Update: Use model file from settings

app.py

@@ -39,17 +39,17 @@ from lib.examples import EXAMPLES
 
 MODEL_BASE = "microsoft/deberta-v3-base"
 REPO_ID    = "ziem-io/deberta_flavour_regressor_multi_head"
-FILENAME   = "deberta_flavour_regressor_multi_head_20250914_1020.safetensors"
 
 # (optional) falls das Model-Repo privat ist:
 HF_TOKEN = os.getenv("HF_TOKEN")  # in Space-Secrets hinterlegen
+MODEL_FILE = os.getenv("MODEL_FILE")  # in Space-Secrets hinterlegen
 
 ##################################################################################
 
 # --- Download Weights ---
 weights_path = hf_hub_download(
     repo_id=REPO_ID, 
-    filename=FILENAME, 
+    filename=MODEL_FILE, 
     token=HF_TOKEN
 )
 
@@ -159,7 +159,7 @@ def predict(review: str):
             for name, score in prediction_flavours.items()
         ],
         "review": review,
-        "model": FILENAME,
+        "model": MODEL_FILE,
         "device": device,
         "duration": round((t_end_flavours - t_start_flavours), 3),
     }