Model/backend/app/plan/router.py

import json
from datetime import datetime

from tqdm import tqdm
import pandas as pd
from etl.epc.Record import EPCRecord
from backend.SearchEpc import SearchEpc
from fastapi import APIRouter, Depends
from sqlalchemy.exc import IntegrityError, OperationalError
from sqlalchemy.orm import sessionmaker
from starlette.responses import Response

from backend.app.config import get_settings, get_prediction_buckets
from backend.app.db.connection import db_engine
from backend.app.db.functions.materials_functions import get_materials
from backend.app.db.functions.portfolio_functions import aggregate_portfolio_recommendations
from backend.app.db.functions.property_functions import (
    create_property, create_property_details_epc, create_property_targets, update_property_data,
    update_or_create_property_spatial_details
)
from backend.app.db.functions.recommendations_functions import (
    create_plan, upload_recommendations, create_scenario
)
from backend.app.db.functions.energy_assessment_functions import get_latest_assessment_by_uprn
from backend.app.db.models.portfolio import rating_lookup
from backend.app.dependencies import validate_token
from backend.app.plan.schemas import PlanTriggerRequest, MdsRequest
from backend.app.plan.utils import get_cleaned
from backend.app.utils import epc_to_sap_lower_bound, sap_to_epc

from backend.ml_models.api import ModelApi
from backend.Property import Property
from backend.apis.GoogleSolarApi import GoogleSolarApi

from recommendations.optimiser.CostOptimiser import CostOptimiser
from recommendations.optimiser.GainOptimiser import GainOptimiser
from recommendations.optimiser.optimiser_functions import prepare_input_measures
from recommendations.Recommendations import Recommendations
from recommendations.Mds import Mds
from utils.logger import setup_logger
from utils.s3 import read_dataframe_from_s3_parquet, read_csv_from_s3
from backend.ml_models.Valuation import PropertyValuation

from etl.bill_savings.EnergyConsumptionModel import EnergyConsumptionModel
from etl.bill_savings.KwhData import KwhData
from etl.spatial.OpenUprnClient import OpenUprnClient
from etl.solar.SolarPhotoSupply import SolarPhotoSupply

logger = setup_logger()

BATCH_SIZE = 5
SCORING_BATCH_SIZE = 400


def patch_epc(patch, epc_records):
    """
    This utility function is useful to patch the epc data if we have data from the customer
    :return:
    """

    for patch_variable, patch_value in patch.items():

        if patch_variable in ["address", "postcode"]:
            continue

        if patch_value == "":
            continue
        if patch_variable in epc_records["original_epc"]:
            epc_records["original_epc"][patch_variable] = patch_value

    return epc_records


def extract_portfolio_aggregation_data(
    input_properties, total_valuation_increase, recommendations, new_epc_bands, property_value_increase_ranges
):
    # We aggregate a number of metrics for the portfolio:
    # 1) A breakdown of the number of properties in each EPC band
    #    a) before retrofit
    #    b) after retrofit
    # 2) Number of units
    # 3) Co2/unit
    #    a) before retrofit
    #    b) after retrofit
    # 4) Energy bill/unit
    #    a) before retrofit
    #    b) after retrofit
    # 5) Average valuation improvement/unit
    # 6) Total cost
    # 7) Cost per unit
    # 8) £ per CO2 saved
    # 9) £ per SAP point

    # We need to construct the underlyind data for this

    # Helper function to reformat the EPC data
    def reformat_epc_data(epc_counts):
        # Define all possible EPC bands in the required order
        epc_bands = ["G", "F", "E", "D", "C", "B", "A"]

        # Create the formatted data list by checking each band in the order
        formatted_data = []
        for band in epc_bands:
            # Get the count from the dictionary, defaulting to 0 if not present
            count = epc_counts.get(band, 0)
            # Append the formatted dictionary to the list
            formatted_data.append({"name": band, band: count})

        return formatted_data

    n_units = len(input_properties)

    agg_data = []
    for p in input_properties:
        # Get the recommendations for the property - we include all properties, even ones without recommendations
        property_recommendations = recommendations.get(p.id, [])

        # Get just the default recommendations
        default_recommendations = [r for r in property_recommendations if r["default"]]

        has_recommendations = len(default_recommendations) > 0

        # We can now calculate multiple outputs based on default recommendations
        carbon_savings = sum([r["co2_equivalent_savings"] for r in default_recommendations])

        pre_retrofit_co2 = p.data["co2-emissions-current"]
        post_retrofit_co2 = pre_retrofit_co2 - carbon_savings

        pre_retrofit_energy_bill = sum(p.current_energy_bill.values())
        post_retrofit_energy_bill = sum(p.current_energy_bill.values()) - sum(
            [r["energy_cost_savings"] for r in default_recommendations]
        )

        pre_retrofit_energy_consumption = p.current_energy_consumption
        post_retrofit_energy_consumption = p.current_energy_consumption - sum(
            [r["kwh_savings"] for r in default_recommendations]
        )

        # Add up energy savings
        cost = sum([r["total"] for r in default_recommendations])
        sap_point_improvement = sum([r["sap_points"] for r in default_recommendations])

        lower_bound_valuation_uplift = (
            property_value_increase_ranges[p.id]["lower_bound_increased_value"] -
            property_value_increase_ranges[p.id]["current_value"]
        )
        upper_bound_valuation_uplift = (
            property_value_increase_ranges[p.id]["upper_bound_increased_value"] -
            property_value_increase_ranges[p.id]["current_value"]
        )

        agg_data.append({
            "pre_retrofit_epc": p.data["current-energy-rating"],
            "post_retrofit_epc": new_epc_bands[p.id],
            "pre_retrofit_co2": pre_retrofit_co2,
            "post_retrofit_co2": post_retrofit_co2,
            "pre_retrofit_energy_bill": pre_retrofit_energy_bill,
            "post_retrofit_energy_bill": post_retrofit_energy_bill,
            "pre_retrofit_energy_consumption": pre_retrofit_energy_consumption,
            "post_retrofit_energy_consumption": post_retrofit_energy_consumption,
            "cost": cost,
            "sap_point_improvement": sap_point_improvement,
            "lower_bound_valuation_uplift": lower_bound_valuation_uplift,
            "upper_bound_valuation_uplift": upper_bound_valuation_uplift,
            "has_recommendations": has_recommendations
        })

    agg_data = pd.DataFrame(agg_data)

    n_units_to_retrofit = agg_data["has_recommendations"].sum()

