New custom level tests.
This commit is contained in:
Cristiano Ferreira
@@ -659,3 +659,19 @@ academic_subjects = [
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"Ecology",
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"International Business"
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]
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grammar_types = [
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"parts of speech",
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"parts of speech - Nouns",
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"parts of speech - Pronouns",
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"parts of speech - Verbs",
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"parts of speech - Adverbs",
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"parts of speech - Adjectives",
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"parts of speech - Conjunctions",
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"parts of speech - Prepositions",
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"parts of speech - Interjections",
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"sentence structure",
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"types of sentences",
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"tenses",
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"active voice and passive voice"
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]
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@@ -1443,18 +1443,29 @@ def parse_conversation(conversation_data):
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return "\n".join(readable_text)
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def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams=None):
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def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, topic=None, all_exams=None):
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gen_multiple_choice_for_text = "Generate " + str(
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quantity) + " multiple choice blank space questions of 4 options for an english level exam, some easy questions, some intermediate " \
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"questions and some advanced questions. Ensure that the questions cover a range of topics such as " \
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"verb tense, subject-verb agreement, pronoun usage, sentence structure, and punctuation. Make sure " \
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"every question only has 1 correct answer."
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quantity) + (" multiple choice blank space questions of 4 options for an english level exam, some easy "
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"questions, some intermediate questions and some advanced questions. Make sure every question "
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"only has 1 correct answer.")
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if topic is None:
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gen_multiple_choice_for_text = gen_multiple_choice_for_text + ("Ensure that the questions cover a range of "
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"topics such as verb tense, subject-verb "
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"agreement, pronoun usage, sentence structure, "
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"and punctuation.")
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else:
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gen_multiple_choice_for_text = gen_multiple_choice_for_text + ("Ensure that the questions are fill the blanks "
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"and cover the grammar "
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"topic of '" + topic + "' and the prompts "
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"are varied.")
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messages = [
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{
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"role": "system",
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"content": (
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'You are a helpful assistant designed to output JSON on this format: {"questions": [{"id": "9", "options": '
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'You are a helpful assistant designed to output JSON on this format: '
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'{"questions": [{"id": "9", "options": '
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'[{"id": "A", "text": '
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'"And"}, {"id": "B", "text": "Cat"}, {"id": "C", "text": '
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'"Happy"}, {"id": "D", "text": "Jump"}], '
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@@ -1473,7 +1484,7 @@ def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams
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GEN_QUESTION_TEMPERATURE)
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if len(question["questions"]) != quantity:
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return gen_multiple_choice_blank_space_utas(quantity, start_id)
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return gen_multiple_choice_blank_space_utas(quantity, start_id, topic, all_exams)
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else:
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if all_exams is not None:
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seen_keys = set()
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@@ -2089,3 +2100,309 @@ def gen_listening_section_4(topic, difficulty, req_exercises, number_of_exercise
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"text": monologue,
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"difficulty": difficulty
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}
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def gen_transformation_exercise(quantity, start_id, difficulty, topic=None):
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json_format = {
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"exercises": [
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{
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"id": 1,
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"first": "first sentence",
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"second": "second sentence",
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"solutions": ["first_missing_word", "second_missing_word"]
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}
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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": ("Create " + str(quantity) + " transformation exercises of " + difficulty + " where the student "
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"has to complete the "
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"second sentences' 2 blank spaces so that it has the same meaning "
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"as the first. Each blank space must correspond to a single word.")
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},
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{
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"role": "user",
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"content": 'The id starts at ' + str(start_id) + '.'
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}
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]
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if topic is not None:
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messages.append({
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"role": "user",
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"content": 'Focus the exercises on the grammar subject of ' + topic + '.'
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})
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
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response["prompt"] = "Complete the second sentence so that it has the same meaning as the first."
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response["difficulty"] = difficulty
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response["topic"] = topic
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return response
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def gen_gap_filling_exercise(quantity, start_id, difficulty, topic=None):
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json_format = {
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"exercises": [
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{
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"id": 1,
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"question": "sentence with a blank space to fill",
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"solutions": ["option 1", "option 2"]
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}
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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": ("Create " + str(quantity) + " gap filling exercises of " + difficulty + " where the student "
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"has to complete the "
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"sentence's blank space (signaled as {{id}}) so that it makes sense. "
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"The blank space must correspond to a single word.")
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},
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{
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"role": "user",
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"content": 'The id starts at ' + str(start_id) + '.'
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}
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]
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if topic is not None:
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messages.append({
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"role": "user",
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"content": 'Focus the exercises on the grammar subject of ' + topic + '.'
