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| import subprocess | |
| import time | |
| from typing import Dict, List, Tuple | |
| import gradio as gr # pylint: disable=import-error | |
| import numpy as np | |
| import pandas as pd | |
| import requests | |
| from symptoms_categories import SYMPTOMS_LIST | |
| from utils import ( | |
| CLIENT_DIR, | |
| CURRENT_DIR, | |
| DEPLOYMENT_DIR, | |
| INPUT_BROWSER_LIMIT, | |
| KEYS_DIR, | |
| SERVER_URL, | |
| TARGET_COLUMNS, | |
| TRAINING_FILENAME, | |
| clean_directory, | |
| get_disease_name, | |
| load_data, | |
| pretty_print, | |
| ) | |
| from concrete.ml.deployment import FHEModelClient | |
| subprocess.Popen(["uvicorn", "server:app"], cwd=CURRENT_DIR) | |
| time.sleep(3) | |
| # pylint: disable=c-extension-no-member,invalid-name | |
| 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 display_default_symptoms_fn(default_disease: str) -> Dict: | |
| """ | |
| Displays the symptoms of a given existing disease. | |
| Args: | |
| default_disease (str): Disease | |
| Returns: | |
| Dict: The according symptoms | |
| """ | |
| df = pd.read_csv(TRAINING_FILENAME) | |
| df_filtred = df[df[TARGET_COLUMNS[1]] == default_disease] | |
| return { | |
| default_symptoms: gr.update( | |
| visible=True, | |
| value=pretty_print( | |
| df_filtred.columns[df_filtred.eq(1).any()].to_list(), delimiter=", " | |
| ), | |
| ) | |
| } | |
| def get_user_symptoms_from_checkboxgroup(checkbox_symptoms: List) -> np.array: | |
| """ | |
| Convert the user symptoms into a binary vector representation. | |
| Args: | |
| checkbox_symptoms (List): A list of user symptoms. | |
| Returns: | |
| np.array: A binary vector representing the user's symptoms. | |
| Raises: | |
| KeyError: If a provided symptom is not recognized as a valid symptom. | |
| """ | |
| symptoms_vector = {key: 0 for key in valid_symptoms} | |
| for pretty_symptom in checkbox_symptoms: | |
| original_symptom = "_".join((pretty_symptom.lower().split(" "))) | |
| if original_symptom not in symptoms_vector.keys(): | |
| raise KeyError( | |
| f"The symptom '{original_symptom}' you provided is not recognized as a valid " | |
| f"symptom.\nHere is the list of valid symptoms: {symptoms_vector}" | |
| ) | |
| symptoms_vector[original_symptom] = 1 | |
| user_symptoms_vect = np.fromiter(symptoms_vector.values(), dtype=float)[np.newaxis, :] | |
| assert all(value == 0 or value == 1 for value in user_symptoms_vect.flatten()) | |
| return user_symptoms_vect | |
| def get_features_fn(*checked_symptoms: Tuple[str]) -> Dict: | |
| """ | |
| Get vector features based on the selected symptoms. | |
| Args: | |
| checked_symptoms (Tuple[str]): User symptoms | |
| Returns: | |
| Dict: The encoded user vector symptoms. | |
| """ | |
| if not any(lst for lst in checked_symptoms if lst): | |
| return { | |
| error_box1: gr.update(visible=True, value="⚠️ Please provide your chief complaints."), | |
| } | |
| if len(pretty_print(checked_symptoms)) < 5: | |
| print("Provide at least 5 symptoms.") | |
| return { | |
| error_box1: gr.update(visible=True, value="⚠️ Provide at least 5 symptoms"), | |
| one_hot_vect: None, | |
| } | |
| return { | |
| error_box1: gr.update(visible=False), | |
| one_hot_vect: gr.update( | |
| visible=False, | |
| value=get_user_symptoms_from_checkboxgroup(pretty_print(checked_symptoms)), | |
| ), | |
| submit_btn: gr.update(value="Data submitted ✅"), | |
| } | |
| def key_gen_fn(user_symptoms: List[str]) -> Dict: | |
| """ | |
| Generate keys for a given user. | |
| Args: | |
| user_symptoms (List[str]): The vector symptoms provided by the user. | |
| Returns: | |
| dict: A dictionary containing the generated keys and related information. | |
| """ | |
| clean_directory() | |
| if is_none(user_symptoms): | |
| print("Error: Please submit your symptoms or select a default disease.") | |
| return { | |
| error_box2: gr.update(visible=True, value="⚠️ Please submit your symptoms first."), | |
| } | |
