import gradio as gr import pandas as pd from typing import Any 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: Any, progress: gr.Progress = gr.Progress()) -> Any: """ Wrapper für den 'Manual Single Run'-Tab, mit polyrhythmischer Analyse und korrigierten Plots. """ try: results = run_seismic_analysis(*args, progress_callback=progress) stats, deltas = results.get("stats", {}), results.get("state_deltas", []) df_time = pd.DataFrame({"Internal Step": range(len(deltas)), "State Change (Delta)": deltas}) spectrum_data = [] if "power_spectrum" in results: spectrum = results["power_spectrum"] # KORREKTUR: Verwende den konsistenten Schlüssel 'frequencies' if spectrum and "frequencies" in spectrum and "power" in spectrum: for freq, power in zip(spectrum["frequencies"], spectrum["power"]): if freq > 0.001: period = 1 / freq if freq > 0 else float('inf') spectrum_data.append({"Period (Steps/Cycle)": period, "Power": power}) df_freq = pd.DataFrame(spectrum_data) periods_list = stats.get('dominant_periods_steps') periods_str = ", ".join(map(str, periods_list)) if periods_list else "N/A" stats_md = f"""### Statistical Signature - **Mean Delta:** {stats.get('mean_delta', 0):.4f} - **Std Dev Delta:** {stats.get('std_delta', 0):.4f} - **Dominant Periods:** {periods_str} Steps/Cycle - **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: str, num_steps: int, seed: int, experiment_name: str, progress: gr.Progress = gr.Progress()) -> Any: """Wrapper für den 'Automated Suite'-Tab, der nun alle Plot-Typen korrekt handhabt.""" try: summary_df, plot_df, all_results = run_auto_suite(model_id, num_steps, 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")) 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) spectrum_data = [] for label, result in all_results.items(): if "power_spectrum" in result: spectrum = result["power_spectrum"] if spectrum and "frequencies" in spectrum and "power" in spectrum: for freq, power in zip(spectrum["frequencies"], spectrum["power"]): if freq > 0.001: period = 1 / freq if freq > 0 else float('inf') spectrum_data.append({"Period (Steps/Cycle)": period, "Power": power, "Experiment": label}) spectrum_df = pd.DataFrame(spectrum_data) spectrum_plot_params = { "x": "Period (Steps/Cycle)", "y": "Power", "color": "Experiment", "title": "Cognitive Frequency Fingerprint (Period 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="Period (Steps/Cycle)", y="Power", title="Frequency Domain (Period)") 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-3-1b-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)