    valuation_improvement_lower_bound_per_unit = (
        agg_data["lower_bound_valuation_uplift"].mean()
    )
    valuation_improvement_upper_bound_per_unit = (
        agg_data["upper_bound_valuation_uplift"].mean()
    )

    total_carbon_saved = agg_data["pre_retrofit_co2"].sum() - agg_data["post_retrofit_co2"].sum()
    total_sap_points = agg_data["sap_point_improvement"].sum()

    def format_money(amount):
        return f"£{amount:,.0f}"

    valuation_improvment_per_unit = str(
        format_money(
            total_valuation_increase / n_units) + (f" ({format_money(valuation_improvement_lower_bound_per_unit)} - "
                                                   f"{format_money(valuation_improvement_upper_bound_per_unit)})")
    )

    valuation_return_on_investment = str(
        str(round(total_valuation_increase / agg_data["cost"].sum(), 2)) +
        f" ("
        f"{agg_data['lower_bound_valuation_uplift'].sum() / agg_data['cost'].sum():,.2f} - "
        f"{agg_data['upper_bound_valuation_uplift'].sum() / agg_data['cost'].sum():,.2f})"
    )

    aggregation_data = {
        "epc_breakdown_pre_retrofit": json.dumps(
            reformat_epc_data(agg_data["pre_retrofit_epc"].value_counts().to_dict())
        ),
        "epc_breakdown_post_retrofit": json.dumps(
            reformat_epc_data(agg_data["post_retrofit_epc"].value_counts().to_dict())
        ),
        "number_of_properties": int(n_units),
        "n_units_to_retrofit": int(n_units_to_retrofit),
        "co2_per_unit_pre_retrofit": str(round(agg_data["pre_retrofit_co2"].mean(), 2)) + "t",
        "co2_per_unit_post_retrofit": str(round(agg_data["post_retrofit_co2"].mean(), 2)) + "t",
        "energy_bill_per_unit_pre_retrofit": format_money(agg_data["pre_retrofit_energy_bill"].mean()),
        "energy_bill_per_unit_post_retrofit": format_money(agg_data["post_retrofit_energy_bill"].mean()),
        "energy_consumption_per_unit_pre_retrofit": str(
            round(agg_data["pre_retrofit_energy_consumption"].mean())) + "kWh",
        "energy_consumption_per_unit_post_retrofit": str(
            round(agg_data["post_retrofit_energy_consumption"].mean())) + "kWh",
        "valuation_improvement_per_unit": valuation_improvment_per_unit,
        "cost_per_unit": format_money(agg_data["cost"].mean()),
        "cost_per_co2_saved": format_money(agg_data["cost"].sum() / total_carbon_saved),
        "cost_per_sap_point": format_money(agg_data["cost"].sum() / total_sap_points),
        "valuation_return_on_investment": valuation_return_on_investment,
        # TODO: Could we add 10yr carbon credits value?
    }

    return aggregation_data


def create_epc_records(epc_searcher: SearchEpc, energy_assessment: dict):
    """
    This function will set up with epc_records dictionary with the newest EPC, the full SAP EPC and the older EPCs
    and will factor in an energy assessment that we have performed for a client.
    :param epc_searcher: An instance of the SearchEpc class
    :param energy_assessment: The energy assessment we have performed. If we have not performed an energy assessment,
                              this should be an empty response as defined by the models's
                              EnergyAssessment.empty_response() method
    """

    newest_epc = epc_searcher.newest_epc.copy()
    if newest_epc["uprn"] == "" and epc_searcher.uprn:
        newest_epc["uprn"] = epc_searcher.uprn

    if not energy_assessment["epc"]:
        energy_assessment_is_newer = False
        return {
            'original_epc': newest_epc,
            'full_sap_epc': epc_searcher.full_sap_epc.copy(),
            'old_data': epc_searcher.older_epcs.copy(),
        }, energy_assessment_is_newer

    epc = energy_assessment["epc"]
    energy_assessment_date = epc["inspection-date"].strftime("%Y-%m-%d")

    # We insert county into the epc, since right now this isn't something that we pull out from the energy
    # assessment
    for col in ["county", "constituency", "constituency-label", "local-authority", "local-authority-label"]:
        epc[col] = newest_epc[col]

    # We check if the energy assessment is newer than the newest EPC
    if pd.to_datetime(energy_assessment_date) > pd.to_datetime(newest_epc["inspection-date"]):
        # In this case, our energy assessment is newer than the EPCs available for this property
        energy_assessment_is_newer = True
        return {
            "original_epc": epc,
            "full_sap_epc": epc_searcher.full_sap_epc.copy(),
            "old_data": epc_searcher.older_epcs.copy() + [newest_epc]
        }, energy_assessment_is_newer

    # We check if the EPC we have produced is contained in the set of EPCs done for the property
    # We do this based on inspection-date and SAP
    epc_in_historicals = [
        x for x in epc_searcher.older_epcs + [newest_epc]
        if x["inspection-date"] == energy_assessment_date and
                   x["current-energy-efficiency"] == epc["current-energy-efficiency"]
    ]
    energy_assessment_is_newer = False

    if epc_in_historicals:
        # Then the EPC we have produced is already in the set of EPCs, and our EPC is older than the newest
        return {
            "original_epc": newest_epc,
            "full_sap_epc": epc_searcher.full_sap_epc.copy(),
            "old_data": epc_searcher.older_epcs.copy()
        }, energy_assessment_is_newer

    # In this case, our EPC is older than the newest publically avaible one, but is not contained in
    # the historicals, so it can't have been lodged, so we include it in the old data
    return {
        'original_epc': newest_epc,
        'full_sap_epc': epc_searcher.full_sap_epc.copy(),
        'old_data': epc_searcher.older_epcs.copy() + [epc],
    }, energy_assessment_is_newer


def get_on_site_data(body: PlanTriggerRequest):
    """
    This function will read in the on-site data from the S3 bucket
    :param body: The request body
    :return:
    """
    patches = []
    if body.patches_file_path:
        patches = read_csv_from_s3(bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.patches_file_path)

    already_installed = []
    if body.already_installed_file_path:
        already_installed = read_csv_from_s3(
            bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.already_installed_file_path
        )

    non_invasive_recommendations = []
    if body.non_invasive_recommendations_file_path:
        non_invasive_recommendations = read_csv_from_s3(
            bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.non_invasive_recommendations_file_path
        )

    return patches, already_installed, non_invasive_recommendations


def extract_property_on_site_recommendations(config, patches, already_installed, non_invasive_recommendations, uprn):
    patch_has_uprn = "uprn" in patches[0] if patches else True
    if patch_has_uprn:
        patch = next((
            x for x in patches if str(x["uprn"]) == str(config["uprn"])
        ), {})
    else:
        patch = next((
            x for x in patches if (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
        ), {})

    property_already_installed = next((
        x for x in already_installed if
        (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
    ), {})

