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import subprocess
import time
from typing import Dict, List, Tuple
import gradio as gr
import numpy as np
import requests
from symptoms_categories import SYMPTOMS_LIST
from utils import (
CLIENT_DIR,
CURRENT_DIR,
DEPLOYMENT_DIR,
INPUT_BROWSER_LIMIT,
KEYS_DIR,
SERVER_URL,
clean_directory,
get_disease_name,
load_data,
pretty_print,
)
from concrete.ml.deployment import FHEModelClient
subprocess.Popen(["uvicorn", "server:app"], cwd=CURRENT_DIR)
time.sleep(3)
# pylint: disable=c-extension-no-member,invalid-name
def is_nan(inputs) -> bool:
"""
Check if the input is NaN.
Args:
inputs (any): The input to be checked.
Returns:
bool: True if the input is NaN or empty, False otherwise.
"""
return inputs is None or (inputs is not None and len(inputs) < 1)
# def fill_in_fn(default_disease: str, *checkbox_symptoms: Tuple[str]) -> Dict:
# """
# Fill in the gr.CheckBoxGroup list with the predefined symptoms of a selected default disease.
# Args:
# default_disease (str): The default disease
# *checkbox_symptoms (Tuple[str]): Tuple of selected symptoms
# Returns:
# dict: The updated gr.CheckBoxesGroup.
# """
# df = pd.read_csv(TRAINING_FILENAME)
# df_filtred = df[df[TARGET_COLUMNS[1]] == default_disease]
# symptoms = pretty_print(df_filtred.columns[df_filtred.eq(1).any()].to_list())
# if any(lst for lst in checkbox_symptoms if lst):
# for sublist in checkbox_symptoms:
# symptoms.extend(sublist)
# return {box: symptoms for box in check_boxes}
def get_user_symptoms_from_checkboxgroup(checkbox_symptoms: List) -> np.array:
"""
Convert the user symptoms into a binary vector representation.
Args:
checkbox_symptoms (list): A list of user symptoms.
Returns:
np.array: A binary vector representing the user's symptoms.
Raises:
KeyError: If a provided symptom is not recognized as a valid symptom.
"""
symptoms_vector = {key: 0 for key in valid_columns}
for pretty_symptom in checkbox_symptoms:
original_symptom = "_".join((pretty_symptom.lower().split(" ")))
if original_symptom not in symptoms_vector.keys():
raise KeyError(
f"The symptom '{original_symptom}' you provided is not recognized as a valid "
f"symptom.\nHere is the list of valid symptoms: {symptoms_vector}"
)
symptoms_vector[original_symptom] = 1
user_symptoms_vect = np.fromiter(symptoms_vector.values(), dtype=float)[np.newaxis, :]
assert all(value == 0 or value == 1 for value in user_symptoms_vect.flatten())
return user_symptoms_vect
def get_features_fn(*checked_symptoms: Tuple[str]) -> Dict:
"""
Get vector features based on the selected symptoms.
Args:
checked_symptoms (Tuple[str]): User symptoms
Returns:
Dict: The encoded user vector symptoms.
"""
if not any(lst for lst in checked_symptoms if lst):
return {
error_box1: gr.update(
visible=True, value="Enter a default disease or select your own symptoms"
),
}
return {
error_box1: gr.update(visible=False),
user_vect_box1: get_user_symptoms_from_checkboxgroup(pretty_print(checked_symptoms)),
next_step_tab2: gr.update(visible=True),
}
def key_gen_fn(user_symptoms: List[str]) -> Dict:
"""
Generate keys for a given user.
Args:
user_symptoms (List[str]): The vector symptoms provided by the user.
Returns:
dict: A dictionary containing the generated keys and related information.
