Create apy.py

apy.py

@@ -0,0 +1,82 @@
+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()