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chore: be closer to our template
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from typing import Dict, List, Any
import numpy as np
from concrete.ml.deployment import FHEModelServer
def from_json(python_object):
if "__class__" in python_object:
return bytes(python_object["__value__"])
def to_json(python_object):
if isinstance(python_object, bytes):
return {"__class__": "bytes", "__value__": list(python_object)}
raise TypeError(repr(python_object) + " is not JSON serializable")
class EndpointHandler:
def __init__(self, path=""):
# For server
self.fhemodel_server = FHEModelServer(path + "/compiled_model")
# Simulate a database of keys
self.key_database = {}
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
date (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# Get method
method = data.pop("method", data)
if method == "save_key":
# Get keys
evaluation_keys = from_json(data.pop("evaluation_keys", data))
uid = np.random.randint(2**32)
while uid in self.key_database.keys():
uid = np.random.randint(2**32)
self.key_database[uid] = evaluation_keys
return {"uid": uid}
elif method == "append_key":
# Get key piece
evaluation_keys = from_json(data.pop("evaluation_keys", data))
uid = data.pop("uid", data)
self.key_database[uid] += evaluation_keys
return
elif method == "inference":
uid = data.pop("uid", data)
assert uid in self.key_database.keys(), f"{uid} not in DB, {self.key_database.keys()=}"
# Get inputs
encrypted_inputs = from_json(data.pop("encrypted_inputs", data))
# Find key in the database
evaluation_keys = self.key_database[uid]
# Run CML prediction
encrypted_prediction = self.fhemodel_server.run(encrypted_inputs, evaluation_keys)
return to_json(encrypted_prediction)
else:
return