Improve gateway service 2

scripts/test_unified_auth.sh

@@ -0,0 +1,39 @@
+#!/bin/bash
+
+echo "Testing Unified Authentication System"
+
+# 1. Get auth token
+echo "1. Getting authentication token..."
+TOKEN=$(curl -s -X POST http://localhost:8000/api/v1/auth/login \
+  -H "Content-Type: application/json" \
+  -d '{"email": "test@bakery.com", "password": "testpass123"}' \
+  | jq -r '.access_token')
+
+echo "Token obtained: ${TOKEN:0:20}..."
+
+# 2. Test data service through gateway
+echo -e "\n2. Testing data service through gateway..."
+curl -s -X GET http://localhost:8000/api/v1/data/sales \
+  -H "Authorization: Bearer $TOKEN" \
+  -H "X-Tenant-ID: test-tenant" \
+  | jq '.'
+
+# 3. Test training service through gateway
+echo -e "\n3. Testing training service through gateway..."
+curl -s -X POST http://localhost:8000/api/v1/training/jobs \
+  -H "Authorization: Bearer $TOKEN" \
+  -H "X-Tenant-ID: test-tenant" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "include_weather": true,
+    "include_traffic": false,
+    "min_data_points": 30
+  }' \
+  | jq '.'
+
+# 4. Test direct service call (should work with headers)
+echo -e "\n4. Testing direct service call..."
+curl -s -X GET http://localhost:8002/health \
+  | jq '.'
+
+echo -e "\nUnified authentication test complete!"

services/training/app/api/training.py

@@ -11,7 +11,7 @@ import structlog
 from app.schemas.training import (
     TrainingJobRequest, 
     TrainingJobResponse,
-    TrainingJobStatus, 
+    TrainingStatus,
     SingleProductTrainingRequest,
     TrainingJobProgress,
     DataValidationRequest,
@@ -89,7 +89,7 @@ async def start_training_job(
 
 @router.get("/jobs", response_model=List[TrainingJobResponse])
 async def get_training_jobs(
-    status: Optional[TrainingJobStatus] = Query(None),
+    status: Optional[TrainingStatus] = Query(None, description="Filter jobs by status"),
     limit: int = Query(100, ge=1, le=1000),
     offset: int = Query(0, ge=0),
     tenant_id: str = Depends(get_current_tenant_id_dep),

services/training/app/schemas/training.py

@@ -1,13 +1,15 @@
 # services/training/app/schemas/training.py
 """
-Pydantic schemas for training service
+Complete schema definitions for training service
+Includes all request/response schemas used by the API endpoints
 """
 
 from pydantic import BaseModel, Field, validator
-from typing import Dict, List, Any, Optional
+from typing import List, Optional, Dict, Any, Union
 from datetime import datetime
 from enum import Enum
 
+
 class TrainingStatus(str, Enum):
     """Training job status enumeration"""
     PENDING = "pending"
@@ -16,33 +18,32 @@ class TrainingStatus(str, Enum):
     FAILED = "failed"
     CANCELLED = "cancelled"
 
+
 class TrainingJobRequest(BaseModel):
     """Request schema for starting a training job"""
-    products: Optional[List[str]] = Field(None, description="Specific products to train (if None, train all)")
+    products: Optional[List[str]] = Field(None, description="Specific products to train (if None, trains all)")
     include_weather: bool = Field(True, description="Include weather data in training")
     include_traffic: bool = Field(True, description="Include traffic data in training")
     start_date: Optional[datetime] = Field(None, description="Start date for training data")
     end_date: Optional[datetime] = Field(None, description="End date for training data")
-    min_data_points: int = Field(30, description="Minimum data points required per product")
-    estimated_duration: Optional[int] = Field(None, description="Estimated duration in minutes")
     
     # Prophet-specific parameters
-    seasonality_mode: str = Field("additive", description="Prophet seasonality mode")
+    seasonality_mode: str = Field("additive", description="Prophet seasonality mode", pattern="^(additive|multiplicative)$")
     daily_seasonality: bool = Field(True, description="Enable daily seasonality")
     weekly_seasonality: bool = Field(True, description="Enable weekly seasonality")
     yearly_seasonality: bool = Field(True, description="Enable yearly seasonality")
     
+    # Advanced configuration
+    force_retrain: bool = Field(False, description="Force retraining even if recent model exists")
+    parallel_training: bool = Field(True, description="Train products in parallel")
+    max_workers: int = Field(4, description="Maximum parallel workers", ge=1, le=10)
+    
     @validator('seasonality_mode')
     def validate_seasonality_mode(cls, v):
         if v not in ['additive', 'multiplicative']:
-            raise ValueError('seasonality_mode must be additive or multiplicative')
+            raise ValueError('seasonality_mode must be either "additive" or "multiplicative"')
         return v
 
