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handler.py

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+import pandas as pd
+import pickle
+from typing import  Dict, List, Any
+import numpy as np
+
+
+# set device
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        self.pipe = pd.read_pickle(r'churn.pkl')
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`list`:. A string representing what the label/class is
+        """
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+        df = pd.DataFrame(data)
+
+        df["TotalCharges"] = df["TotalCharges"].replace(" ", np.nan, regex=False).astype(float)
+        df = df.drop(columns=["customerID"])
+        df = df.drop(columns=["Churn"])
+        
+        # run inference pipeline
+        pred = self.pipe.predict(df)
+
+        # postprocess the prediction
+        return {"pred": pred}

requirements.txt

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+pandas