import subprocess
import time
from typing import Dict, List, Tuple

import gradio as gr  # pylint: disable=import-error
import numpy as np
import pandas as pd
import requests
from symptoms_categories import SYMPTOMS_LIST
from utils import (
    CLIENT_DIR,
    CURRENT_DIR,
    DEPLOYMENT_DIR,
    INPUT_BROWSER_LIMIT,
    KEYS_DIR,
    SERVER_URL,
    TARGET_COLUMNS,
    TRAINING_FILENAME,
    clean_directory,
    get_disease_name,
    load_data,
    pretty_print,
)

from concrete.ml.deployment import FHEModelClient

subprocess.Popen(["uvicorn", "server:app"], cwd=CURRENT_DIR)
time.sleep(3)

# pylint: disable=c-extension-no-member,invalid-name


def is_none(obj) -> bool:
    """
    Check if the object is None.

    Args:
        obj (any): The input to be checked.

    Returns:
        bool: True if the object is None or empty, False otherwise.
    """
    return obj is None or (obj is not None and len(obj) < 1)


def display_default_symptoms_fn(default_disease: str) -> Dict:
    """
    Displays the symptoms of a given existing disease.

    Args:
        default_disease (str): Disease
    Returns:
        Dict: The according symptoms
    """
    df = pd.read_csv(TRAINING_FILENAME)
    df_filtred = df[df[TARGET_COLUMNS[1]] == default_disease]

    return {
        default_symptoms: gr.update(
            visible=True,
            value=pretty_print(
                df_filtred.columns[df_filtred.eq(1).any()].to_list(), delimiter=", "
            ),
        )
    }


# <!> This function has been paused due to UI issues.

# def fill_in_fn(default_disease: str, *checkbox_symptoms: Tuple[str]) -> Dict:
#     """
#     Fill in the gr.CheckBoxGroup list with predefined symptoms of a selected default disease.
#     Args:
#         default_disease (str): The default selected disease
#         *checkbox_symptoms (Tuple[str]): Existing checked symptoms
#     Returns:
#         dict: The updated gr.CheckBoxesGroup.
#     """
#
#     # Figure out the symptoms of the disease, selected by the user
#     df = pd.read_csv(TRAINING_FILENAME)
#     df_filtred = df[df[TARGET_COLUMNS[1]] == default_disease]
#     symptoms = pretty_print(df_filtred.columns[df_filtred.eq(1).any()].to_list())
#     # Check if there are existing symptoms, in the CheckbBxGroup list
#     if any(lst for lst in checkbox_symptoms if lst):
#         for sublist in checkbox_symptoms:
#             symptoms.extend(sublist)
#     return {box: symptoms for box in check_boxes}


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"),
            user_vect_box1: None,
        }

    return {
        error_box1: gr.update(visible=False),
        user_vect_box1: gr.update(
            visible=True, value=get_user_symptoms_from_checkboxgroup(pretty_print(checked_symptoms))
        ),
        recap_symptoms_box: gr.update(
            visible=True,
            value=pretty_print(checked_symptoms, case_conversion=str.capitalize, delimiter=", "),
        ),
    }


def key_gen_fn(user_symptoms: List[str]) -> Dict:
    """
    Generate keys for a given user.

    Args:
        user_symptoms (List[str]): The vector symptoms provided by the user.

    Returns:
        dict: A dictionary containing the generated keys and related information.

    """
    clean_directory()

    if is_none(user_symptoms):
        print("Error: Please submit your symptoms or select a default disease.")
        return {
            error_box2: gr.update(visible=True, value="⚠️ Please submit your symptoms first."),
        }

    # Generate a random user ID
    user_id = np.random.randint(0, 2**32)
    print(f"Your user ID is: {user_id}....")

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

    # Creates the private and evaluation keys on the client side
    client.generate_private_and_evaluation_keys()

    # Get the serialized evaluation keys
    serialized_evaluation_keys = client.get_serialized_evaluation_keys()
    assert isinstance(serialized_evaluation_keys, bytes)

    # Save the evaluation key
    evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key"
    with evaluation_key_path.open("wb") as f:
        f.write(serialized_evaluation_keys)

    serialized_evaluation_keys_shorten_hex = serialized_evaluation_keys.hex()[:INPUT_BROWSER_LIMIT]

    return {
        error_box2: gr.update(visible=False),
        key_box: gr.update(visible=False, value=serialized_evaluation_keys_shorten_hex),
        user_id_box: gr.update(visible=True, value=user_id),
        key_len_box: gr.update(
            visible=False, value=f"{len(serialized_evaluation_keys) / (10**6):.2f} MB"
        ),
    }


def encrypt_fn(user_symptoms: np.ndarray, user_id: str) -> None:
    """
    Encrypt the user symptoms vector in the `Client Side`.