"""
clean_directory()
if is_nan(user_symptoms):
print("Error: Please submit your symptoms or select a default disease.")
return {
error_box2: gr.update(visible=True, value="Please submit your symptoms first"),
}
# Generate a random user ID
user_id = np.random.randint(0, 2**32)
print(f"Your user ID is: {user_id}....")
client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
client.load()
print("CLIENT_LOADED")
# Creates the private and evaluation keys on the client side
client.generate_private_and_evaluation_keys()
# Get the serialized evaluation keys
serialized_evaluation_keys = client.get_serialized_evaluation_keys()
assert isinstance(serialized_evaluation_keys, bytes)
# Save the evaluation key
evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key"
with evaluation_key_path.open("wb") as f:
f.write(serialized_evaluation_keys)
serialized_evaluation_keys_shorten_hex = serialized_evaluation_keys.hex()[:INPUT_BROWSER_LIMIT]
return {
error_box2: gr.update(visible=False),
key_box: serialized_evaluation_keys_shorten_hex,
user_id_box: user_id,
key_len_box: f"{len(serialized_evaluation_keys) / (10**6):.2f} MB",
}
def encrypt_fn(user_symptoms: np.ndarray, user_id: str) -> None:
"""
Encrypt the user symptoms vector in the `Client Side`.
Args:
user_symptoms (List[str]): The vector symptoms provided by the user
user_id (user): The current user's ID
"""
if is_nan(user_id) or is_nan(user_symptoms):
print("Error in encryption step: Provide your symptoms and generate the evaluation keys.")
return {
error_box3: gr.update(
visible=True, value="Please provide your symptoms and generate the evaluation keys."
)
}
# Retrieve the client API
client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
client.load()
user_symptoms = np.fromstring(user_symptoms[2:-2], dtype=int, sep=".").reshape(1, -1)
quant_user_symptoms = client.model.quantize_input(user_symptoms)
encrypted_quantized_user_symptoms = client.quantize_encrypt_serialize(user_symptoms)
assert isinstance(encrypted_quantized_user_symptoms, bytes)
encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_symptoms"
with encrypted_input_path.open("wb") as f:
f.write(encrypted_quantized_user_symptoms)
encrypted_quantized_user_symptoms_shorten_hex = encrypted_quantized_user_symptoms.hex()[
:INPUT_BROWSER_LIMIT
]
return {
error_box3: gr.update(visible=False),
user_vect_box2: user_symptoms,
quant_vect_box: quant_user_symptoms,
enc_vect_box: encrypted_quantized_user_symptoms_shorten_hex,
}
def send_input_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
"""Send the encrypted data and the evaluation key to the server.
Args:
user_id (int): The current user's ID
user_symptoms (numpy.ndarray): The user symptoms
"""
if is_nan(user_id) or is_nan(user_symptoms):
return {
error_box4: gr.update(
visible=True,
value="Please ensure that the evaluation key has been generated "
"and the symptoms have been submitted before sending the data to the server",
)
}
evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key"
encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_symptoms"
if not evaluation_key_path.is_file():
print(
"Error Encountered While Sending Data to the Server: "
f"The key has been generated correctly - {evaluation_key_path.is_file()=}"
)
return {error_box4: gr.update(visible=True, value="Please generate the private key first.")}
if not encrypted_input_path.is_file():
print(
"Error Encountered While Sending Data to the Server: The data has not been encrypted "
f"correctly on the client side - {encrypted_input_path.is_file()=}"
)
return {
error_box4: gr.update(
visible=True,
value="Please encrypt the data with the private key first.",
),
}
# Define the data and files to post
data = {
"user_id": user_id,
"filter": user_symptoms,
}
files = [
("files", open(encrypted_input_path, "rb")),
("files", open(evaluation_key_path, "rb")),
]
# Send the encrypted input and evaluation key to the server
url = SERVER_URL + "send_input"
with requests.post(
url=url,
data=data,
files=files,
) as response:
print(f"Sending Data: {response.ok=}")
return {error_box4: gr.update(visible=False),
next_step_tab3: gr.update(visible=True),srv_resp_send_data_box: "Data sent",}
def run_fhe_fn(user_id: str) -> Dict:
"""Send the encrypted input as well as the evaluation key to the server.
Args:
user_id (int): The current user's ID.
