fix

app.py

@@ -688,7 +688,7 @@ hawk_model = HawkPredictor(
     conv_kernel_size=hawk_config["conv_kernel_size"],
     dropout=hawk_config["dropout"]
 )
-hawk_model.load_state_dict(torch.load(os.path.join(MODELS_DIR, "hawk_best_model.pt"), map_location=device)['model_state_dict'])
+hawk_model.load_state_dict(torch.load(os.path.join(MODELS_DIR, "hawk_best_model.pt"), map_location=device, weights_only=False)['model_state_dict'])
 hawk_model.to(device)
 hawk_model.eval()
 models["hawk"] = hawk_model
@@ -706,7 +706,7 @@ mamba_model = Mamba2Predictor(
     conv_kernel_size=mamba_config["conv_kernel_size"],
     dropout=mamba_config["dropout"]
 )
-mamba_model.load_state_dict(torch.load(os.path.join(MODELS_DIR, "mamba_best_model.pt"), map_location=device)['model_state_dict'])
+mamba_model.load_state_dict(torch.load(os.path.join(MODELS_DIR, "mamba_best_model.pt"), map_location=device, weights_only=False)['model_state_dict'])
 mamba_model.to(device)
 mamba_model.eval()
 models["mamba"] = mamba_model
@@ -724,7 +724,7 @@ xlstm_model = xLSTMPredictor(
     dropout=xlstm_config["dropout"],
     expand_factor=xlstm_config["expand_factor"]
 )
-xlstm_model.load_state_dict(torch.load(os.path.join(MODELS_DIR, "xlstm_best_model.pt"), map_location=device)['model_state_dict'])
+xlstm_model.load_state_dict(torch.load(os.path.join(MODELS_DIR, "xlstm_best_model.pt"), map_location=device, weights_only=False)['model_state_dict'])
 xlstm_model.to(device)
 xlstm_model.eval()
 models["xlstm"] = xlstm_model

requirements.txt

@@ -12,3 +12,5 @@ lightgbm
 gradio==5.43.1
 huggingface-hub==0.36.0
 transformers==4.57.1
+matplotlib
+safetensors