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chore: update
Browse files- ConcreteXGBoostClassifier.pkl +3 -0
- app.py +405 -0
- client_folder/client.zip +3 -0
- client_folder/server.zip +3 -0
- client_folder/versions.json +1 -0
- preprocessing.py +83 -0
- server.py +93 -0
- symptoms_categories.py +226 -0
ConcreteXGBoostClassifier.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:e95ed531c5b4fd4d23330dee9a72884979d93c4be55c1cc25e06efe027253ce4
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size 599833
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app.py
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import pickle as pkl
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import shutil
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from pathlib import Path
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from time import time
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from typing import List, Tuple, Union
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import gradio as gr
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import numpy as np
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import pandas as pd
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from sklearn import metrics, preprocessing
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from sklearn.ensemble import RandomForestClassifier as SklearnRandomForestClassifier
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from sklearn.model_selection import train_test_split
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from concrete.ml.common.serialization.loaders import load, loads
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from concrete.ml.deployment import FHEModelClient, FHEModelDev, FHEModelServer
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from concrete.ml.sklearn import XGBClassifier as ConcreteXGBoostClassifier
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path_to_model = Path("./client_folder").resolve()
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import subprocess
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from preprocessing import ( # pylint: disable=wrong-import-position, no-name-in-module
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map_prediction,
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pretty_print,
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)
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from symptoms_categories import SYMPTOMS_LIST
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ENCRYPTED_DATA_BROWSER_LIMIT = 500
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# This repository's directory
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REPO_DIR = Path(__file__).parent
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print(f"{REPO_DIR=}")
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# subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR)
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# time.sleep(3)
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def load_data():
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# Load data
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df_train = pd.read_csv("./data/Training_preprocessed.csv")
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df_test = pd.read_csv("./data/Testing_preprocessed.csv")
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# Separate the traget from the training set
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# df['prognosis] contains the name of the disease
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# df['y] contains the numeric label of the disease
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y_train = df_train["y"]
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X_train = df_train.drop(columns=["y", "prognosis"], axis=1, errors="ignore")
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y_test = df_train["y"]
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X_test = df_test.drop(columns=["y", "prognosis"], axis=1, errors="ignore")
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return (df_train, X_train, X_test), (df_test, y_train, y_test)
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def load_model(X_train, y_train):
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concrete_args = {"max_depth": 1, "n_bits": 3, "n_estimators": 3, "n_jobs": -1}
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classifier = ConcreteXGBoostClassifier(**concrete_args)
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classifier.fit(X_train, y_train)
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circuit = classifier.compile(X_train)
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return classifier, circuit
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def key_gen():
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# Key serialization
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user_id = np.random.randint(0, 2**32)
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client = FHEModelClient(path_dir=path_to_model, key_dir=f".fhe_keys/{user_id}")
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client.load()
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# The client first need to create the private and evaluation keys.
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client.generate_private_and_evaluation_keys()
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# Get the serialized evaluation keys
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serialized_evaluation_keys = client.get_serialized_evaluation_keys()
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assert isinstance(serialized_evaluation_keys, bytes)
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np.save(f".fhe_keys/{user_id}/eval_key.npy", serialized_evaluation_keys)
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serialized_evaluation_keys_shorten = list(serialized_evaluation_keys)[:200]
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serialized_evaluation_keys_shorten_hex = "".join(
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f"{i:02x}" for i in serialized_evaluation_keys_shorten
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)
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# Evaluation keys can be quite large files but only have to be shared once with the server.