    # Because we have some non-invasive recommendations that match on address and postcode, but not UPRN
    # we need to check existence of uprn
    has_uprn = "uprn" in non_invasive_recommendations[0] if non_invasive_recommendations else True
    if has_uprn:
        property_non_invasive_recommendations = next((
            x for x in non_invasive_recommendations if
            (str(x["uprn"]) == str(uprn))
        ), {})

        # We patch the non-invasive recs that are ['cavity_extract_and_refill']
    else:
        property_non_invasive_recommendations = next((
            x for x in non_invasive_recommendations if
            (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
        ), {})

    if isinstance(property_non_invasive_recommendations.get("recommendations"), str):
        import ast
        property_non_invasive_recommendations["recommendations"] = ast.literal_eval(
            property_non_invasive_recommendations["recommendations"]
        )
        transformed = []
        for rec in property_non_invasive_recommendations["recommendations"]:
            if isinstance(rec, str):
                transformed.append({"type": rec, })
            else:
                transformed.append(rec)

        property_non_invasive_recommendations["recommendations"] = str(transformed)

    return patch, property_already_installed, property_non_invasive_recommendations


router = APIRouter(
    prefix="/plan",
    tags=["plan"],
    dependencies=[Depends(validate_token)],
    responses={404: {"description": "Not found"}}
)


@router.post("/trigger")
async def trigger_plan(body: PlanTriggerRequest):
    logger.info("Connecting to db")
    session = sessionmaker(bind=db_engine)()
    created_at = datetime.now().isoformat()

    # TODO: if the measure is already installed, it should actually be the very first phase

    try:
        session.begin()
        logger.info("Getting the inputs")
        plan_input = read_csv_from_s3(bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.trigger_file_path)
        # If we have patches or overrides, we should read them in here
        patches, already_installed, non_invasive_recommendations = get_on_site_data(body)

        cleaning_data = read_dataframe_from_s3_parquet(
            bucket_name=get_settings().DATA_BUCKET, file_key="sap_change_model/cleaning_dataset.parquet",
        )

        input_properties = []
        for config in tqdm(plan_input):
            # We validate each record in the file. If the record is NOT valid, we need to handle this accordingly
            uprn = config.get("uprn", None)
            if uprn:
                uprn = int(float(uprn))

            epc_searcher = SearchEpc(
                address1=config["address"],
                postcode=config["postcode"],
                uprn=uprn,
                auth_token=get_settings().EPC_AUTH_TOKEN,
                os_api_key=get_settings().ORDNANCE_SURVEY_API_KEY,
            )
            epc_searcher.ordnance_survey_client.built_form = config.get("built_form", None)
            epc_searcher.ordnance_survey_client.property_type = config.get("property_type", None)
            # For the moment, our OS API access is unavailable, so we skip and interpolate
            epc_searcher.find_property(skip_os=True)

            # We check for an energy assessment we have performed on this property:
            energy_assessment = get_latest_assessment_by_uprn(session, uprn if uprn is not None else epc_searcher.uprn)

            # Create a record in db
            property_id, is_new = create_property(
                session, body.portfolio_id, epc_searcher.address_clean, epc_searcher.postcode_clean, epc_searcher.uprn,
                energy_assessment
            )
            if not is_new and not body.multi_plan:
                continue

            if is_new:
                create_property_targets(
                    session,
                    property_id=property_id,
                    portfolio_id=body.portfolio_id,
                    epc_target=body.goal_value,
                    heat_demand_target=None
                )

            # If we have an energy assessment in place, that is newer than all of the previous EPCs, we use that.
            # Otherwise, we use the newest EPC
            # energy_assessment_is_newer will tell us if the energy assessment is newer than the newest EPC that
            # has been publically lodged
            epc_records, energy_assessment["energy_assessment_is_newer"] = create_epc_records(
                epc_searcher, energy_assessment
            )

            patch, property_already_installed, property_non_invasive_recommendations = (
                extract_property_on_site_recommendations(
                    config, patches, already_installed, non_invasive_recommendations, uprn
                )
            )

            epc_records = patch_epc(patch, epc_records)

            prepared_epc = EPCRecord(
                epc_records=epc_records,
                run_mode="newdata",
                cleaning_data=cleaning_data
            )

            input_properties.append(
                Property(
                    id=property_id,
                    is_new=is_new,
                    address=epc_searcher.address_clean,
                    postcode=epc_searcher.postcode_clean,
                    epc_record=prepared_epc,
                    already_installed=property_already_installed,
                    non_invasive_recommendations=property_non_invasive_recommendations,
                    energy_assessment=energy_assessment,
                    **Property.extract_kwargs(config),  # TODO: Depraecate this
                )
            )

        if not input_properties:
            return Response(status_code=204)

        # The materials data could be cached or local so we don't need to make
        # consistent requests to the backend for
        # the same data
        logger.info("Reading in materials and cleaned datasets")
        materials = get_materials(session)
        cleaned = get_cleaned()

        dataset_version = "2024-07-08"
        energy_consumption_client = EnergyConsumptionModel(
            model_paths={
                "heating_kwh": f"model_directory/energy_consumption_model/heating_kwh_{dataset_version}.pkl",
                "hot_water_kwh": f"model_directory/energy_consumption_model/hot_water_kwh_{dataset_version}.pkl"
            },
            dummy_schema_path=f"model_directory/energy_consumption_model/{dataset_version}_dummy_schema.pkl",
            consumption_average_path=f"energy_consumption/{dataset_version}/consumption_averages.parquet",
            cleaned=cleaned,
            environment=get_settings().ENVIRONMENT
        )

        kwh_client = KwhData(bucket=get_settings().DATA_BUCKET, read_consumption_data=True)

        model_api = ModelApi(
            portfolio_id=body.portfolio_id,
            timestamp=created_at,
            prediction_buckets=get_prediction_buckets()
        )

        epcs_for_scoring = kwh_client.transform(data=kwh_client.prepare_epc(input_properties), cleaned=cleaned)

        kwh_preds = model_api.paginated_predictions(
            data=epcs_for_scoring,
            bucket=get_settings().DATA_BUCKET,
            model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
            extract_ids=False,
            batch_size=SCORING_BATCH_SIZE
        )

