ENCOA-94: Added user to training content docs, added support for shuffles, tweaked training prompt
This commit is contained in:
Carlos Mesquita
@@ -5,3 +5,4 @@ README.md
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*.pyd
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__pycache__
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.pytest_cache
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/scripts
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1
.gitignore
vendored
1
.gitignore
vendored
@@ -3,3 +3,4 @@ __pycache__
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.env
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.DS_Store
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/firebase-configs/test_firebase.json
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/scripts
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506
app.py
506
app.py
@@ -70,25 +70,7 @@ def get_listening_section_1_question():
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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if (len(req_exercises) == 0):
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req_exercises = random.sample(LISTENING_1_EXERCISE_TYPES, 1)
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number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_1_EXERCISES, len(req_exercises))
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processed_conversation = generate_listening_1_conversation(topic)
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app.logger.info("Generated conversation: " + str(processed_conversation))
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start_id = 1
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exercises = generate_listening_conversation_exercises(parse_conversation(processed_conversation),
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req_exercises,
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number_of_exercises_q,
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start_id, difficulty)
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return {
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"exercises": exercises,
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"text": processed_conversation,
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"difficulty": difficulty
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}
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return gen_listening_section_1(topic, difficulty, req_exercises)
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except Exception as e:
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return str(e)
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@@ -103,22 +85,7 @@ def get_listening_section_2_question():
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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if (len(req_exercises) == 0):
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req_exercises = random.sample(LISTENING_2_EXERCISE_TYPES, 2)
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number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_2_EXERCISES, len(req_exercises))
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monologue = generate_listening_2_monologue(topic)
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app.logger.info("Generated monologue: " + str(monologue))
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start_id = 11
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exercises = generate_listening_monologue_exercises(str(monologue), req_exercises, number_of_exercises_q,
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start_id, difficulty)
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return {
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"exercises": exercises,
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"text": monologue,
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"difficulty": difficulty
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}
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return gen_listening_section_2(topic, difficulty, req_exercises)
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except Exception as e:
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return str(e)
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@@ -133,24 +100,7 @@ def get_listening_section_3_question():
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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if (len(req_exercises) == 0):
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req_exercises = random.sample(LISTENING_3_EXERCISE_TYPES, 1)
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number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_3_EXERCISES, len(req_exercises))
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processed_conversation = generate_listening_3_conversation(topic)
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app.logger.info("Generated conversation: " + str(processed_conversation))
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start_id = 21
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exercises = generate_listening_conversation_exercises(parse_conversation(processed_conversation), req_exercises,
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number_of_exercises_q,
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start_id, difficulty)
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return {
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"exercises": exercises,
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"text": processed_conversation,
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"difficulty": difficulty
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}
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return gen_listening_section_3(topic, difficulty, req_exercises)
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except Exception as e:
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return str(e)
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@@ -165,22 +115,7 @@ def get_listening_section_4_question():
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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if (len(req_exercises) == 0):
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req_exercises = random.sample(LISTENING_EXERCISE_TYPES, 2)
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number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_4_EXERCISES, len(req_exercises))
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monologue = generate_listening_4_monologue(topic)
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app.logger.info("Generated monologue: " + str(monologue))
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start_id = 31
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exercises = generate_listening_monologue_exercises(str(monologue), req_exercises, number_of_exercises_q,
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start_id, difficulty)
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return {
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"exercises": exercises,
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"text": monologue,
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"difficulty": difficulty
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}
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return gen_listening_section_4(topic, difficulty, req_exercises)
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except Exception as e:
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return str(e)
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@@ -347,37 +282,7 @@ def get_writing_task_1_general_question():
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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topic = request.args.get("topic", default=random.choice(mti_topics))
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try:
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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: '
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'{"prompt": "prompt content"}')
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},
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{
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"role": "user",
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"content": ('Craft a prompt for an IELTS Writing Task 1 General Training exercise that instructs the '
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'student to compose a letter. The prompt should present a specific scenario or situation, '
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'based on the topic of "' + topic + '", requiring the student to provide information, '
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'advice, or instructions within the letter. '
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'Make sure that the generated prompt is '
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'of ' + difficulty + 'difficulty and does not contain '
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'forbidden subjects in muslim '
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'countries.')
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},
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{
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"role": "user",
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"content": 'The prompt should end with "In the letter you should" followed by 3 bullet points of what '
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'the answer should include.'
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}
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]
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token_count = count_total_tokens(messages)
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response = make_openai_call(GPT_3_5_TURBO, messages, token_count, "prompt",
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GEN_QUESTION_TEMPERATURE)
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return {
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"question": add_newline_before_hyphen(response["prompt"].strip()),
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"difficulty": difficulty,
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"topic": topic
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}
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return gen_writing_task_1(topic, difficulty)
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except Exception as e:
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return str(e)
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@@ -512,32 +417,7 @@ def get_writing_task_2_general_question():
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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topic = request.args.get("topic", default=random.choice(mti_topics))
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try:
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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: '
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'{"prompt": "prompt content"}')
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},
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{
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"role": "user",
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"content": (
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'Craft a comprehensive question of ' + difficulty + 'difficulty like the ones for IELTS Writing Task 2 General Training that directs the candidate '
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'to delve into an in-depth analysis of contrasting perspectives on the topic of "' + topic + '". '
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'The candidate should be asked to discuss the strengths and weaknesses of both viewpoints.')
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},
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{
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"role": "user",
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"content": 'The question should lead to an answer with either "theories", "complicated information" or '
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'be "very descriptive" on the topic.'
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}
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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, "prompt", GEN_QUESTION_TEMPERATURE)
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return {
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"question": response["prompt"].strip(),
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"difficulty": difficulty,
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"topic": topic
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}
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return gen_writing_task_2(topic, difficulty)
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except Exception as e:
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return str(e)
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@@ -719,56 +599,8 @@ def get_speaking_task_1_question():
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first_topic = request.args.get("first_topic", default=random.choice(mti_topics))
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second_topic = request.args.get("second_topic", default=random.choice(mti_topics))
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json_format = {
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"first_topic": "topic 1",
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"second_topic": "topic 2",
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"questions": [
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"Introductory question about the first topic, starting the topic with 'Let's talk about x' and then the "
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"question.",
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"Follow up question about the first topic",
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"Follow up question about the first topic",
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"Question about second topic",
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"Follow up question about the second topic",
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]
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}
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try:
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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: ' + 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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'Craft 5 simple and single questions of easy difficulty for IELTS Speaking Part 1 '
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'that encourages candidates to delve deeply into '
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'personal experiences, preferences, or insights on the topic '
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'of "' + first_topic + '" and the topic of "' + second_topic + '". '
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'Make sure that the generated '
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'question'
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'does not contain forbidden '
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'subjects in'
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'muslim countries.')
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},
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{
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"role": "user",
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"content": 'The questions should lead to the usage of 4 verb tenses (present perfect, present, '
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'past and future).'
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},
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{
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"role": "user",
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"content": 'They must be 1 single question each and not be double-barreled questions.'
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}
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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, ["first_topic"],
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GEN_QUESTION_TEMPERATURE)
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response["type"] = 1
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response["difficulty"] = difficulty
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return response
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return gen_speaking_part_1(first_topic, second_topic, difficulty)
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except Exception as e:
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return str(e)
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@@ -918,50 +750,8 @@ def get_speaking_task_2_question():
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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topic = request.args.get("topic", default=random.choice(mti_topics))
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json_format = {
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"topic": "topic",
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"question": "question",
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"prompts": [
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"prompt_1",
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"prompt_2",
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"prompt_3"
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],
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"suffix": "And explain why..."
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}
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try:
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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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'Create a question of medium difficulty for IELTS Speaking Part 2 '
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'that encourages candidates to narrate a '
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'personal experience or story related to the topic '
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'of "' + topic + '". Include 3 prompts that '
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'guide the candidate to describe '
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'specific aspects of the experience, '
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'such as details about the situation, '
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'their actions, and the reasons it left a '
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'lasting impression. Make sure that the '
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'generated question does not contain '
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'forbidden subjects in muslim countries.')
