Improve training code 4

services/training/app/models/training_models.py

@@ -0,0 +1,80 @@
+# services/training/app/models/training_models.py
+"""
+Database models for trained ML models
+"""
+
+from sqlalchemy import Column, String, DateTime, Float, Integer, Text, Boolean, JSON
+from sqlalchemy.ext.declarative import declarative_base
+from datetime import datetime
+import uuid
+
+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
+        }

services/training/app/schemas/training.py

@@ -26,23 +26,6 @@ class TrainingJobRequest(BaseModel):
     start_date: Optional[datetime] = Field(None, description="Start date for training data")
     end_date: Optional[datetime] = Field(None, description="End date for training data")
 
-    # Prophet-specific parameters
-    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 either "additive" or "multiplicative"')
-        return v
-
 
 class SingleProductTrainingRequest(BaseModel):
     """Request schema for training a single product"""

tests/test_onboarding_flow.sh

@@ -326,9 +326,7 @@ BAKERY_DATA="{
     \"address\": \"Calle Gran Vía 123\",
     \"city\": \"Madrid\",
     \"postal_code\": \"28001\",
-    \"phone\": \"+34600123456\",
+    \"phone\": \"+34600123456\"
-    \"latitude\": $MOCK_LATITUDE,
-    \"longitude\": $MOCK_LONGITUDE
 }"
 
 echo "Bakery Data with mock coordinates:"
@@ -562,63 +560,6 @@ if [ "$HTTP_CODE" = "200" ]; then
     fi
 fi
 
-log_step "3.3. Verifying imported sales data"
-
-SALES_LIST_RESPONSE=$(curl -s -X GET "$API_BASE/api/v1/tenants/$TENANT_ID/sales" \
-    -H "Authorization: Bearer $ACCESS_TOKEN")
-
-echo "Sales Data Response:"
-echo "$SALES_LIST_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$SALES_LIST_RESPONSE"
-
-# Check if we actually got any sales data
-SALES_COUNT=$(echo "$SALES_LIST_RESPONSE" | python3 -c "
-import json, sys
-try:
-    data = json.load(sys.stdin)
-    if isinstance(data, list):
-        print(len(data))
-    elif isinstance(data, dict) and 'data' in data:
-        print(len(data['data']) if isinstance(data['data'], list) else 0)
-    else:
-        print(0)
-except:
-    print(0)
-" 2>/dev/null)
-
-if [ "$SALES_COUNT" -gt 0 ]; then
-    log_success "Sales data successfully retrieved!"
-    echo "  Records found: $SALES_COUNT"
-    
-    # Show some sample products found
-    echo "  Sample products found:"
-    echo "$SALES_LIST_RESPONSE" | python3 -c "
-import json, sys
-try:
-    data = json.load(sys.stdin)
-    records = data if isinstance(data, list) else data.get('data', [])
-    products = set()
-    for record in records[:5]:  # First 5 records
-        if isinstance(record, dict) and 'product_name' in record:
-            products.add(record['product_name'])
-    for product in sorted(products):
-        print(f'    - {product}')
-except:
-    pass
-" 2>/dev/null
-else
-    log_warning "No sales data found in database"
-    
-    if [ -n "$RECORDS_CREATED" ] && [ "$RECORDS_CREATED" -gt 0 ]; then
-        log_error "Inconsistency detected: Import reported $RECORDS_CREATED records created, but none found in database"
-        echo "This could indicate:"
-        echo "  1. Records were created but failed timezone validation and were rolled back"
-        echo "  2. Database transaction was not committed"
-        echo "  3. Records were created in a different tenant/schema"
-    else
-        echo "This is expected if the import failed due to timezone or other errors."
-    fi
-fi
-
 echo ""
 
 # =================================================================
@@ -631,40 +572,10 @@ echo ""
 
 log_step "4.1. Starting model training process with real data products"
 
