REFACTOR - Database logic

services/training/app/models/training_models.py

@@ -1,80 +1,11 @@
 # services/training/app/models/training_models.py
 """
-Database models for trained ML models
+Legacy file - TrainedModel has been moved to training.py
+This file is deprecated and should be removed after migration.
 """
 
-from sqlalchemy import Column, String, DateTime, Float, Integer, Text, Boolean, JSON
-from sqlalchemy.ext.declarative import declarative_base
-from datetime import datetime
-import uuid
+# Import the actual model from the correct location
+from .training import TrainedModel
 
-Base = declarative_base()
-
-class TrainedModel(Base):
-    __tablename__ = "trained_models"
-    
-    # Primary identification
-    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
-    tenant_id = Column(String, nullable=False, index=True)
-    product_name = Column(String, nullable=False, index=True)
-    
-    # Model information
-    model_type = Column(String, default="prophet_optimized")
-    model_version = Column(String, default="1.0")
-    job_id = Column(String, nullable=False)
-    
-    # File storage
-    model_path = Column(String, nullable=False)  # Path to the .pkl file
-    metadata_path = Column(String)  # Path to metadata JSON
-    
-    # Training metrics
-    mape = Column(Float)
-    mae = Column(Float)
-    rmse = Column(Float)
-    r2_score = Column(Float)
-    training_samples = Column(Integer)
-    
-    # Hyperparameters and features
-    hyperparameters = Column(JSON)  # Store optimized parameters
-    features_used = Column(JSON)    # List of regressor columns
-    
-    # Model status
-    is_active = Column(Boolean, default=True)
-    is_production = Column(Boolean, default=False)
-    
-    # Timestamps
-    created_at = Column(DateTime, default=datetime.utcnow)
-    last_used_at = Column(DateTime)
-    
-    # Training data info
-    training_start_date = Column(DateTime)
-    training_end_date = Column(DateTime)
-    data_quality_score = Column(Float)
-    
-    # Additional metadata
-    notes = Column(Text)
-    created_by = Column(String)  # User who triggered training
-    
-    def to_dict(self):
-        return {
-            "id": self.id,
-            "tenant_id": self.tenant_id,
-            "product_name": self.product_name,
-            "model_type": self.model_type,
-            "model_version": self.model_version,
-            "model_path": self.model_path,
-            "mape": self.mape,
-            "mae": self.mae,
-            "rmse": self.rmse,
-            "r2_score": self.r2_score,
-            "training_samples": self.training_samples,
-            "hyperparameters": self.hyperparameters,
-            "features_used": self.features_used,
-            "is_active": self.is_active,
-            "is_production": self.is_production,
-            "created_at": self.created_at.isoformat() if self.created_at else None,
-            "last_used_at": self.last_used_at.isoformat() if self.last_used_at else None,
-            "training_start_date": self.training_start_date.isoformat() if self.training_start_date else None,
-            "training_end_date": self.training_end_date.isoformat() if self.training_end_date else None,
-            "data_quality_score": self.data_quality_score
-        }
+# For backward compatibility, re-export the model
+__all__ = ["TrainedModel"]