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# Check the size of the evaluation keys (in MB)
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return [
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serialized_evaluation_keys_shorten_hex,
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user_id,
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f"{len(serialized_evaluation_keys) / (10**6):.2f} MB",
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]
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def encode_quantize_encrypt(user_symptoms, user_id):
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# check if the key has been generated
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client = FHEModelClient(path_dir=path_to_model, key_dir=f".fhe_keys/{user_id}")
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client.load()
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user_symptoms = np.fromstring(user_symptoms[2:-2], dtype=int, sep=".").reshape(1, -1)
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quant_user_symptoms = client.model.quantize_input(user_symptoms)
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encrypted_quantized_user_symptoms = client.quantize_encrypt_serialize(user_symptoms)
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# print(client.model.predict(vect_x, fhe="simulate"), client.model.predict(vect_x, fhe="execute"))
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# pred_s = client.model.fhe_circuit.simulate(quant_vect)
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# pred_fhe = client.model.fhe_circuit.encrypt_run_decrypt(quant_vect) #
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# non alpha -> \X1124, base64 ou en exa
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# Compute size
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np.save(f".fhe_keys/{user_id}/encrypted_quant_vect.npy", encrypted_quantized_user_symptoms)
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encrypted_quantized_encoding_shorten = list(encrypted_quantized_user_symptoms)[:200]
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encrypted_quantized_encoding_shorten_hex = "".join(
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f"{i:02x}" for i in encrypted_quantized_encoding_shorten
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)
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return user_symptoms, quant_user_symptoms, encrypted_quantized_encoding_shorten_hex
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def decrypt_prediction(encrypted_quantized_vect, user_id):
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fhe_api = FHEModelClient(path_dir=path_to_model, key_dir=f".fhe_keys/{user_id}")
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fhe_api.load()
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fhe_api.generate_private_and_evaluation_keys(force=False)
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predictions = fhe_api.deserialize_decrypt_dequantize(encrypted_quantized_vect)
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return predictions
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def get_user_vect_symptoms_from_checkboxgroup(*user_symptoms) -> np.array:
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symptoms_vector = {key: 0 for key in valid_columns}
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for symptom_box in user_symptoms:
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for pretty_symptom in symptom_box:
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symptom = "_".join((pretty_symptom.lower().split(" ")))
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if symptom not in symptoms_vector.keys():
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raise KeyError(
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f"The symptom '{symptom}' you provided is not recognized as a valid "
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f"symptom.\nHere is the list of valid symptoms: {symptoms_vector}"
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)
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symptoms_vector[symptom] = 1.0
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user_symptoms_vect = np.fromiter(symptoms_vector.values(), dtype=float)[np.newaxis, :]
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assert all(value == 0 or value == 1 for value in user_symptoms_vect.flatten())
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return user_symptoms_vect
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def get_user_vect_symptoms_from_default_disease(disease):
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user_symptom_vector = df_test[df_test["prognosis"] == disease].iloc[0].values
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user_symptoms_vect = np.fromiter(user_symptom_vector[:-2], dtype=float)[np.newaxis, :]
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assert all(value == 0 or value == 1 for value in user_symptoms_vect.flatten())
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return user_symptoms_vect
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def get_user_symptoms_from_default_disease(disease):
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df_filtred = df_test[df_test["prognosis"] == disease]
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columns_with_1 = df_filtred.columns[df_filtred.eq(1).any()].to_list()
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return pretty_print(columns_with_1)
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def get_user_symptoms_vector(selected_default_disease, *selected_symptoms):
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if any(lst for lst in selected_symptoms if lst) and (
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selected_default_disease is not None and len(selected_default_disease) > 0
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):
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# If the user has already selected a disease and added more symptoms, raise an error
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if set(pretty_print(selected_symptoms)) - set(
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get_user_symptoms_from_default_disease(selected_default_disease)
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):
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return {
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user_vector_textbox: gr.update(value="An error occurs"),
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error_box: gr.update(
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visible=True, value="Enter a default disease or select your own symptoms"
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),
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}
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# If the user has not selected a default disease or symptoms, an error is raised.
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if not any(lst for lst in selected_symptoms if lst) and (
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selected_default_disease is None
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or (selected_default_disease is not None and len(selected_default_disease) < 1)
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):
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return {
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user_vector_textbox: gr.update(value="An error occurs"),
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error_box: gr.update(
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visible=True, value="Enter a default disease or select your own symptoms"
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),
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}
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# Case 1: The user has checked his own symptoms
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if any(lst for lst in selected_symptoms if lst):
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return {
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user_vector_textbox: get_user_vect_symptoms_from_checkboxgroup(*selected_symptoms),
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}
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# Case 2: The user has selected a default disease
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if selected_default_disease is not None and len(selected_default_disease) > 0:
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return {
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user_vector_textbox: get_user_vect_symptoms_from_default_disease(
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selected_default_disease
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),
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error_box: gr.update(visible=False),
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**{
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box: get_user_symptoms_from_default_disease(selected_default_disease)
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for box in check_boxes
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},
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}
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def clear_all_buttons():
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return {
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user_id_textbox: None,
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eval_key_textbox: None,
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eval_key_len_textbox: None,
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user_vector_textbox: None,
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box_default: None,
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error_box: gr.update(visible=False),
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**{box: None for box in check_boxes},
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}
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if __name__ == "__main__":
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print("Starting demo ...")
