import streamlit as st
import numpy as np
import plotly.graph_objects as go

st.set_page_config(page_title="Quantum EM Cognition Simulator", layout="wide")

st.title("Quantum Electromagnetic Cognition Simulator")

# Sidebar for controls
st.sidebar.header("Simulation Parameters")

# Electromagnetic Fields
st.sidebar.subheader("Electromagnetic Fields")
electric_field = {
    "x": st.sidebar.slider("Electric Field X", -1.0, 1.0, 0.0, 0.01),
    "y": st.sidebar.slider("Electric Field Y", -1.0, 1.0, 0.0, 0.01),
    "z": st.sidebar.slider("Electric Field Z", -1.0, 1.0, 0.0, 0.01),
}
magnetic_field = {
    "x": st.sidebar.slider("Magnetic Field X", -1.0, 1.0, 0.0, 0.01),
    "y": st.sidebar.slider("Magnetic Field Y", -1.0, 1.0, 0.0, 0.01),
    "z": st.sidebar.slider("Magnetic Field Z", -1.0, 1.0, 0.0, 0.01),
}

# Quantum Parameters
st.sidebar.subheader("Quantum Parameters")
psi = st.sidebar.slider("Ψ (Wave Function)", 0.0, 2*np.pi, np.pi, 0.01)
h_bar = st.sidebar.slider("ℏ (Reduced Planck Constant)", 0.1, 2.0, 1.0, 0.01)

# Neural Network Parameters
st.sidebar.subheader("Neural Network")
mass_distribution = st.sidebar.slider("Mass Distribution", 0.1, 2.0, 1.0, 0.01)
temporal_factor = st.sidebar.slider("Temporal Factor", 0.1, 2.0, 1.0, 0.01)

# Create particle system
num_particles = 1000  # Reduced number of particles for better performance
positions = np.random.uniform(-5, 5, (num_particles, 3))

# Update particle positions based on parameters
def update_particles(positions):
    positions += np.array([electric_field["x"], electric_field["y"], electric_field["z"]]) * 0.1
    
    phase = psi * np.sin(positions[:, 0] * h_bar)
    positions[:, 0] += np.cos(phase) * 0.1
    positions[:, 1] += np.sin(phase) * 0.1
    
    mass_effect = mass_distribution * np.sin(positions[:, 0])
    temporal_effect = temporal_factor * np.cos(np.random.random(num_particles) * 2 * np.pi)
    positions[:, 0] += mass_effect * temporal_effect * 0.1
    
    positions[np.abs(positions) > 5] *= -0.9
    
    return positions

positions = update_particles(positions)

# Create the 3D scatter plot
fig = go.Figure(data=[go.Scatter3d(
    x=positions[:, 0],
    y=positions[:, 1],
    z=positions[:, 2],
    mode='markers',
    marker=dict(
        size=2,
        color=positions[:, 2],
        colorscale='Viridis',
        opacity=0.8
    )
)])

# Update the layout
fig.update_layout(
    width=800,
    height=800,
    scene=dict(
        xaxis_title='X',
        yaxis_title='Y',
        zaxis_title='Z',
        aspectmode='cube'
    )
)

# Display the plot
st.plotly_chart(fig)

# Tutorial
st.sidebar.markdown("---")
st.sidebar.subheader("Tutorial")
tutorial_steps = [
    "Welcome to the Quantum EM Cognition Simulator! Here you can explore the intersection of quantum mechanics, electromagnetism, and AI cognition.",
    "Start by adjusting the Electromagnetic Fields. Watch how the particles (representing information) flow and interact.",
    "Now, try changing the Quantum Parameters. Notice how the Ψ (Wave Function) and ℏ (reduced Planck's constant) affect the particle behavior.",
    "Finally, experiment with the Neural Network parameters. The Mass Distribution and Temporal Factor influence how information propagates through the network.",
    "As you adjust these parameters, look for emerging patterns, self-organization, or unusual behaviors. These could represent breakthroughs in AI cognition!",
    "Remember, you're exploring uncharted territory. Your observations could lead to new paradigms in energy-efficient cognition, unified cognitive fields, or even autonomous intelligence.",
    "Enjoy your exploration of this quantum-electromagnetic-cognitive space!"
]

current_step = st.sidebar.radio("Tutorial Step", range(len(tutorial_steps)), format_func=lambda x: f"Step {x+1}")
st.sidebar.write(tutorial_steps[current_step])