Update app.py

app.py

@@ -453,8 +453,8 @@ def process_hdf5_file(uploaded_file, percentage):
 
         # Step 5: Load the HDF5 file and extract the channels and labels
         with h5py.File(uploaded_file.name, 'r') as f:
-            channels = torch.tensor(f['channels']).float() #astype(np.float32)  # Assuming 'channels' dataset in the HDF5 file
-            labels = torch.tensor(f['labels']).long()  # Assuming 'labels' dataset in the HDF5 file
+            channels = np.array(f['channels']).astype(np.complex64) #astype(np.float32)  # Assuming 'channels' dataset in the HDF5 file
+            labels = np.array(f['labels']).astype(np.int32)  # Assuming 'labels' dataset in the HDF5 file
         print(f"Loaded dataset with {channels.shape[0]} samples.")
 
         # Step 7: Tokenize the data using the tokenizer from input_preprocess