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(df_train, X_train, X_test), (df_test, y_train, y_test) = load_data()
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valid_columns = X_train.columns.to_list()
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with gr.Blocks() as demo:
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# Link + images
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gr.Markdown(
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"""
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<p align="center">
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<img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png">
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</p>
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<h2 align="center">Health Prediction On Encrypted Data Using Homomorphic Encryption.</h2>
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<p align="center">
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<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>
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—
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247 |
+
<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>
|
248 |
+
—
|
249 |
+
<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>
|
250 |
+
—
|
251 |
+
<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>
|
252 |
+
</p>
|
253 |
+
|
254 |
+
<p align="center">
|
255 |
+
<img src="https://raw.githubusercontent.com/kcelia/Img/main/demo-img2.png" width="60%" height="60%">
|
256 |
+
</p>
|
257 |
+
"""
|
258 |
+
)
|
259 |
+
|
260 |
+
# Gentle introduction
|
261 |
+
gr.Markdown("## Introduction")
|
262 |
+
gr.Markdown("""Blablabla""")
|
263 |
+
|
264 |
+
# User symptoms
|
265 |
+
gr.Markdown("# Step 1: Provide your symptoms")
|
266 |
+
gr.Markdown("Client side")
|
267 |
+
|
268 |
+
# Default disease, picked from the dataframe
|
269 |
+
with gr.Row():
|
270 |
+
default_diseases = list(set(df_test["prognosis"]))
|
271 |
+
box_default = gr.Dropdown(default_diseases, label="Disease")
|
272 |
+
|
273 |
+
# Box symptoms
|
274 |
+
check_boxes = []
|
275 |
+
for i, category in enumerate(SYMPTOMS_LIST):
|
276 |
+
check_box = gr.CheckboxGroup(
|
277 |
+
pretty_print(category.values()),
|
278 |
+
label=pretty_print(category.keys()),
|
279 |
+
info=f"Symptoms related to `{pretty_print(category.values())}`",
|
280 |
+
max_batch_size=45,
|
281 |
+
)
|
282 |
+
check_boxes.append(check_box)
|
283 |
+
|
284 |
+
# User symptom vector
|
285 |
+
with gr.Row():
|
286 |
+
user_vector_textbox = gr.Textbox(
|
287 |
+
label="User symptoms (vector)",
|
288 |
+
interactive=False,
|
289 |
+
max_lines=100,
|
290 |
+
)
|
291 |
+
error_box = gr.Textbox(label="Error", visible=False)
|
292 |
+
|
293 |
+
with gr.Row():
|
294 |
+
# Submit botton
|
295 |
+
with gr.Column():
|
296 |
+
submit_button = gr.Button("Submit")
|
297 |
+
# Clear botton
|
298 |
+
with gr.Column():
|
299 |
+
clear_button = gr.Button("Clear", style="background-color: yellow;")
|
300 |
+
|
301 |
+
# Click submit botton
|
302 |
+
|
303 |
+
submit_button.click(
|
304 |
+
fn=get_user_symptoms_vector,
|
305 |
+
inputs=[box_default, *check_boxes],
|
306 |
+
outputs=[user_vector_textbox, error_box, *check_boxes],
|
307 |
+
)
|
308 |
+
# Load the model
|
309 |
+
concrete_classifier = load(
|
310 |
+
open("ConcreteRandomForestClassifier.pkl", "r", encoding="utf-8")
|
311 |
+
)
|
312 |
+
|
313 |
+
gr.Markdown("# Step 2: Generate the keys")
|
314 |
+
gr.Markdown("Client side")
|
315 |
+
|
316 |
+
gen_key = gr.Button("Generate the keys and send public part to server")
|
317 |
+
|
318 |
+
with gr.Row():
|
319 |
+
# User ID
|
320 |
+
with gr.Column(scale=1, min_width=600):
|
321 |
+
user_id_textbox = gr.Textbox(
|
322 |
+
label="User ID:",
|
323 |
+
max_lines=4,
|
324 |
+
interactive=False,
|
325 |
+
)
|
326 |
+
# Evaluation key size
|
