Upload 12 files

.gitattributes

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 *.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
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.gitignore

@@ -0,0 +1,3 @@
+.fhe_keys/
+client_dir/
+server_dir/

README.md

@@ -1,12 +1,7 @@
----
-title: Fhe Titanic
-emoji: 🐢
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.18.0
-app_file: app.py
-pinned: false
----
+Titanic Survival Prediction using Fully Homomorphic Encryption (FHE) and Cloud Deployment of Encrypted ML
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Titanic survival code adapted from:
+https://huggingface.co/spaces/gradio/titanic_survival_main
+
+FHE Cloud Deployment code adapted from:
+https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/use_case_examples/deployment/README.md

deployment_logit/client.zip

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+size 129

deployment_logit/server.zip

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deployment_logit/versions.json

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+{"concrete-ml": "1.0.3", "concrete-python": "1.0.0", "python": "3.10.12"}

files/titanic.csv

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+853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
+854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
+855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
+856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
+857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
+858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
+859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
+860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
+861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
+862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
+863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
+864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
+865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
+866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
+867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
+868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
+869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
+870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
+871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
+872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
+873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
+874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
+877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
+878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

requirements.txt

@@ -0,0 +1,7 @@
+scikit-learn
+numpy
+pandas
+concrete-ml==1.0.3
+gradio==3.35.2
+uvicorn>=0.21.0
+fastapi>=0.93.0

