"""Employment extraction entrypoint — llm_setup.llm_call only.

Pure module: no FastAPI imports and no HTTPException.
Called from inbox.tasks.match_inbox_message; no HTTP surface.
"""

from __future__ import annotations

import json
import logging

from employment_agent.decorators import parse_employment_response
from employment_agent.prompt import CURRENT_TITLE,EDUCATION,NO_CITY,NO_COMPANY,city_list_prompt,prompt,user_prompt
from llm_setup import llm_call

logger=logging.getLogger("employment_agent")


def parse_normalized_cities(data,fallback=None):
    """Keep unique proper city names from the list-normalizer JSON."""
    rows=None
    if isinstance(data,dict):
        rows=data.get("cities")
    if not isinstance(rows,list):
        return list(fallback or [])
    out=[]
    seen=set()
    for item in rows:
        if not isinstance(item,str):
            continue
        text=item.strip()
        if not text or text.lower() in (NO_CITY.lower(),"none","null","n/a","-"):
            continue
        key=text.lower()
        if key in seen:
            continue
        seen.add(key)
        out.append(text)
    out.sort(key=str.lower)
    return out or list(fallback or [])


async def normalize_cities(values):
    """OpenAI: messy stored places → the same proper city names the CV agent writes."""
    places=[]
    seen=set()
    for raw in values or []:
        text=(raw or "").strip()
        if not text:
            continue
        key=text.lower()
        if key in seen:
            continue
        seen.add(key)
        places.append(text)
    if not places:
        return []
    try:
        data=await llm_call(
            city_list_prompt(),
            json.dumps({"places":places},ensure_ascii=False),
            json_mode=True,
        )
    except Exception:
        logger.exception("city list normalize failed")
        return places
    return parse_normalized_cities(data,fallback=places)


async def run_employment_agent(*,resume_text=""):
    text=(resume_text or "").strip()
    if not text:
        return {
            "current_employment":NO_COMPANY,
            "education":EDUCATION,
            "current_title":CURRENT_TITLE,
            "linkedin_url":None,
            "phone":None,
            "city":None,
            "skills":[],
            "years_experience":None,
        }
    try:
        data=await llm_call(prompt(),user_prompt(text),json_mode=True)
        return parse_employment_response(data,text)
    except Exception as e:
        logger.exception("employment llm_call failed")
        raise RuntimeError(str(e)) from e