| # 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=False, value=serialized_evaluation_keys_shorten_hex), | |
| user_id_box: gr.update(visible=True, value=user_id), | |
| key_len_box: gr.update( | |
| visible=False, value=f"{len(serialized_evaluation_keys) / (10**6):.2f} MB" | |
| ), | |
| } | |
| def encrypt_fn(user_symptoms: np.ndarray, user_id: str) -> None: | |
| """ | |
| Encrypt the user symptoms vector in the `Client Side`. | |
| Args: | |
| user_symptoms (List[str]): The vector symptoms provided by the user | |
| user_id (user): The current user's ID | |
| """ | |
| if is_none(user_id) or is_none(user_symptoms): | |
| print("Error in encryption step: Provide your symptoms and generate the evaluation keys.") | |
| return { | |
| error_box3: gr.update( | |
| visible=True, | |
| value="⚠️ Please ensure that your symptoms 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_symptoms = np.fromstring(user_symptoms[2:-2], dtype=int, sep=".").reshape(1, -1) | |
| # quant_user_symptoms = client.model.quantize_input(user_symptoms) | |
| encrypted_quantized_user_symptoms = client.quantize_encrypt_serialize(user_symptoms) | |
| assert isinstance(encrypted_quantized_user_symptoms, bytes) | |
| encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_input" | |
| with encrypted_input_path.open("wb") as f: | |
| f.write(encrypted_quantized_user_symptoms) | |
| encrypted_quantized_user_symptoms_shorten_hex = encrypted_quantized_user_symptoms.hex()[ | |
| :INPUT_BROWSER_LIMIT | |
| ] | |
| return { | |
| error_box3: gr.update(visible=False), | |
| one_hot_vect_box: gr.update(visible=True, value=user_symptoms), | |
| enc_vect_box: gr.update(visible=True, value=encrypted_quantized_user_symptoms_shorten_hex), | |
| } | |
| def send_input_fn(user_id: str, user_symptoms: np.ndarray) -> Dict: | |
| """Send the encrypted data and the evaluation key to the server. | |
| Args: | |
| user_id (str): The current user's ID | |
| user_symptoms (np.ndarray): The user symptoms | |
| """ | |
| if is_none(user_id) or is_none(user_symptoms): | |
| 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_symptoms, | |
| } | |
| 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 get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict: | |
| """Retreive the encrypted data from the server. | |
| Args: | |
| user_id (str): The current user's ID | |
| user_symptoms (np.ndarray): The user symptoms | |
| """ | |
| if is_none(user_id) or is_none(user_symptoms): | |
| 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_symptoms: np.ndarray, *checked_symptoms, threshold: int = 0.5 | |
| ) -> Dict: | |
| """Dencrypt the data on the `Client Side`. | |
| Args: | |
| user_id (str): The current user's ID | |
| user_symptoms (np.ndarray): The user symptoms | |
| threshold (float): Probability confidence threshold | |
| Returns: | |
| Decrypted output | |
| """ | |
| if is_none(user_id) or is_none(user_symptoms): | |
| 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 symptoms 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) | |
| top3_diseases = np.argsort(output.flatten())[-3:][::-1] | |
| top3_proba = output[0][top3_diseases] | |
| out = "" | |
| if top3_proba[0] < threshold or abs(top3_proba[0] - top3_proba[1]) < 0.1: | |
| out = ( | |
| "⚠️ The prediction appears uncertain; including more symptoms " | |
| "may improve the results.\n\n" | |
| ) | |
| out = ( | |
| f"{out}Given the symptoms you provided: " | |
| f"{pretty_print(checked_symptoms, case_conversion=str.capitalize, delimiter=', ')}\n\n" | |
| "Here are the top3 predictions:\n\n" | |
| f"1. « {get_disease_name(top3_diseases[0])} » with a probability of {top3_proba[0]:.2%}\n" | |
| f"2. « {get_disease_name(top3_diseases[1])} » with a probability of {top3_proba[1]:.2%}\n" | |
| f"3. « {get_disease_name(top3_diseases[2])} » with a probability of {top3_proba[2]:.2%}\n" | |
| ) | |
| return { | |
| error_box7: gr.update(visible=False), | |
| decrypt_box: out, | |
