started basic works

backend/Property.py

@@ -868,7 +868,7 @@ class Property:
 
         lodgement_date = self.data["lodgement-date"]
         # We check if the lodgement date is more than 10 years old
-        is_expired = (datetime.now() - pd.to_datetime(lodgement_date)) > timedelta(days=3650)
+        is_expired = self.epc_is_expired
 
         # Handle re-baselining
         co2_emissions = self.energy["co2_emissions"]
@@ -1499,3 +1499,13 @@ class Property:
         ]
 
         return self.data.get("mechanical-ventilation") in ventilation_descriptions
+
+    @property
+    def epc_is_expired(self) -> bool:
+        """
+        This property indicates that the EPC is expired. This is based on the lodgement date, where an EPC is
+        valid for 10 years.
+        :return: boolean indicating whether the EPC is expired
+        """
+        lodgement_date = self.data["lodgement-date"]
+        return (datetime.now() - pd.to_datetime(lodgement_date)) > timedelta(days=3650)

backend/engine/engine.py

@@ -943,6 +943,26 @@ async def model_engine(body: PlanTriggerRequest):
         # We also make a tweak - if the property has been flagged for solar but doesn't contain
         # any panel performance, we ensure that we have a 3kWp and 4kWp option for the property
 
+        # TODO: Temp - test re-baselining
+        p = input_properties[0]
+        p.create_base_difference_epc_record(cleaned_lookup=cleaned)
+        scoring_data = p.base_difference_record.df
+        # We just need a recent date to trigger the right models,
+        # as we are only interested in the deltas
+        scoring_data["is_post_sap10_starting"] = True
+        # Score model - SAP re-baselining model
+        model_api.MODEL_URLS["retrofit-sap-baseline-predictions"] = "sapbaselinemodel"
+        model_api.prediction_buckets["retrofit-sap-baseline-predictions"] = "retrofit-sap-baseline-predictions-dev"
+        example_response = model_api.predict_all(
+            df=scoring_data,
+            bucket=get_settings().DATA_BUCKET,
+            model_prefixes=["retrofit-sap-baseline-predictions"],
+            extract_ids=False
+        )
+
+        input_properties[0].data["current-energy-efficiency"] = 58.8
+        input_properties[0].data["current-energy-rating"] = "D"
+
         logger.info("Identifying property recommendations")
         recommendations, recommendations_scoring_data, representative_recommendations = {}, [], {}
         for p in tqdm(input_properties):