"""
if is_nan(user_id): # or is_nan(user_symptoms):
return {
error_box5: gr.update(
visible=True,
value="Please ensure that the evaluation key has been generated "
"and the symptoms have been submitted before sending the data to the server",
)
}
data = {
"user_id": user_id,
}
# Trigger the FHE execution on the encrypted previously sent
url = SERVER_URL + "run_fhe"
with requests.post(
url=url,
data=data,
) as response:
if not response.ok:
return {
error_box5: gr.update(visible=True, value="Please wait."),
fhe_execution_time_box: gr.update(visible=True),
}
else:
print(f"response.ok: {response.ok}, {response.json()} - Computed")
return {
error_box5: gr.update(visible=False),
fhe_execution_time_box: gr.update(value=f"{response.json()} seconds"),
next_step_tab4: gr.update(visible=True)
}
def get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
"""Retreive the encrypted data from the server.
Args:
user_id (int): The current user's ID
user_symptoms (numpy.ndarray): The user symptoms
"""
if is_nan(user_id) or is_nan(user_symptoms):
return {
error_box6: gr.update(
visible=True,
value="Please ensure that the evaluation key has been generated "
"and the symptoms have been submitted before sending the data to the server",
)
}
data = {
"user_id": user_id,
}
# Retrieve the encrypted output
url = SERVER_URL + "get_output"
with requests.post(
url=url,
data=data,
) as response:
if response.ok:
print(f"Receive Data: {response.ok=}")
encrypted_output = response.content
# Save the encrypted output to bytes in a file as it is too large to pass through
# regular Gradio buttons (see https://github.com/gradio-app/gradio/issues/1877)
encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output"
with encrypted_output_path.open("wb") as f:
f.write(encrypted_output)
return {error_box6: gr.update(visible=False), srv_resp_retrieve_data_box: "Data received"}
def decrypt_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
"""Dencrypt the data on the `Client Side`.
Args:
user_id (int): The current user's ID
user_symptoms (numpy.ndarray): The user symptoms
Returns:
Decrypted output
"""
if is_nan(user_id) or is_nan(user_symptoms):
return {
error_box7: gr.update(
visible=True,
value="Please ensure that the symptoms have been submitted and the evaluation "
"key has been generated",
)
}
# Get the encrypted output path
encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output"
if not encrypted_output_path.is_file():
print("Error in decryption step: Please run the FHE execution, first.")
return {
error_box7: gr.update(
visible=True,
value="Please ensure that the symptoms have been submitted, the evaluation "
"key has been generated and step 5 and 6 have been performed on the Server "
"side before decrypting the prediction",
)
}
# Load the encrypted output as bytes
with encrypted_output_path.open("rb") as f:
encrypted_output = f.read()
# Retrieve the client API
client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
client.load()
# Deserialize, decrypt and post-process the encrypted output
output = client.deserialize_decrypt_dequantize(encrypted_output)
return {
error_box7: gr.update(visible=False),
decrypt_target_box: get_disease_name(output.argmax()),
}
def reset_fn():
"""Clear all the box outputs."""
clean_directory()
return {
# disease_box: None,
user_id_box: None,
user_vect_box1: None,
user_vect_box2: None,
quant_vect_box: None,
enc_vect_box: None,
key_box: None,
key_len_box: None,
fhe_execution_time_box: None,
decrypt_target_box: None,
error_box7: gr.update(visible=False),
error_box1: gr.update(visible=False),
error_box2: gr.update(visible=False),
error_box3: gr.update(visible=False),
error_box4: gr.update(visible=False),
error_box5: gr.update(visible=False),
error_box6: gr.update(visible=False),
next_step_tab2: gr.update(visible=False),
next_step_tab3: gr.update(visible=False),
next_step_tab4: gr.update(visible=False),
srv_resp_send_data_box: None,
srv_resp_retrieve_data_box: None,
**{box: None for box in check_boxes},
}
CSS = """
#them {color: orange}
#them {font-size: 25px}
#them {font-weight: bold}
.gradio-container {background-color: white}
.feedback {font-size: 3px !important}
/* .svelte-s1r2yt {color: orange; font-size: 25px, font-weight: bold;} */
/* #them {text-align: center} */
"""
if __name__ == "__main__":
print("Starting demo ...")
