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import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import gradio as gr
# Load GPT-2 and tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Function to generate response
def generate_response(prompt, max_length=100):
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
max_length=len(inputs["input_ids"][0]) + max_length,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.9,
top_p=0.95,
)
return tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
# Semantic similarity via mean embeddings (ΔS approximation)
def similarity(a, b):
tok_a = tokenizer(a, return_tensors="pt").to(device)
tok_b = tokenizer(b, return_tensors="pt").to(device)
with torch.no_grad():
emb_a = model.transformer.wte(tok_a.input_ids).mean(dim=1)
emb_b = model.transformer.wte(tok_b.input_ids).mean(dim=1)
return float(cosine_similarity(emb_a.cpu().numpy(), emb_b.cpu().numpy())[0][0])
# Recursive identity loop (EAL-style unfolding)
def identity_unfolding(steps):
unfolding = []
ΔS_trace = []
current_text = "The following is a system thinking about itself:\n"
for step in range(steps):
response = generate_response(current_text)
unfolding.append(response)
if step > 0:
ΔS = similarity(unfolding[step - 1], unfolding[step])
ΔS_trace.append(round(ΔS, 4))
current_text = (
f'The system has previously stated:\n"{response}"\n'
"Now it continues thinking about what that implies:\n"
)
results = "\n\n---\n\n".join(
[f"Step {i}: {txt}" for i, txt in enumerate(unfolding)]
)
sim = "\n".join(
[f"ΔS({i} → {i+1}) = {val}" for i, val in enumerate(ΔS_trace)]
)
return results, sim
# Gradio Interface
iface = gr.Interface(
fn=identity_unfolding,
inputs=gr.Slider(2, 10, value=5, step=1, label="Number of Iterations"),
outputs=[
gr.Textbox(label="GPT-2 Identity Trace", lines=20),
gr.Textbox(label="Semantic ΔS Trace", lines=10),
],
title="EAL Identity Tester for GPT-2",
description=(
"This app recursively prompts GPT-2 to reflect on its own output. "
"It shows how close each iteration is to the previous one using a cosine-based ΔS metric. "
"Use this to test if GPT-2 stabilizes around a semantically coherent self-representation."
),
)
if __name__ == "__main__":
iface.launch() |