diff --git a/.gitattributes b/.gitattributes index 62eedaa8bf9fa3e882f06f0b3f9469abb487d2b0..8ebefc88f79921a0511f5826c3a20e9b2ef51343 100644 --- a/.gitattributes +++ b/.gitattributes @@ -2,3 +2,6 @@ *.pt filter=lfs diff=lfs merge=lfs -text *.extension filter=lfs diff=lfs merge=lfs -text *.bin filter=lfs diff=lfs merge=lfs -text +data/200_Users_Running_Dataset.csv filter=lfs diff=lfs merge=lfs -text +deployment_files/.fhe_keys/2820248940/encrypted_input filter=lfs diff=lfs merge=lfs -text +deployment_files/server_dir/2820248940_encrypted_input filter=lfs diff=lfs merge=lfs -text diff --git a/__pycache__/app.cpython-310.pyc b/__pycache__/app.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4273dda9d2f5f8899e228548e18bbb3823dfdd6b Binary files /dev/null and b/__pycache__/app.cpython-310.pyc differ diff --git a/__pycache__/dev_dhiria.cpython-310.pyc b/__pycache__/dev_dhiria.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..49b23cbdf9f2100c65d1fc3154ab44a7e948ef8b Binary files /dev/null and b/__pycache__/dev_dhiria.cpython-310.pyc differ diff --git a/__pycache__/server.cpython-310.pyc b/__pycache__/server.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a2a9f1fc52ca9e1b13515d054a2b2653fe426bbe Binary files /dev/null and b/__pycache__/server.cpython-310.pyc differ diff --git a/__pycache__/stuff.cpython-310.pyc b/__pycache__/stuff.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fd468dd39bfb32a66feeed563fd7bfa198fc7cf0 Binary files /dev/null and b/__pycache__/stuff.cpython-310.pyc differ diff --git a/__pycache__/symptoms_categories.cpython-310.pyc b/__pycache__/symptoms_categories.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..833fbf65e295c5c597ca69d2191c16802d77842f Binary files /dev/null and b/__pycache__/symptoms_categories.cpython-310.pyc differ diff --git a/__pycache__/utils.cpython-310.pyc b/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..974ab0578e20448a548e17da91343366fcdefa0d Binary files /dev/null and b/__pycache__/utils.cpython-310.pyc differ diff --git a/app.py b/app.py index ca854b9474b276b47db039280c7d2b72ac9c9802..68e5ec61d515e72d21e212fb1045ffbd180f4aa6 100644 --- a/app.py +++ b/app.py @@ -6,24 +6,28 @@ 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 stuff import get_emoticon, plot_tachometer from utils import ( CLIENT_DIR, CURRENT_DIR, - DEPLOYMENT_DIR, + DEPLOYMENT_DIR_MODEL1, + DEPLOYMENT_DIR_MODEL2, + DEPLOYMENT_DIR_MODEL3, INPUT_BROWSER_LIMIT, KEYS_DIR, SERVER_URL, - TARGET_COLUMNS, - TRAINING_FILENAME, clean_directory, - get_disease_name, - load_data, - pretty_print, ) +from dev_dhiria import frequency_domain, interpolation, statistics from concrete.ml.deployment import FHEModelClient +global_df1 = None +global_df2 = None + +global_output_1 = None +global_output_2 = None + subprocess.Popen(["uvicorn", "server:app"], cwd=CURRENT_DIR) time.sleep(3) @@ -43,92 +47,7 @@ def is_none(obj) -> bool: 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: +def key_gen_fn() -> Dict: """ Generate keys for a given user. @@ -141,17 +60,11 @@ def key_gen_fn(user_symptoms: List[str]) -> Dict: """ 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 = FHEModelClient(path_dir=DEPLOYMENT_DIR_MODEL1, key_dir=KEYS_DIR / f"{user_id}_1") client.load() # Creates the private and evaluation keys on the client side @@ -162,24 +75,35 @@ def key_gen_fn(user_symptoms: List[str]) -> Dict: assert isinstance(serialized_evaluation_keys, bytes) # Save the evaluation key - evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key" + evaluation_key_path = KEYS_DIR / f"{user_id}_1/evaluation_key_1" + with evaluation_key_path.open("wb") as f: + f.write(serialized_evaluation_keys) + + + client = FHEModelClient(path_dir=DEPLOYMENT_DIR_MODEL2, key_dir=KEYS_DIR / f"{user_id}_2") + 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}_2/evaluation_key_2" 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=False, value=user_id), - key_len_box: gr.update( - visible=False, value=f"{len(serialized_evaluation_keys) / (10**6):.2f} MB" - ), gen_key_btn: gr.update(value="Keys have been generated ✅") } -def encrypt_fn(user_symptoms: np.ndarray, user_id: str) -> None: +def encrypt_fn(arr: np.ndarray, user_id: str, input_id: int) -> None: """ Encrypt the user symptoms vector in the `Client Side`. @@ -187,98 +111,110 @@ def encrypt_fn(user_symptoms: np.ndarray, user_id: str) -> None: 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): + if is_none(user_id) or is_none(arr): 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.", - ) + # 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) + if input_id == 1: + client = FHEModelClient(path_dir=DEPLOYMENT_DIR_MODEL1, key_dir=KEYS_DIR / f"{user_id}_1") + client.load() + else: + client = FHEModelClient(path_dir=DEPLOYMENT_DIR_MODEL2, key_dir=KEYS_DIR / f"{user_id}_2") + client.load() - 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" + encrypted_quantized_arr = client.quantize_encrypt_serialize(arr) + assert isinstance(encrypted_quantized_arr, bytes) + encrypted_input_path = KEYS_DIR / f"{user_id}_{input_id}/encrypted_input_{input_id}" 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 - ] + f.write(encrypted_quantized_arr) 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), + # 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: +def send_input_fn(user_id: str, models_layer: int = 1) -> 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 + arr (np.ndarray): The input for a model """ - if is_none(user_id) or is_none(user_symptoms): + if is_none(user_id): 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.", - ) + # 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" + evaluation_key_path_1 = KEYS_DIR / f"{user_id}_1/evaluation_key_1" + evaluation_key_path_2 = KEYS_DIR / f"{user_id}_2/evaluation_key_2" - if not evaluation_key_path.is_file(): + if models_layer == 1: + # First layer of models, we have two encrypted inputs + encrypted_input_path_1 = KEYS_DIR / f"{user_id}_1/encrypted_input_1" + encrypted_input_path_2 = KEYS_DIR / f"{user_id}_2/encrypted_input_2" + else: + encrypted_input_path_3 = KEYS_DIR / f"{user_id}/encrypted_input_3" + + if not evaluation_key_path_1.is_file(): print( "Error Encountered While Sending Data to the Server: " - f"The key has been generated correctly - {evaluation_key_path.is_file()=}" + f"The key has been generated correctly - {evaluation_key_path_1.is_file()=}" ) return { - error_box4: gr.update(visible=True, value="⚠️ Please generate the private key first.") + # error_box4: gr.update(visible=True, value="⚠️ Please generate the private key first.") } - if not encrypted_input_path.is_file(): + if not encrypted_input_path_1.