-# Get unique products from the imported data for training
-# Extract some real product names from the CSV for training
-REAL_PRODUCTS_RAW=$(tail -n +2 "$PREPARED_CSV" | cut -d',' -f2 | sort | uniq | head -3 | tr '\n' ',' | sed 's/,$//')
-
-if [ -z "$REAL_PRODUCTS_RAW" ]; then
-    # Fallback to default products if extraction fails
-    REAL_PRODUCTS_ARRAY='["Pan de molde","Croissants","Magdalenas"]'
-    log_warning "Could not extract real product names, using defaults"
-else
-    # Format for JSON array properly
-    REAL_PRODUCTS_ARRAY='['$(echo "$REAL_PRODUCTS_RAW" | sed 's/,/","/g' | sed 's/^/"/' | sed 's/$/"/')']'
-    log_success "Extracted real products for training: $REAL_PRODUCTS_ARRAY"
-fi
-
-# ✅ FIXED: Training request with correct data types matching TrainingJobRequest schema
-TRAINING_DATA="{
-    \"products\": $REAL_PRODUCTS_ARRAY,
-    \"max_workers\": 4,
-    \"seasonality_mode\": \"additive\",
-    \"daily_seasonality\": true,
-    \"weekly_seasonality\": true,
-    \"yearly_seasonality\": true,
-    \"force_retrain\": false,
-    \"parallel_training\": true
-}"
-
-echo "Training Request:"
-echo "$TRAINING_DATA" | python3 -m json.tool
-
 TRAINING_RESPONSE=$(curl -s -w "\nHTTP_CODE:%{http_code}" -X POST "$API_BASE/api/v1/tenants/$TENANT_ID/training/jobs" \
-    -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $ACCESS_TOKEN" \
-    -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -H "Content-Type: application/json" \
-    -H "X-Tenant-ID: $TENANT_ID" \
+  -d '{}')
-    -d "$TRAINING_DATA")
 
 # Extract HTTP code and response
 HTTP_CODE=$(echo "$TRAINING_RESPONSE" | grep "HTTP_CODE:" | cut -d: -f2)
@@ -685,31 +596,6 @@ if [ "$HTTP_CODE" = "422" ]; then
     echo ""
     echo "Response details:"
     echo "$TRAINING_RESPONSE"
-    
-    # Try a minimal request that should work
-    log_step "4.2. Attempting minimal training request as fallback"
-    
-    MINIMAL_TRAINING_DATA='{"seasonality_mode": "additive"}'
-    
-    FALLBACK_RESPONSE=$(curl -s -w "\nHTTP_CODE:%{http_code}" -X POST "$API_BASE/api/v1/tenants/$TENANT_ID/training/jobs" \
-        -H "Content-Type: application/json" \
-        -H "Authorization: Bearer $ACCESS_TOKEN" \
-        -H "X-Tenant-ID: $TENANT_ID" \
-        -d "$MINIMAL_TRAINING_DATA")
-    
-    FALLBACK_HTTP_CODE=$(echo "$FALLBACK_RESPONSE" | grep "HTTP_CODE:" | cut -d: -f2)
-    FALLBACK_RESPONSE=$(echo "$FALLBACK_RESPONSE" | sed '/HTTP_CODE:/d')
-    
-    echo "Fallback HTTP Status Code: $FALLBACK_HTTP_CODE"
-    echo "Fallback Response:"
-    echo "$FALLBACK_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$FALLBACK_RESPONSE"
-    
-    if [ "$FALLBACK_HTTP_CODE" = "200" ] || [ "$FALLBACK_HTTP_CODE" = "201" ]; then
-        log_success "Minimal training request succeeded"
-        TRAINING_TASK_ID=$(extract_json_field "$FALLBACK_RESPONSE" "job_id")
-    else
-        log_error "Both training requests failed"
-    fi
 else
     # Original success handling
     TRAINING_TASK_ID=$(extract_json_field "$TRAINING_RESPONSE" "job_id")