109 lines
2.6 KiB
Python
109 lines
2.6 KiB
Python
from __future__ import annotations
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from typing import Any, Literal, Optional
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from pydantic import BaseModel, Field
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class Detection(BaseModel):
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label: str
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confidence: float
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bbox: list[float] = Field(description="[x1, y1, x2, y2] 像素坐标")
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label_zh: Optional[str] = None
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class ImagingAnalyzeResponse(BaseModel):
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detections: list[Detection] = []
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annotated_image_base64: Optional[str] = None
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preliminary_diagnosis: str
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findings: str
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recommendations: str
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confidence: float
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model_version: str
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mode: Literal["demo", "real"] = "demo"
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full_report: Optional[str] = None
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disclaimer: str = "本结果仅供辅助决策，不能替代执业医师诊断。"
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class ImagingReportRequest(BaseModel):
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study_type: str = "CT"
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body_part: str = ""
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patient_summary: str = ""
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preliminary_diagnosis: str = ""
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findings: str = ""
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detections: list[Detection] = []
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confidence: float = 0.85
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class ImagingReportResponse(BaseModel):
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findings: str
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impression: str
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recommendations: str
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full_report: str
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model_version: str = "report-v1"
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class PatientInfo(BaseModel):
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age: Optional[int] = None
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gender: Optional[str] = None
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name: Optional[str] = None
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class DecisionRequest(BaseModel):
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chief_complaint: str = ""
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history: str = ""
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exam_findings: str = ""
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diagnosis: str = ""
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medications: str = ""
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imaging_summary: str = ""
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patient: Optional[PatientInfo] = None
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class RiskItem(BaseModel):
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type: str
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description: str
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level: str = "中"
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confidence: float = 0.8
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class SourceRef(BaseModel):
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title: str
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snippet: str = ""
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category: str = ""
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score: float = 0.0
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class DecisionResponse(BaseModel):
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treatment_suggestions: list[dict[str, Any]] = []
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medication_suggestions: list[dict[str, Any]] = []
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nursing_advice: list[str] = []
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follow_up_plan: list[str] = []
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risks: list[RiskItem] = []
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conflicts: list[str] = []
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sources: list[SourceRef] = []
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full_text: str = ""
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engine: str = "fastapi-rag"
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disclaimer: str = "本结果仅供辅助决策，不能替代执业医师诊断。"
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class RagQueryRequest(BaseModel):
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query: str
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top_k: int = 4
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context: str = ""
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class RagQueryResponse(BaseModel):
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answer: str
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sources: list[SourceRef] = []
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engine: str = "langchain-rag"
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class HealthResponse(BaseModel):
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status: str = "ok"
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yolo_available: bool = False
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monai_available: bool = False
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langchain_available: bool = False
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llm_configured: bool = False
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demo_mode: str = "auto"
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knowledge_docs: int = 0
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