"""Datasheet -> IACE 'Grenzen' (ISO 12100 machine limits) extraction.

Hybrid: a deterministic pre-pass pulls high-confidence facts (interfaces, units)
straight from the text; the local LLM (Ollama 35B via llm_cascade, local-first)
does the semantic mapping into the IACE LimitsFormData keys. The LLM must NOT
invent values — any field not supported by the text stays empty and becomes a
follow-up question. Each filled field carries a source snippet (auditability).

Pure + testable: detect_signals / parse_grenzen_json / compute_followups. The
async extract_grenzen() wraps the LLM call (llm_cascade, same as vendor extractor).
"""
import json
import logging
import os
import re

logger = logging.getLogger(__name__)

# Datasheet extraction uses the local 35B (same model as the Compliance Advisor) —
# higher-quality semantic mapping than the default cascade model. Env-overridable.
_DATASHEET_MODEL = os.getenv("CRA_DATASHEET_MODEL", "qwen3.5:35b-a3b")

# IACE Grenzen field keys (must match admin LimitsFormData). label + whether it
# is essential for a usable risk assessment (=> asked as follow-up if empty).
LIMIT_FIELDS = [
    ("machine_designation", "Maschinenbezeichnung", False),
    ("machine_type", "Maschinentyp", False),
    ("manufacturer", "Hersteller", False),
    ("year_of_construction", "Baujahr", False),
    ("general_description", "Allgemeine Beschreibung", True),
    ("intended_purpose", "Verwendungszweck", True),
    ("area_of_use", "Einsatzbereich", True),
    ("operating_modes", "Betriebsarten", True),
    ("variants", "Varianten", False),
    ("foreseeable_misuses", "Vorhersehbare Fehlanwendungen", True),
    ("spatial_limits", "Räumliche Grenzen", True),
    ("temporal_limits", "Zeitliche Grenzen", True),
    ("operating_conditions", "Betriebsbedingungen", True),
    ("energy_supply", "Energieversorgung", True),
    ("mechanical_interfaces", "Mechanische Schnittstellen", False),
    ("electrical_interfaces", "Elektrische Schnittstellen", False),
    ("software_interfaces", "Software-Schnittstellen", False),
    ("pneumatic_hydraulic_interfaces", "Pneumatische/Hydraulische Schnittstellen", False),
    ("person_groups", "Personengruppen", True),
    ("qualification_requirements", "Qualifikationsanforderungen", True),
]
_FIELD_KEYS = [f[0] for f in LIMIT_FIELDS]
_FIELD_LABEL = {f[0]: f[1] for f in LIMIT_FIELDS}
_ESSENTIAL = {f[0] for f in LIMIT_FIELDS if f[2]}

# Deterministic signal detection — high-confidence facts straight from the text.
_INTERFACE_TOKENS = [
    "Ethernet", "EtherCAT", "EtherNet/IP", "PROFINET", "Profinet", "PROFIBUS", "Modbus",
    "CANopen", "CAN", "IO-Link", "OPC UA", "OPC-UA", "Anybus", "RS232", "RS-232", "RS485",
    "RS-485", "USB", "Bluetooth", "WLAN", "WiFi", "Wi-Fi", "MQTT", "REST", "HTTP",
    "Sercos", "DeviceNet", "TCP/IP", "TLS",
]
_UNIT_RE = re.compile(r"\b\d+(?:[.,]\d+)?\s?(?:V|A|kW|bar|mm|cm|°C|Hz|kg|rpm|Achsen|axes|N|W)\b", re.IGNORECASE)


def detect_signals(text: str) -> dict:
    """Deterministic facts: interfaces present + technical units found."""
    t = text or ""
    low = t.lower()
    interfaces = []
    seen = set()
    for tok in _INTERFACE_TOKENS:
        if tok.lower() in low:
            key = tok.lower().replace("-", "").replace("/", "").replace(".", "")
            if key not in seen:
                seen.add(key)
                interfaces.append(tok)
    units = sorted({m.group(0).strip() for m in _UNIT_RE.finditer(t)})
    return {"interfaces": interfaces, "units": units}


def _system_prompt() -> str:
    keys = ", ".join(_FIELD_KEYS)
    return (
        "Du bist ein Sicherheitsingenieur. Extrahiere aus einem Maschinen-/Produkt-"
        "Datenblatt die Maschinengrenzen nach ISO 12100. Gib NUR ein JSON-Objekt zurueck:\n"
        '{"fields": {"<key>": {"value": "<Text oder \\"\\">", "source": "<woertliches Zitat aus dem Datenblatt oder \\"\\">"}}}\n\n'
        f"Erlaubte keys: {keys}\n\n"
        "Regeln:\n"
        "- Fuelle ein Feld NUR, wenn es im Datenblatt steht. Sonst value=\"\".\n"
        "- KEINE Werte erfinden, schaetzen oder annehmen.\n"
        "- 'source' ist ein woertliches Zitat aus dem Text, das den Wert belegt.\n"
        "- foreseeable_misuses / person_groups / qualification_requirements stehen "
        "fast nie im Datenblatt → meist leer lassen.\n"
        "- Nur reines JSON, keine Prosa, keine Code-Fences."
    )