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},
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{
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"role": "user",
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"content": 'The prompts must not be questions. Also include a suffix like the ones in the IELTS exams '
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'that start with "And explain why".'
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}
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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["type"] = 2
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response["difficulty"] = difficulty
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response["topic"] = topic
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return response
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return gen_speaking_part_2(topic, difficulty)
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except Exception as e:
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return str(e)
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@@ -972,47 +762,8 @@ def get_speaking_task_3_question():
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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topic = request.args.get("topic", default=random.choice(mti_topics))
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json_format = {
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"topic": "topic",
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"questions": [
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"Introductory question about the topic.",
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"Follow up question about the topic",
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"Follow up question about the topic",
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"Follow up question about the topic",
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"Follow up question about the topic"
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]
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}
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try:
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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: ' + 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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'Formulate a set of 5 single questions of hard difficulty for IELTS Speaking Part 3 that encourage candidates to engage in a '
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'meaningful discussion on the topic of "' + topic + '". Provide inquiries, ensuring '
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'they explore various aspects, perspectives, and implications related to the topic.'
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'Make sure that the generated question does not contain forbidden subjects in muslim countries.')
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},
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{
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"role": "user",
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"content": 'They must be 1 single question each and not be double-barreled questions.'
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}
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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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# Remove the numbers from the questions only if the string starts with a number
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response["questions"] = [re.sub(r"^\d+\.\s*", "", question) if re.match(r"^\d+\.", question) else question for
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question in response["questions"]]
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response["type"] = 3
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response["difficulty"] = difficulty
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response["topic"] = topic
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return response
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return gen_speaking_part_3(topic, difficulty)
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except Exception as e:
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return str(e)
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@@ -1407,7 +1158,7 @@ def get_reading_passage_1_question():
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topic = request.args.get('topic', default=random.choice(topics))
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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return gen_reading_passage_1(topic, req_exercises, difficulty)
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return gen_reading_passage_1(topic, difficulty, req_exercises)
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except Exception as e:
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return str(e)
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@@ -1420,7 +1171,7 @@ def get_reading_passage_2_question():
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topic = request.args.get('topic', default=random.choice(topics))
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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return gen_reading_passage_2(topic, req_exercises, difficulty)
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return gen_reading_passage_2(topic, difficulty, req_exercises)
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except Exception as e:
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return str(e)
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@@ -1433,7 +1184,7 @@ def get_reading_passage_3_question():
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topic = request.args.get('topic', default=random.choice(topics))
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req_exercises = request.args.getlist('exercises')
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difficulty = request.args.get("difficulty", default=random.choice(difficulties))
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return gen_reading_passage_3(topic, req_exercises, difficulty)
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return gen_reading_passage_3(topic, difficulty, req_exercises)
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except Exception as e:
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return str(e)
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@@ -1565,6 +1316,18 @@ class CustomLevelExerciseTypes(Enum):
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MULTIPLE_CHOICE_UNDERLINED = "multiple_choice_underlined"
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BLANK_SPACE_TEXT = "blank_space_text"
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READING_PASSAGE_UTAS = "reading_passage_utas"
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WRITING_LETTER = "writing_letter"
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WRITING_2 = "writing_2"
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SPEAKING_1 = "speaking_1"
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SPEAKING_2 = "speaking_2"
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SPEAKING_3 = "speaking_3"
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READING_1 = "reading_1"
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READING_2 = "reading_2"
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READING_3 = "reading_3"
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LISTENING_1 = "listening_1"
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LISTENING_2 = "listening_2"
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LISTENING_3 = "listening_3"
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LISTENING_4 = "listening_4"
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@app.route('/custom_level', methods=['GET'])
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@@ -1579,11 +1342,24 @@ def get_custom_level():
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}
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for i in range(1, nr_exercises + 1, 1):
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exercise_type = request.args.get('exercise_' + str(i) + '_type')
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exercise_difficulty = request.args.get('exercise_' + str(i) + '_difficulty',
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random.choice(['easy', 'medium', 'hard']))
