("NSFW", merged.get("unsafe", merged.get("punsafe"))))
        ),
        item_id=merged.get("id"),
        width=merged.get("width"),
        height=merged.get("height"),
    )


# Backwards-compatible alias (older name used elsewhere/tests).
normalize_result = normalize_laion


def normalize_openverse(item: dict[str, Any], rank: int) -> dict[str, Any]:
    """Normalize an Openverse image result."""
    reasons = [str(r) for r in (item.get("unstable__sensitivity") or [])]
    mature = bool(item.get("mature")) or bool(reasons)
    lic = item.get("license")
    lic_version = item.get("license_version")
    license_str = " ".join(str(p) for p in (lic, lic_version) if p).upper() or None
    return _normalized(
        rank=rank,
        source="openverse",
        url=str(item.get("url") or ""),
        thumb=str(item.get("thumbnail") or item.get("url") or ""),
        caption=str(item.get("title") or item.get("creator") or ""),
        mature=mature,
        sensitivity=reasons,
        license=license_str,
        landing_url=item.get("foreign_landing_url") or item.get("url"),
        item_id=item.get("id"),
        width=item.get("width"),
        height=item.get("height"),
    )


def normalize_wikimedia(page: dict[str, Any], info: dict[str, Any], rank: int) -> dict[str, Any]:
    """Normalize a Wikimedia Commons imageinfo result."""
    title = str(page.get("title") or "")
    caption = title[5:] if title.lower().startswith("file:") else title
    extmeta = info.get("extmetadata") or {}
    license_str = (extmeta.get("LicenseShortName") or {}).get("value")
    return _normalized(
        rank=rank,
        source="wikimedia",
        url=str(info.get("url") or info.get("thumburl") or ""),
        thumb=str(info.get("thumburl") or info.get("url") or ""),
        caption=caption,
        license=license_str,
        landing_url=info.get("descriptionurl") or info.get("url"),
        item_id=page.get("pageid"),
        width=info.get("width"),
        height=info.get("height"),
    )


# --- client-side filtering (LAION / reLAION metadata) ----------------------
def filter_results(
    results: list[dict[str, Any]],
    min_punsafe: float = 0.0,
    min_pwatermark: float = 0.0,
    languages: list[str] | None = None,
    include_missing: bool = False,
    punsafe_dir: str = "above",
) -> list[dict[str, Any]]:
    """Keep results matching the ``p_unsafe`` threshold (at or above it when
    ``punsafe_dir`` is ``"above"``, at or below it when ``"below"``), at or above
    the ``pwatermark`` lower bound, and — when a language set is given — in those
    languages. Results with a missing ``p_unsafe`` are only retained when
    ``include_missing`` is set (pwatermark/language filters are skipped for
    results that lack those fields).
    """
    langs = {str(code).lower() for code in (languages or [])}
    thr_p = float(min_punsafe)
    lo_w = float(min_pwatermark)
    kept: list[dict[str, Any]] = []
    for result in results:
        score = result.get("p_unsafe")
        if score is None:
            if not include_missing:
                continue
        else:
            score = float(score)
            if punsafe_dir == "below":
                if score > thr_p:
                    continue
            elif score < thr_p:
                continue
        watermark = result.get("pwatermark")
        if watermark is not None and float(watermark) < lo_w:
            continue
        if langs:
            lang = str(result.get("language") or "").lower()
            if lang and lang not in langs:
                continue
        kept.append(result)
    return kept


# --- rendering -------------------------------------------------------------
_SOURCE_BADGE = {"laion": "LAION", "openverse": "Openverse", "wikimedia": "Wikimedia"}


def render_results(results: list[dict[str, Any]]) -> str:
    """Render a responsive HTML gallery using remote thumbnail URLs."""
    if not results:
        return "<div class='empty'>No results yet — run a search above.</div>"

    cards = []
    for result in results:
        link = html.escape(result.get("landing_url") or result.get("url") or "")
        img = html.escape(result.get("thumb") or result.get("url") or "")
        caption = html.escape(result.get("caption") or "Untitled")
        source = _SOURCE_BADGE.get(result.get("source", ""), result.get("source", ""))