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()=}" + f"correctly on the client side - {encrypted_input_path_1.is_file()=}" ) return { - error_box4: gr.update( - visible=True, - value="⚠️ Please encrypt the data with the private key first.", - ), + # 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, + # "input": user_symptoms, } - files = [ - ("files", open(encrypted_input_path, "rb")), - ("files", open(evaluation_key_path, "rb")), - ] + if models_layer == 1: + files = [ + ("files", open(encrypted_input_path_1, "rb")), + ("files", open(encrypted_input_path_2, "rb")), + ("files", open(evaluation_key_path_1, "rb")), + ("files", open(evaluation_key_path_2, "rb")), + ] + else: + files = [ + ("files", open(encrypted_input_path_3, "rb")), + # ("files", open(evaluation_key_path, "rb")), + ] # Send the encrypted input and evaluation key to the server - url = SERVER_URL + "send_input" + url = SERVER_URL + "send_input_first_layer" with requests.post( url=url, data=data, @@ -309,12 +245,141 @@ def run_fhe_fn(user_id: str) -> Dict: fhe_execution_time_box: None, } + start_time = time.time() data = { "user_id": user_id, } - url = SERVER_URL + "run_fhe" + # Run the first layer + url = SERVER_URL + "run_fhe_first_layer" + 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") + + print(f"First layer done!") + + # Decrypt because ConcreteML doesn't provide output to input + url = SERVER_URL + "get_output_first_layer_1" + 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_1" + + with encrypted_output_path.open("wb") as f: + f.write(encrypted_output) + + url = SERVER_URL + "get_output_first_layer_2" + 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_2" + + with encrypted_output_path.open("wb") as f: + f.write(encrypted_output) + + encrypted_output_path_1 = CLIENT_DIR / f"{user_id}_encrypted_output_1" + encrypted_output_path_2 = CLIENT_DIR / f"{user_id}_encrypted_output_2" + + # Load the encrypted output as bytes + with encrypted_output_path_1.open("rb") as f1, \ + encrypted_output_path_2.open("rb") as f2: + encrypted_output_1 = f1.read() + encrypted_output_2 = f2.read() + + # Retrieve the client API + client = FHEModelClient(path_dir=DEPLOYMENT_DIR_MODEL1, key_dir=KEYS_DIR / f"{user_id}_1") + client.load() + + breakpoint() + # Deserialize, decrypt and post-process the encrypted output + global global_output_1, global_output_2 + global_output_1 = client.deserialize_decrypt_dequantize(encrypted_output_1)[0][0] + min_risk_score = 1.8145127821625648 + max_risk_score = 1.9523557655864805 + global_output_1 = (global_output_1 - min_risk_score) / (max_risk_score - min_risk_score) + + + client = FHEModelClient(path_dir=DEPLOYMENT_DIR_MODEL2, key_dir=KEYS_DIR / f"{user_id}_2") + client.load() + global_output_2 = client.deserialize_decrypt_dequantize(encrypted_output_2) + global_output_2 = int(global_output_2 > 0.6) + + # Now re-encrypt the two values because ConcreteML does not allow + # to use the output of two models as input of a third one. + new_input = np.array([[global_output_1, global_output_2]]) + + # Retrieve the client API + client = FHEModelClient(path_dir=DEPLOYMENT_DIR_MODEL3, 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_second_layer" + with evaluation_key_path.open("wb") as f: + f.write(serialized_evaluation_keys) + encrypted_quantized_arr = client.quantize_encrypt_serialize(new_input) + assert isinstance(encrypted_quantized_arr, bytes) + encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_input_3" + + with encrypted_input_path.open("wb") as f: + f.write(encrypted_quantized_arr) + + # Send it + evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key_second_layer" + 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_second_layer" + with requests.post( + url=url, + data=data, + files=files, + ) as response: + print(f"Sending Data: {response.ok}") + + # Run the second layer + url = SERVER_URL + "run_fhe_second_layer" with requests.post( url=url, data=data, @@ -333,22 +398,25 @@ def run_fhe_fn(user_id: str) -> Dict: else: time.sleep(1) print(f"response.ok: {response.ok}, {response.json()} - Computed") + + print("Second layer done!") + total_time = time.time() - start_time return { error_box5: gr.update(visible=False), - fhe_execution_time_box: gr.update(visible=True, value=f"{response.json():.2f} seconds"), + fhe_execution_time_box: gr.update(visible=True, value=f"{total_time:.2f} seconds"), } -def get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict: - """Retreive the encrypted data from the server. - +def get_output_fn(user_id: str) -> 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): + if is_none(user_id): return { error_box6: gr.update( visible=True, @@ -362,7 +430,7 @@ def get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict: } # Retrieve the encrypted output - url = SERVER_URL + "get_output" + url = SERVER_URL + "get_output_second_layer" with requests.post( url=url, data=data, @@ -374,28 +442,17 @@ def get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict: # 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" + encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output_3" 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"} + 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): +def decrypt_fn(user_id: str) -> Dict: + if is_none(user_id): return { error_box7: gr.update( visible=True, @@ -405,7 +462,7 @@ def decrypt_fn( } # Get the encrypted output path - encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output" + encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output_3" if not encrypted_output_path.is_file(): print("Error in decryption step: Please run the FHE execution, first.") @@ -422,42 +479,46 @@ def decrypt_fn( 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 = FHEModelClient(path_dir=DEPLOYMENT_DIR_MODEL3, 