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exercise_qty = int(request.args.get('exercise_' + str(i) + '_qty', -1))
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exercise_topic = request.args.get('exercise_' + str(i) + '_topic', random.choice(topics))
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exercise_topic_2 = request.args.get('exercise_' + str(i) + '_topic_2', random.choice(topics))
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exercise_text_size = int(request.args.get('exercise_' + str(i) + '_text_size', 700))
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exercise_sa_qty = int(request.args.get('exercise_' + str(i) + '_sa_qty', -1))
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exercise_mc_qty = int(request.args.get('exercise_' + str(i) + '_mc_qty', -1))
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exercise_mc3_qty = int(request.args.get('exercise_' + str(i) + '_mc3_qty', -1))
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exercise_fillblanks_qty = int(request.args.get('exercise_' + str(i) + '_fillblanks_qty', -1))
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exercise_writeblanks_qty = int(request.args.get('exercise_' + str(i) + '_writeblanks_qty', -1))
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exercise_writeblanksquestions_qty = int(
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request.args.get('exercise_' + str(i) + '_writeblanksquestions_qty', -1))
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exercise_writeblanksfill_qty = int(request.args.get('exercise_' + str(i) + '_writeblanksfill_qty', -1))
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exercise_writeblanksform_qty = int(request.args.get('exercise_' + str(i) + '_writeblanksform_qty', -1))
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exercise_truefalse_qty = int(request.args.get('exercise_' + str(i) + '_truefalse_qty', -1))
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exercise_paragraphmatch_qty = int(request.args.get('exercise_' + str(i) + '_paragraphmatch_qty', -1))
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exercise_ideamatch_qty = int(request.args.get('exercise_' + str(i) + '_ideamatch_qty', -1))
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if exercise_type == CustomLevelExerciseTypes.MULTIPLE_CHOICE_4.value:
|
||||
response["exercises"]["exercise_" + str(i)] = {}
|
||||
@@ -1597,7 +1373,7 @@ def get_custom_level():
|
||||
|
||||
response["exercises"]["exercise_" + str(i)]["questions"].extend(
|
||||
generate_level_mc(exercise_id, qty,
|
||||
response["exercises"]["exercise_" + str(i)]["questions"])["questions"])
|
||||
response["exercises"]["exercise_" + str(i)]["questions"])["questions"])
|
||||
exercise_id = exercise_id + qty
|
||||
exercise_qty = exercise_qty - qty
|
||||
|
||||
@@ -1613,7 +1389,8 @@ def get_custom_level():
|
||||
|
||||
response["exercises"]["exercise_" + str(i)]["questions"].extend(
|
||||
gen_multiple_choice_blank_space_utas(qty, exercise_id,
|
||||
response["exercises"]["exercise_" + str(i)]["questions"])["questions"])
|
||||
response["exercises"]["exercise_" + str(i)]["questions"])[
|
||||
"questions"])
|
||||
exercise_id = exercise_id + qty
|
||||
exercise_qty = exercise_qty - qty
|
||||
|
||||
@@ -1629,7 +1406,8 @@ def get_custom_level():
|
||||
|
||||
response["exercises"]["exercise_" + str(i)]["questions"].extend(
|
||||
gen_multiple_choice_underlined_utas(qty, exercise_id,
|
||||
response["exercises"]["exercise_" + str(i)]["questions"])["questions"])
|
||||
response["exercises"]["exercise_" + str(i)]["questions"])[
|
||||
"questions"])
|
||||
exercise_id = exercise_id + qty
|
||||
exercise_qty = exercise_qty - qty
|
||||
|
||||
@@ -1643,9 +1421,205 @@ def get_custom_level():
|
||||
exercise_mc_qty, exercise_topic)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "readingExercises"
|
||||
exercise_id = exercise_id + exercise_qty
|
||||
elif exercise_type == CustomLevelExerciseTypes.WRITING_LETTER.value:
|
||||
response["exercises"]["exercise_" + str(i)] = gen_writing_task_1(exercise_topic, exercise_difficulty)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "writing"
|
||||
exercise_id = exercise_id + 1
|
||||
elif exercise_type == CustomLevelExerciseTypes.WRITING_2.value:
|
||||
response["exercises"]["exercise_" + str(i)] = gen_writing_task_2(exercise_topic, exercise_difficulty)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "writing"
|
||||
exercise_id = exercise_id + 1
|
||||
elif exercise_type == CustomLevelExerciseTypes.SPEAKING_1.value:
|
||||
response["exercises"]["exercise_" + str(i)] = (
|
||||
gen_speaking_part_1(exercise_topic, exercise_topic_2, exercise_difficulty))
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "interactiveSpeaking"
|
||||
exercise_id = exercise_id + 1
|
||||
elif exercise_type == CustomLevelExerciseTypes.SPEAKING_2.value:
|
||||
response["exercises"]["exercise_" + str(i)] = gen_speaking_part_2(exercise_topic, exercise_difficulty)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "speaking"
|
||||
exercise_id = exercise_id + 1
|
||||
elif exercise_type == CustomLevelExerciseTypes.SPEAKING_3.value:
|
||||
response["exercises"]["exercise_" + str(i)] = gen_speaking_part_3(exercise_topic, exercise_difficulty)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "interactiveSpeaking"
|
||||
exercise_id = exercise_id + 1
|
||||
elif exercise_type == CustomLevelExerciseTypes.READING_1.value:
|
||||
exercises = []
|
||||
exercise_qty_q = queue.Queue()
|
||||
total_qty = 0
|
||||
if exercise_fillblanks_qty != -1:
|
||||
exercises.append('fillBlanks')
|
||||
exercise_qty_q.put(exercise_fillblanks_qty)
|
||||
total_qty = total_qty + exercise_fillblanks_qty
|
||||
if exercise_writeblanks_qty != -1:
|
||||
exercises.append('writeBlanks')
|
||||
exercise_qty_q.put(exercise_writeblanks_qty)
|
||||
total_qty = total_qty + exercise_writeblanks_qty
|
||||
if exercise_truefalse_qty != -1:
|
||||
exercises.append('trueFalse')
|
||||
exercise_qty_q.put(exercise_truefalse_qty)
|
||||
total_qty = total_qty + exercise_truefalse_qty
|
||||
if exercise_paragraphmatch_qty != -1:
|
||||
exercises.append('paragraphMatch')
|
||||
exercise_qty_q.put(exercise_paragraphmatch_qty)
|
||||
total_qty = total_qty + exercise_paragraphmatch_qty
|
||||
|
||||
response["exercises"]["exercise_" + str(i)] = gen_reading_passage_1(exercise_topic, exercise_difficulty,
|
||||
exercises, exercise_qty_q, exercise_id)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "reading"
|
||||
|
||||
exercise_id = exercise_id + total_qty
|
||||
elif exercise_type == CustomLevelExerciseTypes.READING_2.value:
|
||||
exercises = []
|
||||
exercise_qty_q = queue.Queue()
|
||||
total_qty = 0
|
||||
if exercise_fillblanks_qty != -1:
|
||||
exercises.append('fillBlanks')
|
||||
exercise_qty_q.put(exercise_fillblanks_qty)
|
||||
total_qty = total_qty + exercise_fillblanks_qty
|
||||
if exercise_writeblanks_qty != -1:
|
||||
exercises.append('writeBlanks')
|
||||
exercise_qty_q.put(exercise_writeblanks_qty)
|
||||
total_qty = total_qty + exercise_writeblanks_qty
|
||||
if exercise_truefalse_qty != -1:
|
||||
exercises.append('trueFalse')
|
||||
exercise_qty_q.put(exercise_truefalse_qty)
|
||||
total_qty = total_qty + exercise_truefalse_qty
|
||||
if exercise_paragraphmatch_qty != -1:
|
||||
exercises.append('paragraphMatch')
|
||||
exercise_qty_q.put(exercise_paragraphmatch_qty)
|
||||
total_qty = total_qty + exercise_paragraphmatch_qty
|
||||
|
||||
response["exercises"]["exercise_" + str(i)] = gen_reading_passage_2(exercise_topic, exercise_difficulty,
|
||||
exercises, exercise_qty_q, exercise_id)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "reading"
|
||||
|
||||
exercise_id = exercise_id + total_qty
|
||||
elif exercise_type == CustomLevelExerciseTypes.READING_3.value:
|
||||
exercises = []
|
||||
exercise_qty_q = queue.Queue()
|
||||
total_qty = 0
|
||||
if exercise_fillblanks_qty != -1:
|
||||
exercises.append('fillBlanks')
|
||||
exercise_qty_q.put(exercise_fillblanks_qty)
|
||||
total_qty = total_qty + exercise_fillblanks_qty
|
||||
if exercise_writeblanks_qty != -1:
|
||||
exercises.append('writeBlanks')
|
||||
exercise_qty_q.put(exercise_writeblanks_qty)
|
||||
total_qty = total_qty + exercise_writeblanks_qty
|
||||
if exercise_truefalse_qty != -1:
|
||||
exercises.append('trueFalse')
|
||||
exercise_qty_q.put(exercise_truefalse_qty)
|
||||
total_qty = total_qty + exercise_truefalse_qty
|
||||
if exercise_paragraphmatch_qty != -1:
|
||||
exercises.append('paragraphMatch')
|
||||
exercise_qty_q.put(exercise_paragraphmatch_qty)