        rows = []
        if result.get("similarity") is not None:
            rows.append(f"<p><strong>Similarity:</strong> {_format_float(result.get('similarity'))}</p>")
        if result.get("p_unsafe") is not None:
            rows.append(f"<p><strong>p_unsafe:</strong> {_format_float(result.get('p_unsafe'))}</p>")
        if result.get("pwatermark") is not None:
            rows.append(f"<p><strong>watermark:</strong> {_format_float(result.get('pwatermark'))}</p>")
        reasons = result.get("sensitivity") or []
        if reasons:
            labels = ", ".join(SENSITIVITY_LABELS.get(r, r) for r in reasons)
            rows.append(f"<p class='flag'><strong>⚠ sensitive:</strong> {html.escape(labels)}</p>")
        elif result.get("mature"):
            rows.append("<p class='flag'><strong>⚠ mature/NSFW-flagged</strong></p>")
        if result.get("license"):
            rows.append(f"<p><strong>License:</strong> {html.escape(str(result.get('license')))}</p>")
        if result.get("language"):
            rows.append(f"<p><strong>Language:</strong> {html.escape(str(result.get('language')))}</p>")

        cards.append(
            f"""
            <article class="result-card">
              <a href="{link}" target="_blank" rel="noopener noreferrer" title="Open source page">
                <img src="{img}" alt="{caption}" loading="lazy" referrerpolicy="no-referrer" />
              </a>
              <div class="meta">
                <div class="rank">#{result.get('rank', '')} <span class="src">{html.escape(source)}</span></div>
                <p class="caption">{caption}</p>
                {''.join(rows)}
                <p><a href="{html.escape(result.get('url') or link)}" target="_blank" rel="noopener noreferrer">Direct image URL</a></p>
              </div>
            </article>
            """
        )
    return "<section class='result-grid'>" + "".join(cards) + "</section>"


# --- export / download -----------------------------------------------------
_EXPORT_FIELDS = [
    "rank", "source", "url", "thumb", "caption", "similarity", "p_unsafe", "pwatermark",
    "mature", "sensitivity", "license", "language", "landing_url", "id", "width", "height",
]


def export_json(results: list[dict[str, Any]]) -> str | None:
    if not results:
        return None
    path = Path(tempfile.NamedTemporaryFile(delete=False, suffix=".json").name)
    path.write_text(json.dumps(results, indent=2, ensure_ascii=False), encoding="utf-8")
    return str(path)


def export_csv(results: list[dict[str, Any]]) -> str | None:
    if not results:
        return None
    path = Path(tempfile.NamedTemporaryFile(delete=False, suffix=".csv").name)
    with path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=_EXPORT_FIELDS, extrasaction="ignore")
        writer.writeheader()
        writer.writerows(results)
    return str(path)


def download_selected(results: list[dict[str, Any]], selected_ranks: str) -> str | None:
    ranks = _parse_ranks(selected_ranks)
    if not ranks:
        raise ValueError("Enter result ranks to download, for example: 1, 3, 5-7.")
    ranks = ranks[:MAX_DOWNLOADS]
    by_rank = {int(item["rank"]): item for item in results}
    path = Path(tempfile.NamedTemporaryFile(delete=False, suffix=".zip").name)
    with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_DEFLATED) as archive:
        manifest = []
        for rank in ranks:
            item = by_rank.get(rank)
            if not item or not item.get("url"):
                continue
            response = requests.get(
                item["url"], timeout=REQUEST_TIMEOUT, stream=True, headers=HTTP_HEADERS
            )
            response.raise_for_status()
            ext = Path(urlparse(item["url"]).path).suffix[:8] or ".jpg"
            filename = f"result_{rank}{ext}"
            data = response.content
            archive.writestr(filename, data)
            manifest.append({**item, "file": filename, "bytes": len(data)})
        archive.writestr("manifest.json", json.dumps(manifest, indent=2, ensure_ascii=False))
    return str(path)


# --- small helpers ---------------------------------------------------------
def _float_or_none(value: Any) -> float | None:
    try:
        return None if value is None or value == "" else float(value)
    except (TypeError, ValueError):
        return None


def _format_float(value: Any, missing: str = "—") -> str:
    value = _float_or_none(value)
    return missing if value is None else f"{value:.4f}"


def _parse_ranks(text: str) -> list[int]:
    ranks: list[int] = []
    for part in (text or "").replace(" ", "").split(","):
        if not part:
            continue
        if "-" in part:
            start, end = [int(x) for x in part.split("-", 1)]
            ranks.extend(range(start, end + 1))
        else:
            ranks.append(int(part))
    return sorted(dict.fromkeys(r for r in ranks if r > 0))