diff --git a/backend/export/property_scenarios/main.py b/backend/export/property_scenarios/main.py
index 8ba1467a..f3ea0100 100644
--- a/backend/export/property_scenarios/main.py
+++ b/backend/export/property_scenarios/main.py
@@ -134,10 +134,18 @@ def handler(event: Mapping[str, Any], context: Optional[Any]) -> Mapping[str, Un
     body_dict = {
         "task_id": "test",
         "subtask_id": "test",
-        "portfolio_id": 647,
+        "portfolio_id": 655,
         "scenario_ids": [],
         "default_plans_only": True,
     }
+
+    body_dict = {
+        "task_id": "test",
+        "subtask_id": "test",
+        "portfolio_id": 655,
+        "scenario_ids": [1174],
+        "default_plans_only": False,
+    }
     :param event: Lambda event containing export request details
     :param context: Lambda context (not used in this handler but included for completeness)
     :return: HTTP response indicating success or failure of the export operation
@@ -159,54 +167,6 @@ def handler(event: Mapping[str, Any], context: Optional[Any]) -> Mapping[str, Un
             with db_read_session() as session:
                 exported_files = process_export(payload, session)
 
-            # Merge with input
-            raw_input1 = pd.read_excel(
-                "/Users/khalimconn-kowlessar/Downloads/eon - 20260323 address sanitisation - Standardised.xlsx",
-                sheet_name="Standardised Asset List",
-            )
-            raw_input2 = pd.read_excel(
-                "/Users/khalimconn-kowlessar/Downloads/eon - 20260323 address sanitisation - Standardised.xlsx",
-                sheet_name="Addresses needing validation",
-            )
-            raw_input = pd.concat([raw_input1, raw_input2], ignore_index=True)
-            raw_input["epc_os_uprn"] = np.where(
-                pd.isnull(raw_input["epc_os_uprn"]),
-                raw_input["ordnance_survey_uprn"],
-                raw_input["epc_os_uprn"],
-            )
-            raw_input["epc_os_uprn"] = raw_input["epc_os_uprn"].astype(int)
-
-            left_df = raw_input[
-                ["epc_os_uprn", "domna_address_1", "landlord_property_type", "landlord_property_type"]].copy()
-
-            combined = left_df.merge(
-                exported_files["default_plans"], how="right",
-                left_on="epc_os_uprn", right_on="uprn"
-            )
-            raw_addresses = pd.read_excel(
-                "/Users/khalimconn-kowlessar/Downloads/North Tyneside Council. EPC D and Below with Type (1).xlsx")
-            raw_addresses = raw_addresses[["UPRN", "Address 1", "Postcode"]]
-            raw_addresses["Address 1"] = raw_addresses["Address 1"].str.replace("  ", " ")
-            raw_addresses = raw_addresses.drop_duplicates("Address 1")
-
-            combined2 = combined.merge(
-                raw_addresses, how="left", left_on="domna_address_1", right_on="Address 1"
-            )
-
-            combined2 = combined2.drop(columns=["landlord_property_id"])
-            combined2 = combined2.rename(columns={"UPRN": "landlord_property_id"})
-            combined2["epc_os_uprn"] = combined2["epc_os_uprn"].astype("Int64")
-            combined2.to_excel("/Users/khalimconn-kowlessar/Downloads/EON - recommended measures for review.xlsx")
-
-            removed = raw_addresses[~raw_addresses["UPRN"].isin(combined2["landlord_property_id"])]
-
-            df2 = pd.read_excel(
-                "/Users/khalimconn-kowlessar/Downloads/20260330 EON - recommended measures for review (1).xlsx"
-            )
-            removed2 = raw_addresses[~raw_addresses["UPRN"].isin(df2["landlord_property_id"])]
-
-            raw_addresses[raw_addresses["Address 1"].duplicated()]
-
             # TODO: Need to handle the exported files - e.g. upload to s3 and email a presigned url
             _ = exported_files
             return {
diff --git a/backend/tests/test_rebaselining_pipeline.py b/backend/tests/test_rebaselining_pipeline.py
index 9fbe1f35..a0c62f34 100644
--- a/backend/tests/test_rebaselining_pipeline.py
+++ b/backend/tests/test_rebaselining_pipeline.py
@@ -2,6 +2,10 @@ import os
 import pickle
 import pandas as pd
 import pytest
+from datetime import datetime
+from backend.ml_models.api import ModelApi
+from backend.app.utils import sap_to_epc
+from backend.app.config import get_prediction_buckets
 
 
 def load_sample_certificates():
@@ -60,12 +64,6 @@ def load_cleaning_data():
 
 @pytest.mark.integration
 def test_rebaselining_pipeline_with_real_data():
-    import pandas as pd
-    from datetime import datetime
-    from backend.ml_models.api import ModelApi
-    from backend.app.utils import sap_to_epc
-    from backend.app.config import get_prediction_buckets
-
     df = load_sample_certificates()
     cleaning_data = load_cleaning_data()
     input_properties = [make_property_from_row(row, cleaning_data=cleaning_data) for _, row in df.iterrows()]