get rid of condition lambda

.github/workflows/_deploy_lambda.yml

@@ -99,11 +99,11 @@ jobs:
             -out=lambdaplan
 
       - name: Terraform Apply
-        if: (inputs.terraform_apply == 'true' || github.ref == 'refs/heads/dev' || github.ref == 'refs/heads/main') && inputs.terraform_destroy != 'true'
+        if: inputs.terraform_apply == 'true' && inputs.terraform_destroy != 'true'
         working-directory: ${{ inputs.lambda_path }}
         run: terraform apply -auto-approve lambdaplan
 
       - name: Terraform Destroy
-        if: inputs.terraform_destroy == 'true'
+        if: inputs.terraform_destroy == 'true' && inputs.terraform_apply != 'true'
         working-directory: ${{ inputs.lambda_path }}
         run: terraform destroy -auto-approve

.github/workflows/deploy_terraform.yml

@@ -16,6 +16,13 @@ jobs:
 
     outputs:
       stage: ${{ steps.set-stage.outputs.stage }}
+      terraform_apply: ${{ steps.set-stage.outputs.terraform_apply }}
+
+    env:
+      AWS_ACCESS_KEY_ID: ${{ secrets.DEV_AWS_ACCESS_KEY_ID }}
+      AWS_SECRET_ACCESS_KEY: ${{ secrets.DEV_AWS_SECRET_ACCESS_KEY }}
+      AWS_REGION: ${{ secrets.DEV_AWS_REGION }}
+      DEV_DB_HOST: ${{ secrets.DEV_DB_HOST }}
 
     env:
       AWS_ACCESS_KEY_ID: ${{ secrets.DEV_AWS_ACCESS_KEY_ID }}
@@ -28,17 +35,18 @@ jobs:
         id: set-stage
         shell: bash
         run: |
-          echo "AWS_ACCESS_KEY_ID is set? ${AWS_ACCESS_KEY_ID:+yes}"
-          echo "AWS_SECRET_ACCESS_KEY is set? ${AWS_SECRET_ACCESS_KEY:+yes}"
-          echo "AWS_REGION=$AWS_REGION"
-          echo "DEV_DB_HOST=$DEV_DB_HOST"
-
           BRANCH="${GITHUB_REF_NAME}"
 
           if [[ "$BRANCH" == "prod" ]]; then
             echo "stage=prod" >> "$GITHUB_OUTPUT"
-          else
+            echo "terraform_apply=false" >> "$GITHUB_OUTPUT"
+          elif [[ "$BRANCH" == "dev" ]]; then
             echo "stage=dev" >> "$GITHUB_OUTPUT"
+            echo "terraform_apply=true" >> "$GITHUB_OUTPUT"
+          else
+            # Feature branch
+            echo "stage=dev" >> "$GITHUB_OUTPUT"
+            echo "terraform_apply=false" >> "$GITHUB_OUTPUT"
           fi
 
   # ============================================================
@@ -105,6 +113,7 @@ jobs:
       stage: ${{ needs.determine_stage.outputs.stage }}
       ecr_repo: address2uprn-${{ needs.determine_stage.outputs.stage }}
       image_digest: ${{ needs.address2uprn_image.outputs.image_digest }}
+      terraform_apply: ${{ needs.determine_stage.outputs.terraform_apply }}
     secrets:
       AWS_ACCESS_KEY_ID: ${{ secrets.DEV_AWS_ACCESS_KEY_ID }}
       AWS_SECRET_ACCESS_KEY: ${{ secrets.DEV_AWS_SECRET_ACCESS_KEY }}
@@ -145,8 +154,7 @@ jobs:
       stage: ${{ needs.determine_stage.outputs.stage }}
       ecr_repo: postcode_splitter-${{ needs.determine_stage.outputs.stage }}
       image_digest: ${{ needs.postcodeSplitter_image.outputs.image_digest }}
-      # This should not be deployed in production!!!!
+      terraform_apply: ${{ needs.determine_stage.outputs.terraform_apply }}
-      terraform_apply: 'true'
     secrets:
       AWS_ACCESS_KEY_ID: ${{ secrets.DEV_AWS_ACCESS_KEY_ID }}
       AWS_SECRET_ACCESS_KEY: ${{ secrets.DEV_AWS_SECRET_ACCESS_KEY }}
@@ -186,9 +194,9 @@ jobs:
       stage: ${{ needs.determine_stage.outputs.stage }}
       ecr_repo: condition-etl-${{ needs.determine_stage.outputs.stage }}
       image_digest: ${{ needs.condition_etl_image.outputs.image_digest }}
+      # terraform_apply: ${{ needs.determine_stage.outputs.terraform_apply }}
       terraform_destroy: 'true'
     secrets:
       AWS_ACCESS_KEY_ID: ${{ secrets.DEV_AWS_ACCESS_KEY_ID }}
       AWS_SECRET_ACCESS_KEY: ${{ secrets.DEV_AWS_SECRET_ACCESS_KEY }}
       AWS_REGION: ${{ secrets.DEV_AWS_REGION }}
-

