"""Three.js 3D BDH Walkthrough — self-contained HTML for st.components.v1.html()"""
def get_threejs_html(bdh_layer_activations, tf_layer_activations):
import json, numpy as np
N_NEURONS = 25 # 5×5 grid per layer
def prep(stats_list, n=N_NEURONS):
layers = []
for s in stats_list:
acts = s["activations"].mean(0)[:n]
acts = (acts - acts.min()) / (acts.max() - acts.min() + 1e-8)
layers.append(acts.tolist())
return layers
bdh_data = json.dumps(prep(bdh_layer_activations))
tf_data = json.dumps(prep(tf_layer_activations))
n_layers = len(bdh_layer_activations)
return f"""
🐉 BDH — Sparse ReLU
🤖 Transformer — Dense GELU
BDH active
BDH silent (~50%)
Transformer (always on)
Drag · Scroll to zoom
3D BDH Walkthrough — {n_layers} layers × {N_NEURONS} neurons (5×5) per side
"""