chore: adding an example from our template with a NeuralNetClassifier.

play_with_endpoint.py

@@ -20,8 +20,7 @@ def from_json(python_object):
         return bytes(python_object["__value__"])
 
 
-# TODO: put the right link `API_URL` for your entryp point
-API_URL = "https://XXXXXXX.us-east-1.aws.endpoints.huggingface.cloud"
+API_URL = "https://zkmlo9jbfzj9ep1j.eu-west-1.aws.endpoints.huggingface.cloud"
 headers = {
     "Authorization": "Bearer " + os.environ.get("HF_TOKEN"),
     "Content-Type": "application/json",
@@ -39,12 +38,13 @@ def query(payload):
 
 path_to_model = Path("compiled_model")
 
-# BEGIN: replace this part with your privacy-preserving application
-from sklearn.datasets import make_classification
+# Iris and NeuralNetClassifier in FHE
 from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_iris
 
-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, y = load_iris(return_X_y=True)
+_, X_test, _, Y_test = train_test_split(X, y, test_size=0.25, random_state=42)
+X_test = X_test.astype("float32")
 
 # Recover parameters for client side
 fhemodel_client = FHEModelClient(path_to_model)
@@ -112,4 +112,3 @@ for i in range(nb_samples):
 print(f"Accuracy on {nb_samples} samples is {nb_good * 1. / nb_samples}")
 print(f"Total time: {time.time() - time_start:.3f} seconds")
 print(f"Duration per inference: {duration / nb_samples:.3f} seconds")
-# END: replace this part with your privacy-preserving application