Model/backend/app/db/functions/recommendations_functions.py

from sqlalchemy import text
from sqlalchemy import insert, delete
from sqlalchemy.orm import Session
from sqlalchemy.exc import SQLAlchemyError
from backend.app.db.models.recommendations import (
    Plan, Recommendation, RecommendationMaterials, PlanRecommendations, Scenario
)
from backend.app.db.models.portfolio import PropertyModel
from backend.app.db.connection import db_session, db_read_session


def prepare_plan_data(
    p, body, scenario_id, eco_packages, valuations, new_sap_points, new_epc, default_recommendations,
    rebaselining_carbon=0, rebaselining_heat_demand=0, rebaselining_kwh=0, rebaselining_bills=0,
):
    """
    Utility function to prepare the data that goes into the production of a plan. Is a fairly rough and unstructured
    function that will need improving in the future
    :param p: Instantiated property
    :param body: request body, PlanTriggerRequest
    :param scenario_id: unique identifier for the scenario
    :param eco_packages: Pre-constructed eco packages for a property
    :param valuations: valuation improvement data
    :param new_sap_points: sap points, post default recommendations
    :param new_epc: new epc rating, post default recommendations
    :param default_recommendations: list of default recommendations for a property
    :param rebaselining_carbon: carbon emissions adjustment for rebaselining
    :param rebaselining_heat_demand: heat demand adjustment for rebaselining
    :param rebaselining_kwh: kwh consumption adjustment for rebaselining
    :param rebaselining_bills: energy bill adjustment for rebaselining
    :return:
    """
    # Plan carbon savings
    co2_savings = sum(
        [r["co2_equivalent_savings"] for r in default_recommendations if not r.get("already_installed", False)]
    )
    post_co2_emissions = p.energy["co2_emissions"] - rebaselining_carbon - co2_savings

    # Plan bill savings
    energy_bill_savings = sum(
        [r["energy_cost_savings"] for r in default_recommendations if not r.get("already_installed", False)]
    )
    post_energy_bill = sum(p.current_energy_bill.values()) - rebaselining_bills - energy_bill_savings

    # energy consumption
    energy_consumption_savings = sum(
        [r["kwh_savings"] for r in default_recommendations if not r.get("already_installed", False)]
    )
    post_energy_consumption = p.current_energy_consumption - rebaselining_kwh - energy_consumption_savings

    valuation_post_retrofit, valuation_increase = None, None
    if valuations["current_value"]:
        valuation_increase = valuations["average_increase"]
        valuation_post_retrofit = valuations["average_increased_value"]

    # plan costing data
    cost_of_works = sum([r["total"] for r in default_recommendations if not r.get("already_installed", False)])
    contingency_cost = sum(
        [r.get("contingency", 0) for r in default_recommendations if not r.get("already_installed", False)]
    )

    return {
        "portfolio_id": body.portfolio_id,
        "property_id": p.id,
        "scenario_id": 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"]
        ),
        "post_sap_points": float(new_sap_points),
        "post_epc_rating": new_epc,
        "post_co2_emissions": float(post_co2_emissions),
        "co2_savings": float(co2_savings),
        "post_energy_bill": float(post_energy_bill),
        "energy_bill_savings": float(energy_bill_savings),
        "post_energy_consumption": float(post_energy_consumption),
        "energy_consumption_savings": float(energy_consumption_savings),
        "valuation_post_retrofit": valuation_post_retrofit,
        "valuation_increase": valuation_increase,
        "cost_of_works": float(cost_of_works),
        "contingency_cost": float(contingency_cost),
        "plan_type": eco_packages.get(p.id, (None, None, None))[2]
    }


def create_plan(session: Session, plan):
    """
    This function will create a record for the plan in the database if it does not exist.
    :param session: The database session
    :param plan: dictionary of data representing a plan to be created
    """
    try:
        new_plan = Plan(**plan)
        session.add(new_plan)
        session.flush()
        session.commit()
        return new_plan.id
    except SQLAlchemyError as e:
        session.rollback()
        raise e


def bulk_create_plans(session: Session, plans_to_create: list[dict]) -> dict[int, int]:
    if not plans_to_create:
        return {}

    payload = [
        {
            "property_id": p["property_id"],
            **p["plan_data"],
        }
        for p in plans_to_create
    ]

    stmt = (
        insert(Plan)
        .values(payload)
        .returning(Plan.id, Plan.property_id)
    )

    result = session.execute(stmt).all()