        # Insert the spatial data
        logger.info("Getting spatial data")
        input_properties = OpenUprnClient.set_spatial_data(input_properties, bucket_name=get_settings().DATA_BUCKET)

        [p.set_features(cleaned=cleaned, kwh_client=kwh_client, kwh_predictions=kwh_preds) for p in input_properties]

        # TODO: If a property is semi-detached, we might get roof surfaces for the main building + the neighbour
        # TODO: If we can't get high image quality, should we use the solar API? Maybe just for semi-detached units with
        #       extensions, since it doesn't seem to do a great job

        logger.info("Performing solar analysis")
        building_solar_config, unit_solar_config = GoogleSolarApi.prepare_input_data(
            input_properties=input_properties,
            energy_consumption_client=energy_consumption_client,
            body=body
        )

        input_properties = GoogleSolarApi.building_solar_analysis(
            building_solar_config=building_solar_config,
            input_properties=input_properties,
            session=session,
            google_solar_api_key=get_settings().GOOGLE_SOLAR_API_KEY
        )

        input_properties = GoogleSolarApi.unit_solar_analysis(
            unit_solar_config=unit_solar_config,
            input_properties=input_properties,
            session=session,
            body=body,
            google_solar_api_key=get_settings().GOOGLE_SOLAR_API_KEY
        )

        logger.info("Identifying property recommendations")
        recommendations = {}
        recommendations_scoring_data = []
        representative_recommendations = {}
        for p in tqdm(input_properties):
            recommender = Recommendations(
                property_instance=p,
                materials=materials,
                exclusions=body.exclusions,
                inclusions=body.inclusions,
                default_u_values=body.default_u_values
            )
            property_recommendations, property_representative_recommendations = recommender.recommend()

            if not property_recommendations:
                continue

            recommendations[p.id] = property_recommendations
            representative_recommendations[p.id] = property_representative_recommendations

            p.create_base_difference_epc_record(cleaned_lookup=cleaned)
            p.adjust_difference_record_with_recommendations(
                property_recommendations, property_representative_recommendations
            )

            recommendations_scoring_data.extend(p.recommendations_scoring_data)

        # TODO: Make sure that number_habitable_rooms has been dropped
        logger.info("Preparing data for scoring in sap change api")
        recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)

        recommendations_scoring_data = recommendations_scoring_data.drop(
            columns=["rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
                     "carbon_ending"]
        )

        all_predictions = model_api.paginated_predictions(
            data=recommendations_scoring_data,
            bucket=get_settings().DATA_BUCKET,
            batch_size=SCORING_BATCH_SIZE
        )

        # 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]

            recommendations_with_impact, impact_summary = (
                Recommendations.calculate_recommendation_impact(
                    property_instance=property_instance,
                    all_predictions=all_predictions,
                    recommendations=recommendations,
                )
            )

            # We use the impact_summary to update the simulation_epcs with the new SAP, heat demand, carbon, cost etc
            # at each phase
            property_instance.update_simulation_epcs(impact_summary)
            scoring_epcs.extend(property_instance.updated_simulation_epcs)
            recommendations[property_id] = recommendations_with_impact

        # We call the API with the scoring epcs
        scoring_epcs = pd.DataFrame(scoring_epcs)
        scoring_epcs = kwh_client.transform(data=scoring_epcs, cleaned=cleaned)

        kwh_simulation_predictions = model_api.paginated_predictions(
            data=scoring_epcs,
            bucket=get_settings().DATA_BUCKET,
            model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
            batch_size=SCORING_BATCH_SIZE
        )

        # We now insert kwh estimates and costs into the recommendations
        # TODO: We should join the methodology which maps the heating and hot water descriptions to the fuel types in
        #       Recommendations, but also the Property class
        logger.info("Calculating tenant savings - kwh and bills")
        for property_id in tqdm([p.id for p in input_properties]):
            property_recommendations = recommendations.get(property_id, [])
            property_instance = [p for p in input_properties if p.id == property_id][0]

            property_current_energy_bill = (
                Recommendations.calculate_recommendation_tenant_savings(
                    property_instance=property_instance,
                    kwh_simulation_predictions=kwh_simulation_predictions,
                    property_recommendations=property_recommendations
                )
            )
            property_instance.current_energy_bill = property_current_energy_bill

        # 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:
            if not recommendations.get(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)

            if not body.optimise:
                if body.goal != "Increasing EPC":
                    raise NotImplementedError("Only EPC optimisation is currently supported")
                solution = []
                for sub_list in input_measures:
                    # Select the entry with the highest gain, and if tied, choose the one with the lowest cost
                    best_measure = max(sub_list, key=lambda x: (x['gain'], -x['cost']))
                    solution.append(best_measure)
            else:

                if body.budget:
                    optimiser = GainOptimiser(
                        input_measures, max_cost=body.budget, max_gain=sap_gain if sap_gain > 0 else 0
                    )
                else:
                    # The minimum gain is the minimum number of SAP points required to get to the target SAP band
                    # If the gain is negative, the optimiser will return an empty solution
                    optimiser = CostOptimiser(
                        input_measures,
                        min_gain=sap_gain
                    )

                optimiser.setup()
                optimiser.solve()
                solution = optimiser.solution

            selected_recommendations = {r["id"] for r in solution}

            # If wall insulation is selected, we also include mechanical ventilation as a best practice measure
            if any(x in [r["type"] for r in solution] for x in [
                "internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"
            ]):
                ventilation_rec = next(
                    (r[0] for r in recommendations[p.id] if r[0]["type"] == "mechanical_ventilation"),
                    None
                )

                # If a matching recommendation was found, add its ID to the selected recommendations
                if ventilation_rec:
                    selected_recommendations.add(ventilation_rec["recommendation_id"])

            # If we have a trickle vents recommendation, we also switch it on. We don't just check the solution
            trickle_vents_rec = next(
                (r[0] for r in recommendations[p.id] if r[0]["type"] == "trickle_vents"),
                None
            )
            # If a matching recommendation was found, add its ID to the selected recommendations
            if trickle_vents_rec:
                selected_recommendations.add(trickle_vents_rec["recommendation_id"])

