Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import numpy as np
-from streamlit_threejs import streamlit_threejs
+import plotly.graph_objects as go
 
 st.set_page_config(page_title="Quantum EM Cognition Simulator", layout="wide")
 
@@ -33,39 +33,55 @@ 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
+num_particles = 1000  # Reduced number of particles for better performance
 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
+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) * 0.01
-    positions[:, 1] += np.sin(phase) * 0.01
+    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 * 0.01
-    
-    colors = (positions + 5) / 10
+    positions[:, 0] += mass_effect * temporal_effect * 0.1
     
     positions[np.abs(positions) > 5] *= -0.9
     
-    return positions, colors
+    return positions
 
-positions, colors = update_particles(positions, colors)
+positions = update_particles(positions)
 
-# Render the scene
-scene = {
-    "type": "points",
-    "points": positions.tolist(),
-    "colors": colors.tolist(),
-    "size": 0.05,
-}
+# 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'
+    )
+)
 
-streamlit_threejs(scene, key="quantum_em_sim", height=600)
+# Display the plot
+st.plotly_chart(fig)
 
 # Tutorial
 st.sidebar.markdown("---")