From 4ebd516d2eae34b733d4deef0fa56331a81dbbe0 Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Fri, 9 Aug 2024 14:57:29 +0100
Subject: [PATCH] refactoring router

---
 backend/app/plan/router.py | 58 +++++++++++++++++++-------------------
 1 file changed, 29 insertions(+), 29 deletions(-)

diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py
index f92d5a10..fbdc2323 100644
--- a/backend/app/plan/router.py
+++ b/backend/app/plan/router.py
@@ -661,15 +661,7 @@ async def trigger_plan(body: PlanTriggerRequest):
             for key, scored in predictions_dict.items():
                 all_predictions[key] = pd.concat([all_predictions[key], scored])
 
-        # We now produce predictions for the kwh models
-
-        # Insert the predictions into the recommendations and run the optimiser
-        # TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
-        #       possibility with heating system
-        # TODO: After optimising, if there are any cheap, quick win measures (e.g. insulate water tank with hot water
-        #      cylinder jacket), we should add these to the recommendations as default
-        raise Exception("Add the cost impacts into the cost model")
-        logger.info("Optimising recommendations")
+        # Insert the predictions into the recommendations, and get the impact summary
         scoring_epcs = []  # For scoring the kwh models
         for property_id in recommendations.keys():
             property_instance = [p for p in input_properties if p.id == property_id][0]
@@ -686,10 +678,33 @@ async def trigger_plan(body: PlanTriggerRequest):
             # at each phase
             property_scoring_epcs = property_instance.update_simulation_epcs(impact_summary)
             scoring_epcs.extend(property_scoring_epcs)
+            recommendations[property_id] = recommendations_with_impact
 
-            input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
+        # We call the API with the scoring epcs
+        scoring_epcs = pd.DataFrame(scoring_epcs)
+        scoring_epcs = kwh_client.transform(data=scoring_epcs, cleaned=cleaned)
 
-            current_sap_points = int(property_instance.data["current-energy-efficiency"])
+        kwh_simulation_predictions = model_api.predict_all(
+            df=scoring_epcs,
+            bucket=get_settings().DATA_BUCKET,
+            prediction_buckets=get_prediction_buckets(),
+            model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
+        )
+
+        # TODO: Costing model, which should include today's costs!
+
+        # Insert the predictions into the recommendations and run the optimiser
+        # TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
+        #       possibility with heating system
+        # TODO: After optimising, if there are any cheap, quick win measures (e.g. insulate water tank with hot water
+        #      cylinder jacket), we should add these to the recommendations as default
+
+        for p in input_properties.keys():
+            if not recommendations[p.id]:
+                continue
+            input_measures = prepare_input_measures(recommendations[p.id], body.goal)
+
+            current_sap_points = int(p.data["current-energy-efficiency"])
             target_sap_points = epc_to_sap_lower_bound(body.goal_value)
             sap_gain = CostOptimiser.calculate_sap_gain_with_slack(target_sap_points - current_sap_points)
 
@@ -716,7 +731,7 @@ async def trigger_plan(body: PlanTriggerRequest):
                 "internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"
             ]):
                 ventilation_rec = next(
-                    (r[0] for r in recommendations_with_impact if r[0]["type"] == "mechanical_ventilation"),
+                    (r[0] for r in recommendations[p.id] if r[0]["type"] == "mechanical_ventilation"),
                     None
                 )
 
@@ -730,29 +745,14 @@ async def trigger_plan(body: PlanTriggerRequest):
                     {**rec, "default": True if rec["recommendation_id"] in selected_recommendations else False}
                     for rec in recommendations_by_type
                 ]
-                for recommendations_by_type in recommendations_with_impact
+                for recommendations_by_type in recommendations[p.id]
             ]
 
             # We'll also unlist the recommendations so they're a bit easier to handle from here onwards
             final_recommendations = [
                 rec for recommendations_by_type in final_recommendations for rec in recommendations_by_type
             ]
-            recommendations[property_id] = final_recommendations
-
-        # We call the API with the scoring epcs
-        scoring_epcs = pd.DataFrame(scoring_epcs)
-        scoring_epcs = add_features_from_code(scoring_epcs)
-        scoring_epcs = add_estimate_annual_kwh(scoring_epcs)
-        # TODO: Drop all potential and env columns
-        kwh_simulation_predictions = model_api.predict_all(
-            df=scoring_epcs,
-            bucket=get_settings().DATA_BUCKET,
-            prediction_buckets=get_prediction_buckets(),
-            model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
-            extract_ids=True
-        )
-
-        # TODO: Costing model, which should include today's costs!
+            recommendations[p.id] = final_recommendations
 
         # We now insert into the recommendations
         for property_id in recommendations.keys():