Update models/peptide_classifiers.py

models/peptide_classifiers.py

@@ -509,7 +509,7 @@ class AffinityModel(nn.Module):
 
 class HemolysisModel:
     def __init__(self, device):
-        self.predictor = xgb.Booster(model_file='/scratch/pranamlab/tong/checkpoints/MOG-DFM/classifier_ckpt/best_model_hemolysis.json')
+        self.predictor = xgb.Booster(model_file='./classifier_ckpt/best_model_hemolysis.json')
         
         self.model = EsmModel.from_pretrained("facebook/esm2_t33_650M_UR50D").to(device)
         self.model.eval()
@@ -547,7 +547,7 @@ class HemolysisModel:
 class NonfoulingModel:
     def __init__(self, device):
         # change model path
-        self.predictor = xgb.Booster(model_file='/scratch/pranamlab/tong/checkpoints/MOG-DFM/classifier_ckpt/best_model_nonfouling.json')
+        self.predictor = xgb.Booster(model_file='./classifier_ckpt/best_model_nonfouling.json')
         
         self.model = EsmModel.from_pretrained("facebook/esm2_t33_650M_UR50D").to(device)
         self.model.eval()
@@ -584,7 +584,7 @@ class NonfoulingModel:
 class SolubilityModel:
     def __init__(self, device):
         # change model path
-        self.predictor = xgb.Booster(model_file='/scratch/pranamlab/tong/checkpoints/MOG-DFM/classifier_ckpt/best_model_solubility.json')
+        self.predictor = xgb.Booster(model_file='./classifier_ckpt/best_model_solubility.json')
         
         self.model = EsmModel.from_pretrained("facebook/esm2_t33_650M_UR50D").to(device)
         self.model.eval()
@@ -670,7 +670,7 @@ class HalfLifeModel:
         output_dim = input_dim // 8
         dropout_rate = 0.3
         self.model = PeptideCNN(input_dim, hidden_dims, output_dim, dropout_rate).to(device)
-        self.model.load_state_dict(torch.load('/scratch/pranamlab/tong/checkpoints/MOG-DFM/classifier_ckpt/best_model_half_life.pth', map_location=device, weights_only=False))
+        self.model.load_state_dict(torch.load('./classifier_ckpt/best_model_half_life.pth', map_location=device, weights_only=False))
         self.model.eval()
 
     def __call__(self, x):