static/app.css

@@ -347,24 +347,3 @@ footer {
 @keyframes spin {
   to { transform: rotate(360deg); }
 }
-
-.card-body.collapsable {
-  display: none;
-  transition: max-height 0.3s ease;
-  overflow: hidden;
-
-}
-
-.card-body.collapsable.show {
-  display: block;
-  max-height: 1000px; 
-}
-
-.toggle-icon {
-  font-size: 1.25rem;
-  user-select: none;
-}
-
-.toggle-icon:hover {
-  color: #007bff;
-}

static/app.js

@@ -499,55 +499,3 @@ function syncThemeColor() {
 }
 
 document.addEventListener('DOMContentLoaded', syncThemeColor);
-
-
-// toggle card visibility
-document.addEventListener('DOMContentLoaded', function() {
-    // 1. Select all plus/minus icons
-    var icons = document.querySelectorAll('.card .toggle-icon');
-
-    icons.forEach(function(icon) {
-      // 2. For each icon, find its corresponding collapsable body
-      var card = icon.closest('.card');
-      if (!card) return;
-
-      var body = card.querySelector('.card-body.collapsable');
-      if (!body) return;
-
-      // 3. Initialize aria‐expanded & icon text based on whether .show is present
-      if (body.classList.contains('show')) {
-        icon.textContent = '–';
-        icon.setAttribute('aria-expanded', 'true');
-      } else {
-        icon.textContent = '+';
-        icon.setAttribute('aria-expanded', 'false');
-      }
-
-      function toggleSection() {
-        // Toggle the 'show' class
-        if (body.classList.contains('show')) {
-          body.classList.remove('show');
-          icon.textContent = '+';
-          icon.setAttribute('aria-expanded', 'false');
-        } else {
-          body.classList.add('show');
-          icon.textContent = '–';
-          icon.setAttribute('aria-expanded', 'true');
-        }
-      }
-
-      // 4. Click listener
-      icon.addEventListener('click', function(e) {
-        e.stopPropagation();
-        toggleSection();
-      });
-
-      // 5. Keydown listener (Enter/Space for accessibility)
-      icon.addEventListener('keydown', function(e) {
-        if (e.key === 'Enter' || e.key === ' ') {
-          e.preventDefault();
-          toggleSection();
-        }
-      });
-    });
-  });

templates/connections.html

@@ -26,7 +26,7 @@
 
 {% block content %}
   <div class="container-fluid">
-    <h2>Verbindungen der letzten 10 Minuten</h2>
+    <h2>Übersicht deiner gültigen Links</h2>
     <span class="ms-3">
       Anzahl Verbindungen: <strong id="totalConnections">0</strong>
     </span>

templates/file_access.html

@@ -62,11 +62,10 @@
       <!-- Detailed Table of Top File Accesses -->
       {% for top20_item in top20 %}
       <div class="card mb-4">
-        <div class="card-header d-flex justify-content-between align-items-center">
-          <span>Top 20 Dateizugriffe ({{ top20_item['category'] }})</span>
-          <span class="toggle-icon" aria-label="collapse" role="button" tabindex="0">+</span>
+        <div class="card-header">
+          Top 20 Dateizugriffe ({{ top20_item['category'] }})
         </div>
-        <div class="card-body collapsable">
+        <div class="card-body">
           <div class="table-responsive">
             <table class="table table-striped">
               <thead>

transcribe_all.py

@@ -1,7 +1,6 @@
 import os
 import sys
 import time
-import torch
 import whisper
 import concurrent.futures
 import json
@@ -19,6 +18,7 @@ with open("transcription_config.yml", "r", encoding="utf-8") as file:
     settings = yaml.safe_load(file)
     folder_list = settings.get("folder_list")
     model_name = settings.get("model_name")
+    device = settings.get("device")
 
 def load_audio_librosa(path: str, sr: int = 16_000) -> np.ndarray:
     audio, orig_sr = librosa.load(path, sr=sr)      # load + resample to 16 kHz
@@ -222,10 +222,7 @@ def process_folder(root_folder):
     else:
         print(f"Checked {checked_files} files. Start to transcribe {len(valid_files)} files.")
     
-    # Choose “cuda” if available, otherwise “cpu”
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print(f"Loading Whisper model on {device}…")
-
+    print("Loading Whisper model...")
     model = whisper.load_model(model_name, device=device)
     
     # Use a thread pool to pre-load files concurrently.