Update app.py

app.py

@@ -4,7 +4,7 @@ from sklearn.metrics.pairwise import cosine_similarity
 import numpy as np
 import gradio as gr
 
-# Load model + tokenizer
+# Load GPT-2 and tokenizer
 model_name = "gpt2"
 tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 model = GPT2LMHeadModel.from_pretrained(model_name)
@@ -12,8 +12,16 @@ model.eval()
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-# Generate response with visible prompt/response formatting
+# Debug log list
+debug_log = []
+
+def debug(msg):
+    print(msg)  # Console log (local)
+    debug_log.append(str(msg))  # Collect for UI
+
+# Generate a GPT-2 response
 def generate_response(prompt, max_length=100):
+    debug(f"Generating response for prompt:\n{prompt}")
     inputs = tokenizer(prompt, return_tensors="pt").to(device)
     outputs = model.generate(
         **inputs,
@@ -23,22 +31,27 @@ def generate_response(prompt, max_length=100):
         temperature=0.9,
         top_p=0.95,
     )
-    return tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+    debug(f"Generated output:\n{result}")
+    return result
 
-# Cosine similarity to estimate ΔS
+# Compute cosine similarity of mean token embeddings
 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])
+    score = float(cosine_similarity(emb_a.cpu().numpy(), emb_b.cpu().numpy())[0][0])
+    debug(f"Similarity between outputs: {score}")
+    return score
 
-# Main loop: identity unfolding
+# Main identity unfolding loop
 def identity_unfolding(n_steps):
     unfolding = []
     ΔS_trace = []
     log = []
+    debug_log.clear()
 
     current_prompt = "The following is a system thinking about itself:\n"
 
@@ -67,7 +80,8 @@ def identity_unfolding(n_steps):
     trace_summary = "\n".join(
         [f"ΔS({i} → {i+1}) = {ΔS_trace[i]}" for i in range(len(ΔS_trace))]
     )
-    return summary, trace_summary
+    debug_output = "\n".join(debug_log)
+    return summary, trace_summary, debug_output
 
 # Gradio interface
 iface = gr.Interface(
@@ -76,12 +90,13 @@ iface = gr.Interface(
     outputs=[
         gr.Textbox(label="Full Trace (Prompts + GPT-2 Outputs)", lines=25),
         gr.Textbox(label="ΔS Semantic Similarity Trace", lines=10),
+        gr.Textbox(label="Debug Log", lines=10),
     ],
     title="GPT-2 Identity Emergence Analyzer (EAL Framework)",
     description=(
         "This app tests whether GPT-2 can recursively reflect on its own outputs. "
         "It uses prompt-based recursion and cosine similarity (ΔS) to measure semantic stability across iterations. "
-        "A stabilizing identity shows high ΔS values close to 1.0 across iterations."
+        "Now includes a visible debug log."
     ),
 )