debugging funding eligibility

backend/Property.py

@@ -22,6 +22,7 @@ from recommendations.recommendation_utils import (
 )
 from backend.ml_models.AnnualBillSavings import AnnualBillSavings
 from backend.app.utils import sap_to_epc
+from backend.Funding import Funding
 import backend.app.assumptions as assumptions
 
 ENVIRONMENT = os.environ.get("ENVIRONMENT", "dev")
@@ -202,6 +203,11 @@ class Property:
         # TODO: We keep this but only temporarily until we add bathrooms, bedrooms, building id to the condition data
         self.parse_kwargs(kwargs)
 
+        # Funding
+        self.gbis_eligibiltiy = None
+        self.eco4_eligibility = None
+        self.whlg_eligibility = None
+
     @classmethod
     def extract_kwargs(cls, kwargs):
         """
@@ -1306,3 +1312,11 @@ class Property:
         )
 
         return electric_consumption
+
+    def insert_funding(self, funding_calulator: Funding):
+        """
+        This method inserts the funding into the property object
+        """
+        self.gbis_eligibiltiy = funding_calulator.gbis_eligibiltiy
+        self.eco4_eligibility = funding_calulator.eco4_eligibility
+        self.whlg_eligibility = funding_calulator.whlg_eligibility

backend/app/plan/router.py

@@ -30,6 +30,7 @@ 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.Funding import Funding
 from backend.apis.GoogleSolarApi import GoogleSolarApi
 
 from recommendations.optimiser.CostOptimiser import CostOptimiser
@@ -751,12 +752,12 @@ async def trigger_plan(body: PlanTriggerRequest):
         # ~~~~~~~~~~~~~~~~
         # Funding
         # ~~~~~~~~~~~~~~~~
-        from backend.Funding import Funding
+
         for p in input_properties:
             funding_calulator = Funding(
                 tenure=body.housing_type,
                 starting_epc=p.data["current-energy-rating"],
-                starting_sap=p.data["current-energy-efficiency"],
+                starting_sap=int(p.data["current-energy-efficiency"]),
                 floor_area=p.floor_area,
                 council_tax_band=None,  # This is seemingly always None at the moment
                 property_recommendations=recommendations[p.id],
@@ -764,7 +765,10 @@ async def trigger_plan(body: PlanTriggerRequest):
                 gbis_abs_rate=20,
                 eco4_abs_rate=20,
             )
-
+            funding_calulator.check_eligibiltiy()
+            # Insert finding
+            p.insert_funding(funding_calulator)
+            
         logger.info("Uploading recommendations to the database")
         # If we have any work to do, we create a new scenario
         engine_scenario = create_scenario(

etl/customers/connells/pilot_remote_assessments.py

@@ -0,0 +1,108 @@
+import os
+import time
+
+from tqdm import tqdm
+import pandas as pd
+from dotenv import load_dotenv
+from etl.find_my_epc.RetrieveFindMyEpc import RetrieveFindMyEpc
+from backend.SearchEpc import SearchEpc
+from utils.s3 import save_csv_to_s3
+
+load_dotenv(dotenv_path="backend/.env")
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
+USER_ID = 8
+PORTFOLIO_ID = 123
+
+
+def app():
+    asset_list = [
+        {"address": "1 Raven Crescent", "postcode": "WV11 2EX", "uprn": 100071188496},
+
+        {"address": "13 Bayliss Avenue", "postcode": "WV11 2EX", "uprn": 100071136271},
+
+        {"address": "30 Southbourne Road", "postcode": "WV10 6ET", "uprn": 100071194376},
+
+        {"address": "96 Marsh Lane", "postcode": "WV10 6RX", "uprn": 100071176297},
+    ]
+    asset_list = pd.DataFrame(asset_list)
+
+    valuations_data = [
+        {'uprn': 100071188496, "valuation": 175_000},
+        {'uprn': 100090136026, "valuation": 183_000},
+        {'uprn': 100071194376, "valuation": 221_000},
+        {'uprn': 100071176297, "valuation": 208_000},
+    ]
+
+    # Pull the additional data
+    extracted_data = []
+    for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
+        add1 = home["address"]
+        pc = home["postcode"]
+        # Retrieve the EPC data
+        epc_searcher = SearchEpc(
+            address1=add1,
+            postcode=pc, uprn=home["uprn"], auth_token=EPC_AUTH_TOKEN, os_api_key=""
+        )
+        epc_searcher.find_property(skip_os=True)
+        if epc_searcher.newest_epc is None:
+            continue
+
+        find_epc_searcher = RetrieveFindMyEpc(address=epc_searcher.newest_epc["address1"],
+                                              postcode=epc_searcher.newest_epc["postcode"])
+        find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data()
+        time.sleep(0.5)
+        # We need uprn
+
+        extracted_data.append(
+            {
+                "uprn": home["uprn"],
+                **find_epc_data,
+            }
+        )
+
+    non_invasive_recommendations = [
+        {
+            "uprn": r["uprn"],
+            "recommendations": r["recommendations"]
+        } for r in extracted_data
+    ]
+
+    filename = f"{USER_ID}/{PORTFOLIO_ID}/asset_list.csv"
+    save_csv_to_s3(
+        dataframe=pd.DataFrame(asset_list),
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=filename
+    )
+
+    # Store the non-invasive recommendations in s3
+    non_invasive_recommendations_filename = f"{USER_ID}/{PORTFOLIO_ID}/non_invasive_recommendations.csv"
+    save_csv_to_s3(
+        dataframe=pd.DataFrame(non_invasive_recommendations),
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=non_invasive_recommendations_filename
+    )
+
+    # Store the valuations data in s3
+    valuations_filename = f"{USER_ID}/{PORTFOLIO_ID}/valuations.csv"
+    save_csv_to_s3(
+        dataframe=pd.DataFrame(valuations_data),
+        bucket_name="retrofit-plan-inputs-dev",
+        file_name=valuations_filename
+    )
+
+    body = {
+        "portfolio_id": str(PORTFOLIO_ID),
+        "housing_type": "Private",
+        "goal": "Increasing EPC",
+        "goal_value": "B",
+        "trigger_file_path": filename,
+        "already_installed_file_path": "",
+        "patches_file_path": "",
+        "non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
+        "valuation_file_path": valuations_filename,
+        "scenario_name": "Wave 3 Packages",
+        "multi_plan": True,
+        "budget": None,
+        "exclusions": []
+    }
+    print(body)