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] + breakpoint() - out = "" + # Load also the data from the first two models (they are already downloaded) + global global_output_1, global_output_2 - 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" - ) + tachometer_plot = plot_tachometer(global_output_1 * 100) + emoticon_image = get_emoticon(global_output_2) + + # Predicted class + predicted_class = np.argmax(output) + + # Labels + labels = { + 0: "Continue what you are doing!", + 1: "Focus on technique!", + 2: "Focus on mental health!", + 3: "Rest!" + } 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" + f"Given your recent running and mental stress statistics, you should... " + f"{labels[predicted_class]}" ) - return { - error_box7: gr.update(visible=False), - decrypt_box: out, - submit_btn: gr.update(value="Submit"), - } + return [ + gr.update(value=out, visible=True), + gr.update(visible=False), + gr.update(value="Submit"), + gr.update(value=tachometer_plot, visible=True), + gr.update(value=emoticon_image, visible=True) + ] def reset_fn(): @@ -466,217 +527,178 @@ def reset_fn(): 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}, + # 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}, + } + + +def process_files(file1, file2): + global global_df1, global_df2 + + # Read the CSV files + df1 = pd.read_csv(file1.name) + df2 = pd.read_csv(file2.name) + + # Store them in global variables to access later + global_df1 = df1 + global_df2 = df2 + + return { + upload_button: gr.update(value="Data uploaded! ✅") + } + + +def encrypt_layer1(user_id_box): + global global_df1, global_df2 + + # INPUT ONE - RUNNING DATA + running_data, risk = statistics(global_df1) + running_data = pd.DataFrame(running_data) + input_model_1 = running_data.iloc[0, :].to_numpy() + input_model_1 = input_model_1.reshape(1, len(input_model_1)) + + # INPUT TWO - MENTAL HEALTH DATA + data = global_df2.iloc[:,2::].T + data.dropna(how='any', inplace=True, axis=0) + data = data.T + data = np.where((data.values > 1000) | (data.values<600), np.median(data.values), data.values) + rr_interpolated = interpolation(data, 4.0) + + results = [] + + for i in range(len(data)): + results.append(frequency_domain(rr_interpolated[i])) + freq_col=['vlf','lf','hf','tot_pow','lf_hf_ratio','peak_vlf','peak_lf','peak_hf'] + freq_features = pd.DataFrame(results, columns = freq_col) + input_model_2 = freq_features.iloc[0, :].to_numpy() + input_model_2 = input_model_2.reshape(1, len(input_model_2)) + + encrypt_fn(input_model_1, user_id_box, 1) + encrypt_fn(input_model_2, user_id_box, 2) + + return { + error_box3: gr.update(visible=False, value="Error"), + encrypt_btn: gr.update(value="Data encrypted! ✅") } + 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: + css = """ + .centered-textbox textarea { + font-size: 24px !important; + text-align: center; + } + .large-emoticon textarea { + font-size: 72px !important; + text-align: center; + } + """ + + with gr.Blocks(theme="light", css=css, title='AtlHEte') as demo: # Link + images gr.Markdown() gr.Markdown( """

- +

""") - gr.Markdown() - gr.Markdown("""

Health Prediction On Encrypted Data Using Fully Homomorphic Encryption

""") - gr.Markdown() - gr.Markdown( - """ -

- Concrete-ML - — - Documentation - — - Community - — - @zama_fhe -

+ + # Title + gr.Markdown(""" + # AtlHEte + ## Data loading + Upload your running time-series, and your PPG. + > The app of AtlHEte would do this automatically. """) - gr.Markdown() - gr.Markdown( - """" -

- -

- """ - ) - 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("
") - gr.Markdown("Client Side") - 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] - ) + file1 = gr.File(label="Upload running time-series") + file2 = gr.File(label="Upload PPG") - 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("
") - gr.Markdown("Client Side") - # 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." - ) + upload_button = gr.Button("Upload") + upload_button.click(process_files, inputs=[file1, file2], outputs=[upload_button]) + # Keys generation + gr.Markdown(""" + ## Keys generation + Generate the TFHE keys. + """) gen_key_btn = gr.Button("Generate the private and evaluation keys.") error_box2 = gr.Textbox(label="Error ❌", visible=False) user_id_box = gr.Textbox(label="User ID:", visible=False) - 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, gen_key_btn, ], ) - # Step 2.2: Encrypt data locally - gr.Markdown("### Encrypt the data") + # Data encryption + gr.Markdown(""" + ## Data encryption + Encrypt both your running time-series and your PPG. + """) 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) + encrypt_btn.click(encrypt_layer1, inputs=[user_id_box], outputs=[error_box3, encrypt_btn]) - 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 Server Side" - ) + + # Data uploading + gr.Markdown(""" + ## Data upload + Upload your data safely to us. + """) error_box4 = gr.Textbox(label="Error ❌", visible=False) - # with gr.Row().style(equal_height=False): - with gr.Row(): - 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) + with gr.Row().style(equal_height=False): + with gr.Row(): + 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], + inputs=[user_id_box], outputs=[error_box4, srv_resp_send_data_box], ) - # ------------------------- Step 3 ------------------------- - gr.Markdown("\n") - gr.Markdown("## Step 3: Run the FHE evaluation") - gr.Markdown("
") - gr.Markdown("Server Side") - 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)." - ) - + # Encrypted processing + gr.Markdown(""" + ## Encrypted processing + Process your encrypted data with AtlHEte! + """) 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) @@ -686,19 +708,12 @@ if __name__ == "__main__": outputs=[fhe_execution_time_box, error_box5], ) - # ------------------------- Step 4 ------------------------- - gr.Markdown("\n") - gr.Markdown("## Step 4: Decrypt the data") - gr.Markdown("
