from __future__ import annotations

from typing import Any, Literal, Optional

from pydantic import BaseModel, Field


class Detection(BaseModel):
    label: str
    confidence: float
    bbox: list[float] = Field(description="[x1, y1, x2, y2] 像素坐标")
    label_zh: Optional[str] = None


class ImagingAnalyzeResponse(BaseModel):
    detections: list[Detection] = []
    annotated_image_base64: Optional[str] = None
    preliminary_diagnosis: str
    findings: str
    recommendations: str
    confidence: float
    model_version: str
    mode: Literal["demo", "real"] = "demo"
    full_report: Optional[str] = None
    disclaimer: str = "本结果仅供辅助决策，不能替代执业医师诊断。"


class ImagingReportRequest(BaseModel):
    study_type: str = "CT"
    body_part: str = ""
    patient_summary: str = ""
    preliminary_diagnosis: str = ""
    findings: str = ""
    detections: list[Detection] = []
    confidence: float = 0.85


class ImagingReportResponse(BaseModel):
    findings: str
    impression: str
    recommendations: str
    full_report: str
    model_version: str = "report-v1"


class PatientInfo(BaseModel):
    age: Optional[int] = None
    gender: Optional[str] = None
    name: Optional[str] = None


class DecisionRequest(BaseModel):
    chief_complaint: str = ""
    history: str = ""
    exam_findings: str = ""
    diagnosis: str = ""
    medications: str = ""
    imaging_summary: str = ""
    patient: Optional[PatientInfo] = None


class RiskItem(BaseModel):
    type: str
    description: str
    level: str = "中"
    confidence: float = 0.8


class SourceRef(BaseModel):
    title: str
    snippet: str = ""
    category: str = ""
    score: float = 0.0


class DecisionResponse(BaseModel):
    treatment_suggestions: list[dict[str, Any]] = []
    medication_suggestions: list[dict[str, Any]] = []
    nursing_advice: list[str] = []
    follow_up_plan: list[str] = []
    risks: list[RiskItem] = []
    conflicts: list[str] = []
    sources: list[SourceRef] = []
    full_text: str = ""
    engine: str = "fastapi-rag"
    disclaimer: str = "本结果仅供辅助决策，不能替代执业医师诊断。"


class RagQueryRequest(BaseModel):
    query: str
    top_k: int = 4
    context: str = ""


class RagQueryResponse(BaseModel):
    answer: str
    sources: list[SourceRef] = []
    engine: str = "langchain-rag"


class HealthResponse(BaseModel):
    status: str = "ok"
    yolo_available: bool = False
    monai_available: bool = False
    langchain_available: bool = False
    llm_configured: bool = False
    demo_mode: str = "auto"
    knowledge_docs: int = 0