handling the case of there not being any suitable recommendations for a property

backend/engine/engine.py

@@ -1043,6 +1043,15 @@ async def model_engine(body: PlanTriggerRequest):
                     work_package=eco_packages[p.id][2]
                 )
 
+                # if handle the empty case
+                if solutions.empty:
+                    scheme = "none"
+                    funded_measures, solution = [], []
+                    (
+                        project_funding, total_uplift, full_project_score, partial_project_score, uplift_project_score
+                    ) = 0, 0, 0, 0, 0
+                else:
+
                     # If the solution isn't eligible, we can't really consider it
                     solutions = solutions[
                         (solutions["is_eligible"] & (solutions["scheme"] != "none")) | (solutions["scheme"] == "none")

recommendations/optimiser/funding_optimiser.py

@@ -502,6 +502,10 @@ def optimise_with_funding_paths(
 
     solutions = pd.DataFrame(solutions)
 
+    if solutions.empty:
+        # We return a blank dataframe
+        return solutions
+
     # Given the scheme, we now check if the packages are eligible. If they *are* eligible, but they don't meet the
     # final upgrade target, we then look to perform a final optimisation pass to meet the target gain.
     solutions["meets_upgrade_target"] = solutions["total_gain"] >= target_gain - 0.1
@@ -779,6 +783,10 @@ def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack
     Thin wrapper over your optimisers.
     Returns: list[dict] selected_options
     """
+
+    if not input_measures:
+        return None, 0.0, 0.0
+
     if budget is not None:
         opt = GainOptimiser(
             input_measures, max_cost=budget, max_gain=(sub_target_gain or float("inf")),