            # We'll use the set of selected recommendations to filter the recommendations to upload
            final_recommendations = [
                [
                    {**rec, "default": True if rec["recommendation_id"] in selected_recommendations else False}
                    for rec in recommendations_by_type
                ]
                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[p.id] = final_recommendations

        logger.info("Uploading recommendations to the database")
        # If we have any work to do, we create a new scenario
        engine_scenario = create_scenario(
            session=session,
            scenario={
                "name": body.scenario_name,
                "created_at": created_at,
                "budget": body.budget,
                "portfolio_id": body.portfolio_id,
                "housing_type": body.housing_type,
                "goal": body.goal,
                "trigger_file_path": body.trigger_file_path,
                "already_installed_file_path": body.already_installed_file_path,
                "patches_file_path": body.patches_file_path,
                "non_invasive_recommendations_file_path": body.non_invasive_recommendations_file_path,
                "exclusions": body.exclusions,
                "multi_plan": body.multi_plan
            }
        )

        property_valuation_increases = []
        session.commit()
        new_epc_bands = {}
        property_value_increase_ranges = {}
        for i in range(0, len(input_properties), BATCH_SIZE):
            try:
                # Take a slice of the input_properties list to make a batch
                batch_properties = input_properties[i:i + BATCH_SIZE]

                for p in batch_properties:
                    recommendations_to_upload = recommendations.get(p.id, [])
                    default_recommendations = [r for r in recommendations_to_upload if r["default"]]
                    total_sap_points = sum([r["sap_points"] for r in default_recommendations])
                    new_sap_points = float(p.data["current-energy-efficiency"]) + total_sap_points
                    new_epc = sap_to_epc(new_sap_points)
                    new_epc_bands[p.id] = new_epc

                    valuations = PropertyValuation.estimate(property_instance=p, target_epc=new_epc)
                    property_value_increase_ranges[p.id] = valuations

                    if p.is_new:
                        property_details_epc = p.get_property_details_epc(
                            portfolio_id=body.portfolio_id, rating_lookup=rating_lookup,
                        )
                        create_property_details_epc(session, property_details_epc)

                        update_or_create_property_spatial_details(session, p.uprn, p.spatial)

                        property_data = p.get_full_property_data(current_valuation=valuations["current_value"])
                        update_property_data(
                            session, property_id=p.id, portfolio_id=body.portfolio_id, property_data=property_data
                        )

                    if not recommendations_to_upload:
                        continue

                    new_plan_id = create_plan(session, {
                        "portfolio_id": body.portfolio_id,
                        "property_id": p.id,
                        "scenario_id": engine_scenario.id,
                        "is_default": True if p.is_new else False,
                        "name": body.scenario_name,
                        "valuation_increase_lower_bound": (
                            valuations["lower_bound_increased_value"] - valuations["current_value"]
                        ),
                        "valuation_increase_upper_bound": (
                            valuations["upper_bound_increased_value"] - valuations["current_value"]
                        ),
                        "valuation_increase_average": (
                            valuations["average_increased_value"] - valuations["current_value"]
                        ),
                    })

                    upload_recommendations(
                        session, recommendations_to_upload, p.id, new_plan_id
                    )

                    property_valuation_increases.append(
                        valuations["average_increased_value"] - valuations["current_value"]
                    )

                # Commit the session after each batch
                session.commit()

            except Exception as e:
                # Rollback the session if an error occurs
                session.rollback()
                print("Failed i = %s" % str(i))
                logger.error(f"An error occurred during batch starting at index {i}: {e}")

        logger.info("Creating portfolio aggregations")
        # We implement this in the simplest way possible which will be just to query the database for all
        # recommendations associated to the portfolio and then aggregate them. This is not the most efficient
        # way to do this, but it's the simplest and will be a process that we can re-use since when we change a
        # recommendation from being default to not default, we'll need to re-run this process to re-calculate the
        # the portfolion level impact

        total_valuation_increase = sum(property_valuation_increases)
        labour_days = round(max(
            [sum(r["labour_days"] for r in rec_group if r["default"]) for p_id, rec_group in recommendations.items()]
        ))

        aggregated_data = extract_portfolio_aggregation_data(
            input_properties=input_properties,
            total_valuation_increase=total_valuation_increase,
            recommendations=recommendations,
            new_epc_bands=new_epc_bands,
            property_value_increase_ranges=property_value_increase_ranges
        )

        aggregate_portfolio_recommendations(
            session,
            portfolio_id=body.portfolio_id,
            scenario_id=engine_scenario.id,
            total_valuation_increase=total_valuation_increase,
            labour_days=labour_days,
            aggregated_data=aggregated_data
        )

        # Commit final changes
        session.commit()
    except IntegrityError:
        logger.error("Database integrity error occurred", exc_info=True)
        session.rollback()
        return Response(status_code=500, content="Database integrity error.")
    except OperationalError:
        logger.error("Database operational error occurred", exc_info=True)
        session.rollback()
        return Response(status_code=500, content="Database operational error.")
    except ValueError:
        logger.error("Value error - possibly due to malformed data", exc_info=True)
        session.rollback()
        return Response(status_code=400, content="Bad request: malformed data.")
    except Exception as e:  # General exception handling
        logger.error(f"An error occurred: {e}")
        session.rollback()
        return Response(status_code=500, content="An unexpected error occurred.")
    finally:
        session.close()

    return Response(status_code=200)


@router.post("/mds")
async def build_mds(body: MdsRequest):
    # TODO: This is a placeholder location for the MDS endpoint, which this is being assembled

    logger.info("Connecting to db")
    session = sessionmaker(bind=db_engine)()
    created_at = datetime.now().isoformat()

    try:
        session.begin()
        logger.info("Getting the inputs")
        plan_input = read_csv_from_s3(bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.trigger_file_path)
        measure_set = body.measures
        optimise_measures = measure_set is not None

        cleaning_data = read_dataframe_from_s3_parquet(
            bucket_name=get_settings().DATA_BUCKET, file_key="sap_change_model/cleaning_dataset.parquet",
        )

        input_properties = []
        for property_id, config in tqdm(enumerate(plan_input), total=len(plan_input)):
            # We validate each record in the file. If the record is NOT valid, we need to handle this accordingly
            uprn = config.get("uprn", None)
            uprn = None if uprn == "" else uprn
            if uprn:
                uprn = int(float(uprn))

            epc_searcher = SearchEpc(
                address1=config["address"],
                postcode=config["postcode"],
                uprn=uprn,
                auth_token=get_settings().EPC_AUTH_TOKEN,
                os_api_key=get_settings().ORDNANCE_SURVEY_API_KEY,
            )
            epc_searcher.ordnance_survey_client.built_form = config.get("built_form", None)
            epc_searcher.ordnance_survey_client.property_type = config.get("property_type", None)
            # For the moment, our OS API access is unavailable, so we skip and interpolate
            epc_searcher.find_property(skip_os=True)

            if config["address"] == "35b High Street":
                print("Performing temporary patch on 35b High Street")
                epc_searcher.newest_epc["uprn"] = 10002911892
                epc_searcher.full_sap_epc["uprn"] = 10002911892

            if config["address"] == "Cobnut Barn":
                print("Performing temporary patch on Cobnut Barn")
                epc_searcher.newest_epc["uprn"] = 10013924689