def parse_grenzen_json(raw: str) -> dict:
    """Parse the LLM response into {key: {value, source}} for known keys only."""
    try:
        data = json.loads(raw)
    except (json.JSONDecodeError, TypeError):
        return {}
    fields = data.get("fields") if isinstance(data, dict) else None
    if not isinstance(fields, dict):
        fields = data if isinstance(data, dict) else {}
    out = {}
    for key in _FIELD_KEYS:
        entry = fields.get(key)
        if isinstance(entry, dict):
            val = str(entry.get("value") or "").strip()
            src = str(entry.get("source") or "").strip()
        elif isinstance(entry, str):
            val, src = entry.strip(), ""
        else:
            continue
        if val:
            out[key] = {"value": val, "source": src}
    return out


_QUESTION = {
    "general_description": "Was tut das Produkt grundsätzlich? (kurze Beschreibung)",
    "intended_purpose": "Wofür ist das Produkt bestimmungsgemäß vorgesehen?",
    "area_of_use": "In welchem Umfeld / welcher Branche wird es eingesetzt?",
    "operating_modes": "Welche Betriebsarten gibt es (Automatik, Einrichten, Wartung …)?",
    "foreseeable_misuses": "Welche vernünftigerweise vorhersehbaren Fehlanwendungen gibt es?",
    "spatial_limits": "Räumliche Grenzen (Abmessungen, Arbeits-/Zugangsbereich)?",
    "temporal_limits": "Zeitliche Grenzen (Lebensdauer, Wartungsintervalle, Betriebsdauer)?",
    "operating_conditions": "Betriebsbedingungen (Temperatur, Feuchte, Umgebung)?",
    "energy_supply": "Energieversorgung (elektrisch, pneumatisch, hydraulisch)?",
    "person_groups": "Welche Personengruppen interagieren mit dem Produkt?",
    "qualification_requirements": "Welche Qualifikation brauchen Bediener/Wartung?",
}


def compute_followups(limits: dict) -> list:
    """Essential ISO-12100 fields still empty → targeted follow-up questions."""
    out = []
    for key in _FIELD_KEYS:
        if key in _ESSENTIAL and not (limits.get(key) or "").strip():
            out.append({"key": key, "label": _FIELD_LABEL[key],
                        "question": _QUESTION.get(key, f"Bitte ergänzen: {_FIELD_LABEL[key]}")})
    return out


def _merge_detected(limits: dict, provenance: dict, signals: dict) -> None:
    """Backfill electrical/software interfaces from the deterministic detector
    when the LLM left them empty (high-confidence facts shouldn't be lost)."""
    ifaces = signals.get("interfaces") or []
    if not ifaces:
        return
    net = [i for i in ifaces if i.lower() not in ("usb",)]
    if net and not limits.get("electrical_interfaces"):
        limits["electrical_interfaces"] = ", ".join(net)
        provenance["electrical_interfaces"] = "deterministisch erkannt: " + ", ".join(net)


async def extract_grenzen(text: str, max_chars: int = 20000) -> dict:
    """Datasheet text -> {limits, provenance, detected, missing, followup}."""
    signals = detect_signals(text or "")
    limits: dict = {}
    provenance: dict = {}
    llm_status = "skipped"  # skipped | ok | empty | unavailable
    excerpt = (text or "")[:max_chars]
    if len(excerpt) >= 200:
        try:
            from compliance.services.llm_cascade import call_with_cascade
            res = await call_with_cascade(
                system=_system_prompt(),
                user=f"Datenblatt-Text:\n\n{excerpt}",
                min_confidence=0.5, max_tokens=4000, model=_DATASHEET_MODEL, think=False,
            )
            parsed = parse_grenzen_json(res.get("text", "") if isinstance(res, dict) else "")
            for key, entry in parsed.items():
                limits[key] = entry["value"]
                provenance[key] = entry.get("source", "")
            llm_status = "ok" if parsed else "empty"
        except Exception as e:
            # best-effort: keep the deterministic facts, but surface the failure so
            # a cold-start/timeout doesn't masquerade as "nothing on the datasheet".
            logger.warning("datasheet LLM extraction failed: %s (%s)", e, type(e).__name__)
            llm_status = "unavailable"

    _merge_detected(limits, provenance, signals)
    return {
        "limits": limits,
        "provenance": provenance,
        "detected": signals,
        "llm_status": llm_status,
        "filled": sorted(limits.keys()),
        "missing": [k for k in _FIELD_KEYS if not (limits.get(k) or "").strip()],
        "followup": compute_followups(limits),
    }