clean_directory()
(X_train, X_test), (y_train, y_test) = load_data()
valid_columns = X_train.columns.to_list()
with gr.Blocks(css=CSS) as demo:
# Link + images
gr.Markdown(
"""
<p align="center">
<img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png">
</p>
<h2 align="center">Health Prediction On Encrypted Data Using Fully Homomorphic Encryption.</h2>
<p align="center">
<a href="https://github.com/zama-ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197972109-faaaff3e-10e2-4ab6-80f5-7531f7cfb08f.png">Concrete-ML</a>
—
<a href="https://docs.zama.ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197976802-fddd34c5-f59a-48d0-9bff-7ad1b00cb1fb.png">Documentation</a>
—
<a href="https://zama.ai/community"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197977153-8c9c01a7-451a-4993-8e10-5a6ed5343d02.png">Community</a>
—
<a href="https://twitter.com/zama_fhe"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197975044-bab9d199-e120-433b-b3be-abd73b211a54.png">@zama_fhe</a>
</p>
<p align="center">
<img width="100%" height="30%" src="https://raw.githubusercontent.com/kcelia/Img/main/health_prediction_img.png">
</p>
"""
)
with gr.Tabs(elem_id="them"):
with gr.TabItem("1. Symptoms Selection") as feature:
gr.Markdown("<span style='color:orange'>Client Side</span>")
gr.Markdown("## Step 1: Provide your symptoms")
gr.Markdown(
"You can provide your health condition either by checking "
"the symptoms available in the boxes or by selecting a known disease with "
"its predefined set of symptoms."
)
# Box symptoms
check_boxes = []
for i, category in enumerate(SYMPTOMS_LIST):
with gr.Accordion(
pretty_print(category.keys()),
open=False,
elem_classes="feedback",
) as accordion:
check_box = gr.CheckboxGroup(
pretty_print(category.values()),
show_label=False,
)
check_boxes.append(check_box)
error_box1 = gr.Textbox(label="Error", visible=False)
# Default disease, picked from the dataframe
# disease_box = gr.Dropdown(list(sorted(set(df_test["prognosis"]))),
# label="Disease:")
# disease_box.change(
# fn=fill_in_fn,
# inputs=[disease_box, *check_boxes],
# outputs=[*check_boxes],
# )
# User symptom vector
user_vect_box1 = gr.Textbox(label="User Symptoms Vector:", interactive=False)
# Submit botton
submit_button = gr.Button("Submit")
# Clear botton
clear_button = gr.Button("Reset Space")
next_step_tab2 = gr.Markdown(
"""
<p align="center">
<img width="80%" height="20%" src="https://raw.githubusercontent.com/kcelia/Img/main/Go-To-Step2.png">
</p>
""", visible=False
)
submit_button.click(
fn=get_features_fn,
inputs=[*check_boxes],
outputs=[user_vect_box1, error_box1, next_step_tab2],
)
with gr.TabItem("2. Data Encryption") as encryption_tab:
gr.Markdown("<span style='color:orange'>Client Side</span>")
gr.Markdown("## Step 2: Generate the keys")
gen_key_btn = gr.Button("Generate the keys")
error_box2 = gr.Textbox(label="Error", visible=False)
with gr.Row():
# User ID
with gr.Column(scale=1, min_width=600):
user_id_box = gr.Textbox(label="User ID:", interactive=False)
# Evaluation key size
with gr.Column(scale=1, min_width=600):
key_len_box = gr.Textbox(label="Evaluation Key Size:", interactive=False)
# Evaluation key (truncated)
with gr.Column(scale=2, min_width=600):
key_box = gr.Textbox(
label="Evaluation key (truncated):",
max_lines=3,
interactive=False,
)
gen_key_btn.click(
key_gen_fn,
inputs=user_vect_box1,
outputs=[
key_box,
user_id_box,
key_len_box,
error_box2,
],
)
gr.Markdown("## Step 3: Encrypt the symptoms")
