Initial commit - production deployment

services/forecasting/app/models/forecasts.py

@@ -0,0 +1,101 @@
+# ================================================================
+# services/forecasting/app/models/forecasts.py
+# ================================================================
+"""
+Forecast models for the forecasting service
+"""
+
+from sqlalchemy import Column, String, Integer, Float, DateTime, Boolean, Text, JSON, UniqueConstraint, Index
+from sqlalchemy.dialects.postgresql import UUID
+from datetime import datetime, timezone
+import uuid
+
+from shared.database.base import Base
+
+class Forecast(Base):
+    """Forecast model for storing prediction results"""
+    __tablename__ = "forecasts"
+
+    __table_args__ = (
+        # Unique constraint to prevent duplicate forecasts
+        # Ensures only one forecast per (tenant, product, date, location) combination
+        UniqueConstraint(
+            'tenant_id', 'inventory_product_id', 'forecast_date', 'location',
+            name='uq_forecast_tenant_product_date_location'
+        ),
+        # Composite index for common query patterns
+        Index('ix_forecasts_tenant_product_date', 'tenant_id', 'inventory_product_id', 'forecast_date'),
+    )
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    inventory_product_id = Column(UUID(as_uuid=True), nullable=False, index=True)  # Reference to inventory service
+    product_name = Column(String(255), nullable=True, index=True)  # Product name (optional - use inventory_product_id as reference)
+    location = Column(String(255), nullable=False, index=True)
+    
+    # Forecast period
+    forecast_date = Column(DateTime(timezone=True), nullable=False, index=True)
+    created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
+    
+    # Prediction results
+    predicted_demand = Column(Float, nullable=False)
+    confidence_lower = Column(Float, nullable=False)
+    confidence_upper = Column(Float, nullable=False)
+    confidence_level = Column(Float, default=0.8)
+    
+    # Model information
+    model_id = Column(String(255), nullable=False)
+    model_version = Column(String(50), nullable=False)
+    algorithm = Column(String(50), default="prophet")
+    
+    # Business context
+    business_type = Column(String(50), default="individual")  # individual or central_workshop
+    day_of_week = Column(Integer, nullable=False)
+    is_holiday = Column(Boolean, default=False)
+    is_weekend = Column(Boolean, default=False)
+    
+    # External factors
+    weather_temperature = Column(Float)
+    weather_precipitation = Column(Float)
+    weather_description = Column(String(100))
+    traffic_volume = Column(Integer)
+    
+    # Metadata
+    processing_time_ms = Column(Integer)
+    features_used = Column(JSON)
+    
+    def __repr__(self):
+        return f"<Forecast(id={self.id}, inventory_product_id={self.inventory_product_id}, date={self.forecast_date})>"
+
+class PredictionBatch(Base):
+    """Batch prediction requests"""
+    __tablename__ = "prediction_batches"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    
+    # Batch information
+    batch_name = Column(String(255), nullable=False)
+    requested_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
+    completed_at = Column(DateTime(timezone=True))
+    
+    # Status
+    status = Column(String(50), default="pending")  # pending, processing, completed, failed
+    total_products = Column(Integer, default=0)
+    completed_products = Column(Integer, default=0)
+    failed_products = Column(Integer, default=0)
+    
+    # Configuration
+    forecast_days = Column(Integer, default=7)
+    business_type = Column(String(50), default="individual")
+    
+    # Results
+    error_message = Column(Text)
+    processing_time_ms = Column(Integer)
+    
+    cancelled_by = Column(String, nullable=True)
+    
+    def __repr__(self):
+        return f"<PredictionBatch(id={self.id}, status={self.status})>"
+
+