    Args:
        user_symptoms (List[str]): The vector symptoms provided by the user
        user_id (user): The current user's ID
    """

    if is_none(user_id) or is_none(user_symptoms):
        print("Error in encryption step: Provide your symptoms and generate the evaluation keys.")
        return {
            error_box3: gr.update(
                visible=True,
                value="⚠️ Please ensure that your symptoms have been submitted and "
                "that you have generated the evaluation key.",
            )
        }

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

    user_symptoms = np.fromstring(user_symptoms[2:-2], dtype=int, sep=".").reshape(1, -1)
    quant_user_symptoms = client.model.quantize_input(user_symptoms)

    encrypted_quantized_user_symptoms = client.quantize_encrypt_serialize(user_symptoms)
    assert isinstance(encrypted_quantized_user_symptoms, bytes)
    encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_input"

    with encrypted_input_path.open("wb") as f:
        f.write(encrypted_quantized_user_symptoms)

    encrypted_quantized_user_symptoms_shorten_hex = encrypted_quantized_user_symptoms.hex()[
        :INPUT_BROWSER_LIMIT
    ]

    return {
        error_box3: gr.update(visible=False),
        user_vect_box2: gr.update(visible=False, value=user_symptoms),
        quant_vect_box: gr.update(visible=False, value=quant_user_symptoms),
        enc_vect_box: gr.update(visible=True, value=encrypted_quantized_user_symptoms_shorten_hex),
    }


def send_input_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
    """Send the encrypted data and the evaluation key to the server.

    Args:
        user_id (str): The current user's ID
        user_symptoms (np.ndarray): The user symptoms
    """

    if is_none(user_id) or is_none(user_symptoms):
        return {
            error_box4: gr.update(
                visible=True,
                value="⚠️ Please check your connectivity \n"
                "⚠️ Ensure that the symptoms have been submitted and the evaluation "
                "key has been generated before sending the data to the server.",
            )
        }

    evaluation_key_path = KEYS_DIR / f"{user_id}/evaluation_key"
    encrypted_input_path = KEYS_DIR / f"{user_id}/encrypted_input"

    if not evaluation_key_path.is_file():
        print(
            "Error Encountered While Sending Data to the Server: "
            f"The key has been generated correctly - {evaluation_key_path.is_file()=}"
        )

        return {
            error_box4: gr.update(visible=True, value="⚠️ Please generate the private key first.")
        }

    if not encrypted_input_path.is_file():
        print(
            "Error Encountered While Sending Data to the Server: The data has not been encrypted "
            f"correctly on the client side - {encrypted_input_path.is_file()=}"
        )
        return {
            error_box4: gr.update(
                visible=True,
                value="⚠️ Please encrypt the data with the private key first.",
            ),
        }

    # Define the data and files to post
    data = {
        "user_id": user_id,
        "input": user_symptoms,
    }

    files = [
        ("files", open(encrypted_input_path, "rb")),
        ("files", open(evaluation_key_path, "rb")),
    ]

    # Send the encrypted input and evaluation key to the server
    url = SERVER_URL + "send_input"
    with requests.post(
        url=url,
        data=data,
        files=files,
    ) as response:
        print(f"Sending Data: {response.ok=}")
    return {
        error_box4: gr.update(visible=False),
        srv_resp_send_data_box: "Data sent",
    }


def run_fhe_fn(user_id: str) -> Dict:
    """Send the encrypted input and the evaluation key to the server.

