Improve AI logic

shared/clients/production_client.py

@@ -16,8 +16,8 @@ logger = structlog.get_logger()
 class ProductionServiceClient(BaseServiceClient):
     """Client for communicating with the Production Service"""
 
-    def __init__(self, config: BaseServiceSettings):
-        super().__init__("production", config)
+    def __init__(self, config: BaseServiceSettings, calling_service_name: str = "unknown"):
+        super().__init__(calling_service_name, config)
 
     def get_service_base_path(self) -> str:
         return "/api/v1"
@@ -63,7 +63,7 @@ class ProductionServiceClient(BaseServiceClient):
                 request_data["recipes_data"] = recipes_data
 
             result = await self.post(
-                "production/generate-schedule",
+                "production/operations/generate-schedule",
                 data=request_data,
                 tenant_id=tenant_id
             )
@@ -404,6 +404,47 @@ class ProductionServiceClient(BaseServiceClient):
                         error=str(e), tenant_id=tenant_id)
             return None
 
+    # ================================================================
+    # ML INSIGHTS: Yield Prediction
+    # ================================================================
+
+    async def trigger_yield_prediction(
+        self,
+        tenant_id: str,
+        recipe_ids: Optional[List[str]] = None,
+        lookback_days: int = 90,
+        min_history_runs: int = 30
+    ) -> Optional[Dict[str, Any]]:
+        """
+        Trigger yield prediction for production recipes.
+
+        Args:
+            tenant_id: Tenant UUID
+            recipe_ids: Specific recipe IDs to analyze. If None, analyzes all recipes
+            lookback_days: Days of historical production to analyze (30-365)
+            min_history_runs: Minimum production runs required (10-100)
+
+        Returns:
+            Dict with prediction results including insights posted
+        """
+        try:
+            data = {
+                "recipe_ids": recipe_ids,
+                "lookback_days": lookback_days,
+                "min_history_runs": min_history_runs
+            }
+            result = await self.post("production/ml/insights/predict-yields", data=data, tenant_id=tenant_id)
+            if result:
+                logger.info("Triggered yield prediction",
+                          recipes_analyzed=result.get('recipes_analyzed', 0),
+                          insights_posted=result.get('total_insights_posted', 0),
+                          tenant_id=tenant_id)
+            return result
+        except Exception as e:
+            logger.error("Error triggering yield prediction",
+                       error=str(e), tenant_id=tenant_id)
+            return None
+
     # ================================================================
     # UTILITY METHODS
     # ================================================================