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Fix template variable interpolation issues in dashboard

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This commit fixes the template interpolation issues where variables like
{{supplier_name}}, {{product_names_joined}}, {{current_stock}}, etc. were
showing as literal strings instead of being replaced with actual values.

Changes made:

1. **Dashboard Service (Orchestrator):**
   - Added missing `current_stock` parameter to default reasoning_data for
     production batches
   - This ensures all required template variables are present when batches
     don't have proper reasoning_data from the database

2. **Production Service:**
   - Updated batch creation to properly populate `product_name` field
   - Improved product name resolution to check forecast data and stock_info
     before falling back to placeholder
   - Added missing `product_id` field to batch_data
   - Added required `planned_duration_minutes` field to batch_data
   - Ensures reasoning_data has all required parameters (product_name,
     predicted_demand, current_stock, confidence_score)

The root cause was that the default reasoning_data used by the dashboard
service when database records lacked proper reasoning_data was missing
required parameters. This resulted in i18n template variables being
displayed as literal {{variable}} strings instead of interpolated values.

Fixes dashboard display issues for:
- Purchase order cards showing {{supplier_name}}, {{product_names_joined}},
  {{days_until_stockout}}
- Production plan items showing {{product_name}}, {{predicted_demand}},
  {{current_stock}}, {{confidence_score}}
This commit is contained in:
Claude
2025-11-20 19:14:50 +00:00
parent 3b845da8d1
commit 3f8c269de4
2 changed files with 18 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
services/production/app/services/production_service.py
View File

@@ -1839,8 +1839,15 @@ class ProductionService:
# Generate reasoning data for JTBD dashboard
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(
product_name=f"Product {product_id}", # TODO: Get actual product name from inventory
product_name=product_name,
predicted_demand=predicted_demand,
current_stock=current_stock,
production_needed=production_needed,
@@ -1849,16 +1856,23 @@ class ProductionService:
)
# 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 = {
'tenant_id': tenant_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
'batch_number': await self._generate_batch_number(session, tenant_id, target_date, batches_created + 1),
'status': 'scheduled',
'priority': 'normal',
'planned_start_time': datetime.combine(target_date, datetime.min.time()),
'planned_end_time': datetime.combine(target_date, datetime.max.time()),
'planned_start_time': planned_start,
'planned_end_time': planned_end,
'planned_quantity': production_needed,
'planned_duration_minutes': duration_minutes,
'reasoning_data': reasoning_data, # NEW: Structured reasoning for i18n
'created_at': datetime.now(timezone.utc),
'updated_at': datetime.now(timezone.utc),