run.py

@@ -0,0 +1,664 @@
+import os
+
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+
+from concrete.ml.sklearn import RandomForestClassifier as ConcreteRandomForestClassifier
+
+import gradio as gr
+
+from utils import (
+    CLIENT_DIR,
+    CURRENT_DIR,
+    DEPLOYMENT_DIR,
+    INPUT_BROWSER_LIMIT,
+    KEYS_DIR,
+    SERVER_URL,
+    TARGET_COLUMNS,
+    TRAINING_FILENAME,
+    clean_directory,
+    load_data,
+    pretty_print,
+)
+
+import requests
+import subprocess
+import time
+from typing import Dict, List, Tuple
+
+from concrete.ml.deployment import FHEModelClient
+
+subprocess.Popen(["uvicorn", "server:app"], cwd=CURRENT_DIR)
+time.sleep(3)
+
+current_dir = os.path.dirname(os.path.realpath(__file__))
+data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))
+
+def is_none(obj) -> bool:
+    """
+    Check if the object is None.
+
+    Args:
+        obj (any): The input to be checked.
+
+    Returns:
+        bool: True if the object is None or empty, False otherwise.
+    """
+    return obj is None or (obj is not None and len(obj) < 1)
+
+def encode_age(df):
+    df.Age = df.Age.fillna(-0.5)
+    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
+    categories = pd.cut(df.Age, bins, labels=False)
+    df.Age = categories
+    return df
+
+
+def encode_fare(df):
+    df.Fare = df.Fare.fillna(-0.5)
+    bins = (-1, 0, 8, 15, 31, 1000)
+    categories = pd.cut(df.Fare, bins, labels=False)
+    df.Fare = categories
+    return df
+
+
+def encode_df(df):
+    df = encode_age(df)
+    df = encode_fare(df)
+    sex_mapping = {"male": 0, "female": 1}
+    df = df.replace({"Sex": sex_mapping})
+    embark_mapping = {"S": 1, "C": 2, "Q": 3}
+    df = df.replace({"Embarked": embark_mapping})
+    df.Embarked = df.Embarked.fillna(0)
+    df["Company"] = 0
+    df.loc[(df["SibSp"] > 0), "Company"] = 1
+    df.loc[(df["Parch"] > 0), "Company"] = 2
+    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
+    df = df[
+        [
+            "PassengerId",
+            "Pclass",
+            "Sex",
+            "Age",
+            "Fare",
+            "Embarked",
+            "Company",
+            "Survived",
+        ]
+    ]
+    return df
+
+
+train = encode_df(data)
+
+X_all = train.drop(["Survived", "PassengerId"], axis=1)
+y_all = train["Survived"]
+
+num_test = 0.20
+X_train, X_test, y_train, y_test = train_test_split(
+    X_all, y_all, test_size=num_test, random_state=23
+)
+
+clf = RandomForestClassifier()
+clf.fit(X_train, y_train)
+predictions = clf.predict(X_test)
+
+n_estimators = 50
+max_depth = 4
+n_bits = 6
+n_jobs_xgb = 1
+n_jobs_gridsearch = -1
+concrete_clf = ConcreteRandomForestClassifier(
+    n_bits=n_bits, n_estimators=n_estimators, max_depth=max_depth, n_jobs=n_jobs_xgb
+)
+concrete_clf.fit(X_train, y_train)
+concrete_predictions = concrete_clf.predict(X_test)
+
+def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
+    if passenger_class is None or embark_point is None:
+        return None
+    df = pd.DataFrame.from_dict(
+        {
+            "Pclass": [passenger_class + 1],
+            "Sex": [0 if is_male else 1],
+            "Age": [age],
+            "Fare": [fare],
+            "Embarked": [embark_point + 1],
+            "Company": [
+                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
+            ]
+        }
+    )
+    df = encode_age(df)
+    df = encode_fare(df)
+    pred = clf.predict_proba(df)[0]
+    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}
+
+def collect_input(passenger_class, is_male, age, company, fare, embark_point):
+    if passenger_class is None or embark_point is None:
+        return None
+    input_dict = {
+            "Pclass": [passenger_class + 1],
+            "Sex": [0 if is_male else 1],
+            "Age": [age],
+            "Fare": [fare],
+            "Embarked": [embark_point + 1],
+            "Company": [
+                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
+            ]
+        }
+    print(input_dict)
+    return input_dict
+
+def clear_predict_survival(input_dict):
+    df = pd.DataFrame.from_dict(input_dict)
+    df = encode_age(df)
+    df = encode_fare(df)
+    pred = clf.predict_proba(df)[0]
+    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}
+
+def concrete_predict_survival(input_dict):
+    df = pd.DataFrame.from_dict(input_dict)
+    df = encode_age(df)
+    df = encode_fare(df)
+    pred = concrete_clf.predict_proba(df)[0]
+    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}
+
+print("\nclear_test    ", clear_predict_survival({'Pclass': [1], 'Sex': [0], 'Age': [25], 'Fare': [20.0], 'Embarked': [2], 'Company': [1]}))
+
+print("encrypted_test", concrete_predict_survival({'Pclass': [1], 'Sex': [0], 'Age': [25], 'Fare': [20.0], 'Embarked': [2], 'Company': [1]}),"\n")
+
+
+def key_gen_fn() -> Dict:
+    """
+    Generate keys for a given user.
+
+    Args:
+        
+    Returns:
+        dict: A dictionary containing the generated keys and related information.
+
+    """
+    clean_directory()
+
+    # 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()
+
+    # 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: gr.update(visible=True, value=serialized_evaluation_keys_shorten_hex),
+        user_id_box: gr.update(visible=True, value=user_id),
+        key_len_box: gr.update(