") - gr.Markdown("Client Side") - gr.Markdown( - "### Get the encrypted data from the Server Side" - ) - + # Download the report + gr.Markdown(""" + ## Download the encrypted report + Download your personalized encrypted report... + """) error_box6 = gr.Textbox(label="Error ❌", visible=False) - - # Step 4.1: Data transmission - # with gr.Row().style(equal_height=True): with gr.Row(): with gr.Column(scale=4): get_output_btn = gr.Button("Get data") @@ -707,65 +722,37 @@ if __name__ == "__main__": get_output_btn.click( get_output_fn, - inputs=[user_id_box, one_hot_vect], + inputs=[user_id_box], outputs=[srv_resp_retrieve_data_box, error_box6], ) - # Step 4.1: Data transmission - gr.Markdown("### Decrypt the output") + # Download the report + gr.Markdown(""" + ## Decrypt the report + Decrypt the report to know how you are doing! + """) 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:") + + # Layout components + with gr.Row(): + tachometer_plot = gr.Plot(label="Running Quality", visible=False) + emoticon_display = gr.Textbox(label="Mental Health", visible=False, elem_classes="large-emoticon") + + with gr.Column(): + decrypt_box = gr.Textbox(label="Decrypted Output:", visible=False, elem_classes="centered-textbox") + 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) ⭐. - """ + inputs=[user_id_box], + outputs=[decrypt_box, + error_box7, + decrypt_btn, + tachometer_plot, + emoticon_display], ) - 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() + demo.launch(favicon_path='atlhete-high-resolution-logo-black-transparent.png') diff --git a/atlhete-high-resolution-logo-black-transparent.png b/atlhete-high-resolution-logo-black-transparent.png new file mode 100644 index 0000000000000000000000000000000000000000..3e763ef872bfd295d2e9816701251d246859f256 Binary files /dev/null and b/atlhete-high-resolution-logo-black-transparent.png differ diff --git a/data/200_Users_Running_Dataset.csv b/data/200_Users_Running_Dataset.csv new file mode 100644 index 0000000000000000000000000000000000000000..80df67f7fc08be4465418f019fd2c8a1af762631 --- /dev/null +++ b/data/200_Users_Running_Dataset.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79f7a734f8f85e763f2feee8344eb3a725b5b9c9e2c50694793411787b4bfe58 +size 38539031 diff --git a/data/data_mental.csv b/data/data_mental.csv new file mode 100644 index 0000000000000000000000000000000000000000..d9faa304b7a18c0707e215614e9467d5e10d4267 --- /dev/null +++ b/data/data_mental.csv @@ -0,0 +1,55 @@ +subject 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b/deployment_files/.fhe_keys/2820248940/15341225681980396668/secretKey_0 differ diff --git a/deployment_files/.fhe_keys/2820248940/encrypted_input b/deployment_files/.fhe_keys/2820248940/encrypted_input new file mode 100644 index 0000000000000000000000000000000000000000..b6e7c358bd71de2ffc1b01370bf3c9a00b179bb5 --- /dev/null +++ b/deployment_files/.fhe_keys/2820248940/encrypted_input @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e509a3079177c527a89695b418b96d9050c72a25f2c7a81d89d17e09e99b2b1 +size 2003208 diff --git a/deployment_files/.fhe_keys/2820248940/evaluation_key b/deployment_files/.fhe_keys/2820248940/evaluation_key new file mode 100644 index 0000000000000000000000000000000000000000..4c0abc9db7e405d0f4b2b025179f3fd38bf6a136 Binary files /dev/null and b/deployment_files/.fhe_keys/2820248940/evaluation_key differ diff --git a/deployment_files/client/client.specs.json b/deployment_files/client/client.specs.json new file mode 100644 index 0000000000000000000000000000000000000000..12a56210a3b6612c0390c15ae8d1c9580f43a623 --- /dev/null +++ b/deployment_files/client/client.specs.json @@ -0,0 +1 @@ +{"keyset":{"lweSecretKeys":[{"id":0,"params":{"lweDimension":1955,"integerPrecision":64,"keyType":"binary"}}],"lweBootstrapKeys":[],"lweKeyswitchKeys":[],"packingKeyswitchKeys":[]},"circuits":[{"inputs":[{"rawInfo":{"shape":{"dimensions":[1,128,1956]},"integerPrecision":64,"isSigned":false},"typeInfo":{"lweCiphertext":{"abstractShape":{"dimensions":[1,128]},"concreteShape":{"dimensions":[1,128,1956]},"integerPrecision":64,"encryption":{"keyId":0,"variance":3.0674321942082692e-30,"lweDimension":1955,"modulus":{"modulus":{"native":{}}}},"compression":"none","encoding":{"integer":{"width":30,"isSigned":true,"mode":{"native":{}}}}}}}],"outputs":[{"rawInfo":{"shape":{"dimensions":[1,41,1956]},"integerPrecision":64,"isSigned":false},"typeInfo":{"lweCiphertext":{"abstractShape":{"dimensions":[1,41]},"concreteShape":{"dimensions":[1,41,1956]},"integerPrecision":64,"encryption":{"keyId":0,"variance":3.0674321942082692e-30,"lweDimension":1955,"modulus":{"modulus":{"native":{}}}},"compression":"none","encoding":{"integer":{"width":30,"isSigned":true,"mode":{"native":{}}}}}}}],"name":"main"}]} \ No newline at end of file diff --git a/deployment_files/client/serialized_processing.json b/deployment_files/client/serialized_processing.json new file mode 100644 index 0000000000000000000000000000000000000000..dec9e6b5584d0529dc78105b6c70fd96c656d700 --- /dev/null +++ b/deployment_files/client/serialized_processing.json @@ -0,0 +1 @@ +{"model_type": {"type_name": "type", "serialized_value": 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diff --git a/deployment_files/server/circuit.mlir b/deployment_files/server/circuit.mlir new file mode 100644 index 0000000000000000000000000000000000000000..37a819b2fbd2f43327be719744823ee21c16fff2 --- /dev/null +++ b/deployment_files/server/circuit.mlir @@ -0,0 +1,18 @@ +module { + func.func @main(%arg0: tensor<1x128x!FHE.esint<30>>) -> tensor<1x41x!FHE.esint<30>> { + %cst = arith.constant 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: tensor<128x41xi31> + %0 = "FHELinalg.matmul_eint_int"(%arg0, %cst) : (tensor<1x128x!FHE.esint<30>>, tensor<128x41xi31>) -> tensor<1x41x!FHE.esint<30>> + %1 = "FHELinalg.to_unsigned"(%0) : (tensor<1x41x!FHE.esint<30>>) -> tensor<1x41x!FHE.eint<30>> + %2 = "FHELinalg.sum"(%arg0) {axes = [1], keep_dims = true} : (tensor<1x128x!FHE.esint<30>>) -> tensor<1x1x!FHE.esint<30>> + %c-928_i31 = arith.constant -928 : i31 + %from_elements = tensor.from_elements %c-928_i31 : tensor<1xi31> + %3 = "FHELinalg.to_unsigned"(%2) : (tensor<1x1x!FHE.esint<30>>) -> tensor<1x1x!FHE.eint<30>> + %4 = "FHELinalg.mul_eint_int"(%3, %from_elements) : (tensor<1x1x!FHE.eint<30>>, tensor<1xi31>) -> tensor<1x1x!FHE.eint<30>> + %5 = "FHELinalg.to_signed"(%1) : (tensor<1x41x!FHE.eint<30>>) -> tensor<1x41x!FHE.esint<30>> + %6 = "FHELinalg.to_signed"(%4) : (tensor<1x1x!FHE.eint<30>>) -> tensor<1x1x!FHE.esint<30>> + %7 = "FHELinalg.sub_eint"(%5, %6) : (tensor<1x41x!FHE.esint<30>>, tensor<1x1x!FHE.esint<30>>) -> tensor<1x41x!FHE.esint<30>> + %cst_0 = arith.constant dense<[[55007587, 64672575, -8325351, 68799429, 27183085, 25897887, 26793162, -37370359, 9691029, -77060549, -79741437, -29642252, 24866418, 19172166, 66008164, 69263236, 5805906, 99861790, -68874205, -57767716, 45355732, 118646, -95272629, 31360153, -38812838, -59540471, -51388670, 24551846, -10687010, -1873264, -20900730, 3388994, 72444835, 7999021, 13874550, -33655965, 3273574, -90201021, -58513218, 54288116, -91177]]> : tensor<1x41xi31> + %8 = "FHELinalg.add_eint_int"(%7, %cst_0) : (tensor<1x41x!FHE.esint<30>>, tensor<1x41xi31>) -> tensor<1x41x!FHE.esint<30>> + return %8 : tensor<1x41x!FHE.esint<30>> + } +} \ No newline at end of file diff --git a/deployment_files/server/configuration.json b/deployment_files/server/configuration.json new file mode 100644 index 0000000000000000000000000000000000000000..edf1ab70a7e5fa021d84d93df9df50266b8b5dca --- /dev/null +++ b/deployment_files/server/configuration.json @@ -0,0 +1 @@ +{"verbose": false, "compiler_debug_mode": false, "compiler_verbose_mode": false, "show_graph": null, "show_bit_width_constraints": null, "show_bit_width_assignments": null, "show_assigned_graph": null, "show_mlir": false, "show_optimizer": null, "show_statistics": null, "dump_artifacts_on_unexpected_failures": true, "enable_unsafe_features": false, "use_insecure_key_cache": false, "insecure_key_cache_location": null, "loop_parallelize": true, "dataflow_parallelize": false, "auto_parallelize": false, "compress_evaluation_keys": false, "p_error": 9.094947017729282e-13, "global_p_error": null, "auto_adjust_rounders": false, "auto_adjust_truncators": false, "single_precision": false, "parameter_selection_strategy": "multi", "show_progress": false, "progress_title": "", "progress_tag": false, "fhe_simulation": false, "fhe_execution": true, "comparison_strategy_preference": [], "bitwise_strategy_preference": [], "shifts_with_promotion": true, "multivariate_strategy_preference": [], "min_max_strategy_preference": [], "composable": false, "use_gpu": false} \ No newline at end of file diff --git a/deployment_files/server/is_simulated b/deployment_files/server/is_simulated new file mode 100644 index 0000000000000000000000000000000000000000..c227083464fb9af8955c90d2924774ee50abb547 --- /dev/null +++ b/deployment_files/server/is_simulated @@ -0,0 +1 @@ +0 \ No newline at end of file diff --git a/deployment_files/server/versions.json b/deployment_files/server/versions.json new file mode 100644 index 0000000000000000000000000000000000000000..df1ddf92466c832a82ab36cbbb7092a3383dfc33 --- /dev/null +++ b/deployment_files/server/versions.json @@ -0,0 +1 @@ +{"concrete-python": "2.5", "concrete-ml": "1.4.0", "python": "3.10.11"} \ No newline at end of file diff --git a/deployment_files/server_dir/2820248940_encrypted_input b/deployment_files/server_dir/2820248940_encrypted_input new file mode 100644 index 0000000000000000000000000000000000000000..b6e7c358bd71de2ffc1b01370bf3c9a00b179bb5 --- /dev/null +++ b/deployment_files/server_dir/2820248940_encrypted_input @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e509a3079177c527a89695b418b96d9050c72a25f2c7a81d89d17e09e99b2b1 +size 2003208 diff --git a/deployment_files/server_dir/2820248940_encrypted_output b/deployment_files/server_dir/2820248940_encrypted_output new file mode 100644 index 0000000000000000000000000000000000000000..dd0b4fe0ad5f85e61fa81f8bc8c5aa2f4a464205 Binary files /dev/null and b/deployment_files/server_dir/2820248940_encrypted_output differ diff --git a/deployment_files/server_dir/2820248940_valuation_key b/deployment_files/server_dir/2820248940_valuation_key new file mode 100644 index 0000000000000000000000000000000000000000..4c0abc9db7e405d0f4b2b025179f3fd38bf6a136 Binary files /dev/null and b/deployment_files/server_dir/2820248940_valuation_key differ diff --git a/deployment_files_generic/.fhe_keys/950347125_1/12513518805967278301/secretKey_0 b/deployment_files_generic/.fhe_keys/950347125_1/12513518805967278301/secretKey_0 new file mode 100644 index 0000000000000000000000000000000000000000..d9cfefa0f5cf4d58add788147647fb999340c904 Binary files /dev/null and b/deployment_files_generic/.fhe_keys/950347125_1/12513518805967278301/secretKey_0 differ diff --git a/deployment_files_generic/.fhe_keys/950347125_1/encrypted_input_1 b/deployment_files_generic/.fhe_keys/950347125_1/encrypted_input_1 new file mode 100644 index 0000000000000000000000000000000000000000..ec5f894f87087464d4fab2a9dfd0ee1bd3814008 Binary files /dev/null and b/deployment_files_generic/.fhe_keys/950347125_1/encrypted_input_1 differ diff --git a/deployment_files_generic/.fhe_keys/950347125_1/evaluation_key_1 b/deployment_files_generic/.fhe_keys/950347125_1/evaluation_key_1 new file mode 100644 index 0000000000000000000000000000000000000000..4c0abc9db7e405d0f4b2b025179f3fd38bf6a136 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file mode 100644 index 0000000000000000000000000000000000000000..4c0abc9db7e405d0f4b2b025179f3fd38bf6a136 Binary files /dev/null and b/deployment_files_generic/.fhe_keys/950347125_2/evaluation_key_2 differ diff --git a/deployment_files_generic/client_dir/950347125_encrypted_output_1 b/deployment_files_generic/client_dir/950347125_encrypted_output_1 new file mode 100644 index 0000000000000000000000000000000000000000..c855ba9fefa3960462186caac59975069b9b0a0f Binary files /dev/null and b/deployment_files_generic/client_dir/950347125_encrypted_output_1 differ diff --git a/deployment_files_generic/client_dir/950347125_encrypted_output_2 b/deployment_files_generic/client_dir/950347125_encrypted_output_2 new file mode 100644 index 0000000000000000000000000000000000000000..e13fab1b2967ed3c7eae63e618e54f20264136d6 Binary files /dev/null and b/deployment_files_generic/client_dir/950347125_encrypted_output_2 differ diff --git a/deployment_files_generic/server_dir/950347125_encrypted_input_model1 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+{"concrete-python": "2.5", "concrete-ml": "1.4.0", "python": "3.10.14"} \ No newline at end of file diff --git a/deployment_files_model2/client.zip b/deployment_files_model2/client.zip new file mode 100644 index 