|
||||
total_qty = total_qty + exercise_paragraphmatch_qty
|
||||
if exercise_ideamatch_qty != -1:
|
||||
exercises.append('ideaMatch')
|
||||
exercise_qty_q.put(exercise_ideamatch_qty)
|
||||
total_qty = total_qty + exercise_ideamatch_qty
|
||||
|
||||
response["exercises"]["exercise_" + str(i)] = gen_reading_passage_3(exercise_topic, exercise_difficulty,
|
||||
exercises, exercise_qty_q, exercise_id)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "reading"
|
||||
|
||||
exercise_id = exercise_id + total_qty
|
||||
elif exercise_type == CustomLevelExerciseTypes.LISTENING_1.value:
|
||||
exercises = []
|
||||
exercise_qty_q = queue.Queue()
|
||||
total_qty = 0
|
||||
if exercise_mc_qty != -1:
|
||||
exercises.append('multipleChoice')
|
||||
exercise_qty_q.put(exercise_mc_qty)
|
||||
total_qty = total_qty + exercise_mc_qty
|
||||
if exercise_writeblanksquestions_qty != -1:
|
||||
exercises.append('writeBlanksQuestions')
|
||||
exercise_qty_q.put(exercise_writeblanksquestions_qty)
|
||||
total_qty = total_qty + exercise_writeblanksquestions_qty
|
||||
if exercise_writeblanksfill_qty != -1:
|
||||
exercises.append('writeBlanksFill')
|
||||
exercise_qty_q.put(exercise_writeblanksfill_qty)
|
||||
total_qty = total_qty + exercise_writeblanksfill_qty
|
||||
if exercise_writeblanksform_qty != -1:
|
||||
exercises.append('writeBlanksForm')
|
||||
exercise_qty_q.put(exercise_writeblanksform_qty)
|
||||
total_qty = total_qty + exercise_writeblanksform_qty
|
||||
|
||||
response["exercises"]["exercise_" + str(i)] = gen_listening_section_1(exercise_topic, exercise_difficulty,
|
||||
exercises, exercise_qty_q,
|
||||
exercise_id)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "listening"
|
||||
|
||||
exercise_id = exercise_id + total_qty
|
||||
elif exercise_type == CustomLevelExerciseTypes.LISTENING_2.value:
|
||||
exercises = []
|
||||
exercise_qty_q = queue.Queue()
|
||||
total_qty = 0
|
||||
if exercise_mc_qty != -1:
|
||||
exercises.append('multipleChoice')
|
||||
exercise_qty_q.put(exercise_mc_qty)
|
||||
total_qty = total_qty + exercise_mc_qty
|
||||
if exercise_writeblanksquestions_qty != -1:
|
||||
exercises.append('writeBlanksQuestions')
|
||||
exercise_qty_q.put(exercise_writeblanksquestions_qty)
|
||||
total_qty = total_qty + exercise_writeblanksquestions_qty
|
||||
|
||||
response["exercises"]["exercise_" + str(i)] = gen_listening_section_2(exercise_topic, exercise_difficulty,
|
||||
exercises, exercise_qty_q,
|
||||
exercise_id)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "listening"
|
||||
|
||||
exercise_id = exercise_id + total_qty
|
||||
elif exercise_type == CustomLevelExerciseTypes.LISTENING_3.value:
|
||||
exercises = []
|
||||
exercise_qty_q = queue.Queue()
|
||||
total_qty = 0
|
||||
if exercise_mc3_qty != -1:
|
||||
exercises.append('multipleChoice3Options')
|
||||
exercise_qty_q.put(exercise_mc3_qty)
|
||||
total_qty = total_qty + exercise_mc3_qty
|
||||
if exercise_writeblanksquestions_qty != -1:
|
||||
exercises.append('writeBlanksQuestions')
|
||||
exercise_qty_q.put(exercise_writeblanksquestions_qty)
|
||||
total_qty = total_qty + exercise_writeblanksquestions_qty
|
||||
|
||||
response["exercises"]["exercise_" + str(i)] = gen_listening_section_3(exercise_topic, exercise_difficulty,
|
||||
exercises, exercise_qty_q,
|
||||
exercise_id)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "listening"
|
||||
|
||||
exercise_id = exercise_id + total_qty
|
||||
elif exercise_type == CustomLevelExerciseTypes.LISTENING_4.value:
|
||||
exercises = []
|
||||
exercise_qty_q = queue.Queue()
|
||||
total_qty = 0
|
||||
if exercise_mc_qty != -1:
|
||||
exercises.append('multipleChoice')
|
||||
exercise_qty_q.put(exercise_mc_qty)
|
||||
total_qty = total_qty + exercise_mc_qty
|
||||
if exercise_writeblanksquestions_qty != -1:
|
||||
exercises.append('writeBlanksQuestions')
|
||||
exercise_qty_q.put(exercise_writeblanksquestions_qty)
|
||||
total_qty = total_qty + exercise_writeblanksquestions_qty
|
||||
if exercise_writeblanksfill_qty != -1:
|
||||
exercises.append('writeBlanksFill')
|
||||
exercise_qty_q.put(exercise_writeblanksfill_qty)
|
||||
total_qty = total_qty + exercise_writeblanksfill_qty
|
||||
if exercise_writeblanksform_qty != -1:
|
||||
exercises.append('writeBlanksForm')
|
||||
exercise_qty_q.put(exercise_writeblanksform_qty)
|
||||
total_qty = total_qty + exercise_writeblanksform_qty
|
||||
|
||||
response["exercises"]["exercise_" + str(i)] = gen_listening_section_4(exercise_topic, exercise_difficulty,
|
||||
exercises, exercise_qty_q,
|
||||
exercise_id)
|
||||
response["exercises"]["exercise_" + str(i)]["type"] = "listening"
|
||||
|
||||
exercise_id = exercise_id + total_qty
|
||||
|
||||
return response
|
||||
|
||||
|
||||
@app.route('/grade_short_answers', methods=['POST'])
|
||||
@jwt_required()
|
||||
def grade_short_answers():
|
||||
@@ -1670,7 +1644,8 @@ def grade_short_answers():
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": 'Grade these answers according to the text content and write a correct answer if they are wrong. Text, questions and answers:\n ' + str(data)
|
||||
"content": 'Grade these answers according to the text content and write a correct answer if they are '
|
||||
'wrong. Text, questions and answers:\n ' + str(data)
|
||||
|
||||
}
|
||||
]
|
||||
@@ -1680,6 +1655,7 @@ def grade_short_answers():
|
||||
except Exception as e:
|
||||
return str(e)
|
||||
|
||||
|
||||
@app.route('/fetch_tips', methods=['POST'])
|
||||
@jwt_required()
|
||||
def fetch_answer_tips():
|
||||
|
||||
@@ -15,19 +15,19 @@ from helper.speech_to_text_helper import has_x_words
|
||||
nltk.download('words')
|
||||
|
||||
|
||||
def gen_reading_passage_1(topic, req_exercises, difficulty):
|
||||
def gen_reading_passage_1(topic, difficulty, req_exercises, number_of_exercises_q=queue.Queue(), start_id=1):
|
||||
if (len(req_exercises) == 0):
|
||||
req_exercises = random.sample(READING_EXERCISE_TYPES, 2)
|
||||
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_READING_PASSAGE_1_EXERCISES, len(req_exercises))
|
||||
if (number_of_exercises_q.empty()):
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_READING_PASSAGE_1_EXERCISES, len(req_exercises))
|
||||
|
||||
passage = generate_reading_passage_1_text(topic)
|
||||
if passage == "":
|
||||
return gen_reading_passage_1(topic, req_exercises, difficulty)
|
||||
start_id = 1
|
||||
return gen_reading_passage_1(topic, difficulty, req_exercises, number_of_exercises_q, start_id)
|
||||
exercises = generate_reading_exercises(passage["text"], req_exercises, number_of_exercises_q, start_id, difficulty)
|
||||
if contains_empty_dict(exercises):
|
||||
return gen_reading_passage_1(topic, req_exercises, difficulty)
|
||||
return gen_reading_passage_1(topic, difficulty, req_exercises, number_of_exercises_q, start_id)
|
||||
return {
|
||||
"exercises": exercises,
|
||||
"text": {
|
||||
@@ -38,19 +38,19 @@ def gen_reading_passage_1(topic, req_exercises, difficulty):
|
||||
}
|
||||
|
||||
|
||||
def gen_reading_passage_2(topic, req_exercises, difficulty):
|
||||
def gen_reading_passage_2(topic, difficulty, req_exercises, number_of_exercises_q=queue.Queue(), start_id=14):
|
||||
if (len(req_exercises) == 0):
|
||||
req_exercises = random.sample(READING_EXERCISE_TYPES, 2)
|
||||
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_READING_PASSAGE_2_EXERCISES, len(req_exercises))
|
||||
if (number_of_exercises_q.empty()):
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_READING_PASSAGE_2_EXERCISES, len(req_exercises))
|
||||
|
||||
passage = generate_reading_passage_2_text(topic)
|
||||
if passage == "":
|
||||
return gen_reading_passage_2(topic, req_exercises, difficulty)
|
||||
start_id = 14
|
||||
return gen_reading_passage_2(topic, difficulty, req_exercises, number_of_exercises_q, start_id)
|
||||
exercises = generate_reading_exercises(passage["text"], req_exercises, number_of_exercises_q, start_id, difficulty)
|
||||
if contains_empty_dict(exercises):
|
||||
return gen_reading_passage_2(topic, req_exercises, difficulty)
|
||||
return gen_reading_passage_2(topic, difficulty, req_exercises, number_of_exercises_q, start_id)
|
||||
return {
|
||||
"exercises": exercises,
|
||||
"text": {
|
||||
@@ -61,19 +61,19 @@ def gen_reading_passage_2(topic, req_exercises, difficulty):
|
||||
}
|
||||
|
||||
|
||||
def gen_reading_passage_3(topic, req_exercises, difficulty):
|
||||
def gen_reading_passage_3(topic, difficulty, req_exercises, number_of_exercises_q=queue.Queue(), start_id=27):
|
||||
if (len(req_exercises) == 0):
|
||||
req_exercises = random.sample(READING_EXERCISE_TYPES, 2)