| submit_btn: gr.update(value="Submit"), | |
| } | |
| def reset_fn(): | |
| """Reset the space and clear all the box outputs.""" | |
| clean_directory() | |
| return { | |
| one_hot_vect: None, | |
| one_hot_vect_box: None, | |
| enc_vect_box: gr.update(visible=True, value=None), | |
| quant_vect_box: gr.update(visible=False, value=None), | |
| user_id_box: gr.update(visible=False, value=None), | |
| default_symptoms: gr.update(visible=True, value=None), | |
| default_disease_box: gr.update(visible=True, value=None), | |
| key_box: gr.update(visible=True, value=None), | |
| key_len_box: gr.update(visible=False, value=None), | |
| fhe_execution_time_box: gr.update(visible=True, value=None), | |
| decrypt_box: None, | |
| submit_btn: gr.update(value="Submit"), | |
| error_box7: gr.update(visible=False), | |
| error_box1: gr.update(visible=False), | |
| error_box2: gr.update(visible=False), | |
| error_box3: gr.update(visible=False), | |
| error_box4: gr.update(visible=False), | |
| error_box5: gr.update(visible=False), | |
| error_box6: gr.update(visible=False), | |
| srv_resp_send_data_box: None, | |
| srv_resp_retrieve_data_box: None, | |
| **{box: None for box in check_boxes}, | |
| } | |
| if __name__ == "__main__": | |
| print("Starting demo ...") | |
| clean_directory() | |
| (X_train, X_test), (y_train, y_test), valid_symptoms, diseases = load_data() | |
| with gr.Blocks() as demo: | |
| # Link + images | |
| gr.Markdown( | |
| """ | |
| <p align="center"> | |
| <img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png"> | |
| </p> | |
| <h2 align="center">Health Prediction On Encrypted Data Using Fully Homomorphic Encryption.</h2> | |
| <p align="center"> | |
| <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> | |
| — | |
| <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> | |
| — | |
| <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> | |
| — | |
| <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> | |
| </p> | |
| <p align="center"> | |
| <img width="65%" height="20%" src="https://raw.githubusercontent.com/kcelia/Img/main/healthcare_prediction.jpg"> | |
| </p> | |
| """ | |
| ) | |
| gr.Markdown("## Notes") | |
| gr.Markdown( | |
| """ | |
| - The private key is used to encrypt and decrypt the data and shall never be shared. | |
| - The evaluation key is a public key that the server needs to process encrypted data. | |
| """ | |
| ) | |
| # ------------------------- Step 1 ------------------------- | |
| gr.Markdown("\n") | |
| gr.Markdown("## Step 1: Select chief complaints") | |
| gr.Markdown("<hr />") | |
| gr.Markdown("<span style='color:grey'>Client Side</span>") | |
| gr.Markdown("Select at least 5 chief complaints from the list below.") | |
| # Step 1.1: Provide symptoms | |
| check_boxes = [] | |
| with gr.Row(): | |
| with gr.Column(): | |
| for category in SYMPTOMS_LIST[:3]: | |
| with gr.Accordion(pretty_print(category.keys()), open=False): | |
| check_box = gr.CheckboxGroup(pretty_print(category.values()), show_label=0) | |
| check_boxes.append(check_box) | |
| with gr.Column(): | |
| for category in SYMPTOMS_LIST[3:6]: | |
| with gr.Accordion(pretty_print(category.keys()), open=False): | |
| check_box = gr.CheckboxGroup(pretty_print(category.values()), show_label=0) | |
| check_boxes.append(check_box) | |
| with gr.Column(): | |
| for category in SYMPTOMS_LIST[6:]: | |
| with gr.Accordion(pretty_print(category.keys()), open=False): | |
| check_box = gr.CheckboxGroup(pretty_print(category.values()), show_label=0) | |
| check_boxes.append(check_box) | |
| error_box1 = gr.Textbox(label="Error ❌", visible=False) | |
| # Default disease, picked from the dataframe | |
| gr.Markdown( | |
| "You can choose an **existing disease** and explore its associated symptoms.", | |
| visible=False, | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| default_disease_box = gr.Dropdown(sorted(diseases), label="Diseases", visible=False) | |
| with gr.Column(scale=5): | |
