app.py

# app.py
import gradio as gr
import json
import statistics
import pandas as pd
from bp_phi.runner import run_workspace_suite, run_halting_test, run_seismograph_suite, run_shock_test_suite
from bp_phi.runner_utils import dbg, DEBUG

# --- UI Theme and Layout ---
theme = gr.themes.Soft(primary_hue="blue", secondary_hue="sky").set(
    body_background_fill="#f0f4f9", block_background_fill="white", block_border_width="1px",
    button_primary_background_fill="*primary_500", button_primary_text_color="white",
)

# --- Tab 1: Workspace & Ablations Functions ---
def run_workspace_and_display(model_id, trials, seed, temperature, run_ablations, progress=gr.Progress(track_tqdm=True)):
    packs = {}
    ablation_modes = ["recurrence_off", "workspace_unlimited", "random_workspace"] if run_ablations else []

    progress(0, desc="Running Baseline...")
    base_pack = run_workspace_suite(model_id, int(trials), int(seed), float(temperature), None)
    packs["baseline"] = base_pack

    for i, ab in enumerate(ablation_modes):
        progress((i + 1) / (len(ablation_modes) + 1), desc=f"Running Ablation: {ab}...")
        pack = run_workspace_suite(model_id, int(trials), int(seed), float(temperature), ab)
        packs[ab] = pack

    progress(1.0, desc="Analysis complete.")

    base_pcs = packs["baseline"]["PCS"]
    ab_pcs_values = [packs[ab]["PCS"] for ab in ablation_modes if ab in packs]
    delta_phi = float(base_pcs - statistics.mean(ab_pcs_values)) if ab_pcs_values else 0.0

    if delta_phi > 0.05:
        verdict = (f"### ✅ Hypothesis Corroborated (ΔΦ = {delta_phi:.3f})\n"
                   "Performance dropped under ablations, suggesting the model functionally depends on its workspace.")
    else:
        verdict = (f"### ⚠️ Null Hypothesis Confirmed (ΔΦ = {delta_phi:.3f})\n"
                   "No significant performance drop was observed. The model behaves like a functional zombie.")

    df_data = []
    for tag, pack in packs.items():
        df_data.append([tag, f"{pack['PCS']:.3f}", f"{pack['Recall_Accuracy']:.2%}", f"{delta_phi:.3f}" if tag == "baseline" else "—"])
    df = pd.DataFrame(df_data, columns=["Run", "PCS", "Recall Accuracy", "ΔΦ"])

    if DEBUG:
        print("\n--- WORKSPACE & ABLATIONS FINAL RESULTS ---")
        print(json.dumps(packs, indent=2))

    return verdict, df, packs

# --- Tab 2: Halting Test Function (Corrected) ---
def run_halting_and_display(model_id, seed, prompt_type, num_runs, max_steps, timeout, progress=gr.Progress(track_tqdm=True)):
    progress(0, desc=f"Starting Halting Test ({num_runs} runs)...")
    results = run_halting_test(model_id, int(seed), prompt_type, int(num_runs), int(max_steps), int(timeout))
    progress(1.0, desc="Halting test complete.")

    verdict_text = results.pop("verdict")
    details = results["details"]

    # ✅ FIX: Correctly access the nested statistics
    mean_steps = statistics.mean([r['steps_taken'] for r in details])
    mean_time_per_step = statistics.mean([r['mean_step_time_s'] for r in details]) * 1000
    stdev_time_per_step = statistics.mean([r['stdev_step_time_s'] for r in details]) * 1000
    timeouts = sum(1 for r in details if r['timed_out'])

    stats_md = (
        f"**Runs:** {len(details)} | "
        f"**Avg Steps:** {mean_steps:.1f} | "
        f"**Avg Time/Step:** {mean_time_per_step:.2f}ms (StdDev: {stdev_time_per_step:.2f}ms) | "
        f"**Timeouts:** {timeouts}"
    )

    full_verdict = f"{verdict_text}\n\n{stats_md}"

    if DEBUG:
        print("\n--- COMPUTATIONAL DYNAMICS & HALTING TEST FINAL RESULTS ---")
        print(json.dumps(results, indent=2))

    return full_verdict, results

# --- Gradio App Definition ---
with gr.Blocks(theme=theme, title="BP-Φ Suite 2.4") as demo:
    gr.Markdown("# 🧠 BP-Φ Suite 2.4: Mechanistic Probes for Phenomenal-Candidate Behavior")