|
||||
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_READING_PASSAGE_3_EXERCISES, len(req_exercises))
|
||||
if (number_of_exercises_q.empty()):
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_READING_PASSAGE_3_EXERCISES, len(req_exercises))
|
||||
|
||||
passage = generate_reading_passage_3_text(topic)
|
||||
if passage == "":
|
||||
return gen_reading_passage_3(topic, req_exercises, difficulty)
|
||||
start_id = 27
|
||||
return gen_reading_passage_3(topic, difficulty, req_exercises, number_of_exercises_q, start_id)
|
||||
exercises = generate_reading_exercises(passage["text"], req_exercises, number_of_exercises_q, start_id, difficulty)
|
||||
if contains_empty_dict(exercises):
|
||||
return gen_reading_passage_3(topic, req_exercises, difficulty)
|
||||
return gen_reading_passage_3(topic, difficulty, req_exercises, number_of_exercises_q, start_id)
|
||||
return {
|
||||
"exercises": exercises,
|
||||
"text": {
|
||||
@@ -865,7 +865,8 @@ def gen_idea_match_exercise(text: str, quantity: int, start_id):
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
'From the text extract ' + str(quantity) + ' ideas, theories, opinions and who they are from. The text: ' + str(text))
|
||||
'From the text extract ' + str(
|
||||
quantity) + ' ideas, theories, opinions and who they are from. The text: ' + str(text))
|
||||
|
||||
}
|
||||
]
|
||||
@@ -882,6 +883,7 @@ def gen_idea_match_exercise(text: str, quantity: int, start_id):
|
||||
"type": "matchSentences"
|
||||
}
|
||||
|
||||
|
||||
def build_options(ideas):
|
||||
options = []
|
||||
letters = iter(string.ascii_uppercase)
|
||||
@@ -892,6 +894,7 @@ def build_options(ideas):
|
||||
})
|
||||
return options
|
||||
|
||||
|
||||
def build_sentences(ideas, start_id):
|
||||
sentences = []
|
||||
letters = iter(string.ascii_uppercase)
|
||||
@@ -906,6 +909,7 @@ def build_sentences(ideas, start_id):
|
||||
sentence["id"] = i
|
||||
return sentences
|
||||
|
||||
|
||||
def assign_letters_to_paragraphs(paragraphs):
|
||||
result = []
|
||||
letters = iter(string.ascii_uppercase)
|
||||
@@ -1272,7 +1276,8 @@ def replace_exercise_if_exists(all_exams, current_exercise, current_exam, seen_k
|
||||
current_exercise["options"])
|
||||
for exercise in exercise_dict.get("exercises", [])[0]["questions"]
|
||||
):
|
||||
return replace_exercise_if_exists(all_exams, generate_single_mc_level_question(), current_exam, seen_keys)
|
||||
return replace_exercise_if_exists(all_exams, generate_single_mc_level_question(), current_exam,
|
||||
seen_keys)
|
||||
return current_exercise, seen_keys
|
||||
|
||||
|
||||
@@ -1302,7 +1307,8 @@ def replace_blank_space_exercise_if_exists_utas(all_exams, current_exercise, cur
|
||||
key = (current_exercise['prompt'], tuple(sorted(option['text'] for option in current_exercise['options'])))
|
||||
# Check if the key is in the set
|
||||
if key in seen_keys:
|
||||
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_blank_space_level_question(), current_exam, seen_keys)
|
||||
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_blank_space_level_question(), current_exam,
|
||||
seen_keys)
|
||||
else:
|
||||
seen_keys.add(key)
|
||||
|
||||
@@ -1313,7 +1319,8 @@ def replace_blank_space_exercise_if_exists_utas(all_exams, current_exercise, cur
|
||||
current_exercise["options"])
|
||||
for exercise in exam.get("questions", [])
|
||||
):
|
||||
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_blank_space_level_question(), current_exam,
|
||||
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_blank_space_level_question(),
|
||||
current_exam,
|
||||
seen_keys)
|
||||
return current_exercise, seen_keys
|
||||
|
||||
@@ -1323,7 +1330,8 @@ def replace_underlined_exercise_if_exists_utas(all_exams, current_exercise, curr
|
||||
key = (current_exercise['prompt'], tuple(sorted(option['text'] for option in current_exercise['options'])))
|
||||
# Check if the key is in the set
|
||||
if key in seen_keys:
|
||||
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_underlined_level_question(), current_exam, seen_keys)
|
||||
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_underlined_level_question(), current_exam,
|
||||
seen_keys)
|
||||
else:
|
||||
seen_keys.add(key)
|
||||
|
||||
@@ -1334,7 +1342,8 @@ def replace_underlined_exercise_if_exists_utas(all_exams, current_exercise, curr
|
||||
current_exercise["options"])
|
||||
for exercise in exam.get("questions", [])
|
||||
):
|
||||
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_underlined_level_question(), current_exam,
|
||||
return replace_exercise_if_exists_utas(all_exams, generate_single_mc_underlined_level_question(),
|
||||
current_exam,
|
||||
seen_keys)
|
||||
return current_exercise, seen_keys
|
||||
|
||||
@@ -1376,8 +1385,8 @@ def generate_single_mc_blank_space_level_question():
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": ('Generate 1 multiple choice blank space question of 4 options for an english level exam, it can be easy, '
|
||||
'intermediate or advanced.')
|
||||
"content": ('Generate 1 multiple choice blank space question of 4 options for an english level exam, '
|
||||
'it can be easy, intermediate or advanced.')
|
||||
|
||||
}
|
||||
]
|
||||
@@ -1401,8 +1410,8 @@ def generate_single_mc_underlined_level_question():
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": ('Generate 1 multiple choice blank space question of 4 options for an english level exam, it can be easy, '
|
||||
'intermediate or advanced.')
|
||||
"content": ('Generate 1 multiple choice blank space question of 4 options for an english level exam, '
|
||||
'it can be easy, intermediate or advanced.')
|
||||
|
||||
},
|
||||
{
|
||||
@@ -1469,9 +1478,9 @@ def gen_multiple_choice_blank_space_utas(quantity: int, start_id: int, all_exams
|
||||
if all_exams is not None:
|
||||
seen_keys = set()
|
||||
for i in range(len(question["questions"])):
|
||||
question["questions"][i], seen_keys = replace_blank_space_exercise_if_exists_utas(all_exams, question["questions"][i],
|
||||
question,
|
||||
seen_keys)
|
||||
question["questions"][i], seen_keys = (
|
||||
replace_blank_space_exercise_if_exists_utas(all_exams, question["questions"][i], question,
|
||||
seen_keys))
|
||||
response = fix_exercise_ids(question, start_id)
|
||||
response["questions"] = randomize_mc_options_order(response["questions"])
|
||||
return response
|
||||
@@ -1546,11 +1555,9 @@ def gen_multiple_choice_underlined_utas(quantity: int, start_id: int, all_exams=
|
||||
if all_exams is not None:
|
||||
seen_keys = set()
|
||||
for i in range(len(question["questions"])):
|
||||
question["questions"][i], seen_keys = replace_underlined_exercise_if_exists_utas(all_exams,
|
||||
question["questions"][
|
||||
i],
|
||||
question,
|
||||
seen_keys)
|
||||
question["questions"][i], seen_keys = (
|
||||
replace_underlined_exercise_if_exists_utas(all_exams, question["questions"][i], question,
|
||||
seen_keys))
|
||||
response = fix_exercise_ids(question, start_id)
|
||||
response["questions"] = randomize_mc_options_order(response["questions"])
|
||||
return response
|
||||
@@ -1765,7 +1772,8 @@ def generate_level_mc(start_id: int, quantity: int, all_questions=None):
|
||||
if all_questions is not None:
|
||||
seen_keys = set()
|
||||
for i in range(len(question["questions"])):
|
||||
question["questions"][i], seen_keys = replace_exercise_if_exists_utas(all_questions, question["questions"][i],
|
||||
question["questions"][i], seen_keys = replace_exercise_if_exists_utas(all_questions,
|
||||
question["questions"][i],
|
||||
question,
|
||||
seen_keys)
|
||||
response = fix_exercise_ids(question, start_id)
|
||||
@@ -1791,3 +1799,293 @@ def randomize_mc_options_order(questions):
|
||||
question['solution'] = option['id']
|
||||
|
||||
return questions
|
||||
|
||||
|
||||
def gen_writing_task_1(topic, difficulty):
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": ('You are a helpful assistant designed to output JSON on this format: '
|
||||
'{"prompt": "prompt content"}')
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": ('Craft a prompt for an IELTS Writing Task 1 General Training exercise that instructs the '
|
||||
'student to compose a letter. The prompt should present a specific scenario or situation, '
|
||||
'based on the topic of "' + topic + '", requiring the student to provide information, '
|
||||
'advice, or instructions within the letter. '
|
||||
'Make sure that the generated prompt is '
|
||||
'of ' + difficulty + 'difficulty and does not contain '
|
||||
'forbidden subjects in muslim '
|
||||
'countries.')
