chore: trying without json

play_with_endpoint.py

@@ -0,0 +1,77 @@
+import numpy as np
+import time
+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
+
+
+API_URL = "https://puqif7goarh132kl.us-east-1.aws.endpoints.huggingface.cloud"
+headers = {
+    "Authorization": "Bearer " + os.environ.get("HF_TOKEN"),
+    "Content-Type": "application/octet-stream",
+}
+
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, data=payload)
+    return response.json()
+
+
+path_to_model = Path("compiled_model")
+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)
+
+# Recover parameters for client side
+fhemodel_client = FHEModelClient(path_to_model)
+
+# Generate the keys
+fhemodel_client.generate_private_and_evaluation_keys()
+evaluation_keys = fhemodel_client.get_serialized_evaluation_keys()
+
+# Test the handler
+nb_good = 0
+nb_samples = len(X_test)
+verbose = False
+time_start = time.time()
+duration = 0
+
+for i in range(nb_samples):
+
+    # Quantize the input and encrypt it
+    encrypted_inputs = fhemodel_client.quantize_encrypt_serialize([X_test[i]])
+
+    # Prepare the payload, including the evaluation keys which are needed server side
+    payload = {
+        "inputs": "fake",
+        "encrypted_inputs": encrypted_inputs,
+        "evaluation_keys": evaluation_keys,
+    }
+
+    # Run the inference on HF servers
+    duration -= time.time()
+    encrypted_prediction = query(payload)
+    duration += time.time()
+
+    encrypted_prediction = encrypted_prediction
+
+    # Decrypt the result and dequantize
+    prediction_proba = fhemodel_client.deserialize_decrypt_dequantize(encrypted_prediction)[0]
+    prediction = np.argmax(prediction_proba)
+
+    if verbose or True:
+        print(f"for {i}-th input, {prediction=} with expected {y_test[i]}")
+
+    # Measure accuracy
+    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")
+print(f"Duration in inferences: {duration} seconds")
+print(f"Duration per inference: {duration / nb_samples} seconds")