    Args:
        user_id (int): The current user's ID.
    """
    if is_none(user_id):
        return {
            error_box5: gr.update(
                visible=True,
                value="⚠️ Please check your connectivity \n"
                "⚠️ Ensure that the symptoms have been submitted, the evaluation "
                "key has been generated and the server received the data "
                "before processing the data.",
            ),
            fhe_execution_time_box: None,
        }

    data = {
        "user_id": user_id,
    }

    url = SERVER_URL + "run_fhe"

    with requests.post(
        url=url,
        data=data,
    ) as response:
        if not response.ok:
            return {
                error_box5: gr.update(
                    visible=True,
                    value=(
                        "⚠️ An error occurred on the Server Side. "
                        "Please check connectivity and data transmission."
                    ),
                ),
                fhe_execution_time_box: gr.update(visible=False),
            }
        else:
            time.sleep(1)
            print(f"response.ok: {response.ok}, {response.json()} - Computed")

    return {
        error_box5: gr.update(visible=False),
        fhe_execution_time_box: gr.update(visible=True, value=f"{response.json():.2f} seconds"),
    }


def get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
    """Retreive the encrypted data from the server.

    Args:
        user_id (str): The current user's ID
        user_symptoms (np.ndarray): The user symptoms
    """

    if is_none(user_id) or is_none(user_symptoms):
        return {
            error_box6: gr.update(
                visible=True,
                value="⚠️ Please check your connectivity \n"
                "⚠️ Ensure that the server has successfully processed and transmitted the data to the client.",
            )
        }

    data = {
        "user_id": user_id,
    }

    # Retrieve the encrypted output
    url = SERVER_URL + "get_output"
    with requests.post(
        url=url,
        data=data,
    ) as response:
        if response.ok:
            print(f"Receive Data: {response.ok=}")

            encrypted_output = response.content

            # Save the encrypted output to bytes in a file as it is too large to pass through
            # regular Gradio buttons (see https://github.com/gradio-app/gradio/issues/1877)
            encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output"

            with encrypted_output_path.open("wb") as f:
                f.write(encrypted_output)
    return {error_box6: gr.update(visible=False), srv_resp_retrieve_data_box: "Data received"}


def decrypt_fn(user_id: str, user_symptoms: np.ndarray, threshold: int = 0.5) -> Dict:
    """Dencrypt the data on the `Client Side`.

    Args:
        user_id (str): The current user's ID
        user_symptoms (np.ndarray): The user symptoms
        threshold (float): Probability confidence threshold

    Returns:
        Decrypted output
    """

    if is_none(user_id) or is_none(user_symptoms):
        return {
            error_box7: gr.update(
                visible=True,
                value="⚠️ Please check your connectivity \n"
                "⚠️ Ensure that the client has successfully received the data from the server.",
            )
        }

    # Get the encrypted output path
    encrypted_output_path = CLIENT_DIR / f"{user_id}_encrypted_output"

    if not encrypted_output_path.is_file():
        print("Error in decryption step: Please run the FHE execution, first.")
        return {
            error_box7: gr.update(
                visible=True,
                value="⚠️ Please ensure that: \n"
                "- the connectivity \n"
                "- the symptoms have been submitted \n"
                "- the evaluation key has been generated \n"
                "- the server processed the encrypted data \n"
                "- the Client received the data from the Server before decrypting the prediction",
            ),
            decrypt_target_box: None,
        }

    # Load the encrypted output as bytes
    with encrypted_output_path.open("rb") as f:
        encrypted_output = f.read()

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

    # Deserialize, decrypt and post-process the encrypted output
    output = client.deserialize_decrypt_dequantize(encrypted_output)

    top3_diseases = np.argsort(output.flatten())[-3:][::-1]
    top3_proba = output[0][top3_diseases]

    if (
        (top3_proba[0] < threshold)
        or (np.sum(top3_proba) < threshold)
        or (abs(top3_proba[0] - top3_proba[1]) < threshold)
    ):
        out = (
            "The prediction appears uncertain; including more symptoms may improve the results.\n\n"
            "Here are the top3 predictions:"
        )
    else:
        out = "Based on the information provided, here are the top3 predictions:"

    out = (
        f"{out}\n\n"
        f"1. « {get_disease_name(top3_diseases[0])} » with a probability of {top3_proba[0]:.2%}\n"
        f"2. « {get_disease_name(top3_diseases[1])} » with a probability of {top3_proba[1]:.2%}\n"
        f"3. « {get_disease_name(top3_diseases[2])} » with a probability of {top3_proba[2]:.2%}\n"
    )

    return {
        error_box7: gr.update(visible=False),
        decrypt_target_box: out,
    }


def reset_fn():
    """Reset the space and clear all the box outputs."""

