Fix AI insights feature issues

services/sales/app/api/analytics.py

@@ -45,3 +45,55 @@ async def get_sales_analytics(
     except Exception as e:
         logger.error("Failed to get sales analytics", error=str(e), tenant_id=tenant_id)
         raise HTTPException(status_code=500, detail=f"Failed to get sales analytics: {str(e)}")
+
+
+@router.get(
+    route_builder.build_analytics_route("products/{product_id}/demand-patterns")
+)
+@analytics_tier_required
+async def get_product_demand_patterns(
+    tenant_id: UUID = Path(..., description="Tenant ID"),
+    product_id: UUID = Path(..., description="Product ID (inventory_product_id)"),
+    start_date: Optional[datetime] = Query(None, description="Start date for analysis"),
+    end_date: Optional[datetime] = Query(None, description="End date for analysis"),
+    min_history_days: int = Query(90, description="Minimum days of history required", ge=30, le=365),
+    current_user: Dict[str, Any] = Depends(get_current_user_dep),
+    sales_service: SalesService = Depends(get_sales_service)
+):
+    """
+    Analyze demand patterns for a specific product (Professional+ tier required).
+
+    Returns:
+    - Demand trends (increasing/decreasing/stable)
+    - Volatility metrics (coefficient of variation)
+    - Weekly seasonal patterns
+    - Peak/low demand days
+    - Statistical summaries
+    """
+    try:
+        patterns = await sales_service.analyze_product_demand_patterns(
+            tenant_id=tenant_id,
+            inventory_product_id=product_id,
+            start_date=start_date,
+            end_date=end_date,
+            min_history_days=min_history_days
+        )
+
+        logger.info(
+            "Retrieved product demand patterns",
+            tenant_id=tenant_id,
+            product_id=product_id
+        )
+        return patterns
+
+    except Exception as e:
+        logger.error(
+            "Failed to get product demand patterns",
+            error=str(e),
+            tenant_id=tenant_id,
+            product_id=product_id
+        )
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to analyze demand patterns: {str(e)}"
+        )