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Database models (tables) for the application.
Uses SQLAlchemy ORM to define table structure.
"""
from sqlalchemy import Column, String, DateTime, ForeignKey, Text, Float, Integer
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.sql import func
from sqlalchemy.orm import relationship
import uuid
from app.database.database import Base
class User(Base):
"""
User table stores user authentication information.
Columns:
id: Unique user identifier (UUID)
full_name: User's full name
email: User's email (unique, used for login)
password_hash: Hashed password (never store plain passwords!)
created_at: When user account was created
last_login: Last successful login timestamp
"""
__tablename__ = "users"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
full_name = Column(String(255), nullable=False)
email = Column(String(255), unique=True, nullable=False, index=True)
password_hash = Column(String(255), nullable=False)
created_at = Column(DateTime(timezone=True), server_default=func.now())
last_login = Column(DateTime(timezone=True), nullable=True)
# Relationships
diagnoses = relationship("Diagnosis", back_populates="user", cascade="all, delete-orphan")
segmentations = relationship("Segmentation", back_populates="user", cascade="all, delete-orphan")
class Diagnosis(Base):
"""
Diagnosis table stores disease diagnosis results.
Columns:
id: Unique diagnosis identifier
user_id: Foreign key to users table
disease_name: Predicted disease (e.g., "Breast Cancer")
severity: Normal or Abnormal
stage: Disease stage (e.g., "Ductal Carcinoma")
confidence_*: Model confidence scores (0-1)
*_image_url: S3 URLs for images (NOT actual image data)
explainability_text: AI-generated explanation
created_at: When diagnosis was performed
"""
__tablename__ = "diagnoses"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
user_id = Column(UUID(as_uuid=True), ForeignKey("users.id", ondelete="CASCADE"), nullable=False)
# Diagnosis results
disease_name = Column(String(100), nullable=False)
severity = Column(String(50), nullable=False)
stage = Column(String(100), nullable=True)
# Confidence scores
confidence_disease = Column(Float, nullable=True)
confidence_severity = Column(Float, nullable=True)
confidence_stage = Column(Float, nullable=True)
# Image URLs (S3 paths, not actual images!)
original_image_url = Column(Text, nullable=False)
heatmap_url = Column(Text, nullable=True)
# Explainability
explainability_text = Column(Text, nullable=True)
# Metadata
created_at = Column(DateTime(timezone=True), server_default=func.now())
# Relationship
user = relationship("User", back_populates="diagnoses")
class Segmentation(Base):
"""
Segmentation table stores cell segmentation results.
Columns:
id: Unique segmentation identifier
user_id: Foreign key to users table
original_image_url: S3 URL for original image
segmented_mask_url: S3 URL for segmentation mask
cell_count: Number of cells detected
created_at: When segmentation was performed
"""
__tablename__ = "segmentations"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
user_id = Column(UUID(as_uuid=True), ForeignKey("users.id", ondelete="CASCADE"), nullable=False)
# Image URLs (S3 paths)
original_image_url = Column(Text, nullable=False)
segmented_mask_url = Column(Text, nullable=False)
# Results
cell_count = Column(Integer, nullable=True)
# Metadata
created_at = Column(DateTime(timezone=True), server_default=func.now())
# Relationship
user = relationship("User", back_populates="segmentations") |