import pickle as pkl import shutil from pathlib import Path from time import time from typing import List, Tuple, Union import gradio as gr import numpy as np import pandas as pd from sklearn import metrics, preprocessing from sklearn.ensemble import RandomForestClassifier as SklearnRandomForestClassifier from sklearn.model_selection import train_test_split from concrete.ml.common.serialization.loaders import load, loads from concrete.ml.deployment import FHEModelClient, FHEModelDev, FHEModelServer from concrete.ml.sklearn import XGBClassifier as ConcreteXGBoostClassifier path_to_model = Path("./client_folder").resolve() import subprocess from preprocessing import ( # pylint: disable=wrong-import-position, no-name-in-module map_prediction, pretty_print, ) from symptoms_categories import SYMPTOMS_LIST ENCRYPTED_DATA_BROWSER_LIMIT = 500 # This repository's directory REPO_DIR = Path(__file__).parent print(f"{REPO_DIR=}") # subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR) # time.sleep(3) def load_data(): # Load data df_train = pd.read_csv("./data/Training_preprocessed.csv") df_test = pd.read_csv("./data/Testing_preprocessed.csv") # Separate the traget from the training set # df['prognosis] contains the name of the disease # df['y] contains the numeric label of the disease y_train = df_train["y"] X_train = df_train.drop(columns=["y", "prognosis"], axis=1, errors="ignore") y_test = df_train["y"] X_test = df_test.drop(columns=["y", "prognosis"], axis=1, errors="ignore") return (df_train, X_train, X_test), (df_test, y_train, y_test) def load_model(X_train, y_train): concrete_args = {"max_depth": 1, "n_bits": 3, "n_estimators": 3, "n_jobs": -1} classifier = ConcreteXGBoostClassifier(**concrete_args) classifier.fit(X_train, y_train) circuit = classifier.compile(X_train) return classifier, circuit def key_gen(): # Key serialization user_id = np.random.randint(0, 2**32) client = FHEModelClient(path_dir=path_to_model, key_dir=f".fhe_keys/{user_id}") client.load() # The client first need to create the private and evaluation keys. 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) np.save(f".fhe_keys/{user_id}/eval_key.npy", serialized_evaluation_keys) serialized_evaluation_keys_shorten = list(serialized_evaluation_keys)[:200] serialized_evaluation_keys_shorten_hex = "".join( f"{i:02x}" for i in serialized_evaluation_keys_shorten ) # Evaluation keys can be quite large files but only have to be shared once with the server. # Check the size of the evaluation keys (in MB) return [ serialized_evaluation_keys_shorten_hex, user_id, f"{len(serialized_evaluation_keys) / (10**6):.2f} MB", ] def encode_quantize_encrypt(user_symptoms, user_id): # check if the key has been generated client = FHEModelClient(path_dir=path_to_model, key_dir=f".fhe_keys/{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) # print(client.model.predict(vect_x, fhe="simulate"), client.model.predict(vect_x, fhe="execute")) # pred_s = client.model.fhe_circuit.simulate(quant_vect) # pred_fhe = client.model.fhe_circuit.encrypt_run_decrypt(quant_vect) # # non alpha -> \X1124, base64 ou en exa # Compute size np.save(f".fhe_keys/{user_id}/encrypted_quant_vect.npy", encrypted_quantized_user_symptoms) encrypted_quantized_encoding_shorten = list(encrypted_quantized_user_symptoms)[:200] encrypted_quantized_encoding_shorten_hex = "".join( f"{i:02x}" for i in encrypted_quantized_encoding_shorten ) return user_symptoms, quant_user_symptoms, encrypted_quantized_encoding_shorten_hex def decrypt_prediction(encrypted_quantized_vect, user_id): fhe_api = FHEModelClient(path_dir=path_to_model, key_dir=f".fhe_keys/{user_id}") fhe_api.load() fhe_api.generate_private_and_evaluation_keys(force=False) predictions = fhe_api.deserialize_decrypt_dequantize(encrypted_quantized_vect) return predictions def get_user_vect_symptoms_from_checkboxgroup(*user_symptoms) -> np.array: symptoms_vector = {key: 0 for key in valid_columns} for symptom_box in user_symptoms: for pretty_symptom in symptom_box: symptom = "_".join((pretty_symptom.lower().split(" "))) if symptom not in symptoms_vector.keys(): raise KeyError( f"The symptom '{symptom}' you provided is not recognized as a valid " f"symptom.\nHere is the list of valid symptoms: {symptoms_vector}" ) symptoms_vector[symptom] = 1.0 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_user_vect_symptoms_from_default_disease(disease): user_symptom_vector = df_test[df_test["prognosis"] == disease].iloc[0].values user_symptoms_vect = np.fromiter(user_symptom_vector[:-2], dtype=float)[np.newaxis, :] assert all(value == 0 or value == 1 for value in user_symptoms_vect.flatten()) return user_symptoms_vect def get_user_symptoms_from_default_disease(disease): df_filtred = df_test[df_test["prognosis"] == disease] columns_with_1 = df_filtred.columns[df_filtred.eq(1).any()].to_list() return pretty_print(columns_with_1) def get_user_symptoms_vector_btn(selected_default_disease, *selected_symptoms): if any(lst for lst in selected_symptoms if lst) and ( selected_default_disease is not None and len(selected_default_disease) > 0 ): # If the user has already selected a disease and added more symptoms, raise an error if set(pretty_print(selected_symptoms)) - set( get_user_symptoms_from_default_disease(selected_default_disease) ): return { user_vector_textbox: gr.update(value="An error occurs"), error_box: gr.update( visible=True, value="Enter a default disease or select your own symptoms" ), } # If the user has not selected a default disease or symptoms, an error is raised. if not any(lst for lst in selected_symptoms if lst) and ( selected_default_disease is None or (selected_default_disease is not None and len(selected_default_disease) < 1) ): return { user_vector_textbox: gr.update(value="An error occurs"), error_box: gr.update( visible=True, value="Enter a default disease or select your own symptoms" ), } # Case 1: The user has checked his own symptoms if any(lst for lst in selected_symptoms if lst): return { user_vector_textbox: get_user_vect_symptoms_from_checkboxgroup(*selected_symptoms), } # Case 2: The user has selected a default disease if selected_default_disease is not None and len(selected_default_disease) > 0: return { user_vector_textbox: get_user_vect_symptoms_from_default_disease( selected_default_disease ), error_box: gr.update(visible=False), **{ box: get_user_symptoms_from_default_disease(selected_default_disease) for box in check_boxes }, } def clear_all_btn(): return { user_id_textbox: None, eval_key_textbox: None, eval_key_len_textbox: None, user_vector_textbox: None, box_default: None, error_box: gr.update(visible=False), **{box: None for box in check_boxes}, } if __name__ == "__main__": print("Starting demo ...") (df_train, X_train, X_test), (df_test, y_train, y_test) = load_data() valid_columns = X_train.columns.to_list() # Load the model with open("ConcreteXGBoostClassifier.pkl", "r", encoding="utf-8") as file: concrete_classifier = load(file) with gr.Blocks() as demo: # Link + images gr.Markdown( """

