backend/agents/gap_analyzer.py

"""Gap analyzer node with context-bus aware clarifying questions."""

from __future__ import annotations

from typing import Any, Dict, List

from langchain_core.messages import AIMessage

from agents.document_gap_analyzer import _collect_structured_gaps
from core.context.context_bus import filter_clarifying_questions
from schemas import AgentState


def _gap_to_question(gap: str) -> str | None:
    g = (gap or "").lower()
    if "pkd" in g:
        return "Podaj główny kod PKD działalności objętej projektem."
    if "nip" in g or "gus" in g or "profilu firmy" in g:
        return "Podaj NIP firmy, aby pobrać dane z rejestru GUS."
    if "opis" in g or "cel" in g:
        return "Opisz cel projektu i planowane wydatki w 2–3 zdaniach."
    if "finans" in g or "przychod" in g:
        return "Podaj przychody firmy z ostatniego roku obrotowego."
    if "wojew" in g or "region" in g:
        return "Uzupełnij województwo realizacji projektu."
    return f"Uzupełnij brakującą informację: {gap}"


def build_clarifying_questions(gaps: List[str], profile: dict | None) -> List[str]:
    profile = profile or {}
    company = dict(profile)
    if hasattr(profile, "model_dump"):
        company = profile.model_dump()
    elif hasattr(profile, "dict"):
        company = profile.dict()

    questions: List[str] = []
    for gap in gaps or []:
        q = _gap_to_question(gap)
        if q and q not in questions:
            questions.append(q)

    return filter_clarifying_questions(questions, company, gaps)


def gap_analyzer_node(state: AgentState) -> Dict[str, Any]:
    gaps = _collect_structured_gaps(state)
    profile_dict: dict = {}
    if state.profile:
        if hasattr(state.profile, "model_dump"):
            profile_dict = state.profile.model_dump()
        elif hasattr(state.profile, "dict"):
            profile_dict = state.profile.dict()
        else:
            profile_dict = dict(state.profile)

    clarifying = build_clarifying_questions(gaps, profile_dict)

    bb = dict(state.blackboard or {})
    bb["data_gaps"] = gaps
    bb["gap_analysis_done"] = True
    bb["clarifying_questions"] = clarifying

    summary = "\n".join(f"- {g}" for g in gaps) if gaps else "Brak krytycznych braków danych."
    return {
        "messages": [
            AIMessage(
                content=f"[GAP ANALYZER] Analiza braków zakończona.\n{summary}"
            )
        ],
        "blackboard": bb,
        "current_agent": "supervisor",
    }