Update BaseModel.py

BaseModel.py

@@ -1,13 +1,35 @@
-from typing import Union, Tuple
+from typing import Tuple, TypedDict, Optional
 import datetime
 
-class BaseModel:
 
+class ProcessedSynapse(TypedDict):
+    id: Optional[str]
+    property_id: Optional[str]
+    listing_id: Optional[str]
+    address: Optional[str]
+    city: Optional[str]
+    state: Optional[str]
+    zip: Optional[str]
+    price: Optional[float]
+    beds: Optional[int]
+    baths: Optional[float]
+    sqft: Optional[int]
+    lot_size: Optional[int]
+    year_built: Optional[int]
+    days_on_market: Optional[int]
+    latitude: Optional[float]
+    longitude: Optional[float]
+    property_type: Optional[str]
+    last_sale_date: Optional[str]
+    hoa_dues: Optional[float]
+    query_date: Optional[str]
+
+
+class BaseModel:
 
     def __init__(self):
         self._load_model()
 
-
     def _load_model(self):
         """
         Perform any actions needed to load the model.
@@ -17,30 +39,28 @@ class BaseModel:
         # Optional model loading
         print("Model loaded.")
 
-
-    def _sale_date_predictor(self, daysOnMarket: int):
+    def _sale_date_predictor(self, days_on_market: int):
         """
         Calculate the expected sale date based on the national average
-        :param daysOnMarket: number of days this house has been on the market
+        :param days_on_market: number of days this house has been on the market
         :return: the predicted sale date, based on the national average of 34 days
         """
         national_average = 34
-        if daysOnMarket < national_average:
-            days_until_sale = national_average - daysOnMarket
+        if days_on_market < national_average:
+            days_until_sale = national_average - days_on_market
             sale_date = datetime.date.today() + datetime.timedelta(days=days_until_sale)
             return sale_date
         else:
             return datetime.date.today() + datetime.timedelta(days=1)
 
-
-    def run_inference(self, input_data: dict[str, Union[str, int, float]]) -> Tuple[float, str]:
+    def run_inference(self, input_data: ProcessedSynapse) -> Tuple[float, str]:
         """
         Predict the sale price and sale date for the house represented by `input_data`
         :param input_data: a formatted Synapse from the validator, representing a currently listed house
         :return: the predicted sale price and predicted sale date for this home
         """
         predicted_sale_price = float(input_data['price']) if ('price' in input_data) else 1.0
-        predicted_sale_date = self._sale_date_predictor(input_data['dom']) if ('dom' in input_data) else datetime.date.today() + datetime.timedelta(days=1)
+        predicted_sale_date = self._sale_date_predictor(input_data['days_on_market']) if ('days_on_market' in input_data) else datetime.date.today() + datetime.timedelta(days=1)
         predicted_sale_date = predicted_sale_date.strftime("%Y-%m-%d")
         print(f"Predicted sale price: {predicted_sale_price}")
         print(f"Predicted sale date: {predicted_sale_date}")