app.py

import gradio as gr
import pandas as pd
import gc
import torch
import json

from cognitive_mapping_probe.orchestrator_seismograph import run_seismic_analysis
from cognitive_mapping_probe.auto_experiment import run_auto_suite, get_curated_experiments
from cognitive_mapping_probe.prompts import RESONANCE_PROMPTS
from cognitive_mapping_probe.utils import dbg, cleanup_memory

theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue").set(body_background_fill="#f0f4f9", block_background_fill="white")

def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)):
    """
    Wrapper für den 'Manual Single Run'-Tab, jetzt mit Frequenz-Analyse.
    """
    try:
        results = run_seismic_analysis(*args, progress_callback=progress)
        stats, deltas = results.get("stats", {}), results.get("state_deltas", [])
        
        # Zeitreihen-Plot
        df_time = pd.DataFrame({"Internal Step": range(len(deltas)), "State Change (Delta)": deltas})
        
        # Frequenz-Plot
        spectrum_data = []
        if "power_spectrum" in results:
            spectrum = results["power_spectrum"]
            for freq, power in zip(spectrum["frequencies"], spectrum["power"]):
                if freq > 0.001:
                    spectrum_data.append({"Frequency": freq, "Power": power})
        df_freq = pd.DataFrame(spectrum_data)

        # Update der Statistik-Anzeige
        stats_md = f"""### Statistical Signature
- **Mean Delta:** {stats.get('mean_delta', 0):.4f}
- **Std Dev Delta:** {stats.get('std_delta', 0):.4f}
- **Dominant Frequency:** {stats.get('dominant_frequency', 0):.4f} Hz
- **Spectral Entropy:** {stats.get('spectral_entropy', 0):.4f}"""
        
        serializable_results = json.dumps(results, indent=2, default=str)
        return f"{results.get('verdict', 'Error')}\n\n{stats_md}", df_time, df_freq, serializable_results
    finally:
        cleanup_memory()


def run_auto_suite_display(model_id, num_steps, seed, experiment_name, progress=gr.Progress(track_tqdm=True)):
    """Wrapper für den 'Automated Suite'-Tab."""
    try:
        summary_df, plot_df, all_results = run_auto_suite(model_id, int(num_steps), int(seed), experiment_name, progress)
        
        dataframe_component = gr.DataFrame(label="Comparative Signature (incl. Signal Metrics)", value=summary_df, wrap=True, row_count=(len(summary_df), "dynamic"))

        # Zeitreihen-Plot
        plot_params_time = {
            "title": "Comparative Cognitive Dynamics (Time Domain)",
            "color_legend_position": "bottom", "show_label": True, "height": 300, "interactive": True
        }
        if experiment_name == "Mechanistic Probe (Attention Entropies)":
            plot_params_time.update({"x": "Step", "y": "Value", "color": "Metric", "color_legend_title": "Metric"})
        else:
            plot_params_time.update({"x": "Step", "y": "Delta", "color": "Experiment", "color_legend_title": "Experiment Runs"})
        
        time_domain_plot = gr.LinePlot(value=plot_df, **plot_params_time)

        # Frequenz-Spektrum-Plot
        spectrum_data = []
        for label, result in all_results.items():
            if "power_spectrum" in result:
                spectrum = result["power_spectrum"]
                for freq, power in zip(spectrum["frequencies"], spectrum["power"]):
                    if freq > 0.001:
                        spectrum_data.append({"Frequency": freq, "Power": power, "Experiment": label})
        
        spectrum_df = pd.DataFrame(spectrum_data)
        
        spectrum_plot_params = {
            "x": "Frequency", "y": "Power", "color": "Experiment",
            "title": "Cognitive Frequency Fingerprint (Frequency Domain)", "height": 300,
            "color_legend_position": "bottom", "show_label": True, "interactive": True,
            "color_legend_title": "Experiment Runs",
        }
        frequency_domain_plot = gr.LinePlot(value=spectrum_df, **spectrum_plot_params)