| default_symptoms = gr.Textbox(label="Related Symptoms:", visible=False) | |
| # User vector symptoms encoded in oneHot representation | |
| one_hot_vect = gr.Textbox(visible=False) | |
| # Submit botton | |
| submit_btn = gr.Button("Submit") | |
| # Clear botton | |
| clear_button = gr.Button("Reset Space 🔁", visible=False) | |
| default_disease_box.change( | |
| fn=display_default_symptoms_fn, inputs=[default_disease_box], outputs=[default_symptoms] | |
| ) | |
| submit_btn.click( | |
| fn=get_features_fn, | |
| inputs=[*check_boxes], | |
| outputs=[one_hot_vect, error_box1, submit_btn], | |
| ) | |
| # ------------------------- Step 2 ------------------------- | |
| gr.Markdown("\n") | |
| gr.Markdown("## Step 2: Encrypt data") | |
| gr.Markdown("<hr />") | |
| gr.Markdown("<span style='color:grey'>Client Side</span>") | |
| # Step 2.1: Key generation | |
| gr.Markdown( | |
| "### Key Generation\n\n" | |
| "In FHE schemes, a secret (enc/dec)ryption keys are generated for encrypting and decrypting data owned by the client. \n\n" | |
| "Additionally, a public evaluation key is generated, enabling external entities to perform homomorphic operations on encrypted data, without the need to decrypt them. \n\n" | |
| "The evaluation key will be transmitted to the server for further processing." | |
| ) | |
| 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=one_hot_vect, | |
| 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) | |
| quant_vect_box = gr.Textbox(label="Quantized Vector:", visible=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| one_hot_vect_box = gr.Textbox(label="User Symptoms Vector:", max_lines=10) | |
| with gr.Column(): | |
| enc_vect_box = gr.Textbox(label="Encrypted Vector:", max_lines=10) | |
| encrypt_btn.click( | |
| encrypt_fn, | |
| inputs=[one_hot_vect, user_id_box], | |
| outputs=[ | |
| one_hot_vect_box, | |
| enc_vect_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, one_hot_vect], | |
| 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" | |
| "This server employs a [Logistic Regression](https://github.com/zama-ai/concrete-ml/tree/release/1.1.x/use_case_examples/disease_prediction) model that has been trained on this [data-set](https://github.com/anujdutt9/Disease-Prediction-from-Symptoms/tree/master/dataset)." | |
| ) | |
| 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, one_hot_vect], | |
| 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:") | |
| decrypt_btn.click( | |
| decrypt_fn, | |
| inputs=[user_id_box, one_hot_vect, *check_boxes], | |
| outputs=[decrypt_box, error_box7, submit_btn], | |
| ) | |
| # ------------------------- End ------------------------- | |
| gr.Markdown( | |
| """The app was built with [Concrete ML](https://github.com/zama-ai/concrete-ml), a Privacy-Preserving Machine Learning (PPML) open-source set of tools by Zama. | |
| Try it yourself and don't forget to star on [Github](https://github.com/zama-ai/concrete-ml) ⭐. | |
| """ | |
| ) | |
| gr.Markdown("\n\n") | |
| gr.Markdown( | |
| """**Please Note**: This space is intended solely for educational and demonstration purposes. | |
| It should not be considered as a replacement for professional medical counsel, diagnosis, or therapy for any health or related issues. | |
| Any questions or concerns about your individual health should be addressed to your doctor or another qualified healthcare provider. | |
| """ | |
| ) | |
| clear_button.click( | |
| reset_fn, | |
| outputs=[ | |
| one_hot_vect_box, | |
| one_hot_vect, | |
| submit_btn, | |
| error_box1, | |
| error_box2, | |
| error_box3, | |
| error_box4, | |
| error_box5, | |
| error_box6, | |
| error_box7, | |
| default_disease_box, | |
| default_symptoms, | |
| user_id_box, | |
| key_len_box, | |
| key_box, | |
| quant_vect_box, | |
| enc_vect_box, | |
| srv_resp_send_data_box, | |
| srv_resp_retrieve_data_box, | |
| fhe_execution_time_box, | |
| decrypt_box, | |
| *check_boxes, | |
| ], | |
| ) | |
| demo.launch() | |