+            visible=True, value=f"{len(serialized_evaluation_keys) / (10**6):.2f} MB"
+        ),
+    }
+
+def encrypt_fn(user_inputs: np.ndarray, user_id: str) -> None:
+    """
+    """
+
+    if is_none(user_id) or is_none(user_inputs):
+        print("Error in encryption step: Provide your inputs and generate the evaluation keys.")
+        return {
+            error_box3: gr.update(
+                visible=True,
+                value="⚠️ Please ensure that your inputs have been submitted and "
+                "that you have generated the evaluation key.",
+            )
+        }
+
+    # Retrieve the client API
+    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
+    client.load()
+
+    # user_inputs = np.fromstring(user_symptoms[2:-2], dtype=int, sep=".").reshape(1, -1)
+    # quant_user_symptoms = client.model.quantize_input(user_inputs)
+    
+    user_inputs_df = pd.DataFrame.from_dict(user_inputs)
+    user_inputs_df = encode_age(user_inputs_df)
+    user_inputs_df = encode_fare(user_inputs_df)
+
+    print("user_inputs to be encrypted =\n", user_inputs_df)
+    print("user_inputs to be encrypted =\n", user_inputs_df.to_numpy())
+    
+    encrypted_quantized_user_inputs = client.quantize_encrypt_serialize(user_inputs_df.to_numpy())
+
+    assert isinstance(encrypted_quantized_user_inputs, bytes)
+    encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_input"
+
+    with encrypted_input_path.open("wb") as f:
+        f.write(encrypted_quantized_user_inputs)
+
+    encrypted_quantized_user_inputs_shorten_hex = encrypted_quantized_user_inputs.hex()[
+        :INPUT_BROWSER_LIMIT
+    ]
+
+    return {
+        error_box3: gr.update(visible=False),
+        input_dict_box: gr.update(visible=True, value=user_inputs),
+        enc_dict_box: gr.update(visible=True, value=encrypted_quantized_user_inputs_shorten_hex),
+    }
+
+def send_input_fn(user_id: str, user_inputs: np.ndarray) -> Dict:
+    """Send the encrypted data and the evaluation key to the server.
+    """
+
+    if is_none(user_id) or is_none(user_inputs):
+        return {
+            error_box4: gr.update(
+                visible=True,
+                value="⚠️ Please check your connectivity \n"
+                "⚠️ Ensure that the symptoms have been submitted and the evaluation "
+                "key has been generated 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_input"
+
+    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,
+        "input": user_inputs,
+    }
+
+    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),
+        srv_resp_send_data_box: "Data sent",
+    }
+
+def run_fhe_fn(user_id: str) -> Dict:
+    """Send the encrypted input and the evaluation key to the server.
+
+    Args:
+        user_id (int): The current user's ID.
+    """
+    if is_none(user_id):
+        return {
+            error_box5: gr.update(
+                visible=True,
+                value="⚠️ Please check your connectivity \n"
+                "⚠️ Ensure that the symptoms have been submitted, the evaluation "
+                "key has been generated and the server received the data "
+                "before processing the data.",
+            ),
+            fhe_execution_time_box: None,
+        }
+
+    data = {
+        "user_id": user_id,
+    }
+
+    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=(
+                        "⚠️ An error occurred on the Server Side. "
+                        "Please check connectivity and data transmission."
+                    ),
+                ),
+                fhe_execution_time_box: gr.update(visible=False),
+            }
+        else:
+            time.sleep(1)
+            print(f"response.ok: {response.ok}, {response.json()} - Computed")
+
+    return {
+        error_box5: gr.update(visible=False),
+        fhe_execution_time_box: gr.update(visible=True, value=f"{response.json():.2f} seconds"),
+    }
+
+def send_input_fn(user_id: str, user_inputs: np.ndarray) -> Dict:
+    """Send the encrypted data and the evaluation key to the server.
+    """
+
+    if is_none(user_id) or is_none(user_inputs):
+        return {
+            error_box4: gr.update(
+                visible=True,
+                value="⚠️ Please check your connectivity \n"
+                "⚠️ Ensure that the symptoms have been submitted and the evaluation "
+                "key has been generated 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_input"
+
+    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,
+        "input": user_inputs,
+    }
+
+    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),
+        srv_resp_send_data_box: "Data sent",
+    }
+
+def get_output_fn(user_id: str, user_inputs: np.ndarray) -> Dict:
+    """Retreive the encrypted data from the server.
+    """
+
+    if is_none(user_id) or is_none(user_inputs):
+        return {
+            error_box6: gr.update(
+                visible=True,
+                value="⚠️ Please check your connectivity \n"
+                "⚠️ Ensure that the server has successfully processed and transmitted the data to the client.",
+            )
+        }
+
+    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_inputs: np.ndarray) -> Dict:
+    """Dencrypt the data on the `Client Side`.
+    Args:
+        user_id (str): The current user's ID
+        user_inputs (np.ndarray): The user inputs