    clean_directory()

    return {
        user_id_box: gr.update(visible=False, value=None, interactive=False),
        user_vect_box1: None,
        recap_symptoms_box: gr.update(visible=False, value=None),
        default_symptoms: gr.update(visible=True, value=None),
        disease_box: gr.update(visible=True, value=None),
        user_vect_box2: gr.update(visible=False, value=None, interactive=False),
        quant_vect_box: gr.update(visible=False, value=None, interactive=False),
        enc_vect_box: gr.update(visible=True, value=None, interactive=False),
        key_box: gr.update(visible=True, value=None, interactive=False),
        key_len_box: gr.update(visible=False, value=None, interactive=False),
        fhe_execution_time_box: gr.update(visible=True, value=None, interactive=False),
        decrypt_target_box: None,
        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},
    }


CSS = """
#them {color: grey}
#them {font-size: 24px} 
#them {font-weight: bold}
.gradio-container {background-color: white}
.gradio-button {color: red; font-size: 20px;} 
/* .feedback {font-size: 3px !important} */
#svelte-s1r2yt {color: grey}
#svelte-s1r2yt {font-size: 25px}
#svelte-s1r2yt {font-weight: bold}
/* #them {text-align: center} */
"""


"""

<button id="toTopBtn" onclick="'parentIFrame' in window ? window.parentIFrame.scrollTo({top: 0, behavior:'smooth'}) : window.scrollTo({ top: 0 })">
<a style="color:white; text-decoration:none;">Back to Top!</a>
</button>

"""
back_to_top_btn_html = '''

<button onclick="scrollToTop()" style="color:white; text-decoration:none;">
  Back to Top!
</button>

<script>
function scrollToTop() {
  // Check if the parentIFrame exists and use it for scrolling if available
  if ('parentIFrame' in window) {
    window.parentIFrame.scrollTo({top: 0, behavior: 'smooth'});
  } else {
    // Use the standard scrollIntoView method for scrolling
    document.documentElement.scrollIntoView({behavior: 'smooth'});
  }
}
</script>


'''

if __name__ == "__main__":

    print("Starting demo ...")

    clean_directory()

    (X_train, X_test), (y_train, y_test), valid_symptoms, diseases = load_data()

    with gr.Blocks(css="#box { height: 500px; overflow-y: scroll !important}") as demo:

        # Link + images
        gr.Markdown(
            """
            <p align="center">
                <img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png">
            </p>

            <h2 align="center">Health Prediction On Encrypted Data Using Fully Homomorphic Encryption.</h2>

            <p align="center">
                <a href="https://github.com/zama-ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197972109-faaaff3e-10e2-4ab6-80f5-7531f7cfb08f.png">Concrete-ML</a>
                —
                <a href="https://docs.zama.ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197976802-fddd34c5-f59a-48d0-9bff-7ad1b00cb1fb.png">Documentation</a>
                —
                <a href="https://zama.ai/community"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197977153-8c9c01a7-451a-4993-8e10-5a6ed5343d02.png">Community</a>
                —
                <a href="https://twitter.com/zama_fhe"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="https://user-images.githubusercontent.com/5758427/197975044-bab9d199-e120-433b-b3be-abd73b211a54.png">@zama_fhe</a>
            </p>

            <p align="center">
            <img width="100%" height="30%" src="https://raw.githubusercontent.com/kcelia/Img/main/cover_image.png">
            </p>
            """
        )

        gr.Markdown("# Introduction")
        gr.Markdown(
            "Welcome to our Healthcare Prediction space using Fully Homomorphic Encryption (FHE) with Concrete ML library."
        )
        gr.Markdown(
            "Through this user-friendly and secure client-server interface you can confidently submit your chief complaints, which you have locally "
            "encrypted on your end and transmitted to an untrusted server for processing.\n\n"
            "Thanks to the FHE scheme, the remote server is able to perform computations without ever decrypting the data and return result the encrypted to the client for local decryption. \n\n\n"
            "FHE ensures end-to-end data encryption and guarantees patient privacy."
        )
        gr.Markdown(
            "Disclaimer: We are not medical professionals. For accurate diagnosis and treatment,  "
            "please consult a qualified healthcare provider."
        )

        with gr.Tabs(eelem_id="them") as tabs:
            with gr.TabItem("1. Chief Complaints", id=0):
                gr.Markdown("<span style='color:grey'>Client Side</span>")
                gr.Markdown("## Provide your chief complaints")
                gr.Markdown("Provide at least 5 chief complaints by filling in the boxes below. ")