327 |
+
with gr.Column(scale=1, min_width=600):
|
328 |
+
eval_key_len_textbox = gr.Textbox(
|
329 |
+
label="Evaluation key size:", max_lines=4, interactive=False
|
330 |
+
)
|
331 |
+
|
332 |
+
with gr.Row():
|
333 |
+
# Evaluation key (truncated)
|
334 |
+
with gr.Column(scale=2, min_width=600):
|
335 |
+
eval_key_textbox = gr.Textbox(
|
336 |
+
label="Evaluation key (truncated):",
|
337 |
+
max_lines=4,
|
338 |
+
interactive=False,
|
339 |
+
)
|
340 |
+
|
341 |
+
gen_key.click(key_gen, outputs=[eval_key_textbox, user_id_textbox, eval_key_len_textbox])
|
342 |
+
|
343 |
+
clear_button.click(
|
344 |
+
clear_all_buttons,
|
345 |
+
outputs=[
|
346 |
+
user_id_textbox,
|
347 |
+
user_vector_textbox,
|
348 |
+
eval_key_textbox,
|
349 |
+
eval_key_len_textbox,
|
350 |
+
box_default,
|
351 |
+
error_box,
|
352 |
+
*check_boxes,
|
353 |
+
],
|
354 |
+
)
|
355 |
+
|
356 |
+
gr.Markdown("# Step 3: Encode the message with the private key")
|
357 |
+
gr.Markdown("Client side")
|
358 |
+
|
359 |
+
encode_msg = gr.Button("Generate the keys and send public part to server")
|
360 |
+
|
361 |
+
with gr.Row():
|
362 |
+
|
363 |
+
with gr.Column(scale=1, min_width=600):
|
364 |
+
vect_textbox = gr.Textbox(
|
365 |
+
label="Vector:",
|
366 |
+
max_lines=4,
|
367 |
+
interactive=False,
|
368 |
+
)
|
369 |
+
|
370 |
+
with gr.Column(scale=1, min_width=600):
|
371 |
+
quant_vect_textbox = gr.Textbox(
|
372 |
+
label="Quant vector:", max_lines=4, interactive=False
|
373 |
+
)
|
374 |
+
|
375 |
+
with gr.Column(scale=1, min_width=600):
|
376 |
+
encrypted_vect_textbox = gr.Textbox(
|
377 |
+
label="Encrypted vector:", max_lines=4, interactive=False
|
378 |
+
)
|
379 |
+
|
380 |
+
encode_msg.click(
|
381 |
+
encode_quantize_encrypt,
|
382 |
+
inputs=[user_vector_textbox, user_id_textbox],
|
383 |
+
outputs=[vect_textbox, quant_vect_textbox, encrypted_vect_textbox],
|
384 |
+
)
|
385 |
+
|
386 |
+
gr.Markdown("# Step 4: Run the FHE evaluation")
|
387 |
+
gr.Markdown("Server side")
|
388 |
+
|
389 |
+
run_fhe = gr.Button("Run the FHE evaluation")
|
390 |
+
|
391 |
+
gr.Markdown("# Step 5: Decrypt the sentiment")
|
392 |
+
gr.Markdown("Server side")
|
393 |
+
|
394 |
+
decrypt_target_botton = gr.Button("Decrypt the sentiment")
|
395 |
+
decrypt_target_textbox = gr.Textbox(
|
396 |
+
label="Encrypted vector:", max_lines=4, interactive=False
|
397 |
+
)
|
398 |
+
|
399 |
+
decrypt_target_botton.click(
|
400 |
+
decrypt_prediction,
|
401 |
+
inputs=[encrypted_vect_textbox, user_id_textbox],
|
402 |
+
outputs=[decrypt_target_textbox],
|
403 |
+
)
|
404 |
+
|
405 |
+
demo.launch()
|
client_folder/client.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d74f69c8847ee0c4d1d1828eea2d81ae0e9f20de866bb8536d391541d68c8f04
|
3 |
+
size 89862
|
client_folder/server.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3240edb4a0f896e56a7a077bf7ebc83a23003c96f96c5096cc80898152053f5b
|
3 |
+
size 1778
|
client_folder/versions.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"concrete-ml": "1.0.0rc2", "concrete-python": "1.0.0", "python": "3.10.6"}
|
preprocessing.py
ADDED
@@ -0,0 +1,83 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
Preliminary preprocessing on the data, such as:
|
3 |
+
- correcting column names
|
4 |
+
- encoding the target column
|
5 |
+
"""
|
6 |
+
|
7 |
+
import pandas as pd
|
8 |
+
from sklearn import preprocessing
|
9 |
+
|
10 |
+
COLUMNS_TO_DROP = ["Unnamed: 133"]
|
11 |
+
TARGET_COLUMN = ["prognosis"]
|
12 |
+
RENAME_COLUMNS = {
|
13 |
+
"scurring": "scurving",
|
14 |
+
"dischromic _patches": "dischromic_patches",
|
15 |
+
"spotting_ urination": "spotting_urination",
|
16 |
+
"foul_smell_of urine": "foul_smell_of_urine",
|
17 |
+
}
|
18 |
+
|
19 |
+
|
20 |
+
def pretty_print(input):
|
21 |
+
"""
|
22 |
+
Prettify the input.
|
23 |
+
|
24 |
+
Args:
|
25 |
+
input: Can be a list of symtoms or a disease.
