From 89d49690b5c9ca4efb89f3879bb7c414098e5ea2 Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Sun, 16 Feb 2025 17:02:51 +0000
Subject: [PATCH] added extraction of windows sap point

---
 etl/customers/remote_assessments/app.py   | 12 ++++++------
 recommendations/WindowsRecommendations.py | 12 ++++++++++--
 2 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/etl/customers/remote_assessments/app.py b/etl/customers/remote_assessments/app.py
index 70ceb76d..cce0f4fb 100644
--- a/etl/customers/remote_assessments/app.py
+++ b/etl/customers/remote_assessments/app.py
@@ -4,7 +4,7 @@ from dotenv import load_dotenv
 from utils.s3 import save_csv_to_s3
 from etl.find_my_epc.AssetListEpcData import AssetListEpcData
 
-PORTFOLIO_ID = 128
+PORTFOLIO_ID = 129
 USER_ID = 8
 
 load_dotenv(dotenv_path="backend/.env")
@@ -19,9 +19,9 @@ def app():
 
     asset_list = [
         {
-            "address": "46",
-            "postcode": "BS6 7BD",
-            "uprn": 61091,
+            "address": "19",
+            "postcode": "IP21 4YJ",
+            "uprn": 2630134524,
         }
     ]
     asset_list = pd.DataFrame(asset_list)
@@ -52,8 +52,8 @@ def app():
 
     valuation_data = [
         {
-            "uprn": 61091,
-            "valuation": 897_000
+            "uprn": 2630134524,
+            "valuation": 96_000
         }
     ]
     # Store valuation data to s3
diff --git a/recommendations/WindowsRecommendations.py b/recommendations/WindowsRecommendations.py
index 1f755369..46e56c93 100644
--- a/recommendations/WindowsRecommendations.py
+++ b/recommendations/WindowsRecommendations.py
@@ -215,21 +215,29 @@ class WindowsRecommendations:
             "glazed-type": glazed_type_ending,
         }
 
+        measure_type = "double_glazing" if not is_secondary_glazing else "secondary_glazing"
+
+        non_invasive_recommendation = next(
+            (r for r in self.property.non_invasive_recommendations if r["type"] in ["windows_glazing", measure_type]),
+            {}
+        )
+
         self.recommendation = [
             {
                 "phase": phase,
                 "parts": [],
                 "type": "windows_glazing",
-                "measure_type": "double_glazing" if not is_secondary_glazing else "secondary_glazing",
+                "measure_type": measure_type,
                 "description": description,
                 "starting_u_value": None,
                 "new_u_value": None,
-                "sap_points": None,
+                "sap_points": non_invasive_recommendation.get("sap_points", None),
                 "already_installed": already_installed,
                 **cost_result,
                 "is_secondary_glazing": is_secondary_glazing,
                 "description_simulation": description_simulation,
                 "simulation_config": simulation_config,
+                "survey": non_invasive_recommendation.get("survey", None),
             }
         ]