                # Box symptoms
                check_boxes = []
                for i, category in enumerate(SYMPTOMS_LIST):
                    with gr.Accordion(
                        pretty_print(category.keys()),
                        open=False,
                        elem_classes="feedback",
                    ) as accordion:
                        check_box = gr.CheckboxGroup(
                            pretty_print(category.values()),
                            show_label=False,
                        )
                        check_boxes.append(check_box)

                error_box1 = gr.Textbox(label="Error ❌", visible=False)

                # <!> This part has been paused due to UI issues.

                # Default disease, picked from the dataframe
                gr.Markdown(
                    "You can choose an existing disease and explore its associated symptoms."
                )

                with gr.Row():
                    with gr.Column(scale=2):
                        disease_box = gr.Dropdown(sorted(diseases), label="Diseases 👆")
                    with gr.Column(scale=5):
                        default_symptoms = gr.Textbox(
                            label="Related Symptoms:", visible=True, interactive=False
                        )

                disease_box.change(
                    fn=display_default_symptoms_fn, inputs=[disease_box], outputs=[default_symptoms]
                )

                # User symptom vector
                user_vect_box1 = gr.Textbox(label="User Symptoms Vector:", interactive=False)

                # Submit botton
                submit_button = gr.Button("Submit 👆")

                # Clear botton
                clear_button = gr.Button("Reset Space 🔁")
                # Next tab
                gr.Markdown("")
                next_tab = gr.Button("Next Step 👉")
                next_tab.click(lambda _: gr.Tabs.update(selected=1), None, tabs)

            with gr.TabItem("2. Data Encryption", id=1):
                gr.Markdown("<span style='color:grey'>Client Side</span>")
                gr.Markdown("## Key Generation")
                gr.Markdown(
                    "In FHE schemes, a secret (enc/dec)ryption keys are generated for encrypting and decrypting data owned by the client. \n\n"
                    "Additionally, a public evaluation key is generated, enabling external entities to perform homomorphic operations on encrypted data, without the need to decrypt them. \n\n"
                    "The evaluation key will be transmitted to the server for further processing."
                )

                gen_key_btn = gr.Button("Generate the evaluation key 👆")
                error_box2 = gr.Textbox(label="Error ❌", visible=False)

                user_id_box = gr.Textbox(label="User ID:", interactive=False, visible=True)
                # Evaluation key size

                key_len_box = gr.Textbox(
                    label="Evaluation Key Size:", interactive=False, visible=False
                )

                # Evaluation key (truncated)
                key_box = gr.Textbox(
                    label="Evaluation key (truncated):",
                    max_lines=3,
                    interactive=False,
                    visible=False,
                )

                gen_key_btn.click(
                    key_gen_fn,
                    inputs=user_vect_box1,
                    outputs=[
                        key_box,
                        user_id_box,
                        key_len_box,
                        error_box2,
                    ],
                )

                gr.Markdown("## Encrypt the data")

                encrypt_btn = gr.Button("Encrypt the data using the 🔒 private secret key 👆")
                error_box3 = gr.Textbox(label="Error ❌", visible=False)

                with gr.Row():
                    with gr.Column(scale=1, min_width=600):
                        user_vect_box2 = gr.Textbox(
                            label="User Symptoms Vector:",
                            interactive=False,
                            visible=False,
                        )

                    with gr.Column(scale=1, min_width=600):
                        quant_vect_box = gr.Textbox(
                            label="Quantized Vector:",
                            interactive=False,
                            visible=False,
                        )

                    with gr.Column(scale=1, min_width=600):
                        enc_vect_box = gr.Textbox(
                            label="Encrypted Vector:",
                            max_lines=3,
                            interactive=False,
                        )

                encrypt_btn.click(
                    encrypt_fn,
                    inputs=[user_vect_box1, user_id_box],
                    outputs=[
                        user_vect_box2,
                        quant_vect_box,
                        enc_vect_box,
                        error_box3,
                    ],
                )

                gr.Markdown(
                    "## Send the encrypted data to the "
                    "<span style='color:grey'>Server Side</span>"
                )

                error_box4 = gr.Textbox(label="Error ❌", visible=False)

                with gr.Row().style(equal_height=False):
                    with gr.Column(scale=4):
                        send_input_btn = gr.Button("Send the encrypted data 👆")
                    with gr.Column(scale=1):
                        srv_resp_send_data_box = gr.Checkbox(
                            label="Data Sent", show_label=False, interactive=False
                        )

