backend/schemas.py

# ruff: noqa: E402
from typing import List, Optional, Dict, Any, Annotated
from pydantic import BaseModel, Field
import operator
from datetime import datetime


class UserUsageData(BaseModel):
    user_id: str
    wizard_iterations_today: int = 0
    tokens_used_month: int = 0
    last_reset_daily: Optional[datetime] = None
    last_reset_monthly: Optional[datetime] = None


class FinancialData(BaseModel):
    revenue: float = Field(0.0, description="Przychody za ostatni rok obrotowy")
    net_profit: float = Field(0.0, description="Zysk netto")
    employment: int = Field(
        0, description="Liczba pełnych etatów (Roczna Jednostka Pracy)"
    )
    assets: float = Field(0.0, description="Suma bilansowa")


class InvestmentPlan(BaseModel):
    description: str
    estimated_cost: float
    br_evidence: bool = False
    green_transform: bool = False
    digitalization: bool = False
    goz_dnsh: bool = False


class CompanyProfile(BaseModel):
    nip: str
    pkd_codes: List[str] = Field(default_factory=list)
    voivodeship: str
    size: str = "MŚP"  # Mikro, Mała, Średnia, Duża
    financials: Optional[FinancialData] = None
    innovation_focus: List[str] = Field(default_factory=list)
    is_in_difficulty: bool = False
    br_evidence: bool = False  # Dowody B+R
    investment_plans: List[InvestmentPlan] = Field(default_factory=list)


class GrantCall(BaseModel):
    id: str = ""
    title: str
    institution: str = ""
    deadline: str
    max_amount: float
    description: str = ""
    relevance_score: float = 0.0
    poetic_match: str = ""  # Dodane dla wirtualnego poety funduszowego
    explanation: Optional[Dict[str, Any]] = None  # Explainable AI Match
    url: str = Field(default="", pattern=r"^(https://.*)?$", description="Link do dokumentacji naboru (musi zaczynać się od https://)")
    status: str = ""
    last_verified: str = ""
    is_outdated_warning: bool = False


from typing import Literal


class CriticFeedback(BaseModel):
    is_approved: bool
    feedback: str
    severity: Literal["low", "medium", "high"]


class AgentState(BaseModel):
    messages: Annotated[list, operator.add]
    profile: Optional[CompanyProfile] = None
    eligible_grants: Optional[List[GrantCall]] = Field(default_factory=list)
    checklist: Optional[Dict[str, bool]] = Field(default_factory=dict)
    verification_results: Optional[Dict[str, Any]] = Field(default_factory=dict)
    timeline_events: Optional[List[Dict]] = Field(default_factory=list)
    wizard_step: int = 0
    current_nabor: Optional[GrantCall] = None
    current_agent: str = "supervisor"
    user_id: str
    tenant_id: str
    program_name: Optional[str] = None

    # 2026 Architecture Upgrades
    blackboard: Optional[Dict[str, Any]] = Field(
        default_factory=dict, description="Wspólna pamięć faktów o firmie"
    )
    task_plan: Optional[List[str]] = Field(
        default_factory=list, description="Lista kroków z Planner Agent"
    )
    interrupt_history: Optional[List[Dict[str, Any]]] = Field(
        default_factory=list, description="Historia przerw Human-in-the-Loop"
    )
    document_versions: Optional[Dict[str, List[str]]] = Field(
        default_factory=dict, description="Wersjonowanie generowanych fragmentów"
    )
    guard_block: bool = False

    # Dodatkowe pola do obsługi cyklu oceny i HitL
    critic_evaluation: Optional[CriticFeedback] = None
    critic_iterations: int = 0
    max_critic_iterations: int = 2
    risk_score: Optional[Dict[str, Any]] = None
    verification_status: str = "pending"
    approved_by_human: bool = False


class SupervisorDecision(BaseModel):
    next_agent: str
    reason: Optional[str] = None


class PlanOutput(BaseModel):
    steps: List[str]


class RiskScoreOutput(BaseModel):
    score: int
    risks: List[str]


class MatchExplanation(BaseModel):
    reason: str
    criteria: List[str]
    risks: str


class MatchOutput(BaseModel):
    relevance_score: float
    poetic_match: str
    explanation: Optional[MatchExplanation] = None


class ProjectQAResponse(BaseModel):
    answer: str = Field(
        description="Twoja odpowiedź na pytanie poparta faktami. Tak/Nie z wyjaśnieniem, wątpliwościami itp."
    )
    sources: List[str] = Field(
        default_factory=list,
        description="Lista źródeł, np. Nazwa dokumentu (paragraf/strona) lub Inne zidentyfikowane źródło",
    )
    confidence: float = Field(
        0.0, description="Poziom pewności we wnioskowaniu, od 0.0 do 1.0"
    )
    recommendation: str = Field(
        description="Konkretna akcja dla użytkownika, np. 'Skoryguj wydatek w budżecie' lub 'To wymaga aneksu'."
    )