    # property_id -> plan_id
    return {row.property_id: row.id for row in result}


def create_scenario(session: Session, scenario: dict) -> int:
    existing_scenario = (
        session.query(Scenario)
        .filter_by(portfolio_id=scenario["portfolio_id"])
        .first()
    )

    scenario["is_default"] = not bool(existing_scenario)

    new_scenario = Scenario(**scenario)
    session.add(new_scenario)
    session.flush()  # ensures ID is populated

    scenario_id = new_scenario.id
    session.commit()

    return scenario_id


def create_recommendation(session: Session, recommendation):
    """
    This function will create a record for the recommendation in the database if it does not exist.
    :param session: The database session
    :param recommendation: dictionary of data representing a recommendation to be created
    """
    try:
        new_recommendation = Recommendation(**recommendation)
        session.add(new_recommendation)
        session.flush()
        session.commit()
        return new_recommendation.id
    except SQLAlchemyError as e:
        session.rollback()
        raise e


def create_recommendation_material(session: Session, recommendation_id, material_id, depth):
    """
    This function will create a record for the recommendation_material in the database if it does not exist.
    :param session: The databse session
    :param recommendation_id: ID of the recommendation
    :param material_id: ID of the material
    :param depth: depth of the material, may be null if a material where depth is not applicable
    """

    new_recommendation_material = RecommendationMaterials(
        recommendation_id=recommendation_id,
        material_id=material_id,
        depth=depth
    )
    session.add(new_recommendation_material)
    session.flush()

    return new_recommendation_material.id


def create_plan_recommendations(session: Session, plan_id, recommendation_ids):
    """
    This function will create records for the plan_recommendation in the database.
    :param session: The database session
    :param plan_id: ID of the plan
    :param recommendation_ids: list of recommendation IDs
    """

    # Prepare a list of dictionaries for bulk insert
    data = [{"plan_id": plan_id, "recommendation_id": rid} for rid in recommendation_ids]

    # Bulk insert using SQLAlchemy's core API
    session.execute(insert(PlanRecommendations).values(data))


def upload_recommendations(session: Session, recommendations_to_upload, property_id, new_plan_id):
    try:
        # Prepare data for bulk insert for Recommendation
        recommendations_data = [
            {
                "property_id": property_id,
                "type": rec["type"],
                "measure_type": rec["measure_type"],
                "description": rec["description"],
                "estimated_cost": float(rec["total"]),
                "default": rec["default"],
                "starting_u_value": float(rec.get("starting_u_value")) if rec.get("starting_u_value") else None,
                "new_u_value": float(rec.get("new_u_value")) if rec.get("new_u_value") else None,
                "sap_points": float(rec["sap_points"]),
                "energy_savings": float(rec["heat_demand"]),
                "kwh_savings": float(rec["kwh_savings"]),
                "co2_equivalent_savings": float(rec["co2_equivalent_savings"]),
                "total_work_hours": float(rec["labour_hours"]),
                "energy_cost_savings": float(rec["energy_cost_savings"]),
                "labour_days": float(rec["labour_days"]),
                "already_installed": rec["already_installed"],
                "heat_demand": float(rec["heat_demand"])
            }
            for rec in recommendations_to_upload
        ]

        # Insert the recommendations, get back the IDs
        stmt = insert(Recommendation).returning(Recommendation.id).values(recommendations_data)
        result = session.execute(stmt)
        uploaded_recommendation_ids = [row[0] for row in result]