-    @validator('min_data_points')
-    def validate_min_data_points(cls, v):
-        if v < 7:
-            raise ValueError('min_data_points must be at least 7')
-        return v
 
 class SingleProductTrainingRequest(BaseModel):
     """Request schema for training a single product"""
@@ -57,6 +58,7 @@ class SingleProductTrainingRequest(BaseModel):
     weekly_seasonality: bool = Field(True, description="Enable weekly seasonality")
     yearly_seasonality: bool = Field(True, description="Enable yearly seasonality")
 
+
 class TrainingJobResponse(BaseModel):
     """Response schema for training job creation"""
     job_id: str = Field(..., description="Unique training job identifier")
@@ -66,17 +68,60 @@ class TrainingJobResponse(BaseModel):
     created_at: datetime = Field(..., description="Job creation timestamp")
     estimated_duration_minutes: int = Field(..., description="Estimated completion time in minutes")
 
-class TrainingStatusResponse(BaseModel):
+
-    """Response schema for training job status"""
+class TrainingJobStatus(BaseModel):
+    """Response schema for training job status checks"""
     job_id: str = Field(..., description="Training job identifier")
     status: TrainingStatus = Field(..., description="Current job status")
     progress: int = Field(0, description="Progress percentage (0-100)")
     current_step: str = Field("", description="Current processing step")
     started_at: datetime = Field(..., description="Job start timestamp")
     completed_at: Optional[datetime] = Field(None, description="Job completion timestamp")
-    results: Optional[Dict[str, Any]] = Field(None, description="Training results")
+    products_total: int = Field(0, description="Total number of products to train")
+    products_completed: int = Field(0, description="Number of products completed")
+    products_failed: int = Field(0, description="Number of products that failed")
     error_message: Optional[str] = Field(None, description="Error message if failed")
 
+
+class TrainingJobProgress(BaseModel):
+    """Schema for real-time training job progress updates"""
+    job_id: str = Field(..., description="Training job identifier")
+    status: TrainingStatus = Field(..., description="Current job status")
+    progress: int = Field(0, description="Progress percentage (0-100)", ge=0, le=100)
+    current_step: str = Field(..., description="Current processing step")
+    current_product: Optional[str] = Field(None, description="Currently training product")
+    products_completed: int = Field(0, description="Number of products completed")
+    products_total: int = Field(0, description="Total number of products")
+    estimated_time_remaining_minutes: Optional[int] = Field(None, description="Estimated time remaining")
+    timestamp: datetime = Field(default_factory=datetime.now, description="Progress update timestamp")
+
+
+class DataValidationRequest(BaseModel):
+    """Request schema for validating training data"""
+    products: Optional[List[str]] = Field(None, description="Specific products to validate (if None, validates all)")
+    min_data_points: int = Field(30, description="Minimum required data points per product", ge=10, le=1000)
+    start_date: Optional[datetime] = Field(None, description="Start date for data validation")
+    end_date: Optional[datetime] = Field(None, description="End date for data validation")
+    
+    @validator('min_data_points')
+    def validate_min_data_points(cls, v):
+        if v < 10:
+            raise ValueError('min_data_points must be at least 10')
+        return v
+
+
+class DataValidationResponse(BaseModel):
+    """Response schema for data validation results"""
+    is_valid: bool = Field(..., description="Whether the data is valid for training")
+    issues: List[str] = Field(default_factory=list, description="List of data quality issues")
+    recommendations: List[str] = Field(default_factory=list, description="Recommendations for improvement")
+    estimated_time_minutes: int = Field(..., description="Estimated training time in minutes")
+    products_analyzed: int = Field(..., description="Number of products analyzed")
+    total_data_points: int = Field(..., description="Total data points available")
+    products_with_insufficient_data: List[str] = Field(default_factory=list, description="Products with insufficient data")
+    data_quality_score: float = Field(0.0, description="Overall data quality score (0-1)", ge=0.0, le=1.0)
+
+
 class ModelInfo(BaseModel):
     """Schema for trained model information"""
     model_id: str = Field(..., description="Unique model identifier")
@@ -89,6 +134,7 @@ class ModelInfo(BaseModel):
     trained_at: datetime = Field(..., description="Training completion timestamp")
     data_period: Dict[str, str] = Field(..., description="Training data period")
 
