Create NextPlaceBaseModel.py

NextPlaceBaseModel.py

@@ -0,0 +1,47 @@
+from typing import Union, Tuple
+import datetime
+
+class NextPlaceBaseModel:
+
+
+    def __init__(self):
+        self._load_model()
+
+
+    def _load_model(self):
+        """
+        Perform any actions needed to load the model.
+        EX: Establish API connections, download an ML model for inference, etc...
+        """
+        print("Loading model...")
+        # Optional model loading
+        print("Model loaded.")
+
+
+    def _sale_date_predictor(self, daysOnMarket: int):
+        """
+        Calculate the expected sale date based on the national average
+        :param daysOnMarket: 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
+            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]:
+        """
+        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 = predicted_sale_date.strftime("%Y-%m-%d")
+        print(f"Predicted sale price: {predicted_sale_price}")
+        print(f"Predicted sale date: {predicted_sale_date}")
+        return predicted_sale_price, predicted_sale_date