|
26 |
+
|
27 |
+
Returns:
|
28 |
+
list: Sorted and prettified input.
|
29 |
+
"""
|
30 |
+
# Convert to a list if necessary
|
31 |
+
if isinstance(input, list):
|
32 |
+
input = list(input)
|
33 |
+
|
34 |
+
# Flatten the list if required
|
35 |
+
pretty_list = []
|
36 |
+
for item in input:
|
37 |
+
if isinstance(item, list):
|
38 |
+
pretty_list.extend(item)
|
39 |
+
else:
|
40 |
+
pretty_list.append(item)
|
41 |
+
|
42 |
+
# Sort and prettify the input
|
43 |
+
pretty_list = sorted([" ".join((item.split("_"))).title() for item in pretty_list])
|
44 |
+
|
45 |
+
return pretty_list
|
46 |
+
|
47 |
+
|
48 |
+
def map_prediction(target_columns=["y", "prognosis"]):
|
49 |
+
df = pd.read_csv("Training_preprocessed.csv")
|
50 |
+
relevent_df = df[target_columns].drop_duplicates().relevent_df.where(df["y"] == 1)
|
51 |
+
prediction = relevent_df[target_columns[1]].dropna().values[0]
|
52 |
+
return prediction
|
53 |
+
|
54 |
+
|
55 |
+
if __name__ == "__main__":
|
56 |
+
|
57 |
+
# Load data
|
58 |
+
df_train = pd.read_csv("Training.csv")
|
59 |
+
df_test = pd.read_csv("Testing.csv")
|
60 |
+
|
61 |
+
# Remove unseless columns
|
62 |
+
df_train.drop(columns=COLUMNS_TO_DROP, axis=1, errors="ignore", inplace=True)
|
63 |
+
df_test.drop(columns=COLUMNS_TO_DROP, axis=1, errors="ignore", inplace=True)
|
64 |
+
|
65 |
+
# Correct some typos in some columns name
|
66 |
+
df_train.rename(columns=RENAME_COLUMNS, inplace=True)
|
67 |
+
df_test.rename(columns=RENAME_COLUMNS, inplace=True)
|
68 |
+
|
69 |
+
# Convert y category labels to y
|
70 |
+
label_encoder = preprocessing.LabelEncoder()
|
71 |
+
label_encoder.fit(df_train[TARGET_COLUMN].values.flatten())
|
72 |
+
|
73 |
+
df_train["y"] = label_encoder.transform(df_train[TARGET_COLUMN].values.flatten())
|
74 |
+
df_test["y"] = label_encoder.transform(df_test[TARGET_COLUMN].values.flatten())
|
75 |
+
|
76 |
+
# Cast X features from int64 to float32
|
77 |
+
float_columns = df_train.columns.drop(TARGET_COLUMN)
|
78 |
+
df_train[float_columns] = df_train[float_columns].astype("float32")
|
79 |
+
df_test[float_columns] = df_test[float_columns].astype("float32")
|
80 |
+
|
81 |
+
# Save preprocessed data
|
82 |
+
df_train.to_csv(path_or_buf="Training_preprocessed.csv", index=False)
|
83 |
+
df_test.to_csv(path_or_buf="Testing_preprocessed.csv", index=False)
|
server.py
ADDED
@@ -0,0 +1,93 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""Server that will listen for GET and POST requests from the client."""
|
2 |
+
|
3 |
+
import time
|
4 |
+
from pathlib import Path
|
5 |
+
from typing import List
|
6 |
+
|
7 |
+
from fastapi import FastAPI, File, Form, UploadFile
|
8 |
+
from fastapi.responses import JSONResponse, Response
|
9 |
+
|
10 |
+
from concrete.ml.deployment import FHEModelServer
|
11 |
+
|
12 |
+
# Initialize an instance of FastAPI
|
13 |
+
app = FastAPI()
|
14 |
+
|
15 |
+
current_dir = Path(__file__).parent
|
16 |
+
|
17 |
+
# Load the model
|
18 |
+
fhe_model = FHEModelServer(Path.joinpath(current_dir, "./client_folder"))
|
19 |
+
|
20 |
+
# Define the default route
|
21 |
+
@app.get("/")
|
22 |
+
def root():
|
23 |
+
return {"message": "Welcome to Your disease prediction with fhe !"}
|
24 |
+
|
25 |
+
|
26 |
+
@app.post("/send_input")
|
27 |
+
def send_input(
|
28 |
+
user_id: str = Form(),
|
29 |
+
filter: str = Form(),
|
30 |
+
files: List[UploadFile] = File(),
|
31 |
+
):
|
32 |
+
"""Send the inputs to the server."""
