"""A local gradio app that detects seizures with EEG using FHE."""
from PIL import Image
import os
import shutil
import subprocess
import time
import gradio as gr
import numpy
import requests
import numpy as np
from itertools import chain

from common import (
    CLIENT_TMP_PATH,
    SEIZURE_DETECTION_MODEL_PATH,
    SERVER_TMP_PATH,
    EXAMPLES,
    INPUT_SHAPE,
    KEYS_PATH,
    REPO_DIR,
    SERVER_URL,
)
from client_server_interface import FHEClient
from concrete.ml.deployment import FHEModelClient
# Uncomment here to have both the server and client in the same terminal
subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR)
time.sleep(3)

def shorten_bytes_object(bytes_object, limit=500):
    """Shorten the input bytes object to a given length.

    Encrypted data is too large for displaying it in the browser using Gradio. This function
    provides a shorten representation of it.

    Args:
        bytes_object (bytes): The input to shorten
        limit (int): The length to consider. Default to 500.

    Returns:
        str: Hexadecimal string shorten representation of the input byte object.

    """
    # Define a shift for better display
    shift = 100
    return bytes_object[shift : limit + shift].hex()

def get_client(user_id):
    """Get the client API.

    Args:
        user_id (int): The current user's ID.

    Returns:
        FHEClient: The client API.
    """
    return FHEClient(
        key_dir=KEYS_PATH / f"seizure_detection_{user_id}"
    )

def get_client_file_path(name, user_id):
    """Get the correct temporary file path for the client.

    Args:
        name (str): The desired file name.
        user_id (int): The current user's ID.

    Returns:
        pathlib.Path: The file path.
    """
    return CLIENT_TMP_PATH / f"{name}_seizure_detection_{user_id}"

def clean_temporary_files(n_keys=20):
    """Clean keys and encrypted images.

    A maximum of n_keys keys and associated temporary files are allowed to be stored. Once this 
    limit is reached, the oldest files are deleted.

    Args:
        n_keys (int): The maximum number of keys and associated files to be stored. Default to 20.

    """
    # Get the oldest key files in the key directory
    key_dirs = sorted(KEYS_PATH.iterdir(), key=os.path.getmtime)

    # If more than n_keys keys are found, remove the oldest
    user_ids = []
    if len(key_dirs) > n_keys:
        n_keys_to_delete = len(key_dirs) - n_keys
        for key_dir in key_dirs[:n_keys_to_delete]:
            user_ids.append(key_dir.name)
            shutil.rmtree(key_dir)

    # Get all the encrypted objects in the temporary folder
    client_files = CLIENT_TMP_PATH.iterdir()
    server_files = SERVER_TMP_PATH.iterdir()

    # Delete all files related to the ids whose keys were deleted
    for file in chain(client_files, server_files):
        for user_id in user_ids:
            if user_id in file.name:
                file.unlink()

def keygen():
    """Generate the private key for seizure detection.

    Returns:
        (user_id, True) (Tuple[int, bool]): The current user's ID and a boolean used for visual display.

    """
    # Clean temporary files
    clean_temporary_files()

    # 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=SEIZURE_DETECTION_MODEL_PATH, key_dir=KEYS_PATH / f"{user_id}")
    client.load()

    print("Super print ici")

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

    print("Super print ici 2")
    # Get the serialized evaluation keys
    serialized_evaluation_keys = client.get_serialized_evaluation_keys()
    assert isinstance(serialized_evaluation_keys, bytes)

    print("Super print ici 3")

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

    print("Super print ici 4")

    return (user_id, True)

def encrypt(user_id, input_image):
    """Encrypt the given image for seizure detection.

    Args:
        user_id (int): The current user's ID.
        input_image (numpy.ndarray): The image to encrypt.

    Returns:
        (input_image, encrypted_image_short) (Tuple[bytes]): The encrypted image and one of its
        representation.