+    Returns:
+        Decrypted output
+    """
+
+    if is_none(user_id) or is_none(user_inputs):
+        return {
+            error_box7: gr.update(
+                visible=True,
+                value="⚠️ Please check your connectivity \n"
+                "⚠️ Ensure that the client has successfully received the data from the server.",
+            )
+        }
+
+    # 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: \n"
+                "- the connectivity \n"
+                "- the inputs have been submitted \n"
+                "- the evaluation key has been generated \n"
+                "- the server processed the encrypted data \n"
+                "- the Client received the data from the Server before decrypting the prediction",
+            ),
+            decrypt_box: None,
+        }
+
+    # 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)
+
+    print("output =\n", output)
+
+    # top3_diseases = np.argsort(output.flatten())[-3:][::-1]
+    # top3_proba = output[0][top3_diseases]
+
+    out = {"Perishes": float(output[0][0]), "Survives": float(output[0][1])}
+
+    print("output =\n", out)
+
+    return {
+        error_box7: gr.update(visible=False),
+        decrypt_box: out,
+        label: out,
+    }
+
+with gr.Blocks() as demo:
+
+    # Step 1.1: Provide inputs
+    with gr.Row():
+        inp = [
+                gr.Dropdown(["first", "second", "third"], type="index"),
+                gr.Checkbox(label="is_male"),
+                gr.Slider(0, 80, value=25),
+                gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
+                gr.Number(value=20),
+                gr.Radio(["S", "C", "Q"], type="index"),
+            ]
+        out = gr.JSON()
+    btn = gr.Button("Run")
+    btn.click(fn=collect_input, inputs=inp, outputs=out)
+
+    # Step 2.1: Key generation
+
+    gen_key_btn = gr.Button("Generate the evaluation key")
+    error_box2 = gr.Textbox(label="Error ❌", visible=False)
+    user_id_box = gr.Textbox(label="User ID:", visible=True)
+    key_len_box = gr.Textbox(label="Evaluation Key Size:", visible=False)
+    key_box = gr.Textbox(label="Evaluation key (truncated):", max_lines=3, visible=False)
+
+    gen_key_btn.click(
+        key_gen_fn,
+        inputs=None,
+        outputs=[
+            key_box,
+            user_id_box,
+            key_len_box,
+            error_box2,
+        ],
+    )
+
+    # # Step 2.2: Encrypt data locally
+    gr.Markdown("### Encrypt the data")
+    encrypt_btn = gr.Button("Encrypt the data using the private secret key")
+    error_box3 = gr.Textbox(label="Error ❌", visible=False)
+
+    with gr.Row():
+        with gr.Column():
+            input_dict_box = gr.Textbox(label="input_dict_box:", max_lines=10)
+        with gr.Column():
+            enc_dict_box = gr.Textbox(label="input_dict_box:", max_lines=10)
+
+    encrypt_btn.click(
+        encrypt_fn,
+        inputs=[out, user_id_box],
+        outputs=[
+            input_dict_box,
+            enc_dict_box,
+            error_box3,
+        ],
+    )
+    # # Step 2.3: Send encrypted data to the server
+    gr.Markdown(
+        "### Send the encrypted data to the <span style='color:grey'>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 data")
+        with gr.Column(scale=1):
+            srv_resp_send_data_box = gr.Checkbox(label="Data Sent", show_label=False)
+
+    send_input_btn.click(
+        send_input_fn,
+        inputs=[user_id_box, out],
+        outputs=[error_box4, srv_resp_send_data_box],
+    )
+
+    # ------------------------- Step 3 -------------------------
+    gr.Markdown("\n")
+    gr.Markdown("## Step 3: Run the FHE evaluation")
+    gr.Markdown("<hr />")
+    gr.Markdown("<span style='color:grey'>Server Side</span>")
+    gr.Markdown(
+        "Once the server receives the encrypted data, it can process and compute the output without ever decrypting the data just as it would on clear data.\n\n"
+    )
+
+    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:", visible=True)
+    run_fhe_btn.click(
+        run_fhe_fn,
+        inputs=[user_id_box],
+        outputs=[fhe_execution_time_box, error_box5],
+    )
+
+    # ------------------------- Step 4 -------------------------
+    gr.Markdown("\n")
+    gr.Markdown("## Step 4: Decrypt the data")
+    gr.Markdown("<hr />")
+    gr.Markdown("<span style='color:grey'>Client Side</span>")
+    gr.Markdown(
+        "### Get the encrypted data from the <span style='color:grey'>Server Side</span>"
+    )
+
+    error_box6 = gr.Textbox(label="Error ❌", visible=False)
+
+    # Step 4.1: Data transmission
+    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)
+
+    get_output_btn.click(
+        get_output_fn,
+        inputs=[user_id_box, out],
+        outputs=[srv_resp_retrieve_data_box, error_box6],
+    )
+
+    # Step 4.1: Data transmission
+    gr.Markdown("### Decrypt the output")
+    decrypt_btn = gr.Button("Decrypt the output using the private secret key")
+    error_box7 = gr.Textbox(label="Error ❌", visible=False)
+    decrypt_box = gr.Textbox(label="Decrypted Output:")
+    label = gr.Label()
+
+    decrypt_btn.click(
+        decrypt_fn,
+        inputs=[user_id_box, out],
+        outputs=[decrypt_box, error_box7, label],
+    )
+
+    # ------------------------- End -------------------------
+
+demo.launch()

