"""
Bank forecast scraper — fetches public research pages, extracts numeric forecasts via LLM.
Forecasts are stored in bank_forecasts and optionally pushed to macro_series_log as consensus.
"""
import json
import logging
import re
from datetime import datetime, timezone, timedelta
from typing import Optional

logger = logging.getLogger(__name__)

# Max article text sent to LLM (chars)
_MAX_TEXT = 6000


def _fetch_text(url: str, timeout: int = 15) -> str:
    """HTTP GET → plain text (strips HTML tags)."""
    import urllib.request
    import html
    req = urllib.request.Request(url, headers={
        "User-Agent": "Mozilla/5.0 (compatible; OpenFin-Research/1.0)"
    })
    with urllib.request.urlopen(req, timeout=timeout) as resp:
        raw = resp.read().decode("utf-8", errors="replace")
    # Strip HTML tags
    text = re.sub(r"<[^>]+>", " ", raw)
    text = html.unescape(text)
    text = re.sub(r"\s+", " ", text).strip()
    return text[:_MAX_TEXT]


def _upcoming_events(conn, days_ahead: int = 14) -> list[dict]:
    """Return ff_calendar events with series_id coming in the next N days."""
    today = datetime.now(timezone.utc).date().isoformat()
    horizon = (datetime.now(timezone.utc).date() + timedelta(days=days_ahead)).isoformat()
    rows = conn.execute(
        """SELECT DISTINCT series_id, event_name, event_date, currency, impact
           FROM ff_calendar
           WHERE series_id IS NOT NULL AND series_id != ''
             AND event_date BETWEEN ? AND ?
             AND impact IN ('High', 'Medium')
           ORDER BY event_date""",
        (today, horizon)
    ).fetchall()
    return [dict(r) for r in rows]


def _llm_extract(article_text: str, upcoming: list[dict]) -> list[dict]:
    """
    Call Claude to extract numeric forecasts for upcoming events from article text.
    Returns list of {series_id, event_name, event_date, forecast_value, unit, confidence, snippet}.
    """
    from services.database import get_config as _cfg
    import anthropic

    api_key = _cfg("anthropic_api_key") or ""
    if not api_key:
        logger.warning("[bank_forecast] No anthropic_api_key configured")
        return []

    if not upcoming:
        return []

    events_block = "\n".join(
        f"- {e['event_name']} (series_id={e['series_id']}, release={e['event_date']}, currency={e['currency']})"
        for e in upcoming
    )

    prompt = f"""You are extracting economic forecasts from a bank research note.

Upcoming economic releases:
{events_block}

From the text below, extract any numeric forecasts or expectations for the above indicators.
Return a JSON array (no commentary, no markdown). Each item:
  {{ "series_id": "...", "event_name": "...", "event_date": "YYYY-MM-DD",
     "forecast_value": <number>, "unit": "%|K|pp|...",
     "confidence": "high|medium|low",
     "snippet": "<exact quote from text, max 120 chars>" }}

Only include entries where a clear numeric forecast is stated. Return [] if nothing found.

TEXT:
{article_text}"""

    client = anthropic.Anthropic(api_key=api_key)
    msg = client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=1024,
        messages=[{"role": "user", "content": prompt}]
    )
    raw = msg.content[0].text.strip()
    # Extract JSON array from response
    m = re.search(r"\[.*\]", raw, re.DOTALL)
    if not m:
        return []
    try:
        return json.loads(m.group())
    except Exception as e:
        logger.warning(f"[bank_forecast] JSON parse error: {e} — raw: {raw[:200]}")
        return []


def scrape_source(conn, source: dict) -> dict:
    """
    Scrape one bank source: fetch URL, extract forecasts via LLM, save to bank_forecasts.
    Returns {source_id, name, fetched, extracted, saved, error}.
    """
    sid = source["id"]
    url = (source.get("url") or "").strip()
    if not url:
        return {"source_id": sid, "name": source["name"], "fetched": False,
                "extracted": 0, "saved": 0, "error": "no URL configured"}