    """
    if user_id == "":
        raise gr.Error("Please generate the private key first.")

    if input_image is None:
        raise gr.Error("Please choose an image first.")


    import numpy as np
    # Resize image if necessary
    if input_image.shape != (32, 32, 1):
        input_image_pil = Image.fromarray(input_image)
        input_image_pil = input_image_pil.resize((32, 32))
        input_image = np.array(input_image_pil)

    # Convert to grayscale and reshape to (1, 1, 224, 224)
    input_image = np.mean(input_image, axis=2).astype(np.float32)
    input_image = input_image.reshape(1, 1, 32, 32)

    # Scale values to 12-bit range (-2048 to 2047)
    input_image = (input_image / 255.0 * 4095 - 2048).astype(np.int16)
    input_image = np.clip(input_image, -2048, 2047)

    print("Processing the image finished")
    # Retrieve the client API
    client = get_client(user_id)

    print("Client retrieved")

    # Pre-process, encrypt and serialize the image
    encrypted_image = client.encrypt_serialize(input_image)

    print("Encrypted image retrieved")

    # Save encrypted_image to bytes in a file, since too large to pass through regular Gradio
    # buttons, https://github.com/gradio-app/gradio/issues/1877
    encrypted_image_path = get_client_file_path("encrypted_image", user_id)

    print("Encrypted image path retrieved")

    with encrypted_image_path.open("wb") as encrypted_image_file:
        encrypted_image_file.write(encrypted_image)

    print("Encrypted image file retrieved")

    # Get a short representation of the encrypted image for display purposes
    encrypted_image_short = encrypted_image[:100]  # Take first 100 bytes

    return encrypted_image_short


def send_input(user_id):
    """Send the encrypted input image as well as the evaluation key to the server.

    Args:
        user_id (int): The current user's ID.
    """
    # Get the evaluation key path
    evaluation_key_path = get_client_file_path("evaluation_key", user_id)

    if user_id == "" or not evaluation_key_path.is_file():
        raise gr.Error("Please generate the private key first.")

    encrypted_input_path = get_client_file_path("encrypted_image", user_id)

    if not encrypted_input_path.is_file():
        raise gr.Error("Please generate the private key and then encrypt an image first.")

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

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

    # Send the encrypted input image and evaluation key to the server
    url = SERVER_URL + "send_input"
    with requests.post(
        url=url,
        data=data,
        files=files,
    ) as response:
        return response.ok

def run_fhe(user_id):
    """Apply the seizure detection model on the encrypted image previously sent using FHE.

    Args:
        user_id (int): The current user's ID.
    """
    data = {
        "user_id": user_id,
    }

    # Trigger the FHE execution on the encrypted image previously sent
    url = SERVER_URL + "run_fhe"
    with requests.post(
        url=url,
        data=data,
    ) as response:
        if response.ok:
            return response.json()
        else:
            raise gr.Error("Please wait for the input image to be sent to the server.")

def get_output(user_id):
    """Retrieve the encrypted output (boolean).

    Args:
        user_id (int): The current user's ID.

    Returns:
        encrypted_output_short (bytes): A representation of the encrypted result.

    """
    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:
            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 = get_client_file_path("encrypted_output", user_id)

            with encrypted_output_path.open("wb") as encrypted_output_file:
                encrypted_output_file.write(encrypted_output)

            # Create a truncated version of the encrypted output for display
            encrypted_output_short = shorten_bytes_object(encrypted_output)

            return encrypted_output_short
        else:
            raise gr.Error("Please wait for the FHE execution to be completed.")

def decrypt_output(user_id):
    """Decrypt the result.

    Args:
        user_id (int): The current user's ID.

    Returns:
        bool: The decrypted output (True if seizure detected, False otherwise)

    """
    if user_id == "":
        raise gr.Error("Please generate the private key first.")

    # Get the encrypted output path
    encrypted_output_path = get_client_file_path("encrypted_output", user_id)

    if not encrypted_output_path.is_file():
        raise gr.Error("Please run the FHE execution first.")