server.py

@@ -0,0 +1,100 @@
+"""Server that will listen for GET and POST requests from the client."""
+
+import time
+from typing import List
+
+from fastapi import FastAPI, File, Form, UploadFile
+from fastapi.responses import JSONResponse, Response
+from utils import DEPLOYMENT_DIR, SERVER_DIR  # pylint: disable=no-name-in-module
+
+from concrete.ml.deployment import FHEModelServer
+
+# Initialize an instance of FastAPI
+app = FastAPI()
+
+# Define the default route
+@app.get("/")
+def root():
+    """
+    Root endpoint of the health prediction API.
+
+    Returns:
+        dict: The welcome message.
+    """
+    return {"message": "Welcome to your disease prediction with FHE!"}
+
+
+@app.post("/send_input")
+def send_input(
+    user_id: str = Form(),
+    files: List[UploadFile] = File(),
+):
+    """Send the inputs to the server."""
+
+    print("\nSend the data to the server ............\n")
+
+    # Receive the Client's files (Evaluation key + Encrypted symptoms)
+    evaluation_key_path = SERVER_DIR / f"{user_id}_valuation_key"
+    encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input"
+
+    # Save the files using the above paths
+    with encrypted_input_path.open("wb") as encrypted_input, evaluation_key_path.open(
+        "wb"
+    ) as evaluation_key:
+        encrypted_input.write(files[0].file.read())
+        evaluation_key.write(files[1].file.read())
+
+
+@app.post("/run_fhe")
+def run_fhe(
+    user_id: str = Form(),
+):
+    """Inference in FHE."""
+
+    print("\nRun in FHE in the server ............\n")
+    evaluation_key_path = SERVER_DIR / f"{user_id}_valuation_key"
+    encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input"
+
+    # Read the files (Evaluation key + Encrypted symptoms) using the above paths
+    with encrypted_input_path.open("rb") as encrypted_output_file, evaluation_key_path.open(
+        "rb"
+    ) as evaluation_key_file:
+        encrypted_output = encrypted_output_file.read()
+        evaluation_key = evaluation_key_file.read()
+
+    # Load the FHE server and the model
+    fhe_server = FHEModelServer(DEPLOYMENT_DIR)
+
+    # Run the FHE execution
+    start = time.time()
+    encrypted_output = fhe_server.run(encrypted_output, evaluation_key)
+    assert isinstance(encrypted_output, bytes)
+    fhe_execution_time = round(time.time() - start, 2)
+
+    # Retrieve the encrypted output path
+    encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output"
+
+    # Write the file using the above path
+    with encrypted_output_path.open("wb") as f:
+        f.write(encrypted_output)
+
+    return JSONResponse(content=fhe_execution_time)
+
+
+@app.post("/get_output")
+def get_output(user_id: str = Form()):
+    """Retrieve the encrypted output from the server."""
+
+    print("\nGet the output from the server ............\n")
+
+    # Path where the encrypted output is saved
+    encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output"
+
+    # Read the file using the above path
+    with encrypted_output_path.open("rb") as f:
+        encrypted_output = f.read()
+
+    time.sleep(1)
+
+    # Send the encrypted output
+    return Response(encrypted_output)