        # Prepare data for bulk insert for RecommendationMaterials
        recommendation_materials_data = [
            {
                "recommendation_id": recommendation_id,
                "material_id": part["id"],
                "depth": int(part["depth"]) if part["depth"] else None,
                "quantity": float(part["quantity"]) if part.get("quantity") else None,
                "quantity_unit": part.get("quantity_unit", None),
                "estimated_cost": float(part.get("total", part.get("total_cost"))),
            }
            for rec, recommendation_id in zip(recommendations_to_upload, uploaded_recommendation_ids)
            for part in rec["parts"]
        ]

        session.bulk_insert_mappings(RecommendationMaterials, recommendation_materials_data)

        # flush the changes to get the newly created IDs
        session.flush()

        create_plan_recommendations(
            session, plan_id=new_plan_id, recommendation_ids=uploaded_recommendation_ids
        )

        # Commit the transaction
        session.commit()

        return True

    except SQLAlchemyError as e:
        # Rollback the transaction in case of an error
        session.rollback()
        print(f"An error occurred: {e}")
        return False


def bulk_upload_recommendations_and_materials(
    session: Session,
    recommendation_payload: list[dict],
):
    if not recommendation_payload:
        return

    # ---------------------------------------------------------
    # 1. Prepare recommendation rows
    # ---------------------------------------------------------
    recommendation_rows = []
    parts_by_index = []
    plan_ids_by_index = []

    for rec in recommendation_payload:
        recommendation_rows.append({
            "property_id": rec["property_id"],
            "type": rec["type"],
            "measure_type": rec["measure_type"],
            "description": rec["description"],
            "estimated_cost": rec["estimated_cost"],
            "default": rec["default"],
            "starting_u_value": rec["starting_u_value"],
            "new_u_value": rec["new_u_value"],
            "sap_points": rec["sap_points"],
            "heat_demand": rec["heat_demand"],
            "kwh_savings": rec["kwh_savings"],
            "co2_equivalent_savings": rec["co2_equivalent_savings"],
            "energy_savings": rec["energy_savings"],
            "energy_cost_savings": rec["energy_cost_savings"],
            "total_work_hours": rec["total_work_hours"],
            "labour_days": rec["labour_days"],
            "already_installed": rec["already_installed"],
        })

        parts_by_index.append(rec["parts"])
        plan_ids_by_index.append(rec["plan_id"])

    # ---------------------------------------------------------
    # 2. Insert recommendations and get IDs
    # ---------------------------------------------------------
    result = session.execute(
        insert(Recommendation)
        .values(recommendation_rows)
        .returning(Recommendation.id)
    )

    recommendation_ids = [row[0] for row in result]

    # ---------------------------------------------------------
    # 3. Insert recommendation materials
    # ---------------------------------------------------------
    materials_rows = []

    for recommendation_id, parts in zip(recommendation_ids, parts_by_index):
        for part in parts:
            materials_rows.append({
                "recommendation_id": recommendation_id,
                "material_id": part["material_id"],
                "depth": part["depth"],
                "quantity": part["quantity"],
                "quantity_unit": part["quantity_unit"],
                "estimated_cost": part["estimated_cost"],
            })

    if materials_rows:
        session.execute(
            insert(RecommendationMaterials).values(materials_rows)
        )