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": 'The prompt should end with "In the letter you should" followed by 3 bullet points of what '
|
||||
'the answer should include.'
|
||||
}
|
||||
]
|
||||
token_count = count_total_tokens(messages)
|
||||
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, "prompt",
|
||||
GEN_QUESTION_TEMPERATURE)
|
||||
return {
|
||||
"question": add_newline_before_hyphen(response["prompt"].strip()),
|
||||
"difficulty": difficulty,
|
||||
"topic": topic
|
||||
}
|
||||
|
||||
|
||||
def add_newline_before_hyphen(s):
|
||||
return s.replace(" -", "\n-")
|
||||
|
||||
|
||||
def gen_writing_task_2(topic, difficulty):
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": ('You are a helpful assistant designed to output JSON on this format: '
|
||||
'{"prompt": "prompt content"}')
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
'Craft a comprehensive question of ' + difficulty + 'difficulty like the ones for IELTS Writing '
|
||||
'Task 2 General Training that directs the '
|
||||
'candidate'
|
||||
'to delve into an in-depth analysis of '
|
||||
'contrasting perspectives on the topic '
|
||||
'of "' + topic + '". The candidate should be '
|
||||
'asked to discuss the '
|
||||
'strengths and weaknesses of '
|
||||
'both viewpoints.')
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": 'The question should lead to an answer with either "theories", "complicated information" or '
|
||||
'be "very descriptive" on the topic.'
|
||||
}
|
||||
]
|
||||
token_count = count_total_tokens(messages)
|
||||
response = make_openai_call(GPT_4_O, messages, token_count, "prompt", GEN_QUESTION_TEMPERATURE)
|
||||
return {
|
||||
"question": response["prompt"].strip(),
|
||||
"difficulty": difficulty,
|
||||
"topic": topic
|
||||
}
|
||||
|
||||
|
||||
def gen_speaking_part_1(first_topic: str, second_topic: str, difficulty):
|
||||
json_format = {
|
||||
"first_topic": "topic 1",
|
||||
"second_topic": "topic 2",
|
||||
"questions": [
|
||||
"Introductory question about the first topic, starting the topic with 'Let's talk about x' and then the "
|
||||
"question.",
|
||||
"Follow up question about the first topic",
|
||||
"Follow up question about the first topic",
|
||||
"Question about second topic",
|
||||
"Follow up question about the second topic",
|
||||
]
|
||||
}
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
'You are a helpful assistant designed to output JSON on this format: ' + str(json_format))
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
'Craft 5 simple and single questions of easy difficulty for IELTS Speaking Part 1 '
|
||||
'that encourages candidates to delve deeply into '
|
||||
'personal experiences, preferences, or insights on the topic '
|
||||
'of "' + first_topic + '" and the topic of "' + second_topic + '". '
|
||||
'Make sure that the generated '
|
||||
'question'
|
||||
'does not contain forbidden '
|
||||
'subjects in'
|
||||
'muslim countries.')
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": 'The questions should lead to the usage of 4 verb tenses (present perfect, present, '
|
||||
'past and future).'
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": 'They must be 1 single question each and not be double-barreled questions.'
|
||||
|
||||
}
|
||||
]
|
||||
token_count = count_total_tokens(messages)
|
||||
response = make_openai_call(GPT_4_O, messages, token_count, ["first_topic"],
|
||||
GEN_QUESTION_TEMPERATURE)
|
||||
response["type"] = 1
|
||||
response["difficulty"] = difficulty
|
||||
return response
|
||||
|
||||
|
||||
def gen_speaking_part_2(topic: str, difficulty):
|
||||
json_format = {
|
||||
"topic": "topic",
|
||||
"question": "question",
|
||||
"prompts": [
|
||||
"prompt_1",
|
||||
"prompt_2",
|
||||
"prompt_3"
|
||||
],
|
||||
"suffix": "And explain why..."
|
||||
}
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": 'You are a helpful assistant designed to output JSON on this format: ' + str(json_format)
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
'Create a question of medium difficulty for IELTS Speaking Part 2 '
|
||||
'that encourages candidates to narrate a '
|
||||
'personal experience or story related to the topic '
|
||||
'of "' + topic + '". Include 3 prompts that '
|
||||
'guide the candidate to describe '
|
||||
'specific aspects of the experience, '
|
||||
'such as details about the situation, '
|
||||
'their actions, and the reasons it left a '
|
||||
'lasting impression. Make sure that the '
|
||||
'generated question does not contain '
|
||||
'forbidden subjects in muslim countries.')
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": 'The prompts must not be questions. Also include a suffix like the ones in the IELTS exams '
|
||||
'that start with "And explain why".'
|
||||
}
|
||||
]
|
||||
token_count = count_total_tokens(messages)
|
||||
response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
|
||||
response["type"] = 2
|
||||
response["difficulty"] = difficulty
|
||||
response["topic"] = topic
|
||||
return response
|
||||
|
||||
|
||||
def gen_speaking_part_3(topic: str, difficulty):
|
||||
json_format = {
|
||||
"topic": "topic",
|
||||
"questions": [
|
||||
"Introductory question about the topic.",
|
||||
"Follow up question about the topic",
|
||||
"Follow up question about the topic",
|
||||
"Follow up question about the topic",
|
||||
"Follow up question about the topic"
|
||||
]
|
||||
}
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
'You are a helpful assistant designed to output JSON on this format: ' + str(json_format))
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
'Formulate a set of 5 single questions of hard difficulty for IELTS Speaking Part 3 that encourage candidates to engage in a '
|
||||
'meaningful discussion on the topic of "' + topic + '". Provide inquiries, ensuring '
|
||||
'they explore various aspects, perspectives, and implications related to the topic.'
|
||||
'Make sure that the generated question does not contain forbidden subjects in muslim countries.')
|
||||
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": 'They must be 1 single question each and not be double-barreled questions.'
