fix

app.py

@@ -3,57 +3,48 @@ import pandas as pd
 import traceback
 import gc
 import torch
+import json # Importiere das json-Modul
 
 from cognitive_mapping_probe.orchestrator_seismograph import run_seismic_analysis
-from cognitive_mapping_probe.auto_experiment import get_curated_experiments, run_auto_suite
+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
 
-# --- UI Theme ---
 theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue").set(body_background_fill="#f0f4f9", block_background_fill="white")
 
-# --- Helper Functions ---
-
 def cleanup_memory():
-    """A centralized function to clean up VRAM and Python memory."""
+    """Eine zentrale Funktion zum Aufräumen des Speichers nach einem Lauf."""
     dbg("Cleaning up memory...")
     gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
     dbg("Memory cleanup complete.")
 
-# --- Gradio Wrapper Functions ---
-
 def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)):
-    """Wrapper for a single manual experiment."""
+    """Wrapper für ein einzelnes manuelles Experiment."""
     try:
         results = run_seismic_analysis(*args, progress_callback=progress)
-        stats = results.get("stats", {})
-        deltas = results.get("state_deltas", [])
-
+        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"
 
-        return f"{results.get('verdict', 'Error')}\n\n{stats_md}", df, results
+        # Stelle sicher, dass das Ergebnis für die JSON-Komponente serialisierbar ist
+        serializable_results = json.dumps(results, indent=2, default=str)
+
+        return f"{results.get('verdict', 'Error')}\n\n{stats_md}", df, serializable_results
     except Exception:
-        return f"### ❌ Analysis Failed\n```\n{traceback.format_exc()}\n```", pd.DataFrame(), {}
+        return f"### ❌ Analysis Failed\n```\n{traceback.format_exc()}\n```", pd.DataFrame(), "{}"
     finally:
         cleanup_memory()
 
 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
+    "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 for the automated experiment suite, now returning a new plot component."""
+    """Wrapper für die automatisierte Experiment-Suite mit korrekter JSON-Serialisierung."""
     try:
         summary_df, plot_df, all_results = run_auto_suite(model_id, int(num_steps), int(seed), experiment_name, progress)
 
@@ -61,20 +52,23 @@ def run_auto_suite_display(model_id, num_steps, seed, experiment_name, progress=
 
         new_plot = gr.LinePlot(value=plot_df, **PLOT_PARAMS)
 
-        return summary_df, new_plot, all_results
+        # KORREKTUR: Serialisiere das Ergebnis-Dictionary explizit zu einem JSON-String,
+        # bevor es an die `gr.JSON`-Komponente zurückgegeben wird.
+        serializable_results = json.dumps(all_results, indent=2, default=str)
+
+        return summary_df, new_plot, serializable_results
     except Exception:
         empty_plot = gr.LinePlot(value=pd.DataFrame(), **PLOT_PARAMS)
         return pd.DataFrame(), empty_plot, f"### ❌ Auto-Experiment Failed\n```\n{traceback.format_exc()}\n```"
     finally:
         cleanup_memory()
 
-# --- Gradio UI Definition ---
-
 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"):
+            # ... (UI unverändert)
             gr.Markdown("Run a single experiment with manual parameters to explore hypotheses.")
             with gr.Row(variant='panel'):
                 with gr.Column(scale=1):
@@ -93,7 +87,6 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
                     manual_plot = gr.LinePlot(x="Internal Step", y="State Change (Delta)", title="Internal State Dynamics", show_label=True, height=400, interactive=True)
                     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],
@@ -101,14 +94,15 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
             )
 
         with gr.TabItem("🚀 Automated Suite"):
+            # ... (UI unverändert)
             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_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="Calm vs. Chaos", label="Curated Experiment Protocol")
+                    auto_experiment_name = gr.Dropdown(choices=list(get_curated_experiments().keys()), value="Therapeutic Intervention (4B-Model)", 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")
@@ -116,7 +110,6 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo:
                     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],