Add AI insights feature

services/production/app/api/ml_insights.py

@@ -7,7 +7,7 @@ Provides endpoints to trigger ML insight generation for:
 - Process efficiency analysis
 """
 
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Request
 from pydantic import BaseModel, Field
 from typing import Optional, List
 from uuid import UUID
@@ -71,6 +71,7 @@ class YieldPredictionResponse(BaseModel):
 async def trigger_yield_prediction(
     tenant_id: str,
     request_data: YieldPredictionRequest,
+    request: Request,
     db: AsyncSession = Depends(get_db)
 ):
     """
@@ -81,10 +82,12 @@ async def trigger_yield_prediction(
     2. Runs the YieldInsightsOrchestrator to predict yields
     3. Generates insights about yield optimization opportunities
     4. Posts insights to AI Insights Service
+    5. Publishes recommendation events to RabbitMQ
 
     Args:
         tenant_id: Tenant UUID
         request_data: Prediction parameters
+        request: FastAPI request (for app state access)
         db: Database session
 
     Returns:
@@ -103,8 +106,13 @@ async def trigger_yield_prediction(
         from shared.clients.recipes_client import RecipesServiceClient
         from app.core.config import settings
 
+        # Get event publisher from app state (if available)
+        event_publisher = getattr(request.app.state, 'event_publisher', None) if hasattr(request, 'app') else None
+
         # Initialize orchestrator and recipes client
-        orchestrator = YieldInsightsOrchestrator()
+        orchestrator = YieldInsightsOrchestrator(
+            event_publisher=event_publisher
+        )
         recipes_client = RecipesServiceClient(settings)
 
         # Get recipes to analyze from recipes service via API
@@ -186,12 +194,18 @@ async def trigger_yield_prediction(
                         continue  # Skip batches without complete data
 
                     production_data.append({
-                        'production_date': batch.actual_start_time,
+                        'production_run_id': str(batch.id),  # Required: unique identifier for each production run
+                        'recipe_id': str(batch.recipe_id),  # Required: recipe identifier
+                        'started_at': batch.actual_start_time,
+                        'completed_at': batch.actual_end_time,  # Optional but useful for duration analysis
+                        'batch_size': float(batch.planned_quantity),  # Use planned_quantity as batch_size
                         'planned_quantity': float(batch.planned_quantity),
                         'actual_quantity': float(batch.actual_quantity),
                         'yield_percentage': yield_pct,
-                        'worker_id': batch.notes or 'unknown',  # Use notes field or default
-                        'batch_number': batch.batch_number
+                        'staff_assigned': batch.staff_assigned if batch.staff_assigned else ['unknown'],
+                        'batch_number': batch.batch_number,
+                        'equipment_id': batch.equipment_used[0] if batch.equipment_used and len(batch.equipment_used) > 0 else None,
+                        'notes': batch.quality_notes  # Optional quality notes
                     })
 
                 if not production_data:
@@ -202,6 +216,14 @@ async def trigger_yield_prediction(
 
                 production_history = pd.DataFrame(production_data)
 
+                # Debug: Log DataFrame columns and sample data
+                logger.debug(
+                    "Production history DataFrame created",
+                    recipe_id=recipe_id,
+                    columns=list(production_history.columns),
+                    sample_data=production_history.head(1).to_dict('records') if len(production_history) > 0 else None
+                )
+
                 # Run yield analysis
                 results = await orchestrator.analyze_and_post_insights(
                     tenant_id=tenant_id,
@@ -291,8 +313,6 @@ async def ml_insights_health():
 # INTERNAL ENDPOINTS (for demo-session service)
 # ================================================================
 
-from fastapi import Request
-
 # Create a separate router for internal endpoints to avoid the tenant prefix
 internal_router = APIRouter(
     tags=["ML Insights - Internal"]
@@ -347,6 +367,7 @@ async def generate_yield_insights_internal(
         result = await trigger_yield_prediction(
             tenant_id=tenant_id,
             request_data=request_data,
+            request=request,
             db=db
         )