app.py

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)

# Debug log list
debug_log = []

def debug(msg):
    print(msg)
    debug_log.append(str(msg))

# Generate 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,
        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,
    )
    result = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
    debug(f"Response:\n{result}")
    return result

# Cosine similarity as ΔS proxy
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)
    score = float(cosine_similarity(emb_a.cpu().numpy(), emb_b.cpu().numpy())[0][0])
    debug(f"Similarity: {score}")
    return score

# Dual unfolding: I (self-view), ¬I (contradiction)
def dual_identity_unfolding(n_steps):
    I_trace, not_I_trace = [], []
    ΔS_I, ΔS_not_I, ΔS_cross = [], [], []
    debug_log.clear()
    log = []

    current_I = "The system begins to think about itself:\n"
    current_not_I = "The system begins to contradict its previous thoughts:\n"

    for step in range(n_steps):
        log.append(f"--- Step {step} ---")

        I = generate_response(current_I)
        not_I = generate_response(current_not_I)

        I_trace.append(I)
        not_I_trace.append(not_I)

        log.append(f"[Iₙ]:\n{I}\n")
        log.append(f"[¬Iₙ]:\n{not_I}\n")

        if step > 0:
            s_I = similarity(I_trace[step - 1], I_trace[step])
            s_not_I = similarity(not_I_trace[step - 1], not_I_trace[step])
            s_cross = similarity(I_trace[step], not_I_trace[step])

            ΔS_I.append(round(s_I, 4))
            ΔS_not_I.append(round(s_not_I, 4))
            ΔS_cross.append(round(s_cross, 4))

            log.append(f"ΔS(I{step - 1} → I{step}) = {s_I}")
            log.append(f"ΔS(¬I{step - 1} → ¬I{step}) = {s_not_I}")
            log.append(f"ΔS(I{step} ↔ ¬I{step}) = {s_cross}\n")
        else:
            log.append("ΔS not applicable for first step.\n")

        current_I = f'The system previously said:\n"{I}"\nNow it reflects further:\n'
        current_not_I = f'The system previously said:\n"{I}"\nNow it contradicts itself:\n'

    result_log = "\n".join(log)
    debug_output = "\n".join(debug_log)

    I_out = "\n\n".join([f"I{n}: {txt}" for n, txt in enumerate(I_trace)])
    not_I_out = "\n\n".join([f"¬I{n}: {txt}" for n, txt in enumerate(not_I_trace)])

    ΔS_out = "\n".join([
        f"Step {i}: ΔS(I) = {ΔS_I[i]}, ΔS(¬I) = {ΔS_not_I[i]}, ΔS Cross = {ΔS_cross[i]}"
        for i in range(len(ΔS_I))
    ])

    return I_out, not_I_out, ΔS_out, debug_output

# Gradio UI
iface = gr.Interface(
    fn=dual_identity_unfolding,
    inputs=gr.Slider(2, 10, value=4, step=1, label="Number of Identity Steps"),
    outputs=[
        gr.Textbox(label="Iₙ (Identity Trace)", lines=20),
        gr.Textbox(label="¬Iₙ (Antithesis Trace)", lines=20),
        gr.Textbox(label="ΔS Trace", lines=12),
        gr.Textbox(label="Debug Log", lines=10),
    ],
    title="GPT-2 Dual Identity Analyzer (EAL Framework)",
    description=(
        "This app evaluates whether GPT-2 can form a stable identity by recursively reflecting "
        "on its own outputs (Iₙ), and simultaneously handle contradictions (¬Iₙ). "
        "ΔS tracks convergence, oscillation, and semantic symmetry."
    ),
)

if __name__ == "__main__":
    iface.launch()