ab0e9b973a
- Set up FastAPI project structure with SQLite database - Create database models for tomato images and severity classifications - Implement image upload and processing endpoints - Develop a segmentation model for tomato disease severity detection - Add API endpoints for analysis and results retrieval - Implement health check endpoint - Set up Alembic for database migrations - Update project documentation
96 lines
2.1 KiB
Python
96 lines
2.1 KiB
Python
from datetime import datetime
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from typing import List, Optional, Dict, Any
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from pydantic import BaseModel, validator
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# TomatoImage schemas
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class TomatoImageBase(BaseModel):
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filename: str
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mime_type: str
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width: Optional[int] = None
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height: Optional[int] = None
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file_size: int
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class TomatoImageCreate(TomatoImageBase):
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file_path: str
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class TomatoImage(TomatoImageBase):
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id: str
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file_path: str
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uploaded_at: datetime
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class Config:
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from_attributes = True
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# SeverityDetail schemas
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class SeverityDetailBase(BaseModel):
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severity_class: str
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confidence: float
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affected_area_percentage: Optional[float] = None
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class SeverityDetailCreate(SeverityDetailBase):
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analysis_id: str
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class SeverityDetail(SeverityDetailBase):
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id: str
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analysis_id: str
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class Config:
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from_attributes = True
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# AnalysisResult schemas
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class AnalysisResultBase(BaseModel):
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model_name: str
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model_version: str
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primary_severity: Optional[str] = None
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severity_confidence: Optional[float] = None
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segmentation_data: Optional[str] = None
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processing_time_ms: Optional[int] = None
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class AnalysisResultCreate(AnalysisResultBase):
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image_id: str
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class AnalysisResult(AnalysisResultBase):
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id: str
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image_id: str
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processed_at: datetime
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severity_details: List[SeverityDetail] = []
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class Config:
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from_attributes = True
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# Response schemas
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class AnalysisResponse(BaseModel):
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id: str
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image: TomatoImage
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primary_severity: Optional[str] = None
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severity_confidence: Optional[float] = None
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severity_details: List[SeverityDetail] = []
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segmentation_data: Optional[Dict[str, Any]] = None
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processed_at: datetime
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model_name: str
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model_version: str
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@validator('segmentation_data', pre=True)
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def parse_segmentation_data(cls, v):
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import json
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if v and isinstance(v, str):
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try:
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return json.loads(v)
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except Exception:
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return None
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return v
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class UploadResponse(BaseModel):
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image: TomatoImage
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message: str = "Image uploaded successfully" |