train.py

@@ -0,0 +1,120 @@
+import os
+import time
+import numpy
+import pandas as pd
+import torch
+
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+
+from sklearn.metrics import average_precision_score
+from sklearn.model_selection import GridSearchCV, train_test_split
+
+from concrete.ml.sklearn import RandomForestClassifier as ConcreteRandomForestClassifier
+
+def train(dev_folder="./dev"):
+    # Download the data-sets
+    if not os.path.isfile("./files/titanic.csv"):
+        raise ValueError(
+            "no dataset"
+        )
+
+    current_dir = os.path.dirname(os.path.realpath(__file__))
+    data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))
+
+    def encode_age(df):
+        df.Age = df.Age.fillna(-0.5)
+        bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
+        categories = pd.cut(df.Age, bins, labels=False)
+        df.Age = categories
+        return df
+
+
+    def encode_fare(df):
+        df.Fare = df.Fare.fillna(-0.5)
+        bins = (-1, 0, 8, 15, 31, 1000)
+        categories = pd.cut(df.Fare, bins, labels=False)
+        df.Fare = categories
+        return df
+
+
+    def encode_df(df):
+        df = encode_age(df)
+        df = encode_fare(df)
+        sex_mapping = {"male": 0, "female": 1}
+        df = df.replace({"Sex": sex_mapping})
+        embark_mapping = {"S": 1, "C": 2, "Q": 3}
+        df = df.replace({"Embarked": embark_mapping})
+        df.Embarked = df.Embarked.fillna(0)
+        df["Company"] = 0
+        df.loc[(df["SibSp"] > 0), "Company"] = 1
+        df.loc[(df["Parch"] > 0), "Company"] = 2
+        df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
+        df = df[
+            [
+                "PassengerId",
+                "Pclass",
+                "Sex",
+                "Age",
+                "Fare",
+                "Embarked",
+                "Company",
+                "Survived",
+            ]
+        ]
+        return df
+
+    train = encode_df(data)
+
+    X_all = train.drop(["Survived", "PassengerId"], axis=1)
+    y_all = train["Survived"]
+
+    num_test = 0.20
+    X_train, X_test, y_train, y_test = train_test_split(
+        X_all, y_all, test_size=num_test, random_state=23
+    )
+
+    # n_estimators = 50
+    # max_depth = 4
+    # n_bits = 6
+    # n_jobs_xgb = 1
+    # n_jobs_gridsearch = -1
+
+    # A gridsearch to find the best parameters
+    parameters = {
+        "n_bits": [6],
+        "max_depth": [4, 8],
+        "n_estimators": [30, 50],
+        "n_jobs": [-1],
+    }
+ 
+    concrete_clf = ConcreteRandomForestClassifier()
+    # concrete_clf.fit(X_train, y_train)
+    # concrete_predictions = concrete_clf.predict(X_test)
+
+    grid_search = GridSearchCV(concrete_clf, parameters, cv=3, n_jobs=-1, scoring="accuracy")
+    grid_search.fit(X_train, y_train)
+
+    # Check the accuracy of the best model
+    print(f"Best score: {grid_search.best_score_}")
+
+    # Check best hyper-parameters
+    print(f"Best parameters: {grid_search.best_params_}")
+
+    # Extract best model
+    best_model = grid_search.best_estimator_
+    assert isinstance(best_model, ConcreteRandomForestClassifier)   
+
+    best_model.compile(X_train)
+
+    # Export the final model such that we can reuse it in a client/server environment
+
+    # Save the model to be pushed to a server later
+    from concrete.ml.deployment import FHEModelDev
+
+    fhe_api = FHEModelDev(dev_folder, best_model)
+    fhe_api.save()
+
+
+if __name__ == "__main__":
+    train()