    # Fetch article text
    try:
        text = _fetch_text(url)
    except Exception as e:
        return {"source_id": sid, "name": source["name"], "fetched": False,
                "extracted": 0, "saved": 0, "error": str(e)}

    # Get upcoming events to focus extraction
    upcoming = _upcoming_events(conn)
    if not upcoming:
        return {"source_id": sid, "name": source["name"], "fetched": True,
                "extracted": 0, "saved": 0, "error": "no upcoming events with series_id"}

    # LLM extraction
    try:
        forecasts = _llm_extract(text, upcoming)
    except Exception as e:
        return {"source_id": sid, "name": source["name"], "fetched": True,
                "extracted": 0, "saved": 0, "error": f"LLM error: {e}"}

    # Save results
    saved = 0
    now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
    for f in forecasts:
        series_id = f.get("series_id", "").strip()
        event_date = f.get("event_date", "").strip()
        forecast_value = f.get("forecast_value")
        if not series_id or not event_date or forecast_value is None:
            continue
        try:
            conn.execute(
                """INSERT INTO bank_forecasts
                   (source_id, series_id, event_name, event_date, forecast_value,
                    unit, confidence, snippet, source_url, extracted_at)
                   VALUES (?,?,?,?,?,?,?,?,?,?)
                   ON CONFLICT(source_id, series_id, event_date) DO UPDATE SET
                     forecast_value=excluded.forecast_value,
                     snippet=excluded.snippet,
                     extracted_at=excluded.extracted_at""",
                (sid, series_id, f.get("event_name", ""), event_date,
                 float(forecast_value), f.get("unit", ""),
                 f.get("confidence", "medium"), f.get("snippet", "")[:200],
                 url, now)
            )
            saved += 1
        except Exception as e:
            logger.warning(f"[bank_forecast] save error {sid}/{series_id}: {e}")

    conn.execute("UPDATE bank_forecast_sources SET last_scraped=? WHERE id=?", (now, sid))
    conn.commit()

    return {"source_id": sid, "name": source["name"], "fetched": True,
            "extracted": len(forecasts), "saved": saved, "error": None}


def scrape_all_active(conn) -> list[dict]:
    """Scrape all active bank sources. Returns list of per-source results."""
    sources = conn.execute(
        "SELECT id, name, url, last_scraped FROM bank_forecast_sources WHERE active=1"
    ).fetchall()
    results = []
    for s in sources:
        res = scrape_source(conn, dict(s))
        results.append(res)
        logger.info(f"[bank_forecast] {res['name']}: fetched={res['fetched']} saved={res['saved']} err={res.get('error')}")
    return results


def push_consensus_to_log(conn, series_id: str, event_date: str) -> Optional[float]:
    """
    Compute average of all bank forecasts for (series_id, event_date),
    push to macro_series_log as source='bank_consensus'.
    Returns consensus value or None.
    """
    rows = conn.execute(
        "SELECT forecast_value FROM bank_forecasts WHERE series_id=? AND event_date=? AND forecast_value IS NOT NULL",
        (series_id, event_date)
    ).fetchall()
    if not rows:
        return None

    values = [r[0] for r in rows]
    consensus = round(sum(values) / len(values), 4)

    # Get event_name from any row
    meta = conn.execute(
        "SELECT event_name FROM bank_forecasts WHERE series_id=? AND event_date=? LIMIT 1",
        (series_id, event_date)
    ).fetchone()
    event_name = meta[0] if meta else series_id

    from services.macro_series_log import log_if_changed
    log_if_changed(
        conn, series_id=series_id, event_name=event_name, event_date=event_date,
        actual_value=None, forecast_value=consensus, previous_value=None,
        source="bank_consensus"
    )
    return consensus


def push_all_consensus(conn) -> list[dict]:
    """Push consensus for every (series_id, event_date) that has bank forecasts."""
    pairs = conn.execute(
        "SELECT DISTINCT series_id, event_date FROM bank_forecasts"
    ).fetchall()
    results = []
    for series_id, event_date in pairs:
        val = push_consensus_to_log(conn, series_id, event_date)
        results.append({"series_id": series_id, "event_date": event_date, "consensus": val})
    return results