Added missing fillBlanks mc variant that was in UTAS to custom level
This commit is contained in:
9
app.py
9
app.py
@@ -1317,6 +1317,7 @@ class CustomLevelExerciseTypes(Enum):
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MULTIPLE_CHOICE_4 = "multiple_choice_4"
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MULTIPLE_CHOICE_4 = "multiple_choice_4"
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MULTIPLE_CHOICE_BLANK_SPACE = "multiple_choice_blank_space"
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MULTIPLE_CHOICE_BLANK_SPACE = "multiple_choice_blank_space"
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MULTIPLE_CHOICE_UNDERLINED = "multiple_choice_underlined"
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MULTIPLE_CHOICE_UNDERLINED = "multiple_choice_underlined"
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FILL_BLANKS_MC = "fill_blanks_mc"
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BLANK_SPACE_TEXT = "blank_space_text"
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BLANK_SPACE_TEXT = "blank_space_text"
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READING_PASSAGE_UTAS = "reading_passage_utas"
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READING_PASSAGE_UTAS = "reading_passage_utas"
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WRITING_LETTER = "writing_letter"
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WRITING_LETTER = "writing_letter"
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@@ -1414,6 +1415,14 @@ def get_custom_level():
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exercise_id = exercise_id + qty
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exercise_id = exercise_id + qty
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exercise_qty = exercise_qty - qty
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exercise_qty = exercise_qty - qty
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elif exercise_type == CustomLevelExerciseTypes.FILL_BLANKS_MC.value:
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response["exercises"]["exercise_" + str(i)] = gen_fill_blanks_mc_utas(
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exercise_qty, exercise_id, exercise_text_size
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)
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response["exercises"]["exercise_" + str(i)]["type"] = "fillBlanks"
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response["exercises"]["exercise_" + str(i)]["variant"] = "mc"
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exercise_id = exercise_id + exercise_qty
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elif exercise_type == CustomLevelExerciseTypes.BLANK_SPACE_TEXT.value:
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elif exercise_type == CustomLevelExerciseTypes.BLANK_SPACE_TEXT.value:
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response["exercises"]["exercise_" + str(i)] = gen_blank_space_text_utas(exercise_qty, exercise_id,
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response["exercises"]["exercise_" + str(i)] = gen_blank_space_text_utas(exercise_qty, exercise_id,
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exercise_text_size)
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exercise_text_size)
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@@ -1563,6 +1563,66 @@ def gen_multiple_choice_underlined_utas(quantity: int, start_id: int, all_exams=
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return response
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return response
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def gen_fill_blanks_mc_utas(quantity: int, start_id: int, size: int, topic=random.choice(mti_topics)):
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json_format = {
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"question": {
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"solutions": [
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{
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"id": "<question id>",
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"solution": "<the option that holds the solution>"
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}
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],
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"words": [
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{
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"id": "<question id>",
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"options": {
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"A": "<a option>",
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"B": "<b option>",
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"C": "<c option>",
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"D": "<d option>"
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}
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}
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],
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"text": "text"
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}
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}
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messages = [
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{
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"role": "system",
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"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
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},
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{
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"role": "user",
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"content": (
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f'Generate a text of at least {size} words about the topic {topic}. Make sure the text is structured '
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'in paragraphs formatted with newlines (\\n\\n) to delimit them.'
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)
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},
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{
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"role": "user",
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"content": (
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f'From the generated text choose {quantity} words (cannot be sequential words) to replace '
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'once with {{id}} where id starts on ' + str(start_id) + ' and is incremented for each word. '
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'For each word choose 4 options, 1 correct and the other ones false. Make sure that only 1 is the '
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'correct one amongst the 4 options and put the solution on the solutions array. '
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'The ids must be ordered throughout the text and the words must be replaced only once. Put the '
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'removed words and respective ids on the words array of the json in the correct order. You can\'t '
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'reference multiple times the same id across the text, if for example one of the chosen words is '
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'"word1" then word1 must be placed in the text with an id once, if word1 is referenced other '
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'times in the text then replace with the actual text of word.'
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)
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}
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]
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token_count = count_total_tokens(messages)
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question = make_openai_call(GPT_4_O, messages, token_count,
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["question"],
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GEN_QUESTION_TEMPERATURE)
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return question["question"]
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def gen_blank_space_text_utas(quantity: int, start_id: int, size: int, topic=random.choice(mti_topics)):
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def gen_blank_space_text_utas(quantity: int, start_id: int, size: int, topic=random.choice(mti_topics)):
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json_format = {
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json_format = {
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"question": {
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"question": {
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