Initial microservices setup from artifacts

services/training/app/schemas/training.py

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+"""
+Training schemas
+"""
+
+from pydantic import BaseModel, Field, validator
+from typing import Optional, Dict, Any, List
+from datetime import datetime
+from enum import Enum
+
+class TrainingJobStatus(str, Enum):
+    """Training job status enum"""
+    QUEUED = "queued"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+
+class TrainingRequest(BaseModel):
+    """Training request schema"""
+    tenant_id: Optional[str] = None  # Will be set from auth
+    force_retrain: bool = Field(default=False, description="Force retrain even if recent models exist")
+    products: Optional[List[str]] = Field(default=None, description="Specific products to train, or None for all")
+    training_days: Optional[int] = Field(default=730, ge=30, le=1095, description="Number of days of historical data to use")
+    
+    @validator('training_days')
+    def validate_training_days(cls, v):
+        if v < 30:
+            raise ValueError('Minimum training days is 30')
+        if v > 1095:
+            raise ValueError('Maximum training days is 1095 (3 years)')
+        return v
+
+class TrainingJobResponse(BaseModel):
+    """Training job response schema"""
+    id: str
+    tenant_id: str
+    status: TrainingJobStatus
+    progress: int
+    current_step: Optional[str]
+    started_at: datetime
+    completed_at: Optional[datetime]
+    duration_seconds: Optional[int]
+    models_trained: Optional[Dict[str, Any]]
+    metrics: Optional[Dict[str, Any]]
+    error_message: Optional[str]
+    
+    class Config:
+        from_attributes = True
+
+class TrainedModelResponse(BaseModel):
+    """Trained model response schema"""
+    id: str
+    product_name: str
+    model_type: str
+    model_version: str
+    mape: Optional[float]
+    rmse: Optional[float]
+    mae: Optional[float]
+    r2_score: Optional[float]
+    training_samples: Optional[int]
+    features_used: Optional[List[str]]
+    is_active: bool
+    created_at: datetime
+    last_used_at: Optional[datetime]
+    
+    class Config:
+        from_attributes = True
+
+class TrainingProgress(BaseModel):
+    """Training progress update schema"""
+    job_id: str
+    progress: int
+    current_step: str
+    estimated_completion: Optional[datetime]
+    
+class TrainingMetrics(BaseModel):
+    """Training metrics schema"""
+    total_jobs: int
+    successful_jobs: int
+    failed_jobs: int
+    average_duration: float
+    models_trained: int
+    active_models: int
+
+class ModelValidationResult(BaseModel):
+    """Model validation result schema"""
+    product_name: str
+    is_valid: bool
+    accuracy_score: float
+    validation_error: Optional[str]
+    recommendations: List[str]