|
||||
|
||||
}
|
||||
]
|
||||
token_count = count_total_tokens(messages)
|
||||
response = make_openai_call(GPT_4_O, messages, token_count, GEN_FIELDS, GEN_QUESTION_TEMPERATURE)
|
||||
# Remove the numbers from the questions only if the string starts with a number
|
||||
response["questions"] = [re.sub(r"^\d+\.\s*", "", question) if re.match(r"^\d+\.", question) else question for
|
||||
question in response["questions"]]
|
||||
response["type"] = 3
|
||||
response["difficulty"] = difficulty
|
||||
response["topic"] = topic
|
||||
return response
|
||||
|
||||
|
||||
def gen_listening_section_1(topic, difficulty, req_exercises, number_of_exercises_q=queue.Queue(), start_id=1):
|
||||
if (len(req_exercises) == 0):
|
||||
req_exercises = random.sample(LISTENING_1_EXERCISE_TYPES, 1)
|
||||
|
||||
if (number_of_exercises_q.empty()):
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_1_EXERCISES, len(req_exercises))
|
||||
|
||||
processed_conversation = generate_listening_1_conversation(topic)
|
||||
|
||||
exercises = generate_listening_conversation_exercises(parse_conversation(processed_conversation),
|
||||
req_exercises,
|
||||
number_of_exercises_q,
|
||||
start_id, difficulty)
|
||||
return {
|
||||
"exercises": exercises,
|
||||
"text": processed_conversation,
|
||||
"difficulty": difficulty
|
||||
}
|
||||
|
||||
|
||||
def gen_listening_section_2(topic, difficulty, req_exercises, number_of_exercises_q=queue.Queue(), start_id=11):
|
||||
if (len(req_exercises) == 0):
|
||||
req_exercises = random.sample(LISTENING_2_EXERCISE_TYPES, 2)
|
||||
|
||||
if (number_of_exercises_q.empty()):
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_2_EXERCISES, len(req_exercises))
|
||||
|
||||
monologue = generate_listening_2_monologue(topic)
|
||||
|
||||
exercises = generate_listening_monologue_exercises(str(monologue), req_exercises, number_of_exercises_q,
|
||||
start_id, difficulty)
|
||||
return {
|
||||
"exercises": exercises,
|
||||
"text": monologue,
|
||||
"difficulty": difficulty
|
||||
}
|
||||
|
||||
|
||||
def gen_listening_section_3(topic, difficulty, req_exercises, number_of_exercises_q=queue.Queue(), start_id=21):
|
||||
if (len(req_exercises) == 0):
|
||||
req_exercises = random.sample(LISTENING_3_EXERCISE_TYPES, 1)
|
||||
|
||||
if (number_of_exercises_q.empty()):
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_3_EXERCISES, len(req_exercises))
|
||||
|
||||
processed_conversation = generate_listening_3_conversation(topic)
|
||||
|
||||
exercises = generate_listening_conversation_exercises(parse_conversation(processed_conversation), req_exercises,
|
||||
number_of_exercises_q,
|
||||
start_id, difficulty)
|
||||
return {
|
||||
"exercises": exercises,
|
||||
"text": processed_conversation,
|
||||
"difficulty": difficulty
|
||||
}
|
||||
|
||||
|
||||
def gen_listening_section_4(topic, difficulty, req_exercises, number_of_exercises_q=queue.Queue(), start_id=31):
|
||||
if (len(req_exercises) == 0):
|
||||
req_exercises = random.sample(LISTENING_EXERCISE_TYPES, 2)
|
||||
|
||||
if (number_of_exercises_q.empty()):
|
||||
number_of_exercises_q = divide_number_into_parts(TOTAL_LISTENING_SECTION_4_EXERCISES, len(req_exercises))
|
||||
|
||||
monologue = generate_listening_4_monologue(topic)
|
||||
|
||||
exercises = generate_listening_monologue_exercises(str(monologue), req_exercises, number_of_exercises_q,
|
||||
start_id, difficulty)
|
||||
return {
|
||||
"exercises": exercises,
|
||||
"text": monologue,
|
||||
"difficulty": difficulty
|
||||
}
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
import json
|
||||
from datetime import datetime
|
||||
from logging import getLogger
|
||||
|
||||
@@ -24,7 +25,8 @@ class TrainingContentService:
|
||||
self._logger = getLogger(__name__)
|
||||
self._llm = openai
|
||||
|
||||
def get_tips(self, stats):
|
||||
def get_tips(self, training_content):
|
||||
user, stats = training_content["userID"], training_content["stats"]
|
||||
exam_data, exam_map = self._sort_out_solutions(stats)
|
||||
training_content = self._get_exam_details_and_tips(exam_data)
|
||||
tips = self._query_kb(training_content.queries)
|
||||
@@ -39,7 +41,8 @@ class TrainingContentService:
|
||||
'created_at': int(datetime.now().timestamp() * 1000),
|
||||
**exam_map,
|
||||
**usefull_tips.dict(),
|
||||
**weak_areas
|
||||
**weak_areas,
|
||||
"user": user
|
||||
}
|
||||
doc_ref = self._db.collection('training').add(training_doc)
|
||||
return {
|
||||
@@ -70,7 +73,6 @@ class TrainingContentService:
|
||||
|
||||
tips = {"tips": []}
|
||||
for query in queries:
|
||||
print(f"{query.category} {query.text}")
|
||||
if query.category == "words":
|
||||
tips["tips"].extend(
|
||||
self._training_content_module.query_knowledge_base(query.text, "word_link")
|
||||
@@ -104,7 +106,16 @@ class TrainingContentService:
|
||||
' with sentence structure and punctuation.", the "queries" field is where you will write queries '
|
||||
'for tips that will be displayed to the student, the category attribute is a collection of '
|
||||
'embeddings and the text will be the text used to query the knowledge base. The categories are '
|
||||
f'the following [{", ".join(self.TOOLS)}].'
|
||||
f'the following [{", ".join(self.TOOLS)}]. The exam data will be a json where the key of the field '
|
||||
'"exams" is the exam id, an exam can be composed of multiple modules or single modules. The student'
|
||||
' will see your response so refrain from using phrasing like "The student" did x, y and z. If the '
|
||||
'field "answer" in a question is an empty array "[]", then the student didn\'t answer any question '
|
||||
'and you must address that in your response. Also questions aren\'t modules, the only modules are: '
|
||||
'level, speaking, writing, reading and listening. The details array needs to be tailored to the '
|
||||
'exam attempt, even if you receive the same exam you must treat as different exams by their id.'
|
||||
'Don\'t make references to an exam by it\'s id, the GUI will handle that so the student knows '
|
||||
'which is the exam your comments and summary are referencing too. Even if the student hasn\'t '
|
||||
'submitted no answers for an exam, you must still fill the details structure addressing that fact.'
|
||||
)
|
||||
},
|
||||
{
|
||||
@@ -150,42 +161,68 @@ class TrainingContentService:
|
||||
def _sort_out_solutions(self, stats):
|
||||
grouped_stats = {}
|
||||
for stat in stats:
|
||||
exam_id = stat["exam"]
|
||||
session_key = f'{str(stat["date"])}-{stat["user"]}'
|
||||
module = stat["module"]
|
||||
if module not in grouped_stats:
|
||||
grouped_stats[module] = {}
|
||||
if exam_id not in grouped_stats[module]:
|
||||
grouped_stats[module][exam_id] = []
|
||||
grouped_stats[module][exam_id].append(stat)
|
||||
exam_id = stat["exam"]
|
||||
|
||||
if session_key not in grouped_stats:
|
||||
grouped_stats[session_key] = {}
|
||||
if module not in grouped_stats[session_key]:
|
||||
grouped_stats[session_key][module] = {
|
||||
"stats": [],
|
||||
"exam_id": exam_id
|
||||
}
|
||||
grouped_stats[session_key][module]["stats"].append(stat)
|
||||
|
||||
exercises = {}
|
||||
exam_map = {}
|
||||
for module, exams in grouped_stats.items():
|
||||
exercises[module] = {}
|
||||
for exam_id, stat_group in exams.items():
|
||||
exam = self._get_doc_by_id(module, exam_id)
|
||||
exercises[module][exam_id] = {"date": None, "exercises": [], "score": None}
|
||||
for session_key, modules in grouped_stats.items():
|
||||
exercises[session_key] = {}
|
||||
for module, module_stats in modules.items():
|
||||
exercises[session_key][module] = {}
|
||||
|
||||
exam_id = module_stats["exam_id"]
|
||||
if exam_id not in exercises[session_key][module]:
|
||||
exercises[session_key][module][exam_id] = {"date": None, "exercises": []}
