Update app.py

app.py

@@ -24,40 +24,45 @@ def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)):
         cleanup_memory()
 
 def run_auto_suite_display(model_id, num_steps, seed, experiment_name, progress=gr.Progress(track_tqdm=True)):
-    """Wrapper, der die speziellen Plots für die verschiedenen Experimente handhaben kann."""
+    """Wrapper, der nun auch die Frequenz-Spektrum-Plots anzeigen kann."""
     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 Statistical Signature", value=summary_df, wrap=True, row_count=(len(summary_df), "dynamic"))
+        dataframe_component = gr.DataFrame(label="Comparative Signature (incl. Signal Metrics)", value=summary_df, wrap=True, row_count=(len(summary_df), "dynamic"))
 
-        # FINALE KORREKTUR: Robuste Plot-Parameter-Logik
+        # Erzeuge den primären Zeitreihen-Plot
         plot_params = {
-            "title": "Comparative Cognitive Dynamics",
-            "color_legend_position": "bottom", "show_label": True, "height": 400, "interactive": True
+            "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.update({"x": "Step", "y": "Value", "color": "Metric", "color_legend_title": "Metric"})
+        else:
+            plot_params.update({"x": "Step", "y": "Delta", "color": "Experiment", "color_legend_title": "Experiment Runs"})
         
-        if experiment_name == "ACT Titration (Point of No Return)":
-            plot_params.update({
-                "x": "Patch Step", "y": "Post-Patch Mean Delta", "color": None,
-                "title": "Attractor Capture Time (ACT) - Phase Transition", "mark": "line",
-                "color_legend_title": None,
-            })
-        elif experiment_name == "Mechanistic Probe (Attention Entropies)":
-            plot_params.update({
-                "x": "Step", "y": "Value", "color": "Metric",
-                "title": "Mechanistic Analysis: State Delta vs. Attention Entropy",
-                "color_legend_title": "Metric",
-            })
-        else: # Default für alle anderen Multi-Lauf-Experimente
-            plot_params.update({
-                 "x": "Step", "y": "Delta", "color": "Experiment",
-                 "color_legend_title": "Experiment Runs",
-            })
+        time_domain_plot = gr.LinePlot(value=plot_df, **plot_params)
 
-        new_plot = gr.LinePlot(value=plot_df, **plot_params)
+        # Erzeuge den 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, new_plot, serializable_results
+        return dataframe_component, time_domain_plot, frequency_domain_plot, serializable_results
     finally:
         cleanup_memory()
 
@@ -98,7 +103,7 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
             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_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(
@@ -110,17 +115,19 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
                 
                 with gr.Column(scale=2):
                     gr.Markdown("### Suite Results Summary")
-                    # Initialisiere den Plot mit den Default-Parametern
-                    auto_plot_output = gr.LinePlot(x="Step", y="Delta", color="Experiment", title="Comparative Cognitive Dynamics")
-                    auto_summary_df = gr.DataFrame(label="Comparative Statistical Signature", wrap=True)
+                    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_plot_output, auto_raw_json]
+                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)
+    demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)