chore: using a database for keys

handler.py

@@ -1,4 +1,3 @@
-import time
 from typing import Dict, List, Any
 import numpy as np
 from concrete.ml.deployment import FHEModelServer

play_with_endpoint.py

@@ -4,9 +4,6 @@ import os, sys
 
 from pathlib import Path
 
-from sklearn.datasets import make_classification
-from sklearn.model_selection import train_test_split
-
 from concrete.ml.deployment import FHEModelClient
 
 import requests
@@ -40,8 +37,13 @@ def query(payload):
 
 
 path_to_model = Path("compiled_model")
+
+# Logistic regression in FHE
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+
 x, y = make_classification(n_samples=1000, class_sep=2, n_features=30, random_state=42)
-_, X_test, _, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
+_, X_test, _, Y_test = train_test_split(x, y, test_size=0.2, random_state=42)
 
 # Recover parameters for client side
 fhemodel_client = FHEModelClient(path_to_model)
@@ -98,11 +100,11 @@ for i in range(nb_samples):
 
     if verbose or True:
         print(
-            f"for {i}-th input, {prediction=} with expected {y_test[i]} in {duration_inference:.3f} seconds"
+            f"for {i}-th input, {prediction=} with expected {Y_test[i]} in {duration_inference:.3f} seconds"
         )
 
     # Measure accuracy
-    nb_good += y_test[i] == prediction
+    nb_good += Y_test[i] == prediction
 
 print(f"Accuracy on {nb_samples} samples is {nb_good * 1. / nb_samples}")
 print(f"Total time: {time.time() - time_start} seconds")