from __future__ import annotations
from typing import Dict, Any
from services.llm import llm
import json


class ProfileAgent:
    """Parses raw CV text into a structured profile using LLM with fallback."""

    def parse(self, cv_text: str) -> Dict[str, Any]:
        if not cv_text:
            return {}
        if not llm.enabled:
            return {
                "full_name": "Unknown",
                "email": "",
                "skills": [],
                "experiences": [],
                "links": {},
                "languages": [],
                "certifications": [],
                "projects": [],
                "work_mode": "",
                "skill_proficiency": {},
            }
        system = (
            "You are a CV parser. Extract JSON with fields: full_name, email, phone, location, "
            "skills (list), experiences (list of {title, company, start_date, end_date, achievements, technologies}), "
            "education (list of {school, degree, field_of_study, start_date, end_date}), links (map with linkedin/portfolio/website if present). "
            "Also extract optional: languages (list of {language, level}), certifications (list of {name, issuer, year}), "
            "projects (list of {title, link, impact}), work_mode (remote/hybrid/on-site if evident), skill_proficiency (map skill->level). "
            "Keep values concise; do not invent information."
        )
        user = f"Parse this CV into JSON with the schema above. Be strict JSON.\n\n{cv_text}"
        resp = llm.generate(system, user, max_tokens=900, agent="parser")
        try:
            return json.loads(resp)
        except Exception:
            return {"raw": resp}