asset_list/app.py

@@ -69,6 +69,7 @@ def app():
     Property UPRN
     """
 
+<<<<<<< HEAD
     data_folder = "/workspaces/model/asset_list/"
     data_filename = "manchester.xlsx"
     sheet_name = "PW0099 - Property List"
@@ -77,6 +78,53 @@ def app():
     address1_method = None
     fulladdress_column = None
     address_cols_to_concat = ["address"]
+=======
+    data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Aspire"
+    data_filename = "ASPIRE ASSET LIST.xlsx"
+    sheet_name = "Asset List"
+    postcode_column = "Postcode"
+    address1_column = None
+    address1_method = "house_number_extraction"
+    fulladdress_column = "Address"
+    address_cols_to_concat = []
+    missing_postcodes_method = None
+    landlord_year_built = None
+    landlord_os_uprn = None
+    landlord_property_type = "Property Type"
+    landlord_built_form = None
+    landlord_wall_construction = None
+    landlord_roof_construction = None
+    landlord_heating_system = None
+    landlord_existing_pv = None
+    landlord_property_id = "LLUPRN"
+    landlord_sap = None
+    outcomes_filename = None
+    outcomes_sheetname = None
+    outcomes_postcode = None
+    outcomes_houseno = None
+    outcomes_id = None
+    outcomes_address = None
+    master_filepaths = []
+    master_id_colnames = []
+    master_to_asset_list_filepath = None
+    phase = False
+    ecosurv_landlords = None
+    asset_list_header = 0
+    landlord_block_reference = None
+
+    # Peabody data for cleaning
+    data_folder = (
+        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting "
+        "Project/data_validation"
+    )
+    data_filename = "to_standardise_uprns.xlsx"
+    sheet_name = "Sheet1"
+    postcode_column = "Postcode"
+    address1_column = None
+    address1_method = "house_number_extraction"
+    fulladdress_column = "Address"
+    address_cols_to_concat = None
+>>>>>>> d4064da36565f87c2b72d10e9f3604cc6c37bdb6
     missing_postcodes_method = None
     landlord_year_built = None
     landlord_os_uprn = None
@@ -238,7 +286,7 @@ def app():
         if skip is not None and not force_retrieve_data:
             if i <= skip:
                 continue
-        chunk = asset_list.standardised_asset_list[i : i + chunk_size]
+        chunk = asset_list.standardised_asset_list[i: i + chunk_size]
         epc_data_chunk, errors_chunk, no_epc_chunk = get_data(
             df=chunk,
             row_id_name=asset_list.DOMNA_PROPERTY_ID,
@@ -381,7 +429,7 @@ def app():
     # Retrieve just the data we need
     epc_df = epc_df[
         [asset_list.DOMNA_PROPERTY_ID] + list(asset_list.EPC_API_DATA_NAMES.keys())
-    ].rename(columns=asset_list.EPC_API_DATA_NAMES)
+        ].rename(columns=asset_list.EPC_API_DATA_NAMES)
 
