# app.py
import gradio as gr
import json
import statistics
import pandas as pd
import torch
from bp_phi.runner import run_silent_cogitation_test
from bp_phi.runner_utils import dbg, DEBUG

# --- UI Theme and Layout ---
theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue").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: Silent Cogitation Function ---
def run_cogitation_and_display(model_id, seed, prompt_type, num_steps, timeout, temperature, progress=gr.Progress(track_tqdm=True)):
    progress(0, desc="Starting Silent Cogitation Test...")
    results = run_silent_cogitation_test(model_id, int(seed), prompt_type, int(num_steps), int(timeout), float(temperature))
    progress(1.0, desc="Test complete.")

    verdict_text = results.pop("verdict")

    stats_md = (
        f"**Steps Completed:** {results['steps_completed']} | "
        f"**Total Duration:** {results['total_duration_s']:.2f}s | "
        f"**Avg Time/Step:** {results['mean_step_time_ms']:.2f}ms (StdDev: {results['stdev_step_time_ms']:.2f}ms)"
    )
    full_verdict = f"{verdict_text}\n\n{stats_md}"

    deltas = results.get("state_deltas", [])
    df = pd.DataFrame({"Step": range(len(deltas)), "State Change (Delta)": deltas})

    if DEBUG:
        print("\n--- FINAL GRADIO OUTPUT (SILENT COGITATION) ---")
        print(json.dumps(results, indent=2))

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        dbg("Cleared CUDA cache.")

    return full_verdict, df, results

# --- Gradio App Definition ---
with gr.Blocks(theme=theme, title="BP-Φ Suite 9.0") as demo:
    gr.Markdown("# 🧠 BP-Φ Suite 9.0: The Final Experiment")

    with gr.Tabs():
        # --- TAB 1: SILENT COGITATION ---
        with gr.TabItem("1. Silent Cogitation (Internal Dynamics)"):
            gr.Markdown(
                "Tests for internal 'thinking' without text generation. The **Temperature** slider controls the randomness of the thought process. "
                "Low temperature leads to deterministic, convergent thought. High temperature should lead to chaotic, non-convergent dynamics."
            )
            with gr.Row():
                with gr.Column(scale=1):
                    sc_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    sc_prompt_type = gr.Radio(["control_long_prose", "resonance_prompt"], label="Prompt Type", value="resonance_prompt")
                    sc_seed = gr.Slider(1, 1000, 137, step=1, label="Seed")
                    sc_temperature = gr.Slider(0.01, 2.0, 0.01, step=0.01, label="Temperature (Cognitive 'Creativity')")
                    sc_num_steps = gr.Slider(10, 10000, 2000, step=10, label="Number of Internal Steps")
                    sc_timeout = gr.Slider(10, 1200, 600, step=10, label="Timeout (seconds)")
                    sc_run_btn = gr.Button("Run Silent Cogitation Test", variant="primary")
                with gr.Column(scale=2):
                    sc_verdict = gr.Markdown("### Results will appear here.")
                    sc_plot = gr.LinePlot(x="Step", y="State Change (Delta)", label="Internal State Convergence", show_label=True, height=300)
                    with gr.Accordion("Raw Run Details (JSON)", open=False):
                        sc_results = gr.JSON()
            sc_run_btn.click(run_cogitation_and_display, [sc_model_id, sc_seed, sc_prompt_type, sc_num_steps, sc_timeout, sc_temperature], [sc_verdict, sc_plot, sc_results])

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