    # ---------------------------------------------------------
    # 4. Insert plan ↔ recommendation links
    # ---------------------------------------------------------
    plan_recommendation_rows = [
        {
            "plan_id": plan_id,
            "recommendation_id": recommendation_id,
        }
        for plan_id, recommendation_id in zip(
            plan_ids_by_index, recommendation_ids
        )
    ]

    session.execute(
        insert(PlanRecommendations).values(plan_recommendation_rows)
    )


def chunked(iterable, size=100):
    for i in range(0, len(iterable), size):
        yield iterable[i:i + size]


def get_property_ids(portfolio_id: int) -> list[int]:
    with db_read_session() as session:
        return [
            pid for (pid,) in
            session.query(PropertyModel.id)
            .filter(PropertyModel.portfolio_id == portfolio_id)
            .all()
        ]


def delete_property_batch(session: Session, property_ids: list[int]):
    if not property_ids:
        return

    params = {"property_ids": property_ids}

    # --------------------------------------------------
    # recommendation_materials (via recommendation)
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM recommendation_materials rm
            USING recommendation r
            WHERE rm.recommendation_id = r.id
              AND r.property_id = ANY(:property_ids)
        """),
        params,
    )

    # --------------------------------------------------
    # plan_recommendations (via plan)
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM plan_recommendations pr
            USING plan p
            WHERE pr.plan_id = p.id
              AND p.property_id = ANY(:property_ids)
        """),
        params,
    )

    # --------------------------------------------------
    # funding_package_measures
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM funding_package_measures fpm
            USING funding_package fp, plan p
            WHERE fpm.funding_package_id = fp.id
              AND fp.plan_id = p.id
              AND p.property_id = ANY(:property_ids)
        """),
        params,
    )

    # --------------------------------------------------
    # inspections (direct)
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM inspections
            WHERE property_id = ANY(:property_ids)
        """),
        params,
    )

    # --------------------------------------------------
    # funding_package
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM funding_package fp
            USING plan p
            WHERE fp.plan_id = p.id
              AND p.property_id = ANY(:property_ids)
        """),
        params,
    )

    # --------------------------------------------------
    # recommendation (direct — CRITICAL FIX)
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM recommendation
            WHERE property_id = ANY(:property_ids)
        """),
        params,
    )

    # --------------------------------------------------
    # plan (direct)
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM plan
            WHERE property_id = ANY(:property_ids)
        """),
        params,
    )

    # --------------------------------------------------
    # property-scoped tables
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM property_details_epc
            WHERE property_id = ANY(:property_ids)
        """),
        params,
    )

    session.execute(
        text("""
            DELETE FROM property_targets
            WHERE property_id = ANY(:property_ids)
        """),
        params,
    )

    # --------------------------------------------------
    # properties LAST
    # --------------------------------------------------
    session.execute(
        text("""
            DELETE FROM property
            WHERE id = ANY(:property_ids)
        """),
        params,
    )


def portfolio_has_properties(portfolio_id: int) -> bool:
    with db_read_session() as session:
        return session.query(
            session.query(PropertyModel)
            .filter(PropertyModel.portfolio_id == portfolio_id)
            .exists()
        ).scalar()


def delete_portfolio_scenarios_if_empty(portfolio_id: int):
    if portfolio_has_properties(portfolio_id):
        print("Properties still exist — skipping scenario deletion")
        return

    with db_session() as session:
        session.execute(
            delete(Scenario)
            .where(Scenario.portfolio_id == portfolio_id)
        )

    print("Deleted scenarios for empty portfolio")


def clear_portfolio_in_batches(
    portfolio_id: int,
    property_batch_size: int = 25,
):
    property_ids = get_property_ids(portfolio_id)

    if not property_ids:
        print("No properties found.")
        delete_portfolio_scenarios_if_empty(portfolio_id)
        return

    total = (len(property_ids) + property_batch_size - 1) // property_batch_size
    import time
    for i, batch in enumerate(chunked(property_ids, property_batch_size), start=1):
        print(f"Deleting batch {i}/{total} ({len(batch)} properties)")
        start_time = time.time()
        with db_session() as session:
            delete_property_batch(session, batch)
        finish_time = time.time()
        print(f"Batch {i} deleted in {finish_time - start_time:.2f} seconds")

    # scenario deletion happens AFTER all properties are gone
    delete_portfolio_scenarios_if_empty(portfolio_id)

    print("Portfolio cleared in batches.")