import neat
from mcpi.minecraft import Minecraft
from mcpi.vec3 import Vec3
import numpy as np

# Connect to the Minecraft server (replace 'localhost' and '4711' with your server's IP and port)
mc = Minecraft.create("localhost", 4711)

# Define the region you want to scan (replace these coordinates with your desired region)
min_x, min_y, min_z = -10, 0, -10
max_x, max_y, max_z = 10, 10, 10

# Function to check if a block is visible
def is_block_visible(x, y, z):
    player_x, player_y, player_z = mc.player.getPos()
    max_distance = 10  # Adjust this value as needed for your use case
    distance = ((x - player_x) ** 2 + (y - player_y) ** 2 + (z - player_z) ** 2) ** 0.5
    return distance <= max_distance

# Function to scan blocks and return block data for visible blocks
def scan_visible_blocks():
    visible_blocks_data = []
    for x in range(min_x, max_x + 1):
        for y in range(min_y, max_y + 1):
            for z in range(min_z, max_z + 1):
                if is_block_visible(x, y, z):
                    block_data = mc.getBlockWithData(x, y, z)
                    # Append the block data as a list or numpy array
                    visible_blocks_data.append([block_data.id, block_data.data])
    return np.array(visible_blocks_data)

# Function to simulate Minecraft controls based on NEAT output
def control_minecraft(output):
    # Map the output to Minecraft controls
    if output == 0:
        mc.player.setPos(0, 0, 0)  # Stop
    elif output == 1:
        mc.player.setPos(1, 0, 0)  # Move forward
    elif output == 2:
        mc.player.setPos(-1, 0, 0)  # Move backward
    elif output == 3:
        mc.player.setPos(0, 0, 1)  # Move right
    elif output == 4:
        mc.player.setPos(0, 0, -1)  # Move left

# Fitness function to evaluate the performance of the neural network
def evaluate_genome(genome, config):
    # Step 1: Data Collection (scan visible blocks)
    block_data = scan_visible_blocks()

    # Step 2: Data Preprocessing (if needed)
    # Normalize the block data using StandardScaler

    # Step 3: Define Neural Network Architecture
    input_shape = (block_data.shape[1],)  # Number of features in the input (block data)
    output_shape = 5  # Number of possible controls (stop, forward, backward, left, right)

    # Step 4: Create and compile the Neural Network Model using the NEAT genome
    model = neat.nn.FeedForwardNetwork.create(genome, config)
    model.compile(loss='mean_squared_error', optimizer='adam')

    # Step 5: Inference (use the model to generate controls)
    # Replace this with actual inference logic using the model's prediction
    output = np.random.randint(0, output_shape)
    control_minecraft(output)

    # Calculate the fitness based on how well the network performs the task
    fitness = 0  # You need to define how to evaluate the fitness based on the task

    return fitness

def main(config_file):
    # Load NEAT configuration
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_file)

    # Create the NEAT population
    population = neat.Population(config)

    # Add a reporter to show the progress of training
    reporter = neat.StdOutReporter(True)
    population.add_reporter(reporter)
    statistics = neat.StatisticsReporter()
    population.add_reporter(statistics)

    # Run NEAT for the specified number of generations
    winner = population.run(evaluate_genome, 10)  # Replace '10' with the desired number of generations

    # Display the best genome
    print('\nBest genome:\n{!s}'.format(winner))

    # Get the neural network from the best genome
    best_model = neat.nn.FeedForwardNetwork.create(winner, config)

    # Use the best model for Minecraft control
    # You can implement the control logic here using the model's prediction

if __name__ == '__main__':
    local_dir = os.path.dirname(__file__)
    config_file = os.path.join(local_dir, 'neat_config.txt')
    main(config_file)