Update app.py

app.py

@@ -7,6 +7,41 @@ import streamlit as st
 
 import json
 from dotenv import load_dotenv
+# --- Reproducibility & Threading ---
+import os, random
+import numpy as np
+
+# Falls torch/cv2 verfügbar sind: Seeds/Threads setzen
+try:
+    import torch
+    TORCH_AVAILABLE = True
+except Exception:
+    TORCH_AVAILABLE = False
+
+try:
+    import cv2
+    CV2_AVAILABLE = True
+except Exception:
+    CV2_AVAILABLE = False
+
+SEED = 42
+random.seed(SEED)
+np.random.seed(SEED)
+os.environ["PYTHONHASHSEED"] = str(SEED)
+
+if TORCH_AVAILABLE:
+    torch.manual_seed(SEED)
+    torch.set_num_threads(1)  # vermeidet non-deterministische Parallel-Reduktionen
+    # Optional (nur wenn keine Fehlermeldung kommt):
+    # torch.use_deterministic_algorithms(True)
+
+if CV2_AVAILABLE:
+    try:
+        cv2.setNumThreads(1)   # OpenCV deterministischer
+    except Exception:
+        pass
+
+
 
 # Import our modular components
 from video_processing import extract_frames_from_video
@@ -172,6 +207,27 @@ def check_password() -> bool:
 
 
 def main():
+        # Diagnose: Versionen anzeigen (hilft bei Abweichungen lokal vs. online)
+    with st.expander("Diagnostics (Versionen)"):
+        versions = {}
+        try:
+            import cv2; versions["opencv"] = cv2.__version__
+        except Exception: versions["opencv"] = "n/a"
+        try:
+            import numpy as _np; versions["numpy"] = _np.__version__
+        except Exception: versions["numpy"] = "n/a"
+        try:
+            import torch as _torch; versions["torch"] = _torch.__version__
+        except Exception: versions["torch"] = "n/a"
+        try:
+            import transformers as _tf; versions["transformers"] = _tf.__version__
+        except Exception: versions["transformers"] = "n/a"
+        try:
+            import PIL; versions["pillow"] = PIL.__version__
+        except Exception: versions["pillow"] = "n/a"
+
+        st.write(versions)
+
     """Main application entry point"""
     # Initialize application
     initialize_app()