pylord/API-BFSI

1from sqlalchemy import Column, String, Float, Integer, DateTime, JSON, ForeignKey, Text2from sqlalchemy.orm import relationship3from database import Base4from datetime import datetime5 6# ==================== USERS TABLE ====================7class User(Base):8    """9    User table for authentication and user management10    """11    __tablename__ = "users"12 13    email = Column(String(100), primary_key=True, index=True)14    full_name = Column(String(100), nullable=False)15    password = Column(String(150), nullable=False)16    created_at = Column(DateTime, default=datetime.utcnow)17 18    # Relationship to predictions19    predictions = relationship("Prediction", back_populates="user", cascade="all, delete-orphan")20 21    def __repr__(self):22        return f"<User(email={self.email}, full_name={self.full_name})>"23 24 25# ==================== PREDICTIONS TABLE ====================26class Prediction(Base):27    """28    Predictions table for storing fraud detection results29    """30    __tablename__ = "predictions"31 32    id = Column(Integer, primary_key=True, autoincrement=True, index=True)33    customer_id = Column(String(50), nullable=False, index=True)34    transaction_id = Column(String(50), nullable=False, unique=True, index=True)35    email = Column(String(100), ForeignKey("users.email", ondelete="CASCADE"), nullable=False)36    risk_score = Column(Float, nullable=False)37    is_fraud = Column(Integer, nullable=False)38    derived_features = Column(JSON, nullable=False)39    explanation = Column(Text, nullable=True)40    timestamp = Column(DateTime, default=datetime.utcnow, index=True)41 42    # Relationship43    user = relationship("User", back_populates="predictions")44 45    def __repr__(self):46        return f"<Prediction(id={self.id}, transaction_id={self.transaction_id}, is_fraud={self.is_fraud})>"