"""
7-Dimension Scoring Engine with Knockout Criterion for EFINTEL Talent Pool.
"""

from decimal import Decimal, ROUND_HALF_UP
from datetime import date, timedelta
from typing import Dict, Any

class TalentScoringEngine:
    # 7-Dimension Official Weighting
    WEIGHT_CASE_STUDY = Decimal('0.25')       # 25% (Knockout if < 70)
    WEIGHT_PORTFOLIO = Decimal('0.15')        # 15%
    WEIGHT_TECH_SPECIFIC = Decimal('0.15')    # 15%
    WEIGHT_CARDINAL_SKILLS = Decimal('0.15')  # 15%
    WEIGHT_EXPERIENCE = Decimal('0.10')       # 10%
    WEIGHT_PRINCIPLES = Decimal('0.10')       # 10%
    WEIGHT_INNOVATION = Decimal('0.10')       # 10%

    KNOCKOUT_THRESHOLD = Decimal('70.0')
    APPROVAL_THRESHOLD = Decimal('80.0')
    RESERVE_THRESHOLD = Decimal('65.0')
    VALIDITY_DAYS = 365

    @classmethod
    def evaluate(
        cls,
        dim_case_study: Decimal,
        dim_portfolio: Decimal,
        dim_tech_specific: Decimal,
        dim_cardinal_skills: Decimal,
        dim_experience: Decimal,
        dim_principles_safety: Decimal,
        dim_innovation_rndi: Decimal,
        evaluation_date: date = None
    ) -> Dict[str, Any]:
        """
        Calculates weighted 7D score, evaluates Knockout condition, determines status and validity.
        """
        d1 = Decimal(str(dim_case_study or 0))
        d2 = Decimal(str(dim_portfolio or 0))
        d3 = Decimal(str(dim_tech_specific or 0))
        d4 = Decimal(str(dim_cardinal_skills or 0))
        d5 = Decimal(str(dim_experience or 0))
        d6 = Decimal(str(dim_principles_safety or 0))
        d7 = Decimal(str(dim_innovation_rndi or 0))

        # Check Knockout condition on Dimension 1 (Caso Práctico / Prueba Técnica)
        is_knockout = d1 < cls.KNOCKOUT_THRESHOLD

        # Calculate weighted total
        weighted_score = (
            (d1 * cls.WEIGHT_CASE_STUDY) +
            (d2 * cls.WEIGHT_PORTFOLIO) +
            (d3 * cls.WEIGHT_TECH_SPECIFIC) +
            (d4 * cls.WEIGHT_CARDINAL_SKILLS) +
            (d5 * cls.WEIGHT_EXPERIENCE) +
            (d6 * cls.WEIGHT_PRINCIPLES) +
            (d7 * cls.WEIGHT_INNOVATION)
        ).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)

        # Determine Final Result Status
        if is_knockout:
            result_status = 'DESCARTADO'
            verdict_message = f"Descartado por Criterio Knockout: Caso Práctico ({d1}/100) inferior al mínimo aprobatorio de {cls.KNOCKOUT_THRESHOLD} puntos."
        elif weighted_score >= cls.APPROVAL_THRESHOLD:
            result_status = 'APROBADO_CARTERA'
            verdict_message = f"¡Aprobado en Cartera de Especialistas! Puntaje de {weighted_score}/100 superior al umbral de excelencia ({cls.APPROVAL_THRESHOLD})."
        elif weighted_score >= cls.RESERVE_THRESHOLD:
            result_status = 'EN_RESERVA'
            verdict_message = f"Calificado en Banco de Reserva ({weighted_score}/100). Cumple estándares operacionales para proyectos secundarios o convocatorias de contingencia."
        else:
            result_status = 'DESCARTADO'
            verdict_message = f"Descartado: Puntaje ponderado ({weighted_score}/100) no alcanza el umbral mínimo de corte ({cls.RESERVE_THRESHOLD})."

        eval_date = evaluation_date or date.today()
        valid_until = eval_date + timedelta(days=cls.VALIDITY_DAYS)

        return {
            'weighted_score': weighted_score,
            'is_knockout': is_knockout,
            'result_status': result_status,
            'verdict_message': verdict_message,
            'valid_until': valid_until,
            'breakdown': {
                'case_study_weighted': (d1 * cls.WEIGHT_CASE_STUDY).quantize(Decimal('0.01')),
                'portfolio_weighted': (d2 * cls.WEIGHT_PORTFOLIO).quantize(Decimal('0.01')),
                'tech_specific_weighted': (d3 * cls.WEIGHT_TECH_SPECIFIC).quantize(Decimal('0.01')),
                'cardinal_skills_weighted': (d4 * cls.WEIGHT_CARDINAL_SKILLS).quantize(Decimal('0.01')),
                'experience_weighted': (d5 * cls.WEIGHT_EXPERIENCE).quantize(Decimal('0.01')),
                'principles_weighted': (d6 * cls.WEIGHT_PRINCIPLES).quantize(Decimal('0.01')),
                'innovation_weighted': (d7 * cls.WEIGHT_INNOVATION).quantize(Decimal('0.01')),
            }
        }