0000000000000000000000000000000000000000..4138f4aad19d7a50679a01a4d6065e6a1fc55ee7 Binary files /dev/null and b/deployment_files_model2/client.zip differ diff --git a/deployment_files_model2/server.zip b/deployment_files_model2/server.zip new file mode 100644 index 0000000000000000000000000000000000000000..9e102c18d86720708682ae898e52fbc58451d7b3 Binary files /dev/null and b/deployment_files_model2/server.zip differ diff --git a/deployment_files_model2/versions.json b/deployment_files_model2/versions.json new file mode 100644 index 0000000000000000000000000000000000000000..f8ed9f0bc888fd32c86b7fbb4b96a6590197130b --- /dev/null +++ b/deployment_files_model2/versions.json @@ -0,0 +1 @@ +{"concrete-python": "2.5", "concrete-ml": "1.4.0", "python": "3.10.14"} \ No newline at end of file diff --git a/deployment_files_model3/client.zip b/deployment_files_model3/client.zip new file mode 100644 index 0000000000000000000000000000000000000000..0ffbbd08a68f8ce92eda5a3d53aa9cdfa299a454 Binary files /dev/null and b/deployment_files_model3/client.zip differ diff --git a/deployment_files_model3/server.zip b/deployment_files_model3/server.zip new file mode 100644 index 0000000000000000000000000000000000000000..3fa5d75710af7bfc0615b670c07a4c2bba0b4951 Binary files /dev/null and b/deployment_files_model3/server.zip differ diff --git a/deployment_files_model3/versions.json b/deployment_files_model3/versions.json new file mode 100644 index 0000000000000000000000000000000000000000..f8ed9f0bc888fd32c86b7fbb4b96a6590197130b --- /dev/null +++ b/deployment_files_model3/versions.json @@ -0,0 +1 @@ +{"concrete-python": "2.5", "concrete-ml": "1.4.0", "python": "3.10.14"} \ No newline at end of file diff --git a/dev_dhiria.py b/dev_dhiria.py new file mode 100644 index 0000000000000000000000000000000000000000..2b80e91c7b690a398c243f44fda9af6500329897 --- /dev/null +++ b/dev_dhiria.py @@ -0,0 +1,208 @@ +"""Generating deployment files.""" +import shutil +import numpy as np +from pathlib import Path +from scipy import signal +from scipy.ndimage import label +from scipy.stats import zscore +from scipy.interpolate import interp1d +from scipy.integrate import trapz +from scipy.interpolate import interp1d + +import pandas as pd +from sklearn.decomposition import PCA +from sklearn.linear_model import LinearRegression as SklearnLinearRegression +from concrete.ml.sklearn import LinearRegression as ConcreteLinearRegression + +from sklearn.model_selection import GridSearchCV, train_test_split +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler + +from concrete.ml.sklearn.xgb import XGBClassifier + +from concrete.ml.sklearn import LogisticRegression as ConcreteLogisticRegression +from concrete.ml.deployment import FHEModelDev + +def interpolation(data, fs = 4.0): + + rr_interpolated=[] + + for i in range(len(data)): + + rr_manual=data[i] + + # Cumulative sum of data + x = np.cumsum(rr_manual) / 1000.0 + f = interp1d(x, rr_manual, kind='cubic',fill_value="extrapolate") + steps = 1 / fs + + xx = np.arange(1, np.max(x), steps) + rr_interpolated.append(f(xx)) + + return rr_interpolated + + +def frequency_domain(rri, fs=4): + ''' + Segement found frequencies in the bands + - Very Low Frequency (VLF): 0-0.04Hz + - Low Frequency (LF): 0.04-0.15Hz + - High Frequency (HF): 0.15-0.4Hz + ''' + + # Estimate the spectral density using Welch's method + fxx, pxx = signal.welch(x=rri, fs=fs) + + cond_vlf = (fxx >= 0) & (fxx < 0.04) + cond_lf = (fxx >= 0.04) & (fxx < 0.15) + cond_hf = (fxx >= 0.15) & (fxx < 0.4) + + # calculate power in each band by integrating the spectral density + vlf = trapz(pxx[cond_vlf], fxx[cond_vlf]) + lf = trapz(pxx[cond_lf], fxx[cond_lf]) + hf = trapz(pxx[cond_hf], fxx[cond_hf]) + + # sum these up to get total power + total_power = vlf + lf + hf + + # find which frequency has the most power in each band + peak_vlf = fxx[cond_vlf][np.argmax(pxx[cond_vlf])] + peak_lf = fxx[cond_lf][np.argmax(pxx[cond_lf])] + peak_hf = fxx[cond_hf][np.argmax(pxx[cond_hf])] + + result=[vlf, lf, hf, total_power, lf/hf, peak_vlf, peak_lf, peak_hf] + + return np.array(result) + +def abs_sum_diff(x): + # Sum of absolute differences (SAD) is a measure of the similarity between signal + return sum(np.abs(np.diff(x))) + + +def statistics(df): + statistics = [] + labels = [] + + for i in range(0, len(np.unique(df['User ID']))): + mean = np.mean(df[df['User ID'] == i]['Stride Length (m)']) + var = np.var(df[df['User ID'] == i]['Stride Length (m)']) + median = np.median(df[df['User ID'] == i]['Stride Length (m)']) + max_s = np.max(df[df['User ID'] == i]['Stride Length (m)']) + min_s = np.min(df[df['User ID'] == i]['Stride Length (m)']) + abs_sum_diff_s = abs_sum_diff(df[df['User ID'] == i]['Stride Length (m)']) + + statistics.append([mean, var, median, max_s, min_s, abs_sum_diff_s]) + + labels.append(df[df['User ID'] == i]['Injury Risk Score'].iloc[0]) + + return statistics, labels + + +def train_model_1(): + # Running quality + df = pd.read_csv('data/200_Users_Running_Dataset.csv') + + data, risk = statistics(df) + data = pd.DataFrame(data) + + X_train, X_test, y_train, y_test = train_test_split(data, risk, test_size=0.2, random_state=42) + + sklearn_lr = SklearnLinearRegression() + sklearn_lr.fit(X_train, y_train) + + concrete_lr = ConcreteLinearRegression(n_bits=8) + concrete_lr.fit(X_train, y_train) + + # Compile the model + fhe_circuit = concrete_lr.compile(X_train) + fhe_circuit.client.keygen(force=False) + + path_to_model = Path("./deployment_files_model1/").resolve() + + if path_to_model.exists(): + shutil.rmtree(path_to_model) + + dev = FHEModelDev(path_to_model, concrete_lr) + + dev.save(via_mlir=True) + + +def train_model_2(): + df = pd.read_csv('data/data_mental.csv') + + labels = df.iloc[:,1].T + data = df.iloc[:,2::].T + + data.dropna(how='any', inplace=True, axis=0) + labels = labels.T + data = data.T + data = np.where((data.values > 1000) | (data.values<600), np.median(data.values), data.values) + labels = np.where((labels.values == 'stress'), 1, 0) + + rr_interpolated = interpolation(data, 4.0) + + results = [] + + for i in range(len(data)): + results.append(frequency_domain(rr_interpolated[i])) + + freq_col=['vlf','lf','hf','tot_pow','lf_hf_ratio','peak_vlf','peak_lf','peak_hf'] + freq_features = pd.DataFrame(results, columns = freq_col) + + X_train, X_test, y_train, y_test = train_test_split(freq_features, labels, test_size=0.3, shuffle=True, stratify=labels) + + scaling = StandardScaler() + X_train = scaling.fit_transform(X_train) + X_test = scaling.transform(X_test) + + + concrete_lr = ConcreteLinearRegression(n_bits=16) + concrete_lr.fit(X_train, y_train) + y_pred_q = concrete_lr.predict(X_test) + y_pred_q = np.where((y_pred_q > 0.6), 1, 0) + + # Compile the model + fhe_circuit = concrete_lr.compile(X_train) + + fhe_circuit.client.keygen(force=False) + + path_to_model = Path("./deployment_files_model2/").resolve() + + if path_to_model.exists(): + shutil.rmtree(path_to_model) + + dev = FHEModelDev(path_to_model, concrete_lr) + + dev.save(via_mlir=True) + + +def train_model_3(): + N_BITS = 3 + df = pd.read_csv('data/dataset_for_last_model.csv') + X_train = df.drop('label', axis=1) # Drop the 'label' column for features + y_train = df['label'] # The 'label' column is the target + print(X_train) + print(y_train) + # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.0, random_state=0) + + model = XGBClassifier(n_jobs=1, n_bits=N_BITS) + model.fit(X_train, y_train) + + # Compile the model + fhe_circuit = model.compile(X_train) + + fhe_circuit.client.keygen(force=False) + + path_to_model = Path("./deployment_files_model3/").resolve() + + if path_to_model.exists(): + shutil.rmtree(path_to_model) + + dev = FHEModelDev(path_to_model, model) + + dev.save(via_mlir=True) + +if __name__ == "__main__": + train_model_1() + train_model_2() + train_model_3() \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 8a01577b48a5f007a04da0201319e264a58beb48..a54549a6bb85829fb0656ed1da0415134d03483b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,3 +2,4 @@ concrete-ml==1.4.0 gradio uvicorn>=0.21.0 fastapi>=0.93.0 +more_itertools diff --git a/server.py b/server.py index dc7b263a506fe1829c6568ff26a8ec8c96309e5b..22163e6232659e506cab2d601aa8dff6df004955 100644 --- a/server.py +++ b/server.py @@ -5,12 +5,14 @@ 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 utils import DEPLOYMENT_DIR_MODEL1, DEPLOYMENT_DIR_MODEL2, DEPLOYMENT_DIR_MODEL3, SERVER_DIR # pylint: disable=no-name-in-module from concrete.ml.deployment import FHEModelServer # Load the FHE server -FHE_SERVER = FHEModelServer(DEPLOYMENT_DIR) +FHE_SERVER_MODEL1 = FHEModelServer(DEPLOYMENT_DIR_MODEL1) +FHE_SERVER_MODEL2 = FHEModelServer(DEPLOYMENT_DIR_MODEL2) +FHE_SERVER_MODEL3 = FHEModelServer(DEPLOYMENT_DIR_MODEL3) # Initialize an instance of FastAPI app = FastAPI() @@ -24,55 +26,130 @@ def root(): Returns: dict: The welcome message. """ - return {"message": "Welcome to your disease prediction with FHE!"} + return {"message": "Welcome to your disease prediction with FHE made by Dhiria with love!"} -@app.post("/send_input") -def send_input( +@app.post("/send_input_first_layer") +def send_input_first_layer( user_id: str = Form(), files: List[UploadFile] = File(), ): - """Send the inputs to the server.""" + """Send the inputs of the first layer of the hierarchical models to the server. + Order of the files: + files[0]: encrypted input_model1 + files[1]: encrypted input_model2 + files[2]: evaluation keys + """ + print('Receiving data for the first layer of models...') + + # Receive the Client's files (Evaluation key + Encrypted inputs for the models) + evaluation_key_path_1 = SERVER_DIR / f"{user_id}_evaluation_key_1" + evaluation_key_path_2 = SERVER_DIR / f"{user_id}_evaluation_key_2" + encrypted_input_path_model1 = SERVER_DIR / f"{user_id}_encrypted_input_model1" + encrypted_input_path_model2 = SERVER_DIR / f"{user_id}_encrypted_input_model2" - print("\nSend the data to the server ............\n") + # Save the files using the above paths + with encrypted_input_path_model1.open("wb") as encrypted_input_model1, \ + encrypted_input_path_model2.open("wb") as encrypted_input_model2, \ + evaluation_key_path_1.open("wb") as evaluation_key_1, \ + evaluation_key_path_2.open("wb") as evaluation_key_2: + encrypted_input_model1.write(files[0].file.read()) + encrypted_input_model2.write(files[1].file.read()) + evaluation_key_1.write(files[2].file.read()) + evaluation_key_2.write(files[3].file.read()) + + +@app.post("/send_input_second_layer") +def send_input_second_layer( + user_id: str = Form(), + files: List[UploadFile] = File(), +): + """Send the input of the second layer of the hierarchical models to the server. + Order of the files: + files[0]: encrypted input_model1 + files[1]: evaluation keys + """ + print('Receiving data for the second layer of models...') - # 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" + # Receive the Client's files (Evaluation key + Encrypted inputs for the models) + evaluation_key_path = SERVER_DIR / f"{user_id}_evaluation_key_second_layer" + encrypted_input_path_model3 = SERVER_DIR / f"{user_id}_encrypted_input_model3" # 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()) + with encrypted_input_path_model3.open("wb") as encrypted_input_model3, \ + evaluation_key_path.open("wb") as evaluation_key: + encrypted_input_model3.write(files[0].file.read()) evaluation_key.write(files[1].file.read()) -@app.post("/run_fhe") -def run_fhe( +@app.post("/run_fhe_first_layer") +def run_fhe_first_layer( + user_id: str = Form(), +): + """Inference in FHE.""" + + print("\nRun in FHE in the server ............