| 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 | |
| theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue").set(body_background_fill="#f0f4f9", block_background_fill="white") | |
| def cleanup_memory(): | |
| """Eine zentrale Funktion zum Aufräumen des Speichers nach jedem Experimentlauf.""" | |
| dbg("Cleaning up memory...") | |
| gc.collect() | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| dbg("Memory cleanup complete.") | |
| def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)): | |
| """ | |
| Wrapper-Funktion für den "Manual Single Run"-Tab. | |
| """ | |
| results = run_seismic_analysis(*args, progress_callback=progress) | |
| stats, deltas = results.get("stats", {}), results.get("state_deltas", []) | |
| df = pd.DataFrame({"Internal Step": range(len(deltas)), "State Change (Delta)": deltas}) | |
| stats_md = f"### Statistical Signature\n- **Mean Delta:** {stats.get('mean_delta', 0):.4f}\n- **Std Dev Delta:** {stats.get('std_delta', 0):.4f}\n- **Max Delta:** {stats.get('max_delta', 0):.4f}\n" | |
| serializable_results = json.dumps(results, indent=2, default=str) | |
| cleanup_memory() | |
| return f"{results.get('verdict', 'Error')}\n\n{stats_md}", df, serializable_results | |
| PLOT_PARAMS = { | |
| "x": "Step", "y": "Delta", "color": "Experiment", | |
| "title": "Comparative Cognitive Dynamics", "color_legend_title": "Experiment Runs", | |
| "color_legend_position": "bottom", "show_label": True, "height": 400, "interactive": True | |
| } | |
| def run_auto_suite_display(model_id, num_steps, seed, experiment_name, progress=gr.Progress(track_tqdm=True)): | |
| """ | |
| Wrapper-Funktion für den "Automated Suite"-Tab. | |
| """ | |
| summary_df, plot_df, all_results = run_auto_suite(model_id, int(num_steps), int(seed), experiment_name, progress) | |
| if "Introspective Report" in summary_df.columns or "Patch Info" in summary_df.columns: | |
| dataframe_component = gr.DataFrame(label="Comparative Statistical Signature", value=summary_df, wrap=True, row_count=(len(summary_df), "dynamic")) | |
| else: | |
| dataframe_component = gr.DataFrame(label="Comparative Statistical Signature", value=summary_df, wrap=True) | |
| new_plot = gr.LinePlot(value=plot_df, **PLOT_PARAMS) | |
| serializable_results = json.dumps(all_results, indent=2, default=str) | |
| cleanup_memory() | |
| return dataframe_component, new_plot, serializable_results | |
| 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.") | |
| manual_plot = gr.LinePlot(x="Internal Step", y="State Change (Delta)", title="Internal State Dynamics", show_label=True, height=400) | |
| 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_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-3-4b-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 Surgery (Patching Deletion into Self-Analysis)", | |
| 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_plot_output = gr.LinePlot(**PLOT_PARAMS) | |
| auto_summary_df = gr.DataFrame(label="Comparative Statistical Signature", wrap=True) | |
| 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_plot_output, auto_raw_json] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860, debug=True) | |