app.py

from concrete.ml.deployment import FHEModelClient
from pathlib import Path
import numpy as np
import gradio as gr
import requests
from sklearn.preprocessing import OneHotEncoder

# Store the server's URL
SERVER_URL = "http://127.0.0.1:7860/"
CURRENT_DIR = Path(__file__).parent
DEPLOYMENT_DIR = CURRENT_DIR / "deployment_files"
KEYS_DIR = DEPLOYMENT_DIR / ".fhe_keys"
CLIENT_DIR = DEPLOYMENT_DIR / "client_dir"
SERVER_DIR = DEPLOYMENT_DIR / "server_dir"


USER_ID = "user_id"
EXAMPLE_CLINICAL_TRIAL_LINK = "https://www.trials4us.co.uk/ongoing-clinical-trials/recruiting-healthy-adults-c23026?_gl=1*1ysp815*_up*MQ..&gclid=Cj0KCQjwr9m3BhDHARIsANut04bHqi5zE3sjS3f8JK2WRN3YEgY4bTfWbvTdZTxkUTSISxXX5ZWL7qEaAowwEALw_wcB&gbraid=0AAAAAD3Qci2k_3IERmM6U1FGDuYVayZWH"




# Define possible categories for fields without predefined categories
additional_categories = {
    "Gender": ["Male", "Female", "Other"],
    "Ethnicity": ["White", "Black or African American", "Asian", "American Indian or Alaska Native", "Native Hawaiian or Other Pacific Islander", "Other"],
    "Geographic_Location": ["North America", "South America", "Europe", "Asia", "Africa", "Australia", "Antarctica"],
    "Smoking_Status": ["Never", "Former", "Current"],
    "Diagnoses_ICD10": ["E11.9", "I10", "J45.909", "M54.5", "F32.9", "K21.9"],
    "Medications": ["Metformin", "Lisinopril", "Atorvastatin", "Amlodipine", "Omeprazole", "Simvastatin", "Levothyroxine", "None"],
    "Allergies": ["Penicillin", "Peanuts", "Shellfish", "Latex", "Bee stings", "None"],
    "Previous_Treatments": ["Chemotherapy", "Radiation Therapy", "Surgery", "Physical Therapy", "Immunotherapy", "None"],
    "Alcohol_Consumption": ["None", "Occasionally", "Regularly", "Heavy"],
    "Exercise_Habits": ["Sedentary", "Light", "Moderate", "Active", "Very Active"],
    "Diet": ["Omnivore", "Vegetarian", "Vegan", "Pescatarian", "Keto", "Mediterranean"],
    "Functional_Status": ["Independent", "Assisted", "Dependent"],
    "Previous_Trial_Participation": ["Yes", "No"]
}

# Define the input components for the form
age_input = gr.Slider(minimum=18, maximum=100, label="Age ", step=1)
gender_input = gr.Radio(choices=additional_categories["Gender"], label="Gender")
ethnicity_input = gr.Radio(choices=additional_categories["Ethnicity"], label="Ethnicity")
geographic_location_input = gr.Radio(choices=additional_categories["Geographic_Location"], label="Geographic Location")
diagnoses_icd10_input = gr.CheckboxGroup(choices=additional_categories["Diagnoses_ICD10"], label="Diagnoses (ICD-10)")
medications_input = gr.CheckboxGroup(choices=additional_categories["Medications"], label="Medications")
allergies_input = gr.CheckboxGroup(choices=additional_categories["Allergies"], label="Allergies")
previous_treatments_input = gr.CheckboxGroup(choices=additional_categories["Previous_Treatments"], label="Previous Treatments")
blood_glucose_level_input = gr.Slider(minimum=0, maximum=300, label="Blood Glucose Level", step=1)
blood_pressure_systolic_input = gr.Slider(minimum=80, maximum=200, label="Blood Pressure (Systolic)", step=1)
blood_pressure_diastolic_input = gr.Slider(minimum=40, maximum=120, label="Blood Pressure (Diastolic)", step=1)
bmi_input = gr.Slider(minimum=10, maximum=50, label="BMI ", step=1)
smoking_status_input = gr.Radio(choices=additional_categories["Smoking_Status"], label="Smoking Status")
alcohol_consumption_input = gr.Radio(choices=additional_categories["Alcohol_Consumption"], label="Alcohol Consumption")
exercise_habits_input = gr.Radio(choices=additional_categories["Exercise_Habits"], label="Exercise Habits")
diet_input = gr.Radio(choices=additional_categories["Diet"], label="Diet")
condition_severity_input = gr.Slider(minimum=1, maximum=10, label="Condition Severity", step=1)
functional_status_input = gr.Radio(choices=additional_categories["Functional_Status"], label="Functional Status")
previous_trial_participation_input = gr.Radio(choices=additional_categories["Previous_Trial_Participation"], label="Previous Trial Participation")


def encrypt_array(user_symptoms: np.ndarray, user_id: str) -> bytes:
    """
    Encrypt the user symptoms vector.