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

    # Retrieve the client API
    client = get_client(user_id)

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

    return "Seizure detected" if decrypted_output else "No seizure detected"

def resize_img(img, width=256, height=256):
    """Resize the image."""
    if img.dtype != numpy.uint8:
        img = img.astype(numpy.uint8)
    img_pil = Image.fromarray(img)
    # Resize the image
    resized_img_pil = img_pil.resize((width, height))
    # Convert back to a NumPy array
    return numpy.array(resized_img_pil)

demo = gr.Blocks()

print("Starting the demo...")
with demo:
    gr.Markdown(
        """
        <h1 align="center">Seizure Detection on Encrypted EEG Data Using Fully Homomorphic Encryption</h1>
        """
    )

    gr.Markdown("## Client side")
    gr.Markdown("### Step 1: Upload an EEG image. ")
    gr.Markdown(
        f"The image will automatically be resized to shape (32, 32). "
        "The image here, however, is displayed in its original resolution."
    )
    with gr.Row():
        input_image = gr.Image(
            value=None, label="Upload an EEG image here.", height=256,
            width=256, sources="upload", interactive=True,
        )

        examples = gr.Examples(
            examples=EXAMPLES, inputs=[input_image], examples_per_page=5, label="Examples to use."
        )

    gr.Markdown("### Step 2: Generate the private key.")
    keygen_button = gr.Button("Generate the private key.")

    with gr.Row():
        keygen_checkbox = gr.Checkbox(label="Private key generated:", interactive=False)

    user_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)

    gr.Markdown("### Step 3: Encrypt the image using FHE.")
    encrypt_button = gr.Button("Encrypt the image using FHE.")

    with gr.Row():
        encrypted_input = gr.Textbox(
            label="Encrypted input representation:", max_lines=2, interactive=False
        )

    gr.Markdown("## Server side")
    gr.Markdown(
        "The encrypted value is received by the server. The server can then compute the seizure "
        "detection directly over encrypted values. Once the computation is finished, the server returns "
        "the encrypted results to the client."
    )
    gr.Markdown("### Step 4: Send the encrypted image to the server.")
    send_input_button = gr.Button("Send the encrypted image to the server.")
    send_input_checkbox = gr.Checkbox(label="Encrypted image sent.", interactive=False)

    gr.Markdown("### Step 5: Run FHE execution.")
    execute_fhe_button = gr.Button("Run FHE execution.")
    fhe_status = gr.Textbox(label="FHE execution status:", max_lines=1, interactive=False)
    fhe_execution_time = gr.Textbox(
        label="Total FHE execution time (in seconds):", max_lines=1, interactive=False
    )
    task_id = gr.Textbox(label="Task ID:", visible=False)

    gr.Markdown("### Step 6: Check FHE execution status and receive the encrypted output from the server.")
    check_status_button = gr.Button("Check FHE execution status")
    get_output_button = gr.Button("Receive the encrypted output from the server.", interactive=False)

    with gr.Row():
        encrypted_output = gr.Textbox(
            label="Encrypted output representation:",
            max_lines=2,
            interactive=False
        )

    gr.Markdown("## Client side")
    gr.Markdown(
        "The encrypted output is sent back to the client, who can finally decrypt it with the "
        "private key. Only the client is aware of the original image and the detection result."
    )

    gr.Markdown("### Step 7: Decrypt the output.")
    decrypt_button = gr.Button("Decrypt the output")

    with gr.Row():
        decrypted_output = gr.Textbox(
            label="Seizure detection result:",
            interactive=False
        )

    # Button to generate the private key
    keygen_button.click(
        keygen,
        outputs=[user_id, keygen_checkbox],
    )

    # Button to encrypt inputs on the client side
    encrypt_button.click(
        encrypt,
        inputs=[user_id, input_image],
        outputs=[encrypted_input],
    )

    # Button to send the encodings to the server using post method
    send_input_button.click(
        send_input, inputs=[user_id], outputs=[send_input_checkbox]
    )

    # Button to send the encodings to the server using post method
    execute_fhe_button.click(run_fhe, inputs=[user_id], outputs=[fhe_execution_time])

    # Button to send the encodings to the server using post method
    get_output_button.click(
        get_output,
        inputs=[user_id],
        outputs=[encrypted_output]
    )

    # Button to decrypt the output on the client side
    decrypt_button.click(
        decrypt_output,
        inputs=[user_id],
        outputs=[decrypted_output],
    )

    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](https://zama.ai/). "
        "Try it yourself and don't forget to star on Github &#11088;."
    )

demo.launch(share=False)