- office mls set

homeharvest/core/scrapers/models.py

@@ -106,6 +106,7 @@ class Agent(Entity):
 
 @dataclass
 class Office(Entity):
+    mls_set: str | None = None
     email: str | None = None
     href: str | None = None
     phones: list[dict] | AgentPhone | None = None

homeharvest/core/scrapers/realtor/__init__.py

@@ -130,7 +130,8 @@ def _parse_fulfillment_id(fulfillment_id: str | None) -> str | None:
 
                 if advertiser.get("office"):  #: has an office
                     processed_advertisers.office = Office(
-                        uuid=_parse_fulfillment_id(advertiser["office"].get("fulfillment_id")) or advertiser["office"].get("mls_set"),
+                        uuid=_parse_fulfillment_id(advertiser["office"].get("fulfillment_id")),
+                        mls_set=advertiser["office"].get("mls_set"),
                         name=advertiser["office"].get("name"),
                         email=advertiser["office"].get("email"),
                         phones=advertiser["office"].get("phones"),

homeharvest/utils.py

@@ -53,6 +53,7 @@
     "builder_id",
     "builder_name",
     "office_id",
+    "office_mls_set",
     "office_name",
     "office_email",
     "office_phones",
@@ -102,6 +103,7 @@ def process_result(result: Property) -> pd.DataFrame:
             prop_data["office_name"] = office_data.name
             prop_data["office_email"] = office_data.email
             prop_data["office_phones"] = office_data.phones
+            prop_data["office_mls_set"] = office_data.mls_set
 
     prop_data["price_per_sqft"] = prop_data["prc_sqft"]
     prop_data["nearby_schools"] = filter(None, prop_data["nearby_schools"]) if prop_data["nearby_schools"] else None

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.4.1"
+version = "0.4.2"
 description = "Real estate scraping library"
 authors = ["Zachary Hampton <[email protected]>", "Cullen Watson <[email protected]>"]
 homepage = "https://github.com/Bunsly/HomeHarvest"