            # Create a record in db
            # TODO: If we productionise the creation of this mds report, we will need to store this in the db
            # property_id, is_new = create_property(
            #     session, body.portfolio_id, epc_searcher.address_clean, epc_searcher.postcode_clean, epc_searcher.uprn
            # )
            # if not is_new:
            #     continue
            #
            # create_property_targets(
            #     session,
            #     property_id=property_id,
            #     portfolio_id=body.portfolio_id,
            #     epc_target=body.goal_value,
            #     heat_demand_target=None
            # )

            epc_records = {
                'original_epc': epc_searcher.newest_epc.copy(),
                'full_sap_epc': epc_searcher.full_sap_epc.copy(),
                'old_data': epc_searcher.older_epcs.copy(),
            }

            # patch = next((
            #     x for x in patches if (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
            # ), {})
            # epc_records = patch_epc(patch, epc_records)

            prepared_epc = EPCRecord(
                epc_records=epc_records,
                run_mode="newdata",
                cleaning_data=cleaning_data
            )

            # property_already_installed = next((
            #     x for x in already_installed if
            #     (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
            # ), {})
            #
            # property_non_invasive_recommendations = next((
            #     x for x in non_invasive_recommendations if
            #     (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
            # ), {})

            if measure_set is None:
                measures = config["measures"] if "measures" in config else None
            else:
                measures = measure_set

            input_properties.append(
                Property(
                    id=property_id,
                    address=epc_searcher.address_clean,
                    postcode=epc_searcher.postcode_clean,
                    epc_record=prepared_epc,
                    # already_installed=property_already_installed,
                    # non_invasive_recommendations=property_non_invasive_recommendations,
                    measures=measures,
                    is_new=is_new,
                    **Property.extract_kwargs(config)
                )
            )

        logger.info("Reading in materials and cleaned datasets")
        materials = get_materials(session)
        cleaned = get_cleaned()

        uprn_filenames = read_dataframe_from_s3_parquet(
            bucket_name=get_settings().DATA_BUCKET, file_key="spatial/filename_meta.parquet"
        )
        photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket=get_settings().DATA_BUCKET)

        logger.info("Getting spatial data")
        for p in tqdm(input_properties):
            p.get_spatial_data(uprn_filenames)

        logger.info("Getting components and epc recommendations")
        recommendations_scoring_data = []
        representative_recommendations = {}
        recommendations = {}

        for p in tqdm(input_properties):
            p.set_features(cleaned, photo_supply_lookup, floor_area_decile_thresholds)

            mds = Mds(property_instance=p, materials=materials, optimise_measures=optimise_measures)
            mds_recommendations, property_representative_recommendations, errors = mds.build()

            if isinstance(errors, list):
                if errors:
                    raise Exception("Errors occurred during MDS build")
            else:
                if any([len(x) for x in errors.values()]):
                    raise Exception("Errors occurred during MDS build")

            recommendations[p.id] = mds_recommendations
            representative_recommendations[p.id] = property_representative_recommendations

            # Build the scoring data
            p.create_base_difference_epc_record(cleaned_lookup=cleaned)
            if optimise_measures:
                for _id, mds_recs in mds_recommendations.items():
                    representative_ids = [r["recommendation_id"] for r in property_representative_recommendations[_id]]
                    simulation_mds_recs = []
                    for recs in mds_recs:
                        simulation_mds_recs.append(
                            [r for r in recs if r["recommendation_id"] in representative_ids]
                        )

                    p.adjust_difference_record_with_recommendations(
                        simulation_mds_recs, property_representative_recommendations[_id]
                    )

                    data = p.recommendations_scoring_data.copy()
                    for d in data:
                        d["id"] = d["id"] + "*" + _id

                    recommendations_scoring_data.extend(data)

            else:
                recommendations_scoring_data.append(
                    p.simulate_all_representative_recommendations(property_representative_recommendations)
                )

        logger.info("Preparing data for scoring in sap change api")
        recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)

        recommendations_scoring_data = recommendations_scoring_data.drop(
            columns=["rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
                     "carbon_ending"]
        )

        model_api = ModelApi(
            portfolio_id=body.portfolio_id, timestamp=created_at, prediction_buckets=get_prediction_buckets()
        )

        all_predictions = {
            "sap_change_predictions": pd.DataFrame(),
            "heat_demand_predictions": pd.DataFrame(),
            "carbon_change_predictions": pd.DataFrame()
        }
        to_loop_over = range(0, recommendations_scoring_data.shape[0], SCORING_BATCH_SIZE)
        for chunk in tqdm(to_loop_over, total=len(to_loop_over)):
            predictions_dict = model_api.predict_all(
                df=recommendations_scoring_data.iloc[chunk:chunk + SCORING_BATCH_SIZE],
            )

            # Append the predictions to the predictions dictionary
            for key, scored in predictions_dict.items():
                all_predictions[key] = pd.concat([all_predictions[key], scored])

        # TODO: 1) walls_insulation_thickness_ending is not being set in the recommendations_scoring_data,
        #           insulation_thickness_ending is being set instead
        #       2)

