Create app.py

app.py

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+import streamlit as st
+import numpy as np
+from streamlit_threejs import streamlit_threejs
+
+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 = 10000
+positions = np.random.uniform(-5, 5, (num_particles, 3))
+colors = np.random.random((num_particles, 3))
+
+# Update particle positions based on parameters
+def update_particles(positions, colors):
+    positions += np.array([electric_field["x"], electric_field["y"], electric_field["z"]]) * 0.01
+    
+    phase = psi * np.sin(positions[:, 0] * h_bar)
+    positions[:, 0] += np.cos(phase) * 0.01
+    positions[:, 1] += np.sin(phase) * 0.01
+    
+    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 * 0.01
+    
+    colors = (positions + 5) / 10
+    
+    positions[np.abs(positions) > 5] *= -0.9
+    
+    return positions, colors
+
+positions, colors = update_particles(positions, colors)
+
+# Render the scene
+scene = {
+    "type": "points",
+    "points": positions.tolist(),
+    "colors": colors.tolist(),
+    "size": 0.05,
+}
+
+streamlit_threejs(scene, key="quantum_em_sim", height=600)
+
+# 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])
+