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})
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
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response["prompt"] = "Complete the sentence."
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response["difficulty"] = difficulty
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response["topic"] = topic
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return response
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def gen_grammar_matching_exercise(quantity, start_id, difficulty, topic=None):
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json_format = {
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"matching_pairs": [
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{
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"left": "word/sentence on left",
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"right": "word/sentence on right",
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}
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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": ("Create " + str(quantity) + " grammar related matching exercises "
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"of " + difficulty + " where the student has to match the "
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"words/sentences on the left with "
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"words/sentences on the right.")
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}
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]
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if topic is not None:
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messages.append({
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"role": "user",
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"content": 'Focus the exercises on the grammar subject of ' + topic + '.'
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})
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
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return {
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"allowRepetition": False,
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"options": build_options_grammar_matching(response["matching_pairs"]),
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"prompt": "Match the words/sentences on the left with the ones on the right.",
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"sentences": build_sentences_grammar_matching(response["matching_pairs"], start_id),
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"type": "matchSentences",
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"difficulty": difficulty,
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"topic": topic
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}
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def gen_cloze_exercise(quantity, start_id, difficulty, topic=None):
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json_format = {
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"text": "the text {{1}} blank spaces {{2}} it",
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"solutions": [
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{
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"id": 1,
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"solution": [
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"with"
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]
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},
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{
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"id": 2,
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"word": [
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"on"
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]
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}
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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": ("Generate a text for a cloze exercise with " + str(quantity) + " blank spaces to fill of " + difficulty + " where the student "
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"has to complete the "
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"blank spaces (signaled as {{id}}) on the text so that it makes sense. "
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"Each blank space must correspond to a single word.")
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},
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{
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"role": "user",
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"content": 'The id starts at ' + str(start_id) + '.'
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}
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]
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if topic is not None:
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messages.append({
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"role": "user",
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"content": 'Focus the exercises on the grammar subject of ' + topic + '.'
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})
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
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response["prompt"] = "Complete the text by adding a word to each gap."
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response["difficulty"] = difficulty
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response["topic"] = topic
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response["maxWords"] = 1
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return response
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def gen_true_false_exercise(quantity: int, start_id, difficulty, topic=None):
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json_format = {
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"questions": [
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{
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"id": 1,
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"prompt": "statement_1",
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"solution": "true/false"
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},
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{
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"id": 2,
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"prompt": "statement_2",
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"solution": "true/false"
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}
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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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'Generate ' + str(
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quantity) + ' ' + difficulty + ' difficulty grammar related statements for a true or false exercise.')
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},
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{
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"role": "user",
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"content": 'The id starts at ' + str(start_id) + '.'
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}
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]
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if topic is not None:
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messages.append({
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"role": "user",
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"content": 'Focus the exercises on the grammar subject of ' + topic + '.'
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})
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
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response["prompt"] = "Decide if the statements are true or false."
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response["difficulty"] = difficulty
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response["topic"] = topic
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return response
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def gen_error_correction_exercise(quantity: int, start_id, difficulty, topic=None):
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json_format = {
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"questions": [
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{
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"id": 1,
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"prompt": "sentence with errors",
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"solution": "corrected sentence"
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},
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{
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"id": 2,
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"prompt": "sentence with errors",
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"solution": "corrected sentence"
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}
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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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'Generate ' + str(
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quantity) + ' ' + difficulty + ' difficulty grammatically incorrect sentences for an exercise where '
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'the user has to fix the sentence.')
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},
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{
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"role": "user",
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"content": 'The id starts at ' + str(start_id) + '.'
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}
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]
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if topic is not None:
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messages.append({
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"role": "user",
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"content": 'Focus the exercises on the grammar subject of ' + topic + '.'
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})
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
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response["prompt"] = "Find the mistakes in the sentence and correct them."
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response["difficulty"] = difficulty
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response["topic"] = topic
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return response
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def build_options_grammar_matching(pairs):
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options = []
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letters = iter(string.ascii_uppercase)
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for pair in pairs:
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options.append({
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"id": next(letters),
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"sentence": pair["left"]
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})
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return options
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def build_sentences_grammar_matching(pairs, start_id):
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sentences = []
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letters = iter(string.ascii_uppercase)
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for pair in pairs:
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sentences.append({
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"solution": next(letters),
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"sentence": pair["right"]
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})
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random.shuffle(sentences)
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for i, sentence in enumerate(sentences, start=start_id):
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sentence["id"] = i
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return sentences
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