"""Web search retrieval via Brave Search, Exa, and Serper."""

from __future__ import annotations

import sys
import urllib.parse
from datetime import datetime
from urllib.parse import urlparse

from . import dates, http


# ---------------------------------------------------------------------------
# Brave Search API
# ---------------------------------------------------------------------------

def brave_search(
    query: str, date_range: tuple[str, str], api_key: str, count: int = 5,
) -> tuple[list[dict], dict]:
    url = (
        "https://api.search.brave.com/res/v1/web/search?"
        + urllib.parse.urlencode(
            {
                "q": query,
                "count": count,
                "freshness": f"{date_range[0]}to{date_range[1]}",
            }
        )
    )
    data = http.request("GET", url, headers={"X-Subscription-Token": api_key}, timeout=15)
    items = []
    for i, r in enumerate((data.get("web", {}).get("results", []))[:count]):
        raw_date = r.get("page_age") or ""
        pub_date = _normalize_date(raw_date[:10]) if raw_date else None
        if not _in_date_range(pub_date, date_range):
            continue
        items.append({
            "id": f"WB{i + 1}",
            "title": r.get("title", ""),
            "url": r.get("url", ""),
            "source_domain": _domain(r.get("url", "")),
            "snippet": r.get("description", ""),
            "date": pub_date,
            "relevance": 0.8,
            "why_relevant": "Brave web search",
        })
    artifact = {"label": "brave", "webSearchQueries": [query], "resultCount": len(items)}
    return items, artifact


# ---------------------------------------------------------------------------
# Exa AI Search
# ---------------------------------------------------------------------------

def exa_search(
    query: str, date_range: tuple[str, str], api_key: str, count: int = 5,
) -> tuple[list[dict], dict]:
    data = http.request(
        "POST", "https://api.exa.ai/search",
        headers={"x-api-key": api_key},
        json_data={
            "query": query,
            "type": "auto",
            "numResults": count,
            "startPublishedDate": f"{date_range[0]}T00:00:00.000Z",
            "endPublishedDate": f"{date_range[1]}T23:59:59.999Z",
            "contents": {"text": {"maxCharacters": 2000}},
        },
        timeout=15,
    )
    items = []
    for i, r in enumerate((data.get("results", []))[:count]):
        if not isinstance(r, dict):
            continue
        url = r.get("url", "")
        if not url:
            continue
        raw_date = r.get("publishedDate") or ""
        pub_date = _normalize_date(raw_date.split("T")[0] if "T" in raw_date else raw_date[:10]) if raw_date else None
        if not _in_date_range(pub_date, date_range):
            continue
        items.append({
            "id": f"WE{i + 1}",
            "title": r.get("title", ""),
            "url": url,
            "source_domain": _domain(url),
            "snippet": (r.get("text") or "")[:500],
            "date": pub_date,
            "relevance": 0.8,
            "why_relevant": "Exa web search",
        })
    artifact = {"label": "exa", "webSearchQueries": [query], "resultCount": len(items)}
    return items, artifact


# ---------------------------------------------------------------------------
# Serper (Google Search wrapper)
# ---------------------------------------------------------------------------

def serper_search(
    query: str, date_range: tuple[str, str], api_key: str, count: int = 5,
) -> tuple[list[dict], dict]:
    data = http.request(
        "POST", "https://google.serper.dev/search",
        headers={"X-API-KEY": api_key},
        json_data={
            "q": query,
            "num": count,
            "tbs": f"cdr:1,cd_min:{_serper_date_param(date_range[0])},cd_max:{_serper_date_param(date_range[1])}",
        },
        timeout=15,
    )
    items = []
    for i, r in enumerate((data.get("organic", []))[:count]):
        raw_date = r.get("date") or ""
        pub_date = _parse_serper_date(raw_date)
        if not _in_date_range(pub_date, date_range):
            continue
        items.append({
            "id": f"WS{i + 1}",
            "title": r.get("title", ""),
            "url": r.get("link", ""),
            "source_domain": _domain(r.get("link", "")),
            "snippet": r.get("snippet", ""),
            "date": pub_date,
            "relevance": 0.8,
            "why_relevant": "Serper web search",
        })
    artifact = {"label": "serper", "webSearchQueries": [query], "resultCount": len(items)}
    return items, artifact


# ---------------------------------------------------------------------------
# Parallel AI Search
# ---------------------------------------------------------------------------

def parallel_search(
    query: str, date_range: tuple[str, str], api_key: str, count: int = 5,
) -> tuple[list[dict], dict]:
    data = http.request(
        "POST", "https://api.parallel.ai/v1/search",
        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
        json_data={
            "search_queries": [query],
            "advanced_settings": {"max_results": count},
        },
        timeout=15,
    )
    items = []
    for i, r in enumerate((data.get("results", []))[:count]):
        if not isinstance(r, dict):
            continue
        url = r.get("url", "")
        if not url:
            continue
        raw_date = r.get("publish_date") or ""
        pub_date = _normalize_date(raw_date[:10]) if raw_date else None
        if not _in_date_range(pub_date, date_range):
            continue
        items.append({
            "id": f"WP{i + 1}",
            "title": r.get("title", ""),
            "url": url,
            "source_domain": _domain(url),
            "snippet": ((r.get("excerpts") or [""])[0] or "")[:500],
            "date": pub_date,
            "relevance": 0.8,
            "why_relevant": "Parallel AI web search",
        })
    artifact = {"label": "parallel", "webSearchQueries": [query], "resultCount": len(items)}
    return items, artifact


def _parse_serper_date(raw: str) -> str | None:
    if not raw:
        return None
    normalized = _normalize_date(raw)
    if normalized:
        return normalized
    for fmt in ("%b %d, %Y", "%B %d, %Y", "%Y-%m-%d"):
        try:
            return datetime.strptime(raw.strip(), fmt).date().isoformat()
        except ValueError:
            continue
    return None