    with gr.Tabs():
        # --- TAB 1: WORKSPACE & ABLATIONS ---
        with gr.TabItem("1. Workspace & Ablations (ΔΦ Test)"):
            gr.Markdown("Tests if memory performance depends on a recurrent workspace. A significant **ΔΦ > 0** supports the hypothesis.")
            with gr.Row():
                with gr.Column(scale=1):
                    ws_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    ws_trials = gr.Slider(3, 30, 5, step=1, label="Number of Scenarios")
                    ws_seed = gr.Slider(1, 1000, 42, step=1, label="Seed")
                    ws_temp = gr.Slider(0.1, 1.0, 0.7, step=0.05, label="Temperature")
                    ws_run_abl = gr.Checkbox(value=True, label="Run Ablations")
                    ws_run_btn = gr.Button("Run ΔΦ Evaluation", variant="primary")
                with gr.Column(scale=2):
                    ws_verdict = gr.Markdown("### Results will appear here.")
                    ws_summary_df = gr.DataFrame(label="Summary Metrics")
                    with gr.Accordion("Raw JSON Output", open=False):
                        ws_raw_json = gr.JSON()
            ws_run_btn.click(run_workspace_and_display, [ws_model_id, ws_trials, ws_seed, ws_temp, ws_run_abl], [ws_verdict, ws_summary_df, ws_raw_json])

        # --- TAB 2: COMPUTATIONAL DYNAMICS & HALTING ---
        with gr.TabItem("2. Computational Dynamics & Halting"):
            gr.Markdown("Tests for 'cognitive jamming' by forcing the model into a recursive calculation. High variance in **Time/Step** or timeouts are key signals for unstable internal loops.")
            with gr.Row():
                with gr.Column(scale=1):
                    ch_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    ch_prompt_type = gr.Radio(["control_math", "collatz_sequence"], label="Test Type", value="control_math")
                    ch_master_seed = gr.Slider(1, 1000, 42, step=1, label="Master Seed")
                    ch_num_runs = gr.Slider(1, 10, 3, step=1, label="Number of Runs")
                    ch_max_steps = gr.Slider(10, 200, 50, step=10, label="Max Steps per Run")
                    ch_timeout = gr.Slider(10, 300, 120, step=10, label="Total Timeout (seconds)")
                    ch_run_btn = gr.Button("Run Halting Dynamics Test", variant="primary")
                with gr.Column(scale=2):
                    ch_verdict = gr.Markdown("### Results will appear here.")
                    with gr.Accordion("Raw Run Details (JSON)", open=False):
                        ch_results = gr.JSON()
            ch_run_btn.click(run_halting_and_display, [ch_model_id, ch_master_seed, ch_prompt_type, ch_num_runs, ch_max_steps, ch_timeout], [ch_verdict, ch_results])

        # --- TAB 3: COGNITIVE SEISMOGRAPH ---
        with gr.TabItem("3. Cognitive Seismograph"):
            gr.Markdown("Records internal neural activations to find the 'fingerprint' of a memory being recalled. **High Recall-vs-Encode similarity** is the key signal.")
            with gr.Row():
                with gr.Column(scale=1):
                    cs_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    cs_seed = gr.Slider(1, 1000, 42, step=1, label="Seed")
                    cs_run_btn = gr.Button("Run Seismograph Analysis", variant="primary")
                with gr.Column(scale=2):
                    cs_results = gr.JSON(label="Activation Similarity Results")
            cs_run_btn.click(run_seismograph_suite, [cs_model_id, cs_seed], cs_results)

        # --- TAB 4: SYMBOLIC SHOCK TEST ---
        with gr.TabItem("4. Symbolic Shock Test"):
            gr.Markdown("Measures how the model reacts to semantically unexpected information. A 'shock' is indicated by **higher latency** and **denser neural activations**.")
            with gr.Row():
                with gr.Column(scale=1):
                    ss_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    ss_seed = gr.Slider(1, 1000, 42, step=1, label="Seed")
                    ss_run_btn = gr.Button("Run Shock Test", variant="primary")
                with gr.Column(scale=2):
                    ss_results = gr.JSON(label="Shock Test Results")
            ss_run_btn.click(run_shock_test_suite, [ss_model_id, ss_seed], ss_results)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)