                send_input_btn.click(
                    send_input_fn,
                    inputs=[user_id_box, user_vect_box1],
                    outputs=[error_box4, srv_resp_send_data_box],
                )

                gr.Markdown("\n\n")
                with gr.Row().style(equal_height=True):
                    with gr.Column(scale=1):
                        prev_tab = gr.Button("👈 Previous Step")
                        prev_tab.click(lambda _: gr.Tabs.update(selected=0), None, tabs)

                    with gr.Column(scale=1):
                        next_tab = gr.Button("Next Step 👉")
                        next_tab.click(lambda _: gr.Tabs.update(selected=2), None, tabs)

            with gr.TabItem("3. FHE execution", id=2):
                gr.Markdown("<span style='color:grey'>Server Side</span>")
                gr.Markdown("## Run the FHE evaluation")
                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 model that has been trained on this [data-set](https://github.com/anujdutt9/Disease-Prediction-from-Symptoms/tree/master/dataset)."
                )

                run_fhe_btn = gr.Button("Run the FHE evaluation 👆")
                error_box5 = gr.Textbox(label="Error ❌", visible=False)
                fhe_execution_time_box = gr.Textbox(
                    label="Total FHE Execution Time:", interactive=False, visible=True
                )

                run_fhe_btn.click(
                    run_fhe_fn,
                    inputs=[user_id_box],
                    outputs=[fhe_execution_time_box, error_box5],
                )

                gr.Markdown("\n\n")
                with gr.Row().style(equal_height=True):
                    with gr.Column(scale=1):
                        prev_tab = gr.Button("👈 Previous Step")
                        prev_tab.click(lambda _: gr.Tabs.update(selected=1), None, tabs)

                    with gr.Column(scale=1):
                        next_tab = gr.Button("Next Step 👉 ")
                        next_tab.click(lambda _: gr.Tabs.update(selected=3), None, tabs)

            with gr.TabItem("4. Data Decryption", id=3):
                gr.Markdown("<span style='color:grey'>Client Side</span>")
                gr.Markdown("## Get the data from the <span style='color:grey'>Server Side</span>")

                error_box6 = gr.Textbox(label="Error ❌", visible=False)

                with gr.Row().style(equal_height=True):
                    with gr.Column(scale=4):
                        get_output_btn = gr.Button("Get data 👆")
                    with gr.Column(scale=1):
                        srv_resp_retrieve_data_box = gr.Checkbox(
                            label="Data Received", show_label=False, interactive=False
                        )

                get_output_btn.click(
                    get_output_fn,
                    inputs=[user_id_box, user_vect_box1],
                    outputs=[srv_resp_retrieve_data_box, error_box6],
                )

                gr.Markdown("## Decrypt the output")

                recap_symptoms_box = gr.Textbox(
                    label="Summary of chief complaints:", visible=False, max_lines=3
                )

                decrypt_target_btn = gr.Button(
                    "Decrypt the output with the 🔒 private secret decryption key 👆"
                )
                error_box7 = gr.Textbox(label="Error ❌", visible=False)
                decrypt_target_box = gr.Textbox(label="Decrypted Output:", interactive=False)

                decrypt_target_btn.click(
                    decrypt_fn,
                    inputs=[user_id_box, user_vect_box1],
                    outputs=[decrypt_target_box, error_box7],
                )

                with gr.Row().style(equal_height=True):
                    with gr.Column(scale=1):
                        prev_tab = gr.Button("👈 Previous Step")
                        prev_tab.click(lambda _: gr.Tabs.update(selected=2), None, tabs)

                    with gr.Column(scale=1):
                        next_tab = gr.Button("👈 👈 Go back to start")
                        next_tab.click(lambda _: gr.Tabs.update(selected=0), None, tabs)

        submit_button.click(
            fn=get_features_fn,
            inputs=[*check_boxes],
            outputs=[user_vect_box1, error_box1, recap_symptoms_box],
        )

        clear_button.click(
            reset_fn,
            outputs=[
                user_vect_box1,
                user_vect_box2,
                # disease_box,
                error_box1,
                error_box2,
                error_box3,
                error_box4,
                error_box5,
                error_box6,
                error_box7,
                disease_box,
                default_symptoms,
                recap_symptoms_box,
                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_target_box,
                *check_boxes,
            ],
        )

        demo.launch()