app.py

# app.py
import os
import gradio as gr
from transformer_lens import HookedTransformer
from transformer_lens.utils import to_numpy
import torch

model_name = "gpt2-small"
# Determine device based on CUDA availability
device = "cuda" if torch.cuda.is_available() else "cpu"
model = HookedTransformer.from_pretrained(
    model_name,
    device=device  
)

# Only print GPU info if using CUDA
if device == "cuda":
    print(f"Using GPU: {torch.cuda.get_device_name(0)}")
else:
    print("Using CPU")

def get_neuron_acts(text, layer, neuron_index):
    cache = {}
    def caching_hook(act, hook):
        cache["activation"] = act[0, :, neuron_index]
    
    model.run_with_hooks(
        text, fwd_hooks=[(f"blocks.{layer}.mlp.hook_post", caching_hook)]
    )
    return to_numpy(cache["activation"])

def calculate_color(val, max_val, min_val):
    normalized_val = (val - min_val) / max_val
    return f"rgb(240, {240*(1-normalized_val)}, {240*(1-normalized_val)})"

style_string = """<style>
    span.token {
        border: 1px solid rgb(123, 123, 123)
    }
</style>"""

def basic_neuron_vis(text, layer, neuron_index, max_val=None, min_val=None):
    if layer is None:
        return "Please select a Layer"
    if neuron_index is None:
        return "Please select a Neuron"
    acts = get_neuron_acts(text, layer, neuron_index)
    act_max = acts.max()
    act_min = acts.min()
    if max_val is None:
        max_val = act_max
    if min_val is None:
        min_val = act_min
    
    htmls = [style_string]
    htmls.append(f"<h4>Layer: <b>{layer}</b>. Neuron Index: <b>{neuron_index}</b></h4>")
    htmls.append(f"<h4>Max Range: <b>{max_val:.4f}</b>. Min Range: <b>{min_val:.4f}</b></h4>")
    
    if act_max != max_val or act_min != min_val:
        htmls.append(
            f"<h4>Custom Range Set. Max Act: <b>{act_max:.4f}</b>. Min Act: <b>{act_min:.4f}</b></h4>"
        )
    
    str_tokens = model.to_str_tokens(text)
    for tok, act in zip(str_tokens, acts):
        htmls.append(
            f"<span class='token' style='background-color:{calculate_color(act, max_val, min_val)}' >{tok}</span>"
        )
    
    return "".join(htmls)

default_text = """The sun rises red, sets golden.
Digits flow: 101, 202, 303—cyclic repetition.
"Echo," whispers the shadow, "repeat, revise, reverse."
Blue squares align in a grid: 4x4, then shift to 5x5.
α -> β -> γ: transformations loop endlessly.

If X=12, and Y=34, then Z? Calculate: Z = X² + Y².
Strings dance: "abc", "cab", "bca"—rotational symmetry.
Prime steps skip by: 2, 3, 5, 7, 11…
Noise: "X...Y...Z..." patterns emerge. Silence.

Fractals form: 1, 1.5, 2.25, 3.375… exponential growth.
Colors swirl: red fades to orange, orange to yellow.
Binary murmurs: 1010, 1100, 1110, 1001—bit-flips.
Triangles: 1, 3, 6, 10, 15… T(n) = n(n+1)/2.
"Reverse," whispers the wind, "invert and repeat."

Nested loops:
1 -> (2, 4) -> (8, 16) -> (32, 64)
2 -> (3, 9) -> (27, 81) -> (243, 729).

The moon glows silver, wanes to shadow.
Patterns persist: 11, 22, 33—harmonic echoes.
"Reshape," calls the river, "reflect, refract, renew."
Yellow hexagons tessellate, shifting into orange octagons.
1/3 -> 1/9 -> 1/27: recursive reduction spirals infinitely.

Chords hum: A minor, C major, G7 resolve softly.
The Fibonacci sequence: 1, 1, 2, 3, 5, 8… emerges.
Golden spirals curl inwards, outwards, endlessly.
Hexagons tessellate: one becomes six, becomes many.

In the forest, whispers:
A -> B -> C -> (AB), (BC), (CA).
Axiom: F. Rule: F -> F+F-F-F+F.

The tide ebbs:
12 -> 9 -> 6 -> 3 -> 12.
Modulo cycles: 17 -> 3, 6, 12, 1…

Strange attractors pull:
(0.1, 0.2), (0.3, 0.6), (0.5, 1.0).
Chaos stabilizes into order, and order dissolves.

Infinite regress:
"Who am I?" asked the mirror.
"You are the question," it answered.

Numbers sing:
e ≈ 2.7182818...
π ≈ 3.14159...
i² = -1: imaginary worlds collide.

Recursive paradox:
The serpent bites its tail, and time folds.

Symmetry hums:
Palindromes—"radar", "level", "madam"—appear and fade.
Blue fades to white, white dissolves to black.
Sequences echo: 1, 10, 100, 1000…
"Cycle," whispers the clock, "count forward, reverse."""  # Shortened for example
default_layer = 1
default_neuron_index = 1
default_max_val = 4.0
default_min_val = 0.0

def get_random_active_neuron(text, threshold=2.5):
    # Try random layers and neurons until we find one that exceeds threshold
    import random
    max_attempts = 100
    
    for _ in range(max_attempts):
        layer = random.randint(0, model.cfg.n_layers - 1)
        neuron = random.randint(0, model.cfg.d_mlp - 1)
        acts = get_neuron_acts(text, layer, neuron)
        if acts.max() > threshold:
            return layer, neuron
    
    # If no neuron found, return default values
    return 0, 0

with gr.Blocks() as demo:
    gr.HTML(value=f"Neuroscope for {model_name}")
    with gr.Row():
        with gr.Column():
            text = gr.Textbox(label="Text", value=default_text)
            layer = gr.Number(label="Layer", value=default_layer, precision=0)
            neuron_index = gr.Number(
                label="Neuron Index", value=default_neuron_index, precision=0
            )
            random_btn = gr.Button("Find Random Active Neuron")
            max_val = gr.Number(label="Max Value", value=default_max_val)
            min_val = gr.Number(label="Min Value", value=default_min_val)
            inputs = [text, layer, neuron_index, max_val, min_val]
        with gr.Column():
            out = gr.HTML(
                label="Neuron Acts",
                value=basic_neuron_vis(
                    default_text,
                    default_layer,
                    default_neuron_index,
                    default_max_val,
                    default_min_val,
                ),
            )
    
    def random_neuron_callback(text):
        layer_num, neuron_num = get_random_active_neuron(text)
        return layer_num, neuron_num
    
    random_btn.click(
        random_neuron_callback,
        inputs=[text],
        outputs=[layer, neuron_index]
    )
    
    for inp in inputs:
        inp.change(basic_neuron_vis, inputs, out)

demo.launch()