Simplify device handling by removing explicit device selection

app.py

@@ -1,23 +1,10 @@
 # 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")
+model = HookedTransformer.from_pretrained(model_name)
 
 def get_neuron_acts(text, layer, neuron_index):
     cache = {}
@@ -92,7 +79,7 @@ Nested loops:
 
 The moon glows silver, wanes to shadow.
 Patterns persist: 11, 22, 33—harmonic echoes.
-"Reshape," calls the river, "reflect, refract, renew."
+“Reshape,” calls the river, “reflect, refract, renew.”
 Yellow hexagons tessellate, shifting into orange octagons.
 1/3 -> 1/9 -> 1/27: recursive reduction spirals infinitely.
 
@@ -129,7 +116,7 @@ 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
+“Cycle,” whispers the clock, “count forward, reverse."""  # Shortened for example
 default_layer = 1
 default_neuron_index = 1
 default_max_val = 4.0