from concrete.ml.deployment import FHEModelClient from pathlib import Path import numpy as np import gradio as gr import requests # 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()