encrypt_btn = gr.Button("Encrypt the symptoms with the private key")
error_box3 = gr.Textbox(label="Error", visible=False)
with gr.Row():
with gr.Column(scale=1, min_width=600):
user_vect_box2 = gr.Textbox(
label="User Symptoms Vector:", interactive=False
)
with gr.Column(scale=1, min_width=600):
quant_vect_box = gr.Textbox(label="Quantized Vector:", interactive=False)
with gr.Column(scale=1, min_width=600):
enc_vect_box = gr.Textbox(
label="Encrypted Vector:", max_lines=3, interactive=False
)
encrypt_btn.click(
encrypt_fn,
inputs=[user_vect_box1, user_id_box],
outputs=[
user_vect_box2,
quant_vect_box,
enc_vect_box,
error_box3,
],
)
gr.Markdown(
"## Step 4: Send the encrypted data to the "
"<span style='color:orange'>Server Side</span>"
)
error_box4 = gr.Textbox(label="Error", visible=False)
with gr.Row().style(equal_height=False):
with gr.Column(scale=4):
send_input_btn = gr.Button("Send the encrypted data")
with gr.Column(scale=1):
srv_resp_send_data_box = gr.Checkbox(
label="Data Sent", show_label=False, interactive=False
)
next_step_tab3 = gr.Markdown(
"""
<p align="center">
<img width="80%" height="20%" src="https://raw.githubusercontent.com/kcelia/Img/main/Go-To-Step3.png">
</p>
""", visible=False
)
send_input_btn.click(
send_input_fn,
inputs=[user_id_box, user_vect_box1],
outputs=[error_box4, srv_resp_send_data_box, next_step_tab3],
)
with gr.TabItem("3. FHE execution") as fhe_tab:
gr.Markdown("<span style='color:orange'>Server Side</span>")
gr.Markdown("## Step 5: Run the FHE evaluation")
run_fhe_btn = gr.Button("Run the FHE evaluation")
error_box5 = gr.Textbox(label="Error", visible=False)
fhe_execution_time_box = gr.Textbox(
label="Total FHE Execution Time:", interactive=False
)
next_step_tab4 = gr.Markdown(
"""
<p align="center">
<img width="80%" height="20%" src="https://raw.githubusercontent.com/kcelia/Img/main/Go-to-Step4.png">
</p>
""", visible=False
)
run_fhe_btn.click(
run_fhe_fn,
inputs=[user_id_box],
outputs=[fhe_execution_time_box, error_box5, next_step_tab4],
)
with gr.TabItem("4. Data Decryption") as decryption_tab:
gr.Markdown("<span style='color:orange'>Client Side</span>")
gr.Markdown(
"## Step 6: Get the data from the <span style='color:orange'>Server Side</span>"
)
error_box6 = gr.Textbox(label="Error", visible=False)
with gr.Row().style(equal_height=True):
with gr.Column(scale=4):
get_output_btn = gr.Button("Get data")
with gr.Column(scale=1):
srv_resp_retrieve_data_box = gr.Checkbox(
label="Data Received", show_label=False, interactive=False
)
get_output_btn.click(
get_output_fn,
inputs=[user_id_box, user_vect_box1],
outputs=[srv_resp_retrieve_data_box, error_box6],
)
gr.Markdown("## Step 7: Decrypt the output")
decrypt_target_btn = gr.Button("Decrypt the output")
error_box7 = gr.Textbox(label="Error", visible=False)
decrypt_target_box = gr.Textbox(abel="Decrypted Output:", interactive=False)
decrypt_target_btn.click(
decrypt_fn,
inputs=[user_id_box, user_vect_box1],
outputs=[decrypt_target_box, error_box7],
)
clear_button.click(
reset_fn,
outputs=[
next_step_tab2,
next_step_tab3,
next_step_tab4,
user_vect_box1,
user_vect_box2,
# disease_box,
error_box1,
error_box2,
error_box3,
error_box4,
error_box5,
error_box6,
error_box7,
user_id_box,
key_len_box,
key_box,
quant_vect_box,
enc_vect_box,
srv_resp_send_data_box,
srv_resp_retrieve_data_box,
fhe_execution_time_box,
decrypt_target_box,
*check_boxes,
],
)
demo.launch()
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