     # Look for columns not in the find my EPC data, which will have happened if we didn't
     # retrieve it in the first place
@@ -398,7 +446,7 @@ def app():
         find_my_epc_data[
             [asset_list.DOMNA_PROPERTY_ID, "epc_has_floor_recommendation"]
             + list(asset_list.FIND_EPC_DATA_NAMES.keys())
-        ].rename(columns=asset_list.FIND_EPC_DATA_NAMES),
+            ].rename(columns=asset_list.FIND_EPC_DATA_NAMES),
         how="left",
         on=asset_list.DOMNA_PROPERTY_ID,
     )

asset_list/mappings/property_type.py

@@ -439,6 +439,11 @@ PROPERTY_MAPPING = {
     'House: Semi Detached: Top Floor': 'house',
     'House: End Terrace: Ground Floor': 'house',
     'Maisonette: Enclosed End Terrace: Mid Floor': 'maisonette',
-    'Flat: Enclosed Mid Terrace: Basement': 'flat'
+    'Flat: Enclosed Mid Terrace: Basement': 'flat',
+
+    'Warden Bungalow': 'bungalow',
+    'Warden Flat': 'flat',
+    'Upper Floor Flat': 'flat',
+    'Extracare Scheme': 'other'
 
 }

backend/Property.py

@@ -1256,7 +1256,8 @@ class Property:
             "biodiesel": "Smokeless Fuel",
             "b30d": "B30K Biofuel",
             "coal": "Coal",
-            "oil": "Oil"
+            "oil": "Oil",
+            "unknown": None  # Handle - anything post 2020 is electricity else gas
         }
 
         self.heating_energy_source = list({
@@ -1326,7 +1327,16 @@ class Property:
         if self.heating_energy_source == "Varied (Community Scheme)":
 
             if self.main_fuel["fuel_type"] in fuel_map:  # We assume when None as it's unknown
-                self.heating_energy_source = fuel_map[self.main_fuel["fuel_type"]]
+                mapped_to = fuel_map[self.main_fuel["fuel_type"]]
+                if mapped_to is None and self.main_fuel["fuel_type"] == "unknown":
+                    # Handle logic based on age band
+                    if self.year_built >= 2020:
+                        self.heating_energy_source = "Electricity"
+                    else:
+                        self.heating_energy_source = "Natural Gas (Community Scheme)"
+
+                else:
+                    self.heating_energy_source = mapped_to
             else:
                 raise NotImplementedError(f"Unhandled fuel {self.main_fuel['fuel_type']}")
 

backend/app/config.py

@@ -1,8 +1,22 @@
+import os
 from functools import lru_cache
-from pydantic_settings import BaseSettings
+from pydantic_settings import BaseSettings, SettingsConfigDict
 from typing import Optional
 
 
+def resolve_env_file() -> Optional[str]:
+    env = os.getenv("ENVIRONMENT", "local")
+
+    if env == "local":
+        return "backend/.env"
+
+    if env == "test":
+        return "backend/.env.test"
+
+    # prod = no env file
+    return None
+
+
 class Settings(BaseSettings):
     API_KEY: str
     API_KEY_NAME: str = "X-API-KEY"
@@ -41,8 +55,10 @@ class Settings(BaseSettings):
     AWS_SECRET_KEY_ID: Optional[str] = None
     AWS_DEFAULT_REGION: Optional[str] = None
 
-    class Config:
+    model_config = SettingsConfigDict(
-        env_file = "backend/.env.local"
+        env_file=resolve_env_file(),
+        env_file_encoding="utf-8",
+    )
 
 
 @lru_cache()

backend/app/plan/utils.py

@@ -24,7 +24,7 @@ def get_cleaned():
 
     cleaned = read_from_s3(
         s3_file_name="cleaned_epc_data/cleaned.bson",
-        bucket_name="retrofit-data-{environment}".format(environment=get_settings().ENVIRONMENT)
+        bucket_name=get_settings().DATA_BUCKET
     )
 
     cleaned = msgpack.unpackb(cleaned, raw=False)

backend/condition/handler/Dockerfile

@@ -15,7 +15,7 @@ ENV DB_HOST=${DEV_DB_HOST}
 ENV DB_PORT=${DEV_DB_PORT}
 ENV DB_NAME=${DEV_DB_NAME}
 
-COPY backend/.env.local backend/.env.local
+COPY backend/.env.test backend/.env
 
 # -----------------------------
 # Copy requirements FIRST (for Docker layer caching)

backend/engine/engine.py

@@ -978,13 +978,15 @@ async def model_engine(body: PlanTriggerRequest):
             recommendations_scoring_data.extend(p.recommendations_scoring_data)
 
