fix inference_app.py

inference_app.py

@@ -287,7 +287,7 @@ class PairedPDB(HeteroData):  # type: ignore
         return graph
       
 #create_graph takes inputs apo_ligand, apo_residue and paired holo as pdb3(ground truth). 
-def create_graph(pdb1, pdb2, pdb3='/home/sukanya/iitm_bisect_pinder_submission/test_out.pdb', k=5):
+def create_graph(pdb1, pdb2, k=5):
     r"""
     Create a heterogeneous graph from two PDB files, with the ligand and receptor
     as separate nodes, and their respective features and edges.
@@ -304,19 +304,19 @@ def create_graph(pdb1, pdb2, pdb3='/home/sukanya/iitm_bisect_pinder_submission/t
     # Extract coordinates from PDB files
     coords1 = torch.tensor(extract_coordinates_from_pdb(pdb1),dtype=torch.float)  
     coords2 = torch.tensor(extract_coordinates_from_pdb(pdb2),dtype=torch.float)  
-    coords3 = torch.tensor(extract_coordinates_from_pdb(pdb3),dtype=torch.float)
+    # coords3 = torch.tensor(extract_coordinates_from_pdb(pdb3),dtype=torch.float)
     # Create the HeteroData object
     data = HeteroData()
 
     # Define ligand node features
     data["ligand"].x = torch.tensor(coords1, dtype=torch.float)
     data["ligand"].pos = coords1
-    data["ligand"].y = torch.tensor(coords3[:len(coords1)], dtype=torch.float)
+    # data["ligand"].y = torch.tensor(coords3[:len(coords1)], dtype=torch.float)
 
     # Define receptor node features
     data["receptor"].x = torch.tensor(coords2, dtype=torch.float)
     data["receptor"].pos = coords2
-    data["receptor"].y = torch.tensor(coords3[len(coords1):], dtype=torch.float)
+    # data["receptor"].y = torch.tensor(coords3[len(coords1):], dtype=torch.float)
 
     # Construct k-NN graph for ligand
     ligand_edge_index = knn_graph(data["ligand"].pos, k=k)
@@ -890,7 +890,7 @@ model.eval()
 def predict (input_seq_1, input_msa_1, input_protein_1, input_seq_2,input_msa_2,  input_protein_2):
     start_time = time.time()
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    data = create_graph(input_protein_1, input_protein_2, './test_out (1).pdb', k=10)
+    data = create_graph(input_protein_1, input_protein_2, k=10)
     
     with torch.no_grad():
         mat, vect = model(data)