utils.py

@@ -0,0 +1,144 @@
+import os
+import shutil
+from pathlib import Path
+from typing import List, Tuple, Union
+
+import numpy
+import pandas
+
+from concrete.ml.sklearn import XGBClassifier as ConcreteXGBoostClassifier
+
+# Max Input to be displayed on the HuggingFace space brower using Gradio
+# Too large inputs, slow down the server: https://github.com/gradio-app/gradio/issues/1877
+INPUT_BROWSER_LIMIT = 400
+
+# Store the server's URL
+SERVER_URL = "http://localhost:8000/"
+
+CURRENT_DIR = Path(__file__).parent
+DEPLOYMENT_DIR = CURRENT_DIR / "deployment_logit"
+KEYS_DIR = DEPLOYMENT_DIR / ".fhe_keys"
+CLIENT_DIR = DEPLOYMENT_DIR / "client_dir"
+SERVER_DIR = DEPLOYMENT_DIR / "server_dir"
+
+ALL_DIRS = [KEYS_DIR, CLIENT_DIR, SERVER_DIR]
+
+# Columns that define the target
+TARGET_COLUMNS = ["prognosis_encoded", "prognosis"]
+
+TRAINING_FILENAME = "./data/Training_preprocessed.csv"
+TESTING_FILENAME = "./data/Testing_preprocessed.csv"
+
+# pylint: disable=invalid-name
+
+
+def pretty_print(
+    inputs, case_conversion=str.title, which_replace: str = "_", to_what: str = " ", delimiter=None
+):
+    """
+    Prettify and sort the input as a list of string.
+
+    Args:
+        inputs (Any): The inputs to be prettified.
+
+    Returns:
+        List: The prettified and sorted list of inputs.
+
+    """
+    # Flatten the list if required
+    pretty_list = []
+    for item in inputs:
+        if isinstance(item, list):
+            pretty_list.extend(item)
+        else:
+            pretty_list.append(item)
+
+    # Sort
+    pretty_list = sorted(list(set(pretty_list)))
+    # Replace
+    pretty_list = [item.replace(which_replace, to_what) for item in pretty_list]
+    pretty_list = [case_conversion(item) for item in pretty_list]
+    if delimiter:
+        pretty_list = f"{delimiter.join(pretty_list)}."
+
+    return pretty_list
+
+
+def clean_directory() -> None:
+    """
+    Clear direcgtories
+    """
+    print("Cleaning...\n")
+    for target_dir in ALL_DIRS:
+        if os.path.exists(target_dir) and os.path.isdir(target_dir):
+            shutil.rmtree(target_dir)
+        target_dir.mkdir(exist_ok=True, parents=True)
+
+
+def get_disease_name(encoded_prediction: int, file_name: str = TRAINING_FILENAME) -> str:
+    """Return the disease name given its encoded label.
+
+    Args:
+        encoded_prediction (int): The encoded prediction
+        file_name (str): The data file path
+
+    Returns:
+        str: The according disease name
+    """
+    df = pandas.read_csv(file_name, usecols=TARGET_COLUMNS).drop_duplicates()
+    disease_name, _ = df[df[TARGET_COLUMNS[0]] == encoded_prediction].values.flatten()
+    return disease_name
+
+
+def load_data() -> Union[Tuple[pandas.DataFrame, numpy.ndarray], List]:
+    """
+    Return the data
+
+    Args:
+        None
+
+    Return:
+        The train, testing set and valid symptoms.
+    """
+    # Load data
+    df_train = pandas.read_csv(TRAINING_FILENAME)
+    df_test = pandas.read_csv(TESTING_FILENAME)
+
+    # Separate the traget from the training / testing set:
+    # TARGET_COLUMNS[0] -> "prognosis_encoded" -> contains the numeric label of the disease
+    # TARGET_COLUMNS[1] -> "prognosis"         -> contains the name of the disease
+
+    y_train = df_train[TARGET_COLUMNS[0]]
+    X_train = df_train.drop(columns=TARGET_COLUMNS, axis=1, errors="ignore")
+
+    y_test = df_test[TARGET_COLUMNS[0]]
+    X_test = df_test.drop(columns=TARGET_COLUMNS, axis=1, errors="ignore")
+
+    return (
+        (X_train, X_test),
+        (y_train, y_test),
+        X_train.columns.to_list(),
+        df_train[TARGET_COLUMNS[1]].unique().tolist(),
+    )
+
+
+def load_model(X_train: pandas.DataFrame, y_train: numpy.ndarray):
+    """
+    Load a pre-trained serialized model
+
+    Args:
+        X_train (pandas.DataFrame): Training set
+        y_train (numpy.ndarray): Targets of the training set
+
+    Return:
+        The Concrete ML model and its circuit
+    """
+    # Parameters
+    concrete_args = {"max_depth": 1, "n_bits": 3, "n_estimators": 3, "n_jobs": -1}
+    classifier = ConcreteXGBoostClassifier(**concrete_args)
+    # Train the model
+    classifier.fit(X_train, y_train)
+    # Compile the model
+    circuit = classifier.compile(X_train)
+
+    return classifier, circuit