|
33 |
+
# Retrieve the encrypted input image and the evaluation key paths
|
34 |
+
encrypted_image_path = 0 # Tcurrent_dir("encrypted_image", user_id, filter)
|
35 |
+
evaluation_key_path = current_dir / ".fhe_keys/{user_id}"
|
36 |
+
|
37 |
+
# Write the files using the above paths
|
38 |
+
with encrypted_image_path.open("wb") as encrypted_image, evaluation_key_path.open(
|
39 |
+
"wb"
|
40 |
+
) as evaluation_key:
|
41 |
+
encrypted_image.write(files[0].file.read())
|
42 |
+
evaluation_key.write(files[1].file.read())
|
43 |
+
|
44 |
+
|
45 |
+
@app.post("/run_fhe")
|
46 |
+
def run_fhe(
|
47 |
+
user_id: str = Form(),
|
48 |
+
filter: str = Form(),
|
49 |
+
):
|
50 |
+
"""Execute the filter on the encrypted input image using FHE."""
|
51 |
+
# Retrieve the encrypted input image and the evaluation key paths
|
52 |
+
encrypted_image_path = get_server_file_path("encrypted_image", user_id, filter)
|
53 |
+
evaluation_key_path = get_server_file_path("evaluation_key", user_id, filter)
|
54 |
+
|
55 |
+
# Read the files using the above paths
|
56 |
+
with encrypted_image_path.open("rb") as encrypted_image_file, evaluation_key_path.open(
|
57 |
+
"rb"
|
58 |
+
) as evaluation_key_file:
|
59 |
+
encrypted_image = encrypted_image_file.read()
|
60 |
+
evaluation_key = evaluation_key_file.read()
|
61 |
+
|
62 |
+
# Load the FHE server
|
63 |
+
fhe_server = FHEServer(FILTERS_PATH / f"{filter}/deployment")
|
64 |
+
|
65 |
+
# Run the FHE execution
|
66 |
+
start = time.time()
|
67 |
+
encrypted_output_image = fhe_server.run(encrypted_image, evaluation_key)
|
68 |
+
fhe_execution_time = round(time.time() - start, 2)
|
69 |
+
|
70 |
+
# Retrieve the encrypted output image path
|
71 |
+
encrypted_output_path = get_server_file_path("encrypted_output", user_id, filter)
|
72 |
+
|
73 |
+
# Write the file using the above path
|
74 |
+
with encrypted_output_path.open("wb") as encrypted_output:
|
75 |
+
encrypted_output.write(encrypted_output_image)
|
76 |
+
|
77 |
+
return JSONResponse(content=fhe_execution_time)
|
78 |
+
|
79 |
+
|
80 |
+
@app.post("/get_output")
|
81 |
+
def get_output(
|
82 |
+
user_id: str = Form(),
|
83 |
+
filter: str = Form(),
|
84 |
+
):
|
85 |
+
"""Retrieve the encrypted output image."""
|
86 |
+
# Retrieve the encrypted output image path
|
87 |
+
encrypted_output_path = get_server_file_path("encrypted_output", user_id, filter)
|
88 |
+
|
89 |
+
# Read the file using the above path
|
90 |
+
with encrypted_output_path.open("rb") as encrypted_output_file:
|
91 |
+
encrypted_output = encrypted_output_file.read()
|
92 |
+
|
93 |
+
return Response(encrypted_output)
|
symptoms_categories.py
ADDED
@@ -0,0 +1,226 @@
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|
|
1 |
+
"""
|
2 |
+
In this file, we roughly split up a list of symptoms, taken from "./training.csv" file, avalaible
|
3 |
+
through: "https://github.com/anujdutt9/Disease-Prediction-from-Symptoms/tree/master/dataset"
|
4 |
+
into medical categories, in order to make the UI more plesant for the users.
|
5 |
+
|
6 |
+
Each variable contains a list of symptoms sthat can be pecific to a part of the body or to a list
|
7 |
+
of similar symptoms.