         logger.info("Preparing data for scoring in sap change api")
-        recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data).drop(
+        recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
-            columns=[
+        if not recommendations_scoring_data.empty:
-                "rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
+            recommendations_scoring_data = recommendations_scoring_data.drop(
-                "carbon_ending"
+                columns=[
-            ]
+                    "rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
-        )
+                    "carbon_ending"
-        # Temp putting this here
+                ]
+            )
+        # TODO: Temp putting this here
         recommendations_scoring_data["is_post_sap10_ending"] = True
 
         all_predictions = await model_api.async_paginated_predictions(

backend/ml_models/api.py

@@ -313,4 +313,15 @@ class ModelApi:
                     logger.error(f"Batch {chunk}-{chunk + batch_size} failed (Attempt {attempts}): {e}")
                     await asyncio.sleep(2 ** attempts)  # exponential backoff
         await self.close_aiohttp_session()
+
+        # Ensure stable output structure for the datagrame to be utilised by other functions downstream
+        for k in all_predictions.keys():
+            if all_predictions[k].empty:
+                col_template = ['id', 'predictions', 'property_id', 'recommendation_id', 'phase'] if (
+                    extract_ids) else ['id', 'predictions']
+
+                all_predictions[k] = pd.DataFrame(
+                    columns=col_template
+                )
+
         return all_predictions

etl/bill_savings/KwhData.py

@@ -196,6 +196,10 @@ class KwhData:
         if save and self.bucket is None:
             raise Exception("bucket not set, cannot save data")
 
+        if data.empty:
+            # If we have no data
+            return data
+
         # TODO: New is a temporary parameter, which will transform the epc descriptions to their transformed features
         #       in anticipation of the new model
 

recommendations/Recommendations.py

@@ -1090,6 +1090,7 @@ class Recommendations:
 
         ashp_cop = ashp_cop if ashp_cop else assumptions.AVERAGE_ASHP_EFFICIENCY
 
+        #
         kwh_impact_table = kwh_simulation_predictions["heating_kwh_predictions"][
             kwh_simulation_predictions["heating_kwh_predictions"]["property_id"] == str(property_instance.id)
             ].merge(

recommendations/RoofRecommendations.py

@@ -331,18 +331,18 @@ class RoofRecommendations:
         """
 
         # Can a non-primary part satisfy loft insulation?
-        primary_needs_loft = component_needs[1]["needs_loft_insulation"]
+        primary_needs_loft = component_needs[0]["needs_loft_insulation"]
         secondary_needs_loft = any(
-            p['needs_loft_insulation'] for idx, p in component_needs.items() if idx != 1
+            p['needs_loft_insulation'] for idx, p in component_needs.items() if idx != 0
         )
 
         if primary_needs_loft and not secondary_needs_loft:
             # Only option is loft
             return "loft"
 
-        primary_needs_sloping = component_needs[1]["needs_sloping_ceiling"]
+        primary_needs_sloping = component_needs[0]["needs_sloping_ceiling"]
         secondary_needs_sloping = any(
-            p['needs_sloping_ceiling'] for idx, p in component_needs.items() if idx != 1
+            p['needs_sloping_ceiling'] for idx, p in component_needs.items() if idx != 0
         )
 
         if primary_needs_sloping and not secondary_needs_sloping:
@@ -418,11 +418,13 @@ class RoofRecommendations:
             return needs_sloping, not needs_loft  # Indicates that the property needs sloping ceiling as we only run
             # this in that case
 
+        roof_components = [x for x in find_my_epc_components if x["component_name"] == "Roof"]
+
         extracted_roof_descriptions = {
             idx: {
                 "description": component["description"],
                 **RoofAttributes(component["description"]).process()
-            } for idx, component in enumerate(find_my_epc_components) if component["component_name"] == "Roof"
+            } for idx, component in enumerate(roof_components)
         }
 
         component_needs = {}

utils/s3.py

@@ -264,6 +264,7 @@ def save_excel_to_s3(df, bucket_name, file_key):
 
 
 def read_csv_from_s3(bucket_name, filepath):
+    logger.info(f"Reading CSV file from S3 bucket '{bucket_name}' with key '{filepath}'")
     s3 = boto3.client('s3')
 
     # Get the object from s3