import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
# Sacred Geometry constants
PHI = (1 + 5**0.5) / 2  # Golden Ratio
# Create the model
def create_sacred_geometry_model(input_dim, output_dim):
    layer_configs = [
        # Seed of Life inspired layers
        {'name': 'Seed_of_Life', 'units': 64, 'layers': 7},  # 7 layers to represent the Seed of Life structure
        
        # Vesica Piscis inspired layers
        {'name': 'Vesica_Piscis', 'units': int(64 * PHI)},  # PHI multiplier for Vesica Piscis layers
        
        # Flower of Life inspired layers
        {'name': 'Flower_of_Life', 'units': int(64 * PHI), 'iterations': 3},  # 3 iterations of Flower of Life layers
        
        # Platonic Solids inspired layers
        {'name': 'Dodecahedron', 'units': 20}  # Dodecahedron with 20 faces
    ]
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    for i, config in enumerate(layer_configs):
        if config['name'] == 'Seed_of_Life':
            for j in range(config['layers']):
                model.add(Dense(units=config['units'], activation='relu', name=f"{config['name']}_{j}"))
        elif config['name'] == 'Flower_of_Life':
            for j in range(config['iterations']):
                model.add(Dense(units=config['units'], activation='relu', name=f"{config['name']}_Iteration_{j}"))
        else:
            model.add(Dense(units=config['units'], activation='relu', name=f"{config['name']}_{i}"))
    model.add(Dense(units=output_dim, activation='sigmoid', name='Output_Layer'))
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model
# Define input and output dimensions
input_dim = 64  # Example input dimension
output_dim = 1  # Example output dimension
# Create the sacred geometry model
model = create_sacred_geometry_model(input_dim, output_dim)
model.summary()
# Save the model
model.save("SacredGeoModel.h5")
print("Model saved as SacredGeoModel.h5")
# Visualization of the model using NetworkX
def visualize_model_networkx(model):
    G = nx.Graph()
    
    layers = model.layers
    positions = {}
    labels = {}
    
    for i, layer in enumerate(layers):
        if isinstance(layer, tf.keras.layers.InputLayer):
            layer_size = layer.input.shape[-1]
            layer_name = 'Input Layer'
        else:
            layer_size = layer.output.shape[-1]
            layer_name = layer.name
        for j in range(layer_size):
            node_id = f"{layer_name}_{j}"
            G.add_node(node_id)
            positions[node_id] = (i, j)
            labels[node_id] = layer_name
        if i > 0:
            prev_layer = layers[i-1]
            prev_layer_size = prev_layer.output.shape[-1] if not isinstance(prev_layer, tf.keras.layers.InputLayer) else prev_layer.input.shape[-1]
            prev_layer_name = 'Input Layer' if isinstance(prev_layer, tf.keras.layers.InputLayer) else prev_layer.name
            
            for j in range(layer_size):
                for k in range(prev_layer_size):
                    G.add_edge(f"{prev_layer_name}_{k}", f"{layer_name}_{j}")
    
    plt.figure(figsize=(14, 14))
    nx.draw(G, pos=positions, with_labels=True, labels=labels, node_size=1000, node_color="lightblue", font_size=5, font_weight="bold", edge_color="gray")
    plt.title("Sacred Geometry Neural Network Visualization")
    plt.show()
visualize_model_networkx(model)