|
8 |
+
"""
|
9 |
+
|
10 |
+
import itertools
|
11 |
+
|
12 |
+
import pandas as pd
|
13 |
+
|
14 |
+
DIGESTIVE_SYSTEM_SYPTOMS = {
|
15 |
+
"Digestive system syptoms": [
|
16 |
+
"stomach_pain",
|
17 |
+
"acidity",
|
18 |
+
"vomiting",
|
19 |
+
"indigestion",
|
20 |
+
"constipation",
|
21 |
+
"abdominal_pain",
|
22 |
+
"diarrhoea",
|
23 |
+
"belly_pain",
|
24 |
+
"nausea",
|
25 |
+
"distention_of_abdomen",
|
26 |
+
"stomach_bleeding",
|
27 |
+
"pain_during_bowel_movements",
|
28 |
+
"passage_of_gases",
|
29 |
+
"brittle_nails",
|
30 |
+
"red_spots_over_body",
|
31 |
+
"swelling_of_stomach",
|
32 |
+
"bloody_stool",
|
33 |
+
"yellowish_skin",
|
34 |
+
"irritation_in_anus",
|
35 |
+
"pain_in_anal_region",
|
36 |
+
"abnormal_menstruation",
|
37 |
+
]
|
38 |
+
}
|
39 |
+
|
40 |
+
SKIN_SYPTOMS = {
|
41 |
+
"Skin related symptoms": [
|
42 |
+
"itching",
|
43 |
+
"skin_rash",
|
44 |
+
"pus_filled_pimples",
|
45 |
+
"blackheads",
|
46 |
+
"scurving",
|
47 |
+
"skin_peeling",
|
48 |
+
"silver_like_dusting",
|
49 |
+
"small_dents_in_nails",
|
50 |
+
"inflammatory_nails",
|
51 |
+
"blister",
|
52 |
+
"red_sore_around_nose",
|
53 |
+
"bruising",
|
54 |
+
"yellow_crust_ooze",
|
55 |
+
"dischromic_patches",
|
56 |
+
"nodal_skin_eruptions",
|
57 |
+
]
|
58 |
+
}
|
59 |
+
|
60 |
+
ORL_SYPTOMS = {
|
61 |
+
"ORL_SYPTOMS": [
|
62 |
+
"loss_of_smell",
|
63 |
+
"continuous_sneezing",
|
64 |
+
"runny_nose",
|
65 |
+
"patches_in_throat",
|
66 |
+
"throat_irritation",
|
67 |
+
"sinus_pressure",
|
68 |
+
"enlarged_thyroid",
|
69 |
+
"loss_of_balance",
|
70 |
+
"unsteadiness",
|
71 |
+
"dizziness",
|
72 |
+
"spinning_movements",
|
73 |
+
]
|
74 |
+
}
|
75 |
+
|
76 |
+
THORAX_SYMPTOMS = {
|
77 |
+
"THORAX_RELATED_SYMPTOMS": [
|
78 |
+
"breathlessness",
|
79 |
+
"chest_pain",
|
80 |
+
"cough",
|
81 |
+
"rusty_sputum",
|
82 |
+
"phlegm",
|
83 |
+
"mucoid_sputum",
|
84 |
+
"congestion",
|
85 |
+
"blood_in_sputum",
|
86 |
+
"fast_heart_rate",
|
87 |
+
]
|
88 |
+
}
|
89 |
+
|
90 |
+
EYES_SYMPTOMS = {
|
91 |
+
"Eyes_related_symptoms": [
|
92 |
+
"sunken_eyes",
|
93 |
+
"redness_of_eyes",
|
94 |
+
"watering_from_eyes",
|
95 |
+
"blurred_and_distorted_vision",
|
96 |
+
"pain_behind_the_eyes",
|
97 |
+
"visual_disturbances",
|
98 |
+
]
|
99 |
+
}
|
100 |
+
|
101 |
+
VASCULAR_LYMPHATIC_SYMPTOMS = {
|
102 |
+
"VASCULAR_LYMPHATIC_SYMPTOMS": [
|
103 |
+
"cold_hands_and_feets",
|
104 |
+
"swollen_blood_vessels",
|
105 |
+
"swollen_legs",
|
106 |
+
"swelled_lymph_nodes",
|
107 |
+
"palpitations",
|
108 |
+
"prominent_veins_on_calf",
|
109 |
+
"yellowing_of_eyes",
|
110 |
+
"puffy_face_and_eyes",
|
111 |
+
"fluid_overload",
|
112 |
+
"fluid_overload.1",
|
113 |
+
"swollen_extremeties",
|
114 |
+
]
|
115 |
+
}
|
116 |
+
|
117 |
+
UROLOGICAL_SYMPTOMS = {
|
118 |
+
"UROLOGICAL_SYMPTOMS": [
|
119 |
+
"burning_micturition",
|
120 |
+
"spotting_urination",
|
121 |
+
"yellow_urine",
|
122 |
