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Improve speaking grading

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This commit is contained in:
Cristiano Ferreira
2024-03-24 01:33:21 +00:00
parent 6aba83f3bb
commit 7049fd86d4
1 changed files with 44 additions and 26 deletions
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70
app.py
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@@ -97,7 +97,8 @@ def get_listening_section_2_question():
app.logger.info("Generated monologue: " + str(monologue)) app.logger.info("Generated monologue: " + str(monologue))
start_id = 11 start_id = 11
exercises = generate_listening_monologue_exercises(monologue, req_exercises, number_of_exercises_q, start_id, difficulty) exercises = generate_listening_monologue_exercises(monologue, req_exercises, number_of_exercises_q, start_id,
difficulty)
return { return {
"exercises": exercises, "exercises": exercises,
"text": monologue, "text": monologue,
@@ -158,7 +159,8 @@ def get_listening_section_4_question():
app.logger.info("Generated monologue: " + str(monologue)) app.logger.info("Generated monologue: " + str(monologue))
start_id = 31 start_id = 31
exercises = generate_listening_monologue_exercises(monologue, req_exercises, number_of_exercises_q, start_id, difficulty) exercises = generate_listening_monologue_exercises(monologue, req_exercises, number_of_exercises_q, start_id,
difficulty)
return { return {
"exercises": exercises, "exercises": exercises,
"text": monologue, "text": monologue,
@@ -257,9 +259,9 @@ def get_writing_task_1_general_question():
try: try:
gen_wt1_question = "Craft a prompt for an IELTS Writing Task 1 General Training exercise that instructs the " \ gen_wt1_question = "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, " \ "student to compose a letter. The prompt should present a specific scenario or situation, " \
"based on the topic of '" + topic + "', "\ "based on the topic of '" + topic + "', " \
"requiring the student to provide information, advice, or instructions within the letter. " \ "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." "Make sure that the generated prompt is of " + difficulty + " difficulty and does not contain forbidden subjects in muslim countries."
token_count = count_tokens(gen_wt1_question)["n_tokens"] token_count = count_tokens(gen_wt1_question)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_wt1_question, token_count, None, response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_wt1_question, token_count, None,
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
@@ -317,10 +319,10 @@ def get_writing_task_2_general_question():
topic = request.args.get("topic", default=random.choice(mti_topics)) topic = request.args.get("topic", default=random.choice(mti_topics))
try: try:
gen_wt2_question = "Craft a comprehensive question of " + difficulty + " difficulty for IELTS Writing Task 2 General Training that directs the candidate " \ gen_wt2_question = "Craft a comprehensive question of " + difficulty + " difficulty 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 " \ "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, provide evidence or " \ "should be asked to discuss the strengths and weaknesses of both viewpoints, provide evidence or " \
"examples, and present a well-rounded argument before concluding with their personal opinion on the " \ "examples, and present a well-rounded argument before concluding with their personal opinion on the " \
"subject." "subject."
token_count = count_tokens(gen_wt2_question)["n_tokens"] token_count = count_tokens(gen_wt2_question)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_wt2_question, token_count, None, response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_wt2_question, token_count, None,
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
@@ -378,7 +380,7 @@ def grade_speaking_task_1():
token_count = count_tokens(message)["n_tokens"] token_count = count_tokens(message)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count, response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
["comment"], ["comment"],
GEN_QUESTION_TEMPERATURE) GRADING_TEMPERATURE)
perfect_answer_message = ("Provide a perfect answer according to ielts grading system to the following " perfect_answer_message = ("Provide a perfect answer according to ielts grading system to the following "
"Speaking Part 1 question: '" + question + "'") "Speaking Part 1 question: '" + question + "'")
token_count = count_tokens(perfect_answer_message)["n_tokens"] token_count = count_tokens(perfect_answer_message)["n_tokens"]
@@ -389,6 +391,11 @@ def grade_speaking_task_1():
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
response['transcript'] = answer response['transcript'] = answer
response['fixed_text'] = get_speaking_corrections(answer) response['fixed_text'] = get_speaking_corrections(answer)
if response["overall"] == "0.0" or response["overall"] == 0.0:
response["overall"] = round((response["task_response"]["Fluency and Coherence"] +
response["task_response"]["Lexical Resource"] + response["task_response"][
"Grammatical Range and Accuracy"] + response["task_response"][
"Pronunciation"]) / 4, 1)
return response return response
else: else:
return { return {
@@ -413,10 +420,10 @@ def get_speaking_task_1_question():
topic = request.args.get("topic", default=random.choice(mti_topics)) topic = request.args.get("topic", default=random.choice(mti_topics))
try: try:
gen_sp1_question = "Craft a thought-provoking question of " + difficulty + " difficulty for IELTS Speaking Part 1 that encourages candidates to delve deeply " \ gen_sp1_question = "Craft a thought-provoking question of " + difficulty + " difficulty for IELTS Speaking Part 1 that encourages candidates to delve deeply " \
"into personal experiences, preferences, or insights on the topic of '" + topic + "'. Instruct the candidate to offer " \ "into personal experiences, preferences, or insights on the topic of '" + topic + "'. Instruct the candidate to offer " \
"not only detailed descriptions but also provide nuanced explanations, examples, or anecdotes to enrich " \ "not only detailed descriptions but also provide nuanced explanations, examples, or anecdotes to enrich " \
"their response. Make sure that the generated question does not contain forbidden subjects in muslim countries." \ "their response. Make sure that the generated question does not contain forbidden subjects in muslim countries." \
"Provide your response in this json format: {'topic': 'topic','question': 'question'}" "Provide your response in this json format: {'topic': 'topic','question': 'question'}"
token_count = count_tokens(gen_sp1_question)["n_tokens"] token_count = count_tokens(gen_sp1_question)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_sp1_question, token_count, GEN_FIELDS, response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_sp1_question, token_count, GEN_FIELDS,
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
@@ -453,7 +460,7 @@ def grade_speaking_task_2():
token_count = count_tokens(message)["n_tokens"] token_count = count_tokens(message)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count, response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
["comment"], ["comment"],
GEN_QUESTION_TEMPERATURE) GRADING_TEMPERATURE)
perfect_answer_message = ("Provide a perfect answer according to ielts grading system to the following " perfect_answer_message = ("Provide a perfect answer according to ielts grading system to the following "
"Speaking Part 2 question: '" + question + "'") "Speaking Part 2 question: '" + question + "'")
token_count = count_tokens(perfect_answer_message)["n_tokens"] token_count = count_tokens(perfect_answer_message)["n_tokens"]
@@ -464,6 +471,12 @@ def grade_speaking_task_2():
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
response['transcript'] = answer response['transcript'] = answer
response['fixed_text'] = get_speaking_corrections(answer) response['fixed_text'] = get_speaking_corrections(answer)
if response["overall"] == "0.0" or response["overall"] == 0.0:
response["overall"] = round((response["task_response"]["Fluency and Coherence"] +
response["task_response"]["Lexical Resource"] + response["task_response"][
"Grammatical Range and Accuracy"] + response["task_response"][
"Pronunciation"]) / 4, 1)
return response return response
else: else