New

.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

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(
             """
             <p align="center">
-                <img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png">
+                <img width=300 src="file/atlhete-high-resolution-logo-black-transparent.png">
             </p>
             """)
-        gr.Markdown()
-        gr.Markdown("""<h2 align="center">Health Prediction On Encrypted Data Using Fully Homomorphic Encryption</h2>""")
-        gr.Markdown()
-        gr.Markdown(
-            """
-            <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>
+        
+        # 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(
-            """"
-            <p align="center">
-            <img width="65%" height="25%" 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]
-        )
+            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("<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."
-        )
+        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 <span style='color:grey'>Server Side</span>"
-        )
+
+        # 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("<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)."
-        )
-
+        # Encrypted processing
+        gr.Markdown("""
+            ## Encrypted processing
+            Process your <span style='color:grey'>encrypted data</span> 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("<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>"
-        )
-
+        # 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')

data/200_Users_Running_Dataset.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79f7a734f8f85e763f2feee8344eb3a725b5b9c9e2c50694793411787b4bfe58
+size 38539031

data/dataset_for_last_model.csv

@@ -0,0 +1,97 @@
+stressed,running_score,label
+0,0.9,0
+0,0.2,1
+1,0.8,2
+1,0.3,3
+0,0.7,0
+0,0.1,1
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+0,0.7,0
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+0,0.7,0
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+0,0.9,0
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data_for_demo/focus_on_technique_mental.csv

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deployment_files/.fhe_keys/2820248940/encrypted_input

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e509a3079177c527a89695b418b96d9050c72a25f2c7a81d89d17e09e99b2b1
+size 2003208

deployment_files/client/client.specs.json

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+{"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"}]}

deployment_files/client/serialized_processing.json

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deployment_files/client/versions.json

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+{"concrete-python": "2.5", "concrete-ml": "1.4.0", "python": "3.10.11"}

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>>
+  }
+}

deployment_files/server/configuration.json

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deployment_files/server/is_simulated

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deployment_files/server/versions.json

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deployment_files/server_dir/2820248940_encrypted_input

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+size 2003208

deployment_files_model1/versions.json

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+{"concrete-python": "2.5", "concrete-ml": "1.4.0", "python": "3.10.14"}