+
"bladder_discomfort",
|
123 |
+
"foul_smell_of_urine",
|
124 |
+
"continuous_feel_of_urine",
|
125 |
+
"polyuria",
|
126 |
+
"dark_urine",
|
127 |
+
]
|
128 |
+
}
|
129 |
+
|
130 |
+
MUSCULOSKELETAL_SYMPTOMS = {
|
131 |
+
"MUSCULOSKELETAL_SYMPTOMS": [
|
132 |
+
"joint_pain",
|
133 |
+
"muscle_wasting",
|
134 |
+
"muscle_pain",
|
135 |
+
"muscle_weakness",
|
136 |
+
"knee_pain",
|
137 |
+
"stiff_neck",
|
138 |
+
"swelling_joints",
|
139 |
+
"movement_stiffness",
|
140 |
+
"hip_joint_pain",
|
141 |
+
"painful_walking",
|
142 |
+
"weakness_of_one_body_side",
|
143 |
+
"neck_pain",
|
144 |
+
"back_pain",
|
145 |
+
"weakness_in_limbs",
|
146 |
+
"cramps",
|
147 |
+
]
|
148 |
+
}
|
149 |
+
|
150 |
+
FEELING_SYMPTOMS = {
|
151 |
+
"FEELING_SYPTOMS": [
|
152 |
+
"anxiety",
|
153 |
+
"restlessness",
|
154 |
+
"lethargy",
|
155 |
+
"mood_swings",
|
156 |
+
"depression",
|
157 |
+
"irritability",
|
158 |
+
"lack_of_concentration",
|
159 |
+
"fatigue",
|
160 |
+
"malaise",
|
161 |
+
"weight_gain",
|
162 |
+
"increased_appetite",
|
163 |
+
"weight_loss",
|
164 |
+
"loss_of_appetite",
|
165 |
+
"obesity",
|
166 |
+
"excessive_hunger",
|
167 |
+
]
|
168 |
+
}
|
169 |
+
|
170 |
+
OTHER_SYPTOMS = {
|
171 |
+
"OTHER_SYPTOMS": [
|
172 |
+
"ulcers_on_tongue",
|
173 |
+
"shivering",
|
174 |
+
"chills",
|
175 |
+
"irregular_sugar_level",
|
176 |
+
"high_fever",
|
177 |
+
"slurred_speech",
|
178 |
+
"sweating",
|
179 |
+
"internal_itching",
|
180 |
+
"mild_fever",
|
181 |
+
"toxic_look_(typhos)",
|
182 |
+
"acute_liver_failure",
|
183 |
+
"dehydration",
|
184 |
+
"headache",
|
185 |
+
"extra_marital_contacts",
|
186 |
+
"drying_and_tingling_lips",
|
187 |
+
"altered_sensorium",
|
188 |
+
]
|
189 |
+
}
|
190 |
+
|
191 |
+
PATIENT_HISTORY = {
|
192 |
+
"PATIENT_HISTORY": [
|
193 |
+
"family_history",
|
194 |
+
"receiving_blood_transfusion",
|
195 |
+
"receiving_unsterile_injections",
|
196 |
+
"history_of_alcohol_consumption",
|
197 |
+
"coma",
|
198 |
+
]
|
199 |
+
}
|
200 |
+
|
201 |
+
SYMPTOMS_LIST = [
|
202 |
+
SKIN_SYPTOMS,
|
203 |
+
EYES_SYMPTOMS,
|
204 |
+
ORL_SYPTOMS,
|
205 |
+
THORAX_SYMPTOMS,
|
206 |
+
DIGESTIVE_SYSTEM_SYPTOMS,
|
207 |
+
UROLOGICAL_SYMPTOMS,
|
208 |
+
VASCULAR_LYMPHATIC_SYMPTOMS,
|
209 |
+
MUSCULOSKELETAL_SYMPTOMS,
|
210 |
+
FEELING_SYMPTOMS,
|
211 |
+
PATIENT_HISTORY,
|
212 |
+
OTHER_SYPTOMS,
|
213 |
+
]
|
214 |
+
|
215 |
+
|
216 |
+
def test(file_path="./Training.csv"):
|
217 |
+
df = pd.read_csv(file_path, index_col=0)
|
218 |
+
valid_column = df.columns
|
219 |
+
all_symptoms = [category.values() for category in SYMPTOMS_LIST]
|
220 |
+
all_symptoms = list(itertools.chain.from_iterable(all_symptoms))
|
221 |
+
all_symptoms = list(itertools.chain.from_iterable(all_symptoms))
|
222 |
+
set(valid_column) - set(all_symptoms), set(all_symptoms) - set(valid_column)
|
223 |
+
|
224 |
+
|
225 |
+
if __name__ == "__main__":
|
226 |
+
test()
|