Add xgboost regression class

StreamServer/src/analytic/health/indoor_model.py

@@ -0,0 +1,19 @@
+import joblib
+
+
+class XgboostIndoorModel:
+    MODEL_PATH = 'xgboost_model.pkl'
+
+    def __init__(self):
+        self.__model = joblib.load(self.MODEL_PATH)
+
+    def predict(self, X: list) -> float:
+        """
+        Predict the indoor temperature based on the input features
+        Input order : ['outdoor_temp', 'outdoor_feels_like', 'outdoor_pressure',
+       'outdoor_humidity', 'outdoor_pm25', 'outdoor_pm10']
+        """
+        if len(X) != 6:
+            raise ValueError(f"Expected 6 features, got {len(X)}")
+        
+        return self.__model.predict([X])[0]