Health Prediction On Encrypted Data Using Homomorphic Encryption.

Concrete-ML — Documentation — Community — @zama_fhe

""" ) # Gentle introduction gr.Markdown("## Introduction") gr.Markdown("""Blablabla""") # User symptoms gr.Markdown("# Step 1: Provide your symptoms") gr.Markdown("Client side") # Default disease, picked from the dataframe with gr.Row(): default_diseases = list(set(df_test["prognosis"])) box_default = gr.Dropdown(default_diseases, label="Disease") # Box symptoms check_boxes = [] for i, category in enumerate(SYMPTOMS_LIST): check_box = gr.CheckboxGroup( pretty_print(category.values()), label=pretty_print(category.keys()), info=f"Symptoms related to `{pretty_print(category.values())}`", max_batch_size=45, ) check_boxes.append(check_box) # User symptom vector with gr.Row(): user_vector_textbox = gr.Textbox( label="User symptoms (vector)", interactive=False, max_lines=100, ) error_box = gr.Textbox(label="Error", visible=False) with gr.Row(): # Submit botton with gr.Column(): submit_button = gr.Button("Submit") # Clear botton with gr.Column(): clear_button = gr.Button("Clear", style="background-color: yellow;") # Click submit botton submit_button.click( fn=get_user_symptoms_vector_btn, inputs=[box_default, *check_boxes], outputs=[user_vector_textbox, error_box, *check_boxes], ) gr.Markdown("# Step 2: Generate the keys") gr.Markdown("Client side") gen_key = gr.Button("Generate the keys and send public part to server") with gr.Row(): # User ID with gr.Column(scale=1, min_width=600): user_id_textbox = gr.Textbox( label="User ID:", max_lines=4, interactive=False, ) # Evaluation key size with gr.Column(scale=1, min_width=600): eval_key_len_textbox = gr.Textbox( label="Evaluation key size:", max_lines=4, interactive=False ) with gr.Row(): # Evaluation key (truncated) with gr.Column(scale=2, min_width=600): eval_key_textbox = gr.Textbox( label="Evaluation key (truncated):", max_lines=4, interactive=False, ) gen_key.click(key_gen, outputs=[eval_key_textbox, user_id_textbox, eval_key_len_textbox]) clear_button.click( clear_all_btn, outputs=[ user_id_textbox, user_vector_textbox, eval_key_textbox, eval_key_len_textbox, box_default, error_box, *check_boxes, ], ) gr.Markdown("# Step 3: Encode the message with the private key") gr.Markdown("Client side") encode_msg = gr.Button("Generate the keys and send public part to server") with gr.Row(): with gr.Column(scale=1, min_width=600): vect_textbox = gr.Textbox( label="Vector:", max_lines=4, interactive=False, ) with gr.Column(scale=1, min_width=600): quant_vect_textbox = gr.Textbox( label="Quant vector:", max_lines=4, interactive=False ) with gr.Column(scale=1, min_width=600): encrypted_vect_textbox = gr.Textbox( label="Encrypted vector:", max_lines=4, interactive=False ) encode_msg.click( encode_quantize_encrypt, inputs=[user_vector_textbox, user_id_textbox], outputs=[vect_textbox, quant_vect_textbox, encrypted_vect_textbox], ) gr.Markdown("# Step 4: Run the FHE evaluation") gr.Markdown("Server side") run_fhe = gr.Button("Run the FHE evaluation") gr.Markdown("# Step 5: Decrypt the sentiment") gr.Markdown("Server side") decrypt_target_botton = gr.Button("Decrypt the sentiment") decrypt_target_textbox = gr.Textbox( label="Encrypted vector:", max_lines=4, interactive=False ) decrypt_target_botton.click( decrypt_prediction, inputs=[encrypted_vect_textbox, user_id_textbox], outputs=[decrypt_target_textbox], ) demo.launch()