+
 class ProductTrainingResult(BaseModel):
     """Schema for individual product training result"""
     product_name: str = Field(..., description="Product name")
@@ -97,6 +143,8 @@ class ProductTrainingResult(BaseModel):
     data_points: int = Field(..., description="Number of data points used")
     error_message: Optional[str] = Field(None, description="Error message if failed")
     trained_at: datetime = Field(..., description="Training completion timestamp")
+    training_duration_seconds: Optional[float] = Field(None, description="Training duration in seconds")
+
 
 class TrainingResultsResponse(BaseModel):
     """Response schema for complete training results"""
@@ -110,6 +158,7 @@ class TrainingResultsResponse(BaseModel):
     summary: Dict[str, Any] = Field(..., description="Training summary statistics")
     completed_at: datetime = Field(..., description="Job completion timestamp")
 
+
 class TrainingValidationResult(BaseModel):
     """Schema for training data validation results"""
     is_valid: bool = Field(..., description="Whether the data is valid for training")
@@ -119,6 +168,7 @@ class TrainingValidationResult(BaseModel):
     products_analyzed: int = Field(..., description="Number of products analyzed")
     total_data_points: int = Field(..., description="Total data points available")
 
+
 class TrainingMetrics(BaseModel):
     """Schema for training performance metrics"""
     mae: float = Field(..., description="Mean Absolute Error")
@@ -129,6 +179,7 @@ class TrainingMetrics(BaseModel):
     mean_actual: float = Field(..., description="Mean of actual values")
     mean_predicted: float = Field(..., description="Mean of predicted values")
 
+
 class ExternalDataConfig(BaseModel):
     """Configuration for external data sources"""
     weather_enabled: bool = Field(True, description="Enable weather data")
@@ -142,6 +193,7 @@ class ExternalDataConfig(BaseModel):
         description="Traffic features to include"
     )
 
+
 class TrainingJobConfig(BaseModel):
     """Complete training job configuration"""
     external_data: ExternalDataConfig = Field(default_factory=ExternalDataConfig)
@@ -163,6 +215,7 @@ class TrainingJobConfig(BaseModel):
         description="Data validation parameters"
     )
 
+
 class TrainedModelResponse(BaseModel):
     """Response schema for trained model information"""
     model_id: str = Field(..., description="Unique model identifier")
@@ -179,3 +232,60 @@ class TrainedModelResponse(BaseModel):
     created_at: datetime = Field(..., description="Model creation timestamp")
     data_period_start: Optional[datetime] = Field(None, description="Training data start date")
     data_period_end: Optional[datetime] = Field(None, description="Training data end date")
+
+
+class ModelTrainingStats(BaseModel):
+    """Schema for model training statistics"""
+    total_models: int = Field(..., description="Total number of trained models")
+    active_models: int = Field(..., description="Number of active models")
+    last_training_date: Optional[datetime] = Field(None, description="Last training date")
+    avg_training_time_minutes: float = Field(..., description="Average training time in minutes")
+    success_rate: float = Field(..., description="Training success rate (0-1)")
+
+
+class BulkTrainingRequest(BaseModel):
+    """Request schema for bulk training operations"""
+    tenant_ids: List[str] = Field(..., description="List of tenant IDs to train")
+    config: TrainingJobConfig = Field(default_factory=TrainingJobConfig, description="Training configuration")
+    priority: int = Field(1, description="Training priority (1-10)", ge=1, le=10)
+    schedule_time: Optional[datetime] = Field(None, description="Schedule training for specific time")
+
+
+class TrainingScheduleResponse(BaseModel):
+    """Response schema for scheduled training jobs"""
+    schedule_id: str = Field(..., description="Unique schedule identifier")
+    tenant_ids: List[str] = Field(..., description="Scheduled tenant IDs")
+    scheduled_time: datetime = Field(..., description="Scheduled execution time")
+    status: str = Field(..., description="Schedule status")
+    created_at: datetime = Field(..., description="Schedule creation timestamp")
+
+
+# WebSocket response schemas for real-time updates
+class TrainingProgressUpdate(BaseModel):
+    """WebSocket message for training progress updates"""
+    type: str = Field("training_progress", description="Message type")
+    job_id: str = Field(..., description="Training job identifier")
+    progress: TrainingJobProgress = Field(..., description="Progress information")
+
+
+class TrainingCompletedUpdate(BaseModel):
+    """WebSocket message for training completion"""
+    type: str = Field("training_completed", description="Message type")
+    job_id: str = Field(..., description="Training job identifier")
+    results: TrainingResultsResponse = Field(..., description="Training results")
+
+
+class TrainingErrorUpdate(BaseModel):
+    """WebSocket message for training errors"""
+    type: str = Field("training_error", description="Message type")
+    job_id: str = Field(..., description="Training job identifier")
+    error: str = Field(..., description="Error message")
+    timestamp: datetime = Field(default_factory=datetime.now, description="Error timestamp")
+
+
+# Union type for all WebSocket messages
+TrainingWebSocketMessage = Union[
+    TrainingProgressUpdate,
+    TrainingCompletedUpdate,
+    TrainingErrorUpdate
+]