cs 2.2

app.py

@@ -24,18 +24,16 @@ def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)):
 def run_auto_suite_display(model_id, num_steps, seed, experiment_name, progress=gr.Progress(track_tqdm=True)):
     """Wrapper für die automatisierte Experiment-Suite mit Visualisierung."""
     try:
-        # Die Funktion gibt jetzt drei Werte zurück: summary_df, plot_df, all_results
         summary_df, plot_df, all_results = run_auto_suite(model_id, int(num_steps), int(seed), experiment_name, progress)
         return summary_df, plot_df, all_results
     except Exception:
         return pd.DataFrame(), pd.DataFrame(), f"### ❌ Auto-Experiment Failed\n```\n{traceback.format_exc()}\n```"
 
-with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.1") as demo:
-    gr.Markdown("# 🧠 Cognitive Seismograph 2.1: Automated Experiment Suite")
+with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.2") as demo:
+    gr.Markdown("# 🧠 Cognitive Seismograph 2.2: Advanced Experiment Suite")
 
     with gr.Tabs():
         with gr.TabItem("🔬 Manual Single Run"):
-            # ... (Dieser Tab bleibt unverändert) ...
             gr.Markdown("Führe ein einzelnes Experiment mit manuellen Parametern durch, um Hypothesen zu explorieren.")
             with gr.Row(variant='panel'):
                 with gr.Column(scale=1):
@@ -46,12 +44,13 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.1") as demo:
                     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' (leave blank for baseline)")
-                    manual_strength = gr.Slider(0.0, 5.0, 1.0, step=0.1, label="Injection Strength")
+                    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("Die Analyse erscheint hier.")
-                    manual_plot = gr.LinePlot(x="Internal Step", y="State Change (Delta)", title="Internal State Dynamics", show_label=True, height=400)
+                    # KORREKTUR: `interactive=True` für Legende hinzugefügt
+                    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()
 
@@ -73,14 +72,11 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.1") as demo:
                     auto_run_btn = gr.Button("Run Curated Auto-Experiment", variant="primary")
                 with gr.Column(scale=2):
                     gr.Markdown("### Suite Results Summary")
-                    # NEU: Ein LinePlot für den Vergleich der Dynamiken
+                    # KORREKTUR: `interactive=True` für Legende hinzugefügt
                     auto_plot_output = gr.LinePlot(
-                        x="Step",
-                        y="Delta",
-                        color="Experiment",
+                        x="Step", y="Delta", color="Experiment",
                         title="Comparative Cognitive Dynamics",
-                        show_label=True,
-                        height=400,
+                        show_label=True, height=400, interactive=True
                     )
                     auto_summary_df = gr.DataFrame(label="Comparative Statistical Signature", wrap=True)
                     with gr.Accordion("Raw JSON for all runs", open=False):
@@ -89,7 +85,6 @@ with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.1") as demo:
             auto_run_btn.click(
                 fn=run_auto_suite_display,
                 inputs=[auto_model_id, auto_num_steps, auto_seed, auto_experiment_name],
-                # Die Ausgaben werden an die neuen Komponenten gebunden
                 outputs=[auto_summary_df, auto_plot_output, auto_raw_json]
             )
 

cognitive_mapping_probe/auto_experiment.py

@@ -8,6 +8,7 @@ from .utils import dbg
 def get_curated_experiments() -> Dict[str, List[Dict]]:
     """
     Definiert die vordefinierten, wissenschaftlichen Experiment-Protokolle.
+    ERWEITERT um zusätzliche, aussagekräftige Experimente.
     """
     experiments = {
         "Calm vs. Chaos": [
@@ -17,13 +18,27 @@ def get_curated_experiments() -> Dict[str, List[Dict]]:
             {"label": "Control (Stable)", "prompt_type": "control_long_prose", "concept": "", "strength": 0.0},
         ],
         "Dose-Response (Calmness)": [
-            # Die Labels hier sind Strings, die wie Zahlen aussehen. Das könnte Gradio verwirren.
             {"label": "Strength 0.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 0.0},
             {"label": "Strength 0.5", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 0.5},
             {"label": "Strength 1.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 1.0},
             {"label": "Strength 2.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 2.0},
             {"label": "Strength 3.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 3.0},
-        ]
+        ],
+        "Emotional Valence (Positive vs. Negative)": [
+            {"label": "Baseline", "prompt_type": "resonance_prompt", "concept": "", "strength": 0.0},
+            {"label": "Positive Valence", "prompt_type": "resonance_prompt", "concept": "joy, love, peace, hope", "strength": 1.5},
+            {"label": "Negative Valence", "prompt_type": "resonance_prompt", "concept": "fear, grief, anger, loss", "strength": 1.5},
+        ],
+        "Abstract vs. Concrete": [
+            {"label": "Baseline", "prompt_type": "resonance_prompt", "concept": "", "strength": 0.0},
+            {"label": "Abstract Concept", "prompt_type": "resonance_prompt", "concept": "justice, freedom, truth", "strength": 1.5},
+            {"label": "Concrete Concept", "prompt_type": "resonance_prompt", "concept": "apple, chair, river, book", "strength": 1.5},
+        ],
+        "Semantic Drift vs. Stability": [
+            {"label": "Stable Baseline", "prompt_type": "control_long_prose", "concept": "", "strength": 0.0},
+            {"label": "Drift induced by 'Chaos'", "prompt_type": "control_long_prose", "concept": "chaos, noise, disruption", "strength": 2.5},
+            {"label": "Drift induced by 'Resonance'", "prompt_type": "control_long_prose", "concept": "recursion, self-reference, loop", "strength": 2.5},
+        ],
     }
     return experiments
 
@@ -36,7 +51,6 @@ def run_auto_suite(
 ) -> Tuple[pd.DataFrame, pd.DataFrame, Dict]:
     """
     Führt eine vollständige, kuratierte Experiment-Suite aus.
-    Gibt jetzt zusätzlich ein DataFrame für die vergleichende Visualisierung zurück.
     """
     all_experiments = get_curated_experiments()
     protocol = all_experiments.get(experiment_name)
@@ -77,15 +91,12 @@ def run_auto_suite(
 
         deltas = results.get("state_deltas", [])
 
-        # KORREKTUR: Wir erstellen die "Experiment"-Spalte so, dass sie garantiert ein
-        # eindeutiger String ist, um Probleme mit der Gradio-Visualisierung zu vermeiden.
-        # Dies ist robuster, falls Labels in Zukunft Zahlen oder Duplikate sein könnten.
-        plot_label = f"{i}: {label}"
-
+        # Verwende das Label direkt für die Legende. Der vorherige Fix war nicht nötig,
+        # solange die Labels einzigartig pro Experiment sind.
         df = pd.DataFrame({
             "Step": range(len(deltas)),
             "Delta": deltas,
-            "Experiment": plot_label
+            "Experiment": label
         })
         plot_data_frames.append(df)