        # TODO: TEMP
        for p in plan_input:
            if p["uprn"]:
                p["uprn"] = str(int(float(p["uprn"])))

        import re
        from backend.ml_models.AnnualBillSavings import AnnualBillSavings

        if optimise_measures:
            results = []
            for p in input_properties:

                sap_before = int(p.data["current-energy-efficiency"])
                epc_before = p.data["current-energy-rating"]
                heat_demand_before = p.data["energy-consumption-current"]
                carbon_before = p.data["co2-emissions-current"]
                current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
                    epc_energy_consumption=heat_demand_before * p.floor_area,
                    current_epc_rating=epc_before,
                )
                current_energy_bill = AnnualBillSavings.calculate_annual_bill(current_adjusted_energy)

                package_comparison = []
                for _id in recommendations[p.id].keys():

                    sap_prediction = all_predictions["sap_change_predictions"][
                        (all_predictions["sap_change_predictions"]["property_id"] == str(p.id)) &
                        (all_predictions["sap_change_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
                        ].copy().reset_index(drop=True)
                    sap_prediction["row_id"] = sap_prediction.index

                    heat_demand_prediction = all_predictions["heat_demand_predictions"][
                        (all_predictions["heat_demand_predictions"]["property_id"] == str(p.id)) &
                        (all_predictions["heat_demand_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
                        ].copy().reset_index(drop=True)
                    heat_demand_prediction["row_id"] = heat_demand_prediction.index

                    carbon_prediction = all_predictions["carbon_change_predictions"][
                        (all_predictions["carbon_change_predictions"]["property_id"] == str(p.id)) &
                        (all_predictions["carbon_change_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
                        ].copy().reset_index(drop=True)
                    carbon_prediction["row_id"] = carbon_prediction.index

                    epc_target = body.goal_value
                    if epc_before == epc_target:
                        continue

                    sap_target = epc_to_sap_lower_bound(epc_target)
                    # Define the measures
                    sap_threshold_barrier = sap_prediction[sap_prediction["predictions"] >= sap_target]
                    meets_threshold = True
                    if sap_threshold_barrier.empty:
                        sap_threshold_barrier = sap_prediction.tail(1)
                        meets_threshold = False
                    sap_threshold_barrier = sap_threshold_barrier.head(1)

                    sap_prediction = sap_prediction[
                        sap_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
                        ]
                    heat_demand_prediction = heat_demand_prediction[
                        heat_demand_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
                        ]
                    carbon_prediction = carbon_prediction[
                        carbon_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
                        ]

                    reverse_map = {v: k for k, v in Mds.format_map.items()}

                    selected_measures = [
                        reverse_map[x.split("-")[0]] for x in sap_prediction["recommendation_id"].values
                    ]
                    selected_measure_ids = [x.split("*")[0] for x in sap_prediction["recommendation_id"].values]

                    costs = [
                        r["total"] for r in representative_recommendations[p.id][_id] if
                        r["recommendation_id"] in selected_measure_ids
                    ]
                    costs = sum(costs)

                    sap_after = sap_prediction["predictions"].values[-1]
                    epc_after = sap_to_epc(sap_after)
                    heat_demand_after = heat_demand_prediction["predictions"].values[-1]
                    carbon_after = carbon_prediction["predictions"].values[-1]

                    expected_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
                        epc_energy_consumption=heat_demand_after * p.floor_area,
                        current_epc_rating=epc_before,
                    )

                    expected_energy_bill = AnnualBillSavings.calculate_annual_bill(expected_adjusted_energy)

                    bill_savings = current_energy_bill - expected_energy_bill
                    energy_savings = current_adjusted_energy - expected_adjusted_energy

                    package_comparison.append(
                        {
                            "id": _id,
                            "cost": costs,
                            "measures": selected_measures,
                            "sap_before": sap_before,
                            "sap_after": sap_after,
                            "epc_before": epc_before,
                            "epc_after": epc_after,
                            "heat_demand_before": heat_demand_before,
                            "heat_demand_after": heat_demand_after,
                            "carbon_before": carbon_before,
                            "carbon_after": carbon_after,
                            "bill_savings": bill_savings,
                            "energy_savings": energy_savings,
                            "current_energy_bill": current_energy_bill,
                            "meets_threshold": meets_threshold
                        }
                    )

                package_comparison = pd.DataFrame(package_comparison)
                # Find the smallest cost package
                if not package_comparison.empty:

                    # We check if any of the packages meet the threshold
                    # If none of them do, take the one that gets closest to the target
                    if package_comparison["meets_threshold"].any():
                        package_comparison = package_comparison[package_comparison["meets_threshold"]]
                        package_comparison = package_comparison.sort_values("cost")
                    else:
                        package_comparison = package_comparison.sort_values("sap_after", ascending=False)

                    package_comparison = package_comparison.head(1).to_dict("records")[0]
                else:
                    package_comparison = {
                        "measures": [],
                        "sap_before": sap_before,
                        "sap_after": sap_before,
                        "epc_before": epc_before,
                        "epc_after": epc_before,
                        "heat_demand_before": heat_demand_before,
                        "heat_demand_after": heat_demand_before,
                        "carbon_before": carbon_before,
                        "carbon_after": carbon_before,
                        "bill_savings": 0,
                        "energy_savings": 0,
                        "current_energy_bill": current_energy_bill,
                        "meets_threshold": False
                    }

                config = [c for c in plan_input if c["uprn"] == str(p.uprn)]
                if not config:
                    config = {"address": None, "postcode": None}
                else:
                    config = config[0]

                results.append({
                    "config_address": config["address"],
                    "config_postcode": config["postcode"],
                    "uprn": p.uprn,
                    "address": p.address,
                    "postcode": p.postcode,
                    "measures": package_comparison["measures"],
                    "year_of_epc": p.data['lodgement-date'],
                    "sap_before": package_comparison["sap_before"],
                    "sap_after": package_comparison["sap_after"],
                    "epc_before": package_comparison["epc_before"],
                    "epc_after": package_comparison["epc_after"],
                    "heat_demand_before": package_comparison["heat_demand_before"],
                    "heat_demand_after": package_comparison["heat_demand_after"],
                    "carbon_before": package_comparison["carbon_before"],
                    "carbon_after": package_comparison["carbon_after"],
                    "bill_savings": round(package_comparison["bill_savings"], 2),
                    "energy_savings": round(package_comparison["energy_savings"], 2),
                    "current_energy_bill": round(package_comparison["current_energy_bill"], 2),
                    "EWI": "EWI" if "external_wall_insulation" in package_comparison["measures"] else None,
                    "CWI": "CWI" if "cavity_wall_insulation" in package_comparison["measures"] else None,
                    "LI": "LI" if "loft_insulation" in package_comparison["measures"] else None,
                    "ASHP Htg": "ASHP Htg" if "air_source_heat_pump" in package_comparison["measures"] else None,
                    "Elec Storage": (
                        "Elec Storage Htrs (Out of scope -Prov sum only)" if "high_heat_retention_storage_heaters" in
                                                                             package_comparison["measures"] else None
                    ),
                    "Solar PV": "Solar PV" if "solar_pv" in package_comparison["measures"] else None,
                })

            results = pd.DataFrame(results)

            # For the different measures, we check the impact with a few debugging functions

            walls_check, hhr_check = check_mds(results, input_properties, recommendations, optimise_measures)

            results.to_excel("optimised mds_results 5th June.xlsx")

        results = []
        for p in input_properties:
            measures = p.measures
            property_recommendations = [r['type'] for r in representative_recommendations[p.id]]