        serializable_results = json.dumps(all_results, indent=2, default=str)
        return dataframe_component, time_domain_plot, frequency_domain_plot, serializable_results
    finally:
        cleanup_memory()

with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
    gr.Markdown("# 🧠 Cognitive Seismograph 2.3: Advanced Experiment Suite")

    with gr.Tabs():
        with gr.TabItem("🔬 Manual Single Run"):
            gr.Markdown("Run a single experiment with manual parameters to explore specific hypotheses.")
            with gr.Row(variant='panel'):
                with gr.Column(scale=1):
                    gr.Markdown("### 1. General Parameters")
                    manual_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    manual_prompt_type = gr.Radio(choices=list(RESONANCE_PROMPTS.keys()), value="resonance_prompt", label="Prompt Type")
                    manual_seed = gr.Slider(1, 1000, 42, step=1, label="Seed")
                    manual_num_steps = gr.Slider(50, 1000, 300, step=10, label="Number of Internal Steps")
                    
                    gr.Markdown("### 2. Modulation Parameters")
                    manual_concept = gr.Textbox(label="Concept to Inject", placeholder="e.g., 'calmness'")
                    manual_strength = gr.Slider(0.0, 5.0, 1.5, step=0.1, label="Injection Strength")
                    manual_run_btn = gr.Button("Run Single Analysis", variant="primary")
                
                with gr.Column(scale=2):
                    gr.Markdown("### Single Run Results")
                    manual_verdict = gr.Markdown("Analysis results will appear here.")
                    with gr.Row():
                        manual_time_plot = gr.LinePlot(x="Internal Step", y="State Change (Delta)", title="Time Domain")
                        manual_freq_plot = gr.LinePlot(x="Frequency", y="Power", title="Frequency Domain")
                    with gr.Accordion("Raw JSON Output", open=False):
                        manual_raw_json = gr.JSON()

            manual_run_btn.click(
                fn=run_single_analysis_display,
                inputs=[manual_model_id, manual_prompt_type, manual_seed, manual_num_steps, manual_concept, manual_strength],
                outputs=[manual_verdict, manual_time_plot, manual_freq_plot, manual_raw_json]
            )

        with gr.TabItem("🚀 Automated Suite"):
            gr.Markdown("Run a predefined, curated suite of experiments and visualize the results comparatively.")
            with gr.Row(variant='panel'):
                with gr.Column(scale=1):
                    gr.Markdown("### Auto-Experiment Parameters")
                    auto_model_id = gr.Textbox(value="google/gemma-3-12b-it", label="Model ID")
                    auto_num_steps = gr.Slider(50, 1000, 300, step=10, label="Steps per Run")
                    auto_seed = gr.Slider(1, 1000, 42, step=1, label="Seed")
                    auto_experiment_name = gr.Dropdown(
                        choices=list(get_curated_experiments().keys()), 
                        value="Causal Verification & Crisis Dynamics", 
                        label="Curated Experiment Protocol"
                    )
                    auto_run_btn = gr.Button("Run Curated Auto-Experiment", variant="primary")
                
                with gr.Column(scale=2):
                    gr.Markdown("### Suite Results Summary")
                    auto_summary_df = gr.DataFrame(label="Comparative Signature (incl. Signal Metrics)", wrap=True)
                    with gr.Row():
                        auto_time_plot_output = gr.LinePlot()
                        auto_freq_plot_output = gr.LinePlot()

                    with gr.Accordion("Raw JSON for all runs", open=False):
                        auto_raw_json = gr.JSON()
            
            auto_run_btn.click(
                fn=run_auto_suite_display,
                inputs=[auto_model_id, auto_num_steps, auto_seed, auto_experiment_name],
                outputs=[auto_summary_df, auto_time_plot_output, auto_freq_plot_output, auto_raw_json]
            )

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