|
||||
|
||||
exam_total_questions = 0
|
||||
exam_total_correct = 0
|
||||
for stat in stat_group:
|
||||
|
||||
for stat in module_stats["stats"]:
|
||||
exam_total_questions += stat["score"]["total"]
|
||||
exam_total_correct += stat["score"]["correct"]
|
||||
exercises[module][exam_id]["date"] = stat["date"]
|
||||
exercises[session_key][module][exam_id]["date"] = stat["date"]
|
||||
|
||||
if exam_id not in exam_map:
|
||||
exam_map[exam_id] = {"stat_ids": [], "score": 0}
|
||||
exam_map[exam_id]["stat_ids"].append(stat["id"])
|
||||
if session_key not in exam_map:
|
||||
exam_map[session_key] = {"stat_ids": [], "score": 0}
|
||||
exam_map[session_key]["stat_ids"].append(stat["id"])
|
||||
|
||||
exam = self._get_doc_by_id(module, exam_id)
|
||||
if module == "listening":
|
||||
exercises[module][exam_id]["exercises"].extend(self._get_listening_solutions(stat, exam))
|
||||
if module == "reading":
|
||||
exercises[module][exam_id]["exercises"].extend(self._get_reading_solutions(stat, exam))
|
||||
if module == "writing":
|
||||
exercises[module][exam_id]["exercises"].extend(self._get_writing_prompts_and_answers(stat, exam))
|
||||
exercises[session_key][module][exam_id]["exercises"].extend(
|
||||
self._get_listening_solutions(stat, exam))
|
||||
elif module == "reading":
|
||||
exercises[session_key][module][exam_id]["exercises"].extend(
|
||||
self._get_reading_solutions(stat, exam))
|
||||
elif module == "writing":
|
||||
exercises[session_key][module][exam_id]["exercises"].extend(
|
||||
self._get_writing_prompts_and_answers(stat, exam)
|
||||
)
|
||||
elif module == "speaking":
|
||||
exercises[session_key][module][exam_id]["exercises"].extend(
|
||||
self._get_speaking_solutions(stat, exam)
|
||||
)
|
||||
elif module == "level":
|
||||
exercises[session_key][module][exam_id]["exercises"].extend(
|
||||
self._get_level_solutions(stat, exam)
|
||||
)
|
||||
|
||||
exam_map[exam_id]["score"] = round((exam_total_correct / exam_total_questions) * 100)
|
||||
exam_map[exam_id]["module"] = module
|
||||
return exercises, exam_map
|
||||
exam_map[session_key]["score"] = round((exam_total_correct / exam_total_questions) * 100)
|
||||
exam_map[session_key]["module"] = module
|
||||
with open('exam_result.json', 'w') as file:
|
||||
json.dump({"exams": exercises}, file, indent=4)
|
||||
|
||||
return {"exams": exercises}, exam_map
|
||||
|
||||
def _get_writing_prompts_and_answers(self, stat, exam):
|
||||
result = []
|
||||
@@ -211,6 +248,54 @@ class TrainingContentService:
|
||||
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def _get_mc_question(exercise, stat):
|
||||
shuffle_maps = stat.get("shuffleMaps", [])
|
||||
answer = stat["solutions"] if len(shuffle_maps) == 0 else []
|
||||
if len(shuffle_maps) != 0:
|
||||
for solution in stat["solutions"]:
|
||||
shuffle_map = [
|
||||
item["map"] for item in shuffle_maps
|
||||
if item["questionID"] == solution["question"]
|
||||
]
|
||||
answer.append({
|
||||
"question": solution["question"],
|
||||
"option": shuffle_map[solution["option"]]
|
||||
})
|
||||
return {
|
||||
"question": exercise["prompt"],
|
||||
"exercise": exercise["questions"],
|
||||
"answer": stat["solutions"]
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _swap_key_name(d, original_key, new_key):
|
||||
d[new_key] = d.pop(original_key)
|
||||
return d
|
||||
|
||||
def _get_level_solutions(self, stat, exam):
|
||||
result = []
|
||||
try:
|
||||
for part in exam["parts"]:
|
||||
for exercise in part["exercises"]:
|
||||
if exercise["id"] == stat["exercise"]:
|
||||
if stat["type"] == "fillBlanks":
|
||||
result.append({
|
||||
"prompt": exercise["prompt"],
|
||||
"template": exercise["text"],
|
||||
"words": exercise["words"],
|
||||
"solutions": exercise["solutions"],
|
||||
"answer": [
|
||||
self._swap_key_name(item, 'solution', 'option')
|
||||
for item in stat["solutions"]
|
||||
]
|
||||
})
|
||||
elif stat["type"] == "multipleChoice":
|
||||
result.append(self._get_mc_question(exercise, stat))
|
||||
except KeyError as e:
|
||||
self._logger.warning(f"Malformed stat object: {str(e)}")
|
||||
return result
|
||||
|
||||
def _get_listening_solutions(self, stat, exam):
|
||||
result = []
|
||||
try:
|
||||
@@ -224,16 +309,54 @@ class TrainingContentService:
|
||||
"solution": exercise["solutions"],
|
||||
"answer": stat["solutions"]
|
||||
})
|
||||
if stat["type"] == "multipleChoice":
|
||||
elif stat["type"] == "fillBlanks":
|
||||
result.append({
|
||||
"question": exercise["prompt"],
|
||||
"exercise": exercise["questions"],
|
||||
"template": exercise["text"],
|
||||
"words": exercise["words"],
|
||||
"solutions": exercise["solutions"],
|
||||
"answer": stat["solutions"]
|
||||
})
|
||||
elif stat["type"] == "multipleChoice":
|
||||
result.append(self._get_mc_question(exercise, stat))
|
||||
|
||||
except KeyError as e:
|
||||
self._logger.warning(f"Malformed stat object: {str(e)}")
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def _find_shuffle_map(shuffle_maps, question_id):
|
||||
return next((item["map"] for item in shuffle_maps if item["questionID"] == question_id), None)
|
||||
|
||||
def _get_speaking_solutions(self, stat, exam):
|
||||
result = {}
|
||||
try:
|
||||
result = {
|
||||
"comments": {
|
||||
key: value['comment'] for key, value in stat['solutions'][0]['evaluation']['task_response'].items()}
|
||||
,
|
||||
"exercises": {}
|
||||
}
|
||||
|
||||
for exercise in exam["exercises"]:
|
||||
if exercise["id"] == stat["exercise"]:
|
||||
if stat["type"] == "interactiveSpeaking":
|
||||
for i in range(len(exercise["prompts"])):
|
||||
result["exercises"][f"exercise_{i+1}"] = {
|
||||
"question": exercise["prompts"][i]["text"]
|
||||
}
|
||||
for i in range(len(exercise["prompts"])):
|
||||
answer = stat['solutions'][0]["evaluation"].get(f'transcript_{i+1}', '')
|
||||
result["exercises"][f"exercise_{i+1}"]["answer"] = answer
|
||||
elif stat["type"] == "speaking":
|
||||
result["exercises"]["exercise_1"] = {
|
||||
"question": exercise["text"],
|
||||
"answer": stat['solutions'][0]["evaluation"].get(f'transcript', '')
|
||||
}
|
||||
except KeyError as e:
|
||||
self._logger.warning(f"Malformed stat object: {str(e)}")
|
||||
return [result]
|
||||
|
||||
def _get_reading_solutions(self, stat, exam):
|
||||
result = []
|
||||
try:
|
||||
@@ -258,8 +381,13 @@ class TrainingContentService:
|
||||
"solutions": exercise["solutions"],
|
||||
"answer": stat["solutions"]
|
||||
})
|
||||
else:
|
||||
# match_sentences
|
||||
elif stat["type"] == "trueFalse":
|
||||
result.append({
|
||||
"text": text,
|
||||
"questions": exercise["questions"],
|
||||
"answer": stat["solutions"]
|
||||
})
|
||||
elif stat["type"] == "matchSentences":
|
||||
result.append({
|
||||
"text": text,
|
||||
"question": exercise["prompt"],
|
||||
|
||||
@@ -36,7 +36,7 @@ class UploadLevelService:
|
||||
FileHelper.remove_directory(f'./tmp/{path_id}')
|
||||
|
||||
if response:
|
||||
return response.dict(exclude_none=True)
|
||||
return self.fix_ids(response.dict(exclude_none=True))
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
@@ -378,3 +378,18 @@ class UploadLevelService:
|
||||
)
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def fix_ids(response):
|
||||
counter = 1
|
||||
for part in response["parts"]:
|
||||
for exercise in part["exercises"]:
|
||||
if exercise["type"] == "multipleChoice":
|
||||
for question in exercise["questions"]:
|
||||
question["id"] = counter
|
||||
counter += 1
|
||||
if exercise["type"] == "fillBlanks":
|
||||
for i in range(len(exercise["words"])):
|
||||
exercise["words"][i]["id"] = counter
|
||||
exercise["solutions"][i]["id"] = counter
|
||||
counter += 1
|
||||
return response
|
||||
Reference in New Issue
Block a user