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Merge pull request #23 from ualsweb/claude/bakery-control-panel-01BWootSHj63zv1UyHGrDc1M

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Fix template variable interpolation issues in dashboard
This commit is contained in:
ualsweb
2025-11-20 20:16:07 +01:00
committed by GitHub
parent 3b845da8d1 3f8c269de4
commit 399309c572
2 changed files with 18 additions and 3 deletions
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1
services/orchestrator/app/services/dashboard_service.py
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@@ -602,6 +602,7 @@ class DashboardService:
"parameters": { "parameters": {
"product_name": batch.get("product_name", "Product"), "product_name": batch.get("product_name", "Product"),
"predicted_demand": batch.get("planned_quantity", 0), "predicted_demand": batch.get("planned_quantity", 0),
"current_stock": 0, # Default to 0 if not available
"confidence_score": 85 "confidence_score": 85
} }
} }

20
services/production/app/services/production_service.py
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@@ -1839,8 +1839,15 @@ class ProductionService:
# Generate reasoning data for JTBD dashboard # Generate reasoning data for JTBD dashboard
from shared.schemas.reasoning_types import create_batch_reasoning_forecast_demand from shared.schemas.reasoning_types import create_batch_reasoning_forecast_demand
# Try to get product name from forecast, stock_info, or use placeholder
product_name = (
forecast.get('product_name') or
(stock_info.get('product_name') if stock_info else None) or
f"Product {product_id}"
)
reasoning_data = create_batch_reasoning_forecast_demand( reasoning_data = create_batch_reasoning_forecast_demand(
product_name=f"Product {product_id}", # TODO: Get actual product name from inventory product_name=product_name,
predicted_demand=predicted_demand, predicted_demand=predicted_demand,
current_stock=current_stock, current_stock=current_stock,
production_needed=production_needed, production_needed=production_needed,
@@ -1849,16 +1856,23 @@ class ProductionService:
) )
# Create production batch # Create production batch
planned_start = datetime.combine(target_date, datetime.min.time())
planned_end = datetime.combine(target_date, datetime.max.time())
duration_minutes = int((planned_end - planned_start).total_seconds() / 60)
batch_data = { batch_data = {
'tenant_id': tenant_id, 'tenant_id': tenant_id,
'schedule_id': schedule.id, 'schedule_id': schedule.id,
'product_id': product_id, # Product ID from forecast
'product_name': product_name, # Product name resolved above
'recipe_id': product_id, # Assuming recipe_id matches product_id 'recipe_id': product_id, # Assuming recipe_id matches product_id
'batch_number': await self._generate_batch_number(session, tenant_id, target_date, batches_created + 1), 'batch_number': await self._generate_batch_number(session, tenant_id, target_date, batches_created + 1),
'status': 'scheduled', 'status': 'scheduled',
'priority': 'normal', 'priority': 'normal',
'planned_start_time': datetime.combine(target_date, datetime.min.time()), 'planned_start_time': planned_start,
'planned_end_time': datetime.combine(target_date, datetime.max.time()), 'planned_end_time': planned_end,
'planned_quantity': production_needed, 'planned_quantity': production_needed,
'planned_duration_minutes': duration_minutes,
'reasoning_data': reasoning_data, # NEW: Structured reasoning for i18n 'reasoning_data': reasoning_data, # NEW: Structured reasoning for i18n
'created_at': datetime.now(timezone.utc), 'created_at': datetime.now(timezone.utc),
'updated_at': datetime.now(timezone.utc), 'updated_at': datetime.now(timezone.utc),