Update app.py

app.py

@@ -72,16 +72,16 @@ def compute_loss(model, inputs):
     return loss
 
 # Load the data from pickle files (replace with your local paths)
-with open("/datasets/AmelieSchreiber/binding_sites_random_split_by_family/train_sequences_chunked_by_family.pkl", "rb") as f:
+with open("./datasets/train_sequences_chunked_by_family.pkl", "rb") as f:
     train_sequences = pickle.load(f)
 
-with open("/datasets/AmelieSchreiber/binding_sites_random_split_by_family/test_sequences_chunked_by_family.pkl", "rb") as f:
+with open("./datasets/test_sequences_chunked_by_family.pkl", "rb") as f:
     test_sequences = pickle.load(f)
 
-with open("/datasets/AmelieSchreiber/binding_sites_random_split_by_family/train_labels_chunked_by_family.pkl", "rb") as f:
+with open("./datasets/train_labels_chunked_by_family.pkl", "rb") as f:
     train_labels = pickle.load(f)
 
-with open("/datasets/AmelieSchreiber/binding_sites_random_split_by_family/test_labels_chunked_by_family.pkl", "rb") as f:
+with open("./datasets/test_labels_chunked_by_family.pkl", "rb") as f:
     test_labels = pickle.load(f)
 
 # Tokenization
@@ -104,3 +104,11 @@ flat_train_labels = [label for sublist in train_labels for label in sublist]
 class_weights = compute_class_weight(class_weight='balanced', classes=classes, y=flat_train_labels)
 accelerator = Accelerator()
 class_weights = torch.tensor(class_weights, dtype=torch.float32).to(accelerator.device)
+
+dubug_result = class_weights
+demo = gr.Blocks(title="DEMO FOR ESMBind")
+
+with demo:
+    gr.Markdown("# DEMO FOR ESMBind")
+    gr.Textbox(dubug_result)
+demo.launch()