tryout no plot

streamlit_simulation/app_backup_hug.py

@@ -132,29 +132,23 @@ def load_lightgbm_model():
 def load_transformer_model_and_dataset():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    try:
-        with st.spinner("🔄 Loading transformer model..."):
-            # Load model
-            model = load_moment_model()
-            checkpoint_path = hf_hub_download(
-            repo_id="dlaj/energy-forecasting-files",
-            filename="transformer_model/model_final.pth",
-            repo_type="dataset"
-            )
-            model.load_state_dict(torch.load(checkpoint_path, map_location=device))
-            model.to(device)
-            model.eval()
+    # Load model
+    model = load_moment_model()
+    checkpoint_path = hf_hub_download(
+    repo_id="dlaj/energy-forecasting-files",
+    filename="transformer_model/model_final.pth",
+    repo_type="dataset"
+    )
+    model.load_state_dict(torch.load(checkpoint_path, map_location=device))
+    model.to(device)
+    model.eval()
 
-            # Datasets
-            train_dataset = InformerDataset(data_split="train", forecast_horizon=FORECAST_HORIZON, random_seed=13)
-            test_dataset = InformerDataset(data_split="test", forecast_horizon=FORECAST_HORIZON, random_seed=13)
-            test_dataset.scaler = train_dataset.scaler
+    # Datasets
+    train_dataset = InformerDataset(data_split="train", forecast_horizon=FORECAST_HORIZON, random_seed=13)
+    test_dataset = InformerDataset(data_split="test", forecast_horizon=FORECAST_HORIZON, random_seed=13)
+    test_dataset.scaler = train_dataset.scaler
 
-            return model, test_dataset, device
-        
-    except Exception as e:
-        st.error(f"❌ Fehler beim Laden des Transformer-Modells: {e}")
-        raise e
+    return model, test_dataset, device
 
 @st.cache_data
 def load_data():