dieya_risk_prediction_backend/app/schemas/risk_prediction.py

"""Risk prediction request and response contracts."""

from typing import Literal

from pydantic import Field

from app.domain.risk_engine import RiskLevel
from app.schemas.common import ApiSchema


class RiskIndicator(ApiSchema):
    name: str = Field(min_length=1, max_length=64)
    value: float = Field(ge=0, le=100)
    weight: float = Field(default=1, gt=0, le=100)


class RiskAssessmentRequest(ApiSchema):
    subject_id: str = Field(min_length=1, max_length=128)
    indicators: list[RiskIndicator] = Field(default_factory=list, max_length=50)
    product_category: str | None = Field(default=None, max_length=100)
    material_type: str | None = Field(default=None, max_length=200)
    supplier: str | None = Field(default=None, max_length=300)
    factory: str | None = Field(default=None, max_length=300)
    stage_new: str | None = Field(default=None, max_length=100)
    defect_level: str | None = Field(default=None, max_length=10)


class RiskFactorResult(ApiSchema):
    name: str
    value: float
    weight: float
    contribution: float


class CategoryWarning(ApiSchema):
    category: str
    anomaly_count: int
    risk_level: str
    similar_category: str | None = None
    similar_count: int | None = None
    suggestion: str | None = None


class SupplierFrequency(ApiSchema):
    supplier_name: str | None = None
    cooperation_count: int | None = None
    anomaly_count: int | None = None
    alpha: float | None = None
    tier: str | None = None
    suggestion: str | None = None
    frequency_basis: str = "暂无匹配采购订单"
    note: str | None = None
    data_available: bool = False


class StandardJudgment(ApiSchema):
    category: str
    risk_point: str
    rule: str
    source: str | None = None
    status: str = "待检测"


class RiskDimensionScore(ApiSchema):
    dimension: str
    score: float
    weight: float
    status: str = "database"
    reason: str | None = None
    sample_size: int | None = None
    raw_value: float | int | None = None


class SimilarCase(ApiSchema):
    case_id: int | None = None
    date: str | None = None
    product: str | None = None
    category: str | None = None
    material: str | None = None
    anomaly_type: str | None = None
    description: str | None = None
    root_cause: str | None = None
    defect_level: str | None = None
    result: str | None = None
    problem: str | None = None
    solution: str | None = None
    similarity: float | None = None


class AiSolutionRisk(ApiSchema):
    level: str
    title: str
    detail: str


class AiSolutionReport(ApiSchema):
    title: str = "AI 案例推理生成"
    confidence: str | None = None
    analysis_base: str | None = None
    standard_fallback_enabled: bool = False
    standards: list[StandardJudgment] = Field(default_factory=list)
    similar_cases: list[SimilarCase] = Field(default_factory=list)
    risks: list[AiSolutionRisk] = Field(default_factory=list)
    suggestions: list[str] = Field(default_factory=list)
    warning: str | None = None


class ReportCard(ApiSchema):
    """A response card explicitly appended to the risk report.

    The card is intentionally a superset of the five assistant card types so
    that the report page and both export formats can consume the exact same
    payload without type-specific ad-hoc transformations.
    """

    type: Literal["supplier", "material", "standard", "compliance", "solution"]
    title: str = "风险分析卡片"
    name: str | None = None
    party_type: str | None = None
    material: str | None = None
    category: str | None = None
    question: str | None = None
    anomaly_count: int | None = None
    cooperation_count: int | None = None
    frequency_coefficient: float | None = None
    supplier_base_score: float | None = None
    supplier_score: float | None = None
    tier: str | None = None
    matched_count: int | None = None
    detected_values: list[dict[str, str]] = Field(default_factory=list)
    top_problems: list[dict[str, int | str]] = Field(default_factory=list)
    frequent_problems: list[dict[str, int | str]] = Field(default_factory=list)
    standards: list[StandardJudgment] = Field(default_factory=list)
    similar_cases: list[SimilarCase] = Field(default_factory=list)
    confidence: str | None = None
    analysis_base: str | None = None
    standard_fallback_enabled: bool = False
    risks: list[AiSolutionRisk] = Field(default_factory=list)
    suggestions: list[str] = Field(default_factory=list)
    note: str | None = None
    warning: str | None = None
    frequency_basis: str | None = None
    data_available: bool | None = None


class RiskAssessmentResult(ApiSchema):
    subject_id: str
    score: float
    level: RiskLevel
    model_version: str
    factors: list[RiskFactorResult]
    risk_badge: str | None = None
    alert_text: str | None = None
    category_warning: CategoryWarning | None = None
    supplier_freq: SupplierFrequency | None = None
    standard_judgment: list[StandardJudgment] = Field(default_factory=list)
    dimension_scores: list[RiskDimensionScore] = Field(default_factory=list)
    similar_cases: list[SimilarCase] = Field(default_factory=list)
    ai_suggestions: list[str] = Field(default_factory=list)
    ai_solution_cards: list[AiSolutionReport] = Field(default_factory=list)
    report_cards: list[ReportCard] = Field(default_factory=list)


class ModelInfo(ApiSchema):
    model_version: str
    score_range: str = "0-100"
    levels: tuple[RiskLevel, ...] = (
        RiskLevel.LOW,
        RiskLevel.MODERATELY_LOW,
        RiskLevel.MEDIUM,
        RiskLevel.HIGH,
        RiskLevel.VERY_HIGH,
    )