Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.

TASK : using GUI interface , plotly module below code we have make advanced visualization interface , make below logic detail 
import torch
import gym
import numpy as np
from typing import List, Tuple
from art.attacks.evasion import FastGradientMethod, ProjectedGradientDescent, CarliniL2Method
from art.defences.preprocessor import JpegCompression, FeatureSqueezing
from art.estimators.classification import PyTorchClassifier

# Define a simple PyTorch model for image classification
class SimpleModel(torch.nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 16, kernel_size=3)
        self.fc1 = torch.nn.Linear(16 * 30 * 30, 10)

    def forward(self, x):
        x = torch.nn.functional.relu(self.conv1(x))
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        return x

# Dataset placeholder (randomly generated data)
class DummyDataset(torch.utils.data.Dataset):
    def __init__(self, num_samples: int = 100):
        self.data = torch.randn(num_samples, 3, 32, 32)
        self.labels = torch.randint(0, 10, (num_samples,))

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx], self.labels[idx]

# Create a PyTorchClassifier required by ART
def create_art_classifier(model: torch.nn.Module) -> PyTorchClassifier:
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    classifier = PyTorchClassifier(
        model=model,
        loss=criterion,
        optimizer=optimizer,
        input_shape=(3, 32, 32),
        nb_classes=10,
        device_type='cpu'  # Change to 'gpu' if using a GPU
    )
    return classifier

# List of attacks using ART module
def get_attack_list(classifier) -> List:
    attack_list = [
        FastGradientMethod(estimator=classifier, eps=0.1),
        ProjectedGradientDescent(estimator=classifier, eps=0.1),
        CarliniL2Method(classifier=classifier),
    ]
    return attack_list

# List of defenses using ART module
def get_defense_list() -> List:
    # Define clip_values for images (min and max pixel values)
    clip_values = (0.0, 1.0)  # Adjust depending on image preprocessing, e.g., (0, 255) if not normalized

    defense_list = [
        JpegCompression(clip_values=clip_values, quality=50),
        FeatureSqueezing(bit_depth=1,clip_values=clip_values),
    ]
    return defense_list


# Custom environment for attack-defense reinforcement learning
class AttackDefenseEnv(gym.Env):
    def __init__(self, classifier, attack_list: List, defense_list: List):
        super(AttackDefenseEnv, self).__init__()
        self.attack_list = attack_list
        self.defense_list = defense_list
        self.classifier = classifier

        self.action_space = gym.spaces.Discrete(len(defense_list))  # Number of defenses
        self.observation_space = gym.spaces.Discrete(len(attack_list))  # Number of attacks

        self.current_attack = None
        self.reset()

    def step(self, action):
        defense = self.defense_list[action]
        attack = self.attack_list[self.current_attack]

        # Simulate applying the attack and defense (dummy values for this example)
        success_rate = np.random.rand()  # Placeholder for actual attack-defense success rate

        reward = -success_rate  # Minimize the success rate of the attack (maximize defense)
        done = True  # One attack-defense interaction per step
        info = {"attack": attack.__class__.__name__, "defense": defense.__class__.__name__}

        return self.current_attack, reward, done, info

    def reset(self):
        self.current_attack = np.random.randint(0, len(self.attack_list))  # Random attack
        return self.current_attack

    def render(self, mode='human'):
        print(f"Current attack: {self.attack_list[self.current_attack].__class__.__name__}")

    def close(self):
        pass

# Function to run the RL loop
def run_rl(env: gym.Env, num_episodes: int = 10):
    for episode in range(num_episodes):
        obs = env.reset()
        done = False
        while not done:
            action = env.action_space.sample()  # Random defense action
            obs, reward, done, info = env.step(action)
            env.render()
            print(f"Episode: {episode + 1}, Attack: {info['attack']}, Defense: {info['defense']}, Reward: {reward}")

# Main logic to run everything end-to-end
if __name__ == "__main__":
    # Initialize dummy dataset and model
    dataset = DummyDataset()
    model = SimpleModel()

    # Create ART classifier
    classifier = create_art_classifier(model)

    # Get list of attacks and defenses
    attacks = get_attack_list(classifier)
    defenses = get_defense_list()

    # Create attack-defense environment
    env = AttackDefenseEnv(classifier, attacks, defenses)

    # Run reinforcement learning loop
    run_rl(env, num_episodes=10)