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
47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
from pathlib import Path
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from typing import List
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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# Base settings
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PROJECT_NAME: str = "Tomato Severity Segmentation API"
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API_V1_STR: str = "/api"
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DEBUG: bool = True
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# CORS
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CORS_ORIGINS: List[str] = ["*"]
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# Paths
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BASE_DIR: Path = Path(__file__).resolve().parent.parent.parent
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STORAGE_DIR: Path = BASE_DIR / "storage"
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# Database
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DB_DIR: Path = STORAGE_DIR / "db"
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DB_DIR.mkdir(parents=True, exist_ok=True)
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SQLALCHEMY_DATABASE_URL: str = f"sqlite:///{DB_DIR}/db.sqlite"
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# Image storage
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IMAGE_DIR: Path = STORAGE_DIR / "images"
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IMAGE_DIR.mkdir(parents=True, exist_ok=True)
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MAX_IMAGE_SIZE: int = 10 * 1024 * 1024 # 10MB
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ALLOWED_IMAGE_TYPES: List[str] = ["image/jpeg", "image/png"]
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# Model settings
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MODEL_DIR: Path = STORAGE_DIR / "models"
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MODEL_DIR.mkdir(parents=True, exist_ok=True)
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DEFAULT_MODEL_NAME: str = "tomato_severity_model"
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# Severity classifications
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SEVERITY_CLASSES: List[str] = [
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"healthy",
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"early_blight",
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"late_blight",
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"bacterial_spot",
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"septoria_leaf_spot"
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]
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class Config:
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env_file = ".env"
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case_sensitive = True
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settings = Settings() |