\n") + evaluation_key_path_1 = SERVER_DIR / f"{user_id}_evaluation_key_1" + evaluation_key_path_2 = SERVER_DIR / f"{user_id}_evaluation_key_2" + encrypted_input_path_model1 = SERVER_DIR / f"{user_id}_encrypted_input_model1" + encrypted_input_path_model2 = SERVER_DIR / f"{user_id}_encrypted_input_model2" + + # Read the files (Evaluation key + Encrypted symptoms) using the above paths + with encrypted_input_path_model1.open("rb") as encrypted_output_file_model1, \ + encrypted_input_path_model2.open("rb") as encrypted_output_file_model2, \ + evaluation_key_path_1.open("rb") as evaluation_key_file_1, \ + evaluation_key_path_2.open("rb") as evaluation_key_file_2: + encrypted_output_model1 = encrypted_output_file_model1.read() + encrypted_output_model2 = encrypted_output_file_model2.read() + evaluation_key_1 = evaluation_key_file_1.read() + evaluation_key_2 = evaluation_key_file_2.read() + + # Run the FHE execution of the first layer of models + + start = time.time() + encrypted_output_model1 = FHE_SERVER_MODEL1.run(encrypted_output_model1, evaluation_key_1) + encrypted_output_model2 = FHE_SERVER_MODEL2.run(encrypted_output_model2, evaluation_key_2) + assert isinstance(encrypted_output_model1, bytes) + assert isinstance(encrypted_output_model2, bytes) + fhe_execution_time = round(time.time() - start, 2) + + # Retrieve the encrypted output path + encrypted_output_path_model1 = SERVER_DIR / f"{user_id}_encrypted_output_model1" + encrypted_output_path_model2 = SERVER_DIR / f"{user_id}_encrypted_output_model2" + + # Write the file using the above path + with encrypted_output_path_model1.open("wb") as f1, \ + encrypted_output_path_model2.open("wb") as f2: + f1.write(encrypted_output_model1) + f2.write(encrypted_output_model2) + + return JSONResponse(content=fhe_execution_time) + + +@app.post("/run_fhe_second_layer") +def run_fhe_second_layer( 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" + evaluation_key_path = SERVER_DIR / f"{user_id}_evaluation_key_second_layer" + encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input_model3" # 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: + 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() # Run the FHE execution start = time.time() - encrypted_output = FHE_SERVER.run(encrypted_output, evaluation_key) + encrypted_output = FHE_SERVER_MODEL3.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" + encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output_model3" # Write the file using the above path with encrypted_output_path.open("wb") as f: @@ -81,14 +158,58 @@ def run_fhe( return JSONResponse(content=fhe_execution_time) -@app.post("/get_output") -def get_output(user_id: str = Form()): - """Retrieve the encrypted output from the server.""" +@app.post("/get_output_first_layer_1") +def get_output_first_layer_1(user_id: str = Form()): + """Retrieve the encrypted outputs of the first layers of models from the server.""" + + print("\nGet the output from the server ............\n") + + # Path where the encrypted output is saved + encrypted_output_path_model1 = SERVER_DIR / f"{user_id}_encrypted_output_model1" + # encrypted_output_path_model2 = SERVER_DIR / f"{user_id}_encrypted_output_model2" + + # Read the file using the above path + with encrypted_output_path_model1.open("rb") as f1: + # encrypted_output_path_model2.open("rb") as f2: + encrypted_output_1 = f1.read() + # encrypted_output_2 = f2.read() + + time.sleep(1) + + # Send the encrypted output + return Response(encrypted_output_1) + + +@app.post("/get_output_first_layer_2") +def get_output_first_layer_2(user_id: str = Form()): + """Retrieve the encrypted outputs of the first layers of models from the server.""" + + print("\nGet the output from the server ............\n") + + # Path where the encrypted output is saved + # encrypted_output_path_model1 = SERVER_DIR / f"{user_id}_encrypted_output_model1" + encrypted_output_path_model2 = SERVER_DIR / f"{user_id}_encrypted_output_model2" + + # Read the file using the above path + with encrypted_output_path_model2.open("rb") as f2: + # encrypted_output_path_model2.open("rb") as f2: + # encrypted_output_1 = f1.read() + encrypted_output_2 = f2.read() + + time.sleep(1) + + # Send the encrypted output + return Response(encrypted_output_2) + + +@app.post("/get_output_second_layer") +def get_output_second_layer(user_id: str = Form()): + """Retrieve the encrypted outputs of the first layers of models 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" + encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output_model3" # Read the file using the above path with encrypted_output_path.open("rb") as f: diff --git a/stuff.py b/stuff.py new file mode 100644 index 0000000000000000000000000000000000000000..9746c9eaafb12601125889b868946627936e71ee --- /dev/null +++ b/stuff.py @@ -0,0 +1,36 @@ +import matplotlib.pyplot as plt +import numpy as np + + +def plot_tachometer(quality): + fig, ax = plt.subplots(figsize=(6, 3), subplot_kw={'projection': 'polar'}) + + # Create the base for the tachometer (semi-circle) correctly oriented (0 on the left, 100 on the right) + theta = np.linspace(np.pi, 0, 100) # Flip the range from pi to 0 + ax.plot(theta, np.ones_like(theta), lw=10, color="lightgrey") + + # Plot the current value as a red needle + theta_value = np.pi - (quality / 100.0) * np.pi # Subtract to flip needle direction + ax.plot([theta_value, theta_value], [0, 1], lw=3, color="red") + + # Add labels and customize the plot + ax.set_xticks(np.linspace(np.pi, 0, 5)) # Correctly set the ticks from left (0) to right (100) + ax.set_xticklabels(['0', '25', '50', '75', '100']) # Labels + ax.set_yticks([]) # No radial ticks + ax.spines['polar'].set_visible(False) + + # Add a title for clarity + ax.set_title(f"Running Quality: {quality:.1f}/100", va='bottom') + + # Use tight layout to avoid cropping and ensure all elements fit well + plt.tight_layout(pad=2.0) + + return fig + + +# Emoticon for Model 2 +def get_emoticon(mental_health): + if mental_health == 0: + return "😎" + elif mental_health == 1: + return "🤯" \ No newline at end of file diff --git a/test.py b/test.py new file mode 100644 index 0000000000000000000000000000000000000000..879b411423075ba9bd45caf660c5ba22a28d9649 --- /dev/null +++ b/test.py @@ -0,0 +1,5 @@ +import pandas as pd + +df = pd.read_csv('data/data_mental.csv') + +breakpoint() \ No newline at end of file diff --git a/utils.py b/utils.py index ddd6e189b14686a73c0298548c5e84f52da58a4f..6e2d2b3851fc413323a07e732b25671de41a73be 100644 --- a/utils.py +++ b/utils.py @@ -16,7 +16,10 @@ INPUT_BROWSER_LIMIT = 380 SERVER_URL = "http://localhost:8000/" CURRENT_DIR = Path(__file__).parent -DEPLOYMENT_DIR = CURRENT_DIR / "deployment_files" +DEPLOYMENT_DIR = CURRENT_DIR / "deployment_files_generic" +DEPLOYMENT_DIR_MODEL1 = CURRENT_DIR / "deployment_files_model1" +DEPLOYMENT_DIR_MODEL2 = CURRENT_DIR / "deployment_files_model2" +DEPLOYMENT_DIR_MODEL3 = CURRENT_DIR / "deployment_files_model3" KEYS_DIR = DEPLOYMENT_DIR / ".fhe_keys" CLIENT_DIR = DEPLOYMENT_DIR / "client_dir" SERVER_DIR = DEPLOYMENT_DIR / "server_dir"