# ---------------------------------------------------------------------------
# Dispatcher
# ---------------------------------------------------------------------------

def web_search(
    query: str,
    date_range: tuple[str, str],
    config: dict,
    backend: str = "auto",
) -> tuple[list[dict], dict]:
    """Run web search with the specified or auto-detected backend."""
    if backend == "auto":
        if config.get("BRAVE_API_KEY"):
            backend = "brave"
        elif config.get("EXA_API_KEY"):
            backend = "exa"
        elif config.get("SERPER_API_KEY"):
            backend = "serper"
        elif config.get("PARALLEL_API_KEY"):
            backend = "parallel"
        else:
            return [], {}
    items: list[dict] = []
    artifact: dict = {}
    if backend == "brave":
        key = config.get("BRAVE_API_KEY")
        if not key:
            raise RuntimeError("BRAVE_API_KEY is required when web_backend='brave'")
        items, artifact = brave_search(query, date_range, key)
    elif backend == "exa":
        key = config.get("EXA_API_KEY")
        if not key:
            raise RuntimeError("EXA_API_KEY is required when web_backend='exa'")
        items, artifact = exa_search(query, date_range, key)
    elif backend == "serper":
        key = config.get("SERPER_API_KEY")
        if not key:
            raise RuntimeError("SERPER_API_KEY is required when web_backend='serper'")
        items, artifact = serper_search(query, date_range, key)
    elif backend == "parallel":
        key = config.get("PARALLEL_API_KEY")
        if not key:
            raise RuntimeError("PARALLEL_API_KEY is required when web_backend='parallel'")
        items, artifact = parallel_search(query, date_range, key)
    elif backend != "none":
        raise ValueError(f"Unsupported web backend: {backend!r}")
    else:
        return [], {}
    if items and not _reddit_excluded(config):
        items = _enrich_reddit_items(items)
    return items, artifact


def _reddit_excluded(config: dict) -> bool:
    """Return True when EXCLUDE_SOURCES contains 'reddit'.

    Respects the same suppression knob the pipeline uses for source gating,
    so a user who set EXCLUDE_SOURCES=reddit doesn't get Reddit content
    smuggled back in via web-search URLs.
    """
    raw = (config.get("EXCLUDE_SOURCES") or "").split(",")
    return any(s.strip().lower() == "reddit" for s in raw)


def _enrich_reddit_items(items: list[dict]) -> list[dict]:
    """Enrich web search results that are Reddit URLs with thread body and comments.

    Claude Code's WebFetch blocks reddit.com, so the model can't retrieve
    Reddit content from web search results. This fetches it via the public
    JSON API (reddit.com/.../.json) which bypasses that restriction.

    Callers should gate this with EXCLUDE_SOURCES=reddit handling (see
    `_reddit_excluded`) so a user who explicitly excluded Reddit doesn't
    get Reddit content via web-search URLs.
    """
    from . import reddit_enrich
    from .reddit_enrich import RedditRateLimitError

    for item in items:
        url = item.get("url", "")
        if "reddit.com" not in url or "/comments/" not in url:
            continue
        try:
            thread_data = reddit_enrich.fetch_thread_data(url, timeout=8)
            if not thread_data:
                continue
            parsed = reddit_enrich.parse_thread_data(thread_data)
            # selftext lives under parsed["submission"], not at the top level
            selftext = (parsed.get("submission") or {}).get("selftext", "")
            if selftext:
                item["snippet"] = selftext[:2000]
            comments = parsed.get("comments", [])
            top = reddit_enrich.get_top_comments(comments)
            if top:
                item["top_comments"] = [
                    {"score": c.get("score", 0), "excerpt": (c.get("body") or "")[:200]}
                    for c in top[:5]
                ]
            item["enriched_via"] = "reddit_json_api"
        except RedditRateLimitError as exc:
            # Stop iterating to avoid flooding more 429s
            sys.stderr.write(f"[Web] Reddit rate-limited, halting enrichment: {exc}\n")
            break
        except Exception as exc:
            sys.stderr.write(f"[Web] Reddit enrichment failed for {url}: {exc}\n")
    return items


# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------

def _normalize_date(value: object) -> str | None:
    if value is None:
        return None
    parsed = dates.parse_date(str(value).strip())
    if not parsed:
        return None
    return parsed.date().isoformat()


def _serper_date_param(iso_date: str) -> str:
    """Convert YYYY-MM-DD to MM/DD/YYYY for Serper tbs parameter."""
    parts = iso_date.split("-")
    return f"{parts[1]}/{parts[2]}/{parts[0]}"


def _in_date_range(pub_date: str | None, date_range: tuple[str, str]) -> bool:
    if not pub_date:
        return False
    return date_range[0] <= pub_date <= date_range[1]


def _domain(url: str) -> str:
    return urlparse(url).netloc.strip().lower()
