Update app.py

app.py

@@ -1,99 +1,3 @@
-import torch
-import numpy as np
-import gc
-from typing import Dict, Any, Optional, List
-
-from .llm_iface import get_or_load_model, LLM
-from .resonance_seismograph import run_cogitation_loop, run_silent_cogitation_seismic
-from .concepts import get_concept_vector
-from .introspection import generate_introspective_report
-from .signal_analysis import analyze_cognitive_signal, get_power_spectrum_for_plotting
-from .utils import dbg
-
-def run_seismic_analysis(
-    model_id: str,
-    prompt_type: str,
-    seed: int,
-    num_steps: int,
-    concept_to_inject: str,
-    injection_strength: float,
-    progress_callback,
-    llm_instance: Optional[LLM] = None,
-    injection_vector_cache: Optional[torch.Tensor] = None
-) -> Dict[str, Any]:
-    """
-    Orchestriert eine einzelne seismische Analyse und integriert nun standardmäßig
-    die fortgeschrittene Signal-Analyse.
-    """
-    local_llm_instance = False
-    if llm_instance is None:
-        progress_callback(0.0, desc=f"Loading model '{model_id}'...")
-        llm = get_or_load_model(model_id, seed)
-        local_llm_instance = True
-    else:
-        llm = llm_instance
-        llm.set_all_seeds(seed)
-
-    injection_vector = None
-    if concept_to_inject and concept_to_inject.strip():
-        if injection_vector_cache is not None:
-            dbg(f"Using cached injection vector for '{concept_to_inject}'.")
-            injection_vector = injection_vector_cache
-        else:
-            progress_callback(0.2, desc=f"Vectorizing '{concept_to_inject}'...")
-            injection_vector = get_concept_vector(llm, concept_to_inject.strip())
-
-    progress_callback(0.3, desc=f"Recording dynamics for '{prompt_type}'...")
-
-    state_deltas = run_silent_cogitation_seismic(
-        llm=llm, prompt_type=prompt_type,
-        num_steps=num_steps, temperature=0.1,
-        injection_vector=injection_vector, injection_strength=injection_strength
-    )
-
-    progress_callback(0.9, desc="Analyzing...")
-    
-    stats = {}
-    results = {}
-    verdict = "### ⚠️ Analysis Warning\nNo state changes recorded."
-
-    if state_deltas:
-        deltas_np = np.array(state_deltas)
-        stats = { 
-            "mean_delta": float(np.mean(deltas_np)), 
-            "std_delta": float(np.std(deltas_np)), 
-            "max_delta": float(np.max(deltas_np)), 
-            "min_delta": float(np.min(deltas_np)), 
-        }
-        
-        # FINALE KORREKTUR: Führe die Signal-Analyse hier standardmäßig durch
-        signal_metrics = analyze_cognitive_signal(deltas_np)
-        stats.update(signal_metrics)
-
-        freqs, power = get_power_spectrum_for_plotting(deltas_np)
-
-        verdict = f"### ✅ Seismic Analysis Complete\nRecorded {len(deltas_np)} steps for '{prompt_type}'."
-        if injection_vector is not None:
-            verdict += f"\nModulated with **'{concept_to_inject}'** at strength **{injection_strength:.2f}**."
-        
-        results["power_spectrum"] = {"frequencies": freqs.tolist(), "power": power.tolist()}
-
-    results.update({ "verdict": verdict, "stats": stats, "state_deltas": state_deltas })
-
-    if local_llm_instance:
-        dbg(f"Releasing locally created model instance for '{model_id}'.")
-        del llm, injection_vector
-        gc.collect()
-        if torch.cuda.is_available(): torch.cuda.empty_cache()
-
-    return results
-
-# Die anderen Orchestrator-Funktionen (run_triangulation_probe, run_causal_surgery_probe, run_act_titration_probe)
-# bleiben unverändert, da sie ihre eigene, spezifische Analyse-Logik enthalten.```
-[File Ends] `cognitive_mapping_probe/orchestrator_seismograph.py`
-
-[File Begins] `app.py`
-```python
 import gradio as gr
 import pandas as pd
 import gc