tryout

streamlit_simulation/app.py

@@ -142,23 +142,29 @@ def load_lightgbm_model():
 def load_transformer_model_and_dataset():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    # 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()
+    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()
 
-    # 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
+            return model, test_dataset, device
+        
+    except Exception as e:
+        st.error(f"❌ Fehler beim Laden des Transformer-Modells: {e}")
+        raise e
 
 @st.cache_data
 def load_data():

streamlit_simulation/app_backup_hug.py

@@ -132,23 +132,29 @@ def load_lightgbm_model():
 def load_transformer_model_and_dataset():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    # 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()
+    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()
 
-    # 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
+            return model, test_dataset, device
+        
+    except Exception as e:
+        st.error(f"❌ Fehler beim Laden des Transformer-Modells: {e}")
+        raise e
 
 @st.cache_data
 def load_data():