Add default for topic on custom level and random reorder for multiple choice options.

app.py

@@ -1,3 +1,4 @@
+import random
 import threading
 from functools import reduce
 
@@ -1512,7 +1513,7 @@ def get_custom_level():
     for i in range(1, nr_exercises + 1, 1):
         exercise_type = request.args.get('exercise_' + str(i) + '_type')
         exercise_qty = int(request.args.get('exercise_' + str(i) + '_qty', -1))
-        exercise_topic = request.args.get('exercise_' + str(i) + '_topic')
+        exercise_topic = request.args.get('exercise_' + str(i) + '_topic', random.choice(topics))
         exercise_text_size = int(request.args.get('exercise_' + str(i) + '_text_size', -1))
         exercise_sa_qty = int(request.args.get('exercise_' + str(i) + '_sa_qty', -1))
         exercise_mc_qty = int(request.args.get('exercise_' + str(i) + '_mc_qty', -1))

helper/exercises.py

@@ -1368,7 +1368,9 @@ def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams
                 question["questions"][i], seen_keys = replace_exercise_if_exists_utas(all_exams, question["questions"][i],
                                                                                       question,
                                                                                       seen_keys)
-        return fix_exercise_ids(question, start_id)
+        response = fix_exercise_ids(question, start_id)
+        response["questions"] = randomize_mc_options_order(response["questions"])
+        return response
 
 
 def gen_multiple_choice_underlined_utas(quantity: int, start_id: int):
@@ -1437,7 +1439,9 @@ def gen_multiple_choice_underlined_utas(quantity: int, start_id: int):
     if len(question["questions"]) != quantity:
         return gen_multiple_choice_level(quantity, start_id)
     else:
-        return fix_exercise_ids(question, start_id)
+        response = fix_exercise_ids(question, start_id)
+        response["questions"] = randomize_mc_options_order(response["questions"])
+        return response
 
 
 def gen_blank_space_text_utas(quantity: int, start_id: int, size: int, topic=random.choice(mti_topics)):
@@ -1590,7 +1594,9 @@ def gen_text_multiple_choice_utas(text: str, start_id: int, mc_quantity: int):
     if len(question["questions"]) != mc_quantity:
         return gen_multiple_choice_level(mc_quantity, start_id)
     else:
-        return fix_exercise_ids(question, start_id)["questions"]
+        response = fix_exercise_ids(question, start_id)
+        response["questions"] = randomize_mc_options_order(response["questions"])
+        return response
 
 
 def generate_level_mc(start_id: int, quantity: int):
@@ -1644,4 +1650,26 @@ def generate_level_mc(start_id: int, quantity: int):
     question = make_openai_call(GPT_4_O, messages, token_count, ["questions"],
                                 GEN_QUESTION_TEMPERATURE)
 
-    return fix_exercise_ids(question, start_id)
+    response = fix_exercise_ids(question, start_id)
+    response["questions"] = randomize_mc_options_order(response["questions"])
+    return response
+
+
+def randomize_mc_options_order(questions):
+    option_ids = ['A', 'B', 'C', 'D']
+
+    for question in questions:
+        # Store the original solution text
+        original_solution_text = next(
+            option['text'] for option in question['options'] if option['id'] == question['solution'])
+
+        # Shuffle the options
+        random.shuffle(question['options'])
+
+        # Update the option ids and find the new solution id
+        for idx, option in enumerate(question['options']):
+            option['id'] = option_ids[idx]
+            if option['text'] == original_solution_text:
+                question['solution'] = option['id']
+
+    return questions