            # TODO: Check high heat retention storage heaters - looks like it's excluded controls!

            sap_prediction = all_predictions["sap_change_predictions"][
                all_predictions["sap_change_predictions"]["property_id"] == str(p.id)
                ]

            heat_demand_prediction = all_predictions["heat_demand_predictions"][
                all_predictions["heat_demand_predictions"]["property_id"] == str(p.id)
                ]

            carbon_prediction = all_predictions["carbon_change_predictions"][
                all_predictions["carbon_change_predictions"]["property_id"] == str(p.id)
                ]

            # Get a before and after for SAP, heat demand, CO2 and also calculate energy bill and energy savings
            sap_before = int(p.data["current-energy-efficiency"])
            sap_after = sap_prediction["predictions"].values[0] if measures else sap_before

            epc_before = p.data["current-energy-rating"]
            epc_after = sap_to_epc(sap_after) if measures else epc_before

            heat_demand_before = p.data["energy-consumption-current"]
            heat_demand_after = heat_demand_prediction["predictions"].values[0] if measures else heat_demand_before

            carbon_before = p.data["co2-emissions-current"]
            carbon_after = carbon_prediction["predictions"].values[0] if measures else carbon_before

            # Estimate bill savings

            from backend.ml_models.AnnualBillSavings import AnnualBillSavings
            current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
                epc_energy_consumption=heat_demand_before * p.floor_area,
                current_epc_rating=epc_before,
            )

            # TODO: This isn't quite right as this is based on EVERY possible measure, not just the ones that are
            #       actually implemented
            expected_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
                epc_energy_consumption=heat_demand_after * p.floor_area,
                current_epc_rating=epc_before,
            )

            # TODO: We should determine if the home is gas & electricity or just electricity

            # Determine if the heating and hotwater was previously electric only or both

            current_energy_bill = AnnualBillSavings.calculate_annual_bill(
                kwh=current_adjusted_energy,
            )
            expected_energy_bill = AnnualBillSavings.calculate_annual_bill(
                kwh=expected_adjusted_energy,
            )

            bill_savings = current_energy_bill - expected_energy_bill
            energy_savings = current_adjusted_energy - expected_adjusted_energy

            config = [c for c in plan_input if c["uprn"] == str(p.uprn)]
            if not config:
                config = {"address": None, "postcode": None}
            else:
                config = config[0]

            to_append = {
                "config_address": config["address"],
                "config_postcode": config["postcode"],
                "uprn": p.uprn,
                "address": p.address,
                "postcode": p.postcode,
                "measures": measures,
                "property_recommendations": property_recommendations,
                "year_of_epc": p.data['lodgement-date'],
                "sap_before": sap_before,
                "sap_after": sap_after,
                "epc_before": epc_before,
                "epc_after": epc_after,
                "heat_demand_before": heat_demand_before,
                "heat_demand_after": heat_demand_after,
                "carbon_before": carbon_before,
                "carbon_after": carbon_after,
                "bill_savings": round(bill_savings, 2),
                "energy_savings": round(energy_savings, 2),
                "current_energy_bill": round(current_energy_bill, 2),
                "fuel_type": p.main_fuel["fuel_type"],
            }
            results.append(to_append)

        results = pd.DataFrame(results)
        results["sap_uplift"] = results["sap_after"] - results["sap_before"]

        # results.to_excel("mds_results 5th June.xlsx")

        walls_check, hhr_check = check_mds(results, input_properties, recommendations, optimise_measures)

    except IntegrityError:
        logger.error("Database integrity error occurred", exc_info=True)
        session.rollback()
        return Response(status_code=500, content="Database integrity error.")
    except OperationalError:
        logger.error("Database operational error occurred", exc_info=True)
        session.rollback()
        return Response(status_code=500, content="Database operational error.")
    except ValueError:
        logger.error("Value error - possibly due to malformed data", exc_info=True)
        session.rollback()
        return Response(status_code=400, content="Bad request: malformed data.")
    except Exception as e:  # General exception handling
        logger.error(f"An error occurred: {e}")
        session.rollback()
        return Response(status_code=500, content="An unexpected error occurred.")
    finally:
        session.close()


def check_mds(results, input_properties, recommendations, optimise_measures):
    import ast
    walls_check = []
    hhr_check = []
    for p in input_properties:
        res = results[results["uprn"] == p.uprn]
        wall = p.walls
        heating = p.main_heating
        heating_controls = p.main_heating_controls

        if optimise_measures:
            measures = res["measures"].values[0]
        else:
            measures = [list(z.keys())[0] for z in res["measures"].values[0]]

        wall_recommendation = [
            x for x in measures if
            x in ["internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"]
        ]

        hhr_recommendation = [
            x for x in measures if
            x in ["high_heat_retention_storage_heaters"]
        ]

        if optimise_measures:
            possible_measures = [ast.literal_eval(x) for x in list(recommendations[p.id].keys())]
            # Unlist them
            possible_measures = [x for sublist in possible_measures for x in sublist]
            possible_measures = list(set(possible_measures))
        else:
            possible_measures = p.measures

        if wall_recommendation:
            if len(wall_recommendation) > 1:
                raise Exception("something went wrong")
            wall_recommendation = wall_recommendation[0]
        else:
            wall_recommendation = None

        hhr_recommendation = hhr_recommendation[0] if hhr_recommendation else None

        walls_check.append(
            {
                "uprn": p.uprn,
                "address": p.address,
                "postcode": p.postcode,
                "property_type": p.data['property-type'],
                "conservation_status": p.spatial["conservation_status"],
                "is_listed_building": p.spatial["is_listed_building"],
                "is_heritage_building": p.spatial["is_heritage_building"],
                "wall": wall["clean_description"],
                "recommendation": wall_recommendation,
                "possible_measures": possible_measures,
                "selected_measures": res["measures"].values[0],
            }
        )

        hhr_check.append(
            {
                "uprn": p.uprn,
                "address": p.address,
                "postcode": p.postcode,
                "heating": heating["clean_description"],
                "heating_controls": heating_controls["clean_description"],
                "recommendation": hhr_recommendation,
                "possible_measures": possible_measures,
                "selected_measures": res["measures"].values[0],
            }
        )

    walls_check = pd.DataFrame(walls_check)
    hhr_check = pd.DataFrame(hhr_check)

    return walls_check, hhr_check