The 'not' part will be handled externally, so removed it

app.py

@@ -26,26 +26,26 @@ def calculate_similarity(student_inputs, mentor_inputs):
     Then append a 'not' to such options and place them at same index as in student_inputs.
     Also we need to take care of value shifts after adding 'not' to some options, so use a new list
     """
-    student = []
-    mentor = []
-
-    for i in range(len(options)):
-        if options[i] in student_inputs:
-            student.append(options[i])
-        else:
-            student.append(f"NOT {options[i]}")
-
-    for i in range(len(options)):
-        if options[i] in mentor_inputs:
-            mentor.append(options[i])
-        else:
-            mentor.append(f"NOT {options[i]}")
-
-    print(f"Student inputs: {student}")
-    print(f"Mentor inputs: {mentor}")
-
-    student_tokens = tokenizer(student, return_tensors="pt", padding=True)
-    mentor_tokens = tokenizer(mentor, return_tensors="pt", padding=True)
+    # student = []
+    # mentor = []
+
+    # for i in range(len(options)):
+    #     if options[i] in student_inputs:
+    #         student.append(options[i])
+    #     else:
+    #         student.append(f"NOT {options[i]}")
+
+    # for i in range(len(options)):
+    #     if options[i] in mentor_inputs:
+    #         mentor.append(options[i])
+    #     else:
+    #         mentor.append(f"NOT {options[i]}")
+
+    # print(f"Student inputs: {student}")
+    # print(f"Mentor inputs: {mentor}")
+
+    student_tokens = tokenizer(student_inputs, return_tensors="pt", padding=True)
+    mentor_tokens = tokenizer(mentor_inputs, return_tensors="pt", padding=True)
 
     with torch.no_grad():
         student_output = model(**student_tokens).last_hidden_state.mean(dim=1)