"""Turn a free-text intake message into a normalized proposal via local Qwen.

The model only EXTRACTS structure; it never decides to write anything. New-vs-existing is
finalized in M2 against the CRM matcher — here `intent` is the model's first read.
"""
import re

import spark

SYSTEM = (
    "You extract structured investor-intake data from a short message a venture-fund "
    "team member typed. Reply with ONLY a JSON object, no prose, with these keys:\n"
    '  "intent": "new_investor" if the message introduces a new investor or prospect, '
    '"meeting_note" if it logs a note/update about an investor, else "unclear".\n'
    '  "investor_name": the investing firm or entity name (e.g. "Acme Capital"), or null.\n'
    '  "contact_name": the individual person mentioned, or null.\n'
    '  "contact_email": the person\'s email if explicitly present, else null. Never invent one.\n'
    '  "contact_title": the person\'s role/title if stated, else null.\n'
    '  "note": any meeting note, context, or next step, else null.\n'
    "Use null (not empty string) for anything not present. Output JSON only."
)

_EMAIL_RE = re.compile(r"[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}")
_VALID_INTENTS = {"new_investor", "meeting_note", "unclear"}
_FIELDS = ("intent", "investor_name", "contact_name", "contact_email", "contact_title", "note")


def _clean(v):
    if v is None:
        return None
    s = str(v).strip()
    if not s or s.lower() in ("null", "none", "n/a", "na", "unknown"):
        return None
    return s


def normalize(raw, source_text=""):
    """Coerce the model's dict into a stable proposal shape; salvage an email from the
    source text if the model missed one. Returns a dict with all _FIELDS keys."""
    raw = raw or {}
    out = {k: _clean(raw.get(k)) for k in _FIELDS}

    intent = (out["intent"] or "").lower().replace("-", "_").replace(" ", "_")
    out["intent"] = intent if intent in _VALID_INTENTS else "unclear"

    # Email integrity: only accept an address that literally appears in the source message.
    # The model is unreliable for verbatim strings and must never mint an address — anything
    # not present in what the human typed is dropped (a wrong email in the CRM is worse than
    # none). This both salvages a missed address and rejects a hallucinated one.
    m = _EMAIL_RE.search(source_text or "")
    out["contact_email"] = m.group(0).rstrip(".,;:!?)]}>\"'") if m else None

    # An intake with no firm AND no person is not actionable.
    if not out["investor_name"] and not out["contact_name"]:
        out["intent"] = "unclear"
    return out


def parse_message(text, parse_fn=spark.parse_json):
    """Parse one intake message. `parse_fn` is injectable for tests (defaults to Spark/Qwen).
    Returns a normalized proposal dict. On a model/transport failure, raises (caller decides)."""
    raw = parse_fn(text, system=SYSTEM, max_tokens=400)
    return normalize(raw, source_text=text)