Updated

.ipynb_checkpoints/app-Copy1-checkpoint.py

@@ -181,18 +181,6 @@ def generate_results_text(pdb_id, segment, residues_by_bracket, protein_residues
     
     return result_str
 
-def predict_utils(sequence):
-    input_ids = tokenizer(" ".join(sequence), return_tensors="pt").input_ids.to(device)
-    with torch.no_grad():
-        outputs = model(input_ids).logits.detach().cpu().numpy().squeeze()
-
-    # Calculate scores and normalize them
-    raw_scores = expit(outputs[:, 1] - outputs[:, 0])
-    normalized_scores = normalize_scores(raw_scores)
-
-    return raw_scores,normalized_scores
-
-
 def process_pdb(pdb_id_or_file, segment, score_type='normalized'):
     # Determine if input is a PDB ID or file path
     if pdb_id_or_file.endswith('.pdb'):

.ipynb_checkpoints/app-checkpoint.py

@@ -181,6 +181,18 @@ def generate_results_text(pdb_id, segment, residues_by_bracket, protein_residues
     
     return result_str
 
+
+def predict_utils(sequence):
+    input_ids = tokenizer(" ".join(sequence), return_tensors="pt").input_ids.to(device)
+    with torch.no_grad():
+        outputs = model(input_ids).logits.detach().cpu().numpy().squeeze()
+
+    # Calculate scores and normalize them
+    raw_scores = expit(outputs[:, 1] - outputs[:, 0])
+    normalized_scores = normalize_scores(raw_scores)
+
+    return raw_scores,normalized_scores
+    
 def process_pdb(pdb_id_or_file, segment, score_type='normalized'):
     # Determine if input is a PDB ID or file path
     if pdb_id_or_file.endswith('.pdb'):

app-Copy1.py

@@ -181,18 +181,6 @@ def generate_results_text(pdb_id, segment, residues_by_bracket, protein_residues
     
     return result_str
 
-def predict_utils(sequence):
-    input_ids = tokenizer(" ".join(sequence), return_tensors="pt").input_ids.to(device)
-    with torch.no_grad():
-        outputs = model(input_ids).logits.detach().cpu().numpy().squeeze()
-
-    # Calculate scores and normalize them
-    raw_scores = expit(outputs[:, 1] - outputs[:, 0])
-    normalized_scores = normalize_scores(raw_scores)
-
-    return raw_scores,normalized_scores
-
-
 def process_pdb(pdb_id_or_file, segment, score_type='normalized'):
     # Determine if input is a PDB ID or file path
     if pdb_id_or_file.endswith('.pdb'):

app.py

@@ -181,6 +181,18 @@ def generate_results_text(pdb_id, segment, residues_by_bracket, protein_residues
     
     return result_str
 
+
+def predict_utils(sequence):
+    input_ids = tokenizer(" ".join(sequence), return_tensors="pt").input_ids.to(device)
+    with torch.no_grad():
+        outputs = model(input_ids).logits.detach().cpu().numpy().squeeze()
+
+    # Calculate scores and normalize them
+    raw_scores = expit(outputs[:, 1] - outputs[:, 0])
+    normalized_scores = normalize_scores(raw_scores)
+
+    return raw_scores,normalized_scores
+    
 def process_pdb(pdb_id_or_file, segment, score_type='normalized'):
     # Determine if input is a PDB ID or file path
     if pdb_id_or_file.endswith('.pdb'):