    Args:
        user_symptoms (np.ndarray): The vector of symptoms provided by the user.
        user_id (str): The current user's ID.
    
    Returns:
        bytes: Encrypted and serialized symptoms.
    """

    # Retrieve the client API
    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
    client.load()

    # Ensure the symptoms are properly formatted as an array
    user_symptoms = np.array(user_symptoms).reshape(1, -1)

    # Encrypt and serialize the symptoms
    encrypted_quantized_user_symptoms = client.quantize_encrypt_serialize(user_symptoms)
    
    # Ensure the encryption process returned bytes
    assert isinstance(encrypted_quantized_user_symptoms, bytes)

    # Save the encrypted data to a file (optional)
    encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_input"
    with encrypted_input_path.open("wb") as f:
        f.write(encrypted_quantized_user_symptoms)

    # Return the encrypted data
    return encrypted_quantized_user_symptoms


def decrypt_result(encrypted_answer: bytes, user_id: str) -> bool:
    """
    Decrypt the encrypted result.

    Args:
        encrypted_answer (bytes): The encrypted result.
        user_id (str): The current user's ID.
    
    Returns:
        bool: The decrypted result.
    """

    # Retrieve the client API
    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{user_id}")
    client.load()

    # Decrypt the result
    decrypted_result = client.decrypt_deserialize(encrypted_answer)

    # Return the decrypted result
    return decrypted_result



def encode_categorical_data(data):
    categories = ["Gender", "Ethnicity", "Geographic_Location", "Smoking_Status", "Alcohol_Consumption", "Exercise_Habits", "Diet", "Functional_Status", "Previous_Trial_Participation"]
    encoded_data = []
    for i in range(len(categories)):
        sub_cats = additional_categories[categories[i]]
        if data[i] in sub_cats:
            encoded_data.append(sub_cats.index(data[i]) + 1)
        else:
            encoded_data.append(0)
        
    return encoded_data


def process_patient_data(age, gender, ethnicity, geographic_location, diagnoses_icd10, medications, allergies, previous_treatments, blood_glucose_level, blood_pressure_systolic, blood_pressure_diastolic, bmi, smoking_status, alcohol_consumption, exercise_habits, diet, condition_severity, functional_status, previous_trial_participation):    
    
    # Encode the data
    categorical_data = [gender, ethnicity, geographic_location, smoking_status, alcohol_consumption, exercise_habits, diet, functional_status, previous_trial_participation]
    print(f"Categorical data: {categorical_data}")
    encoded_categorical_data = encode_categorical_data(categorical_data)
    numerical_data = np.array([age, blood_glucose_level, blood_pressure_systolic, blood_pressure_diastolic, bmi, condition_severity])
    print(f"Numerical data: {numerical_data}")
    print(f"One-hot encoded data: {encoded_categorical_data}")
    combined_data = np.hstack((numerical_data, encoded_categorical_data))
    print(f"Combined data: {combined_data}")
    encrypted_array = encrypt_array(combined_data, "user_id")

    # Send the encrypted data to the server 
    response = requests.post(SERVER_URL, data=encrypted_array)

    # Check if the data was sent successfully
    if response.status_code == 200:
        print("Data sent successfully.")
    else:
        print("Error sending data.")
    
    # Decrypt the result
    decrypted_result = decrypt_result(response.content, USER_ID)
    
    # If the answer is True, return the link
    if decrypted_result:
        return (
            f"Encrypted data: {encrypted_array}",
            f"Decrypted result: {decrypted_result}",
            f"You may now access the link to the [clinical trial]({EXAMPLE_CLINICAL_TRIAL_LINK})"
        )
    else:
        return (
            f"Encrypted data: {encrypted_array}",
            f"Decrypted result: {decrypted_result}",
            f"Unfortunately, there are no clinical trials available for the provided criteria."
        )

# Create the Gradio interface
demo = gr.Interface(
    fn=process_patient_data, 
    inputs=[
        age_input, gender_input, ethnicity_input, geographic_location_input, diagnoses_icd10_input, medications_input, allergies_input, previous_treatments_input, blood_glucose_level_input, blood_pressure_systolic_input, blood_pressure_diastolic_input, bmi_input, smoking_status_input, alcohol_consumption_input, exercise_habits_input, diet_input, condition_severity_input, functional_status_input, previous_trial_participation_input
    ], 
    outputs="text",
    title="Patient Data Criteria Form",
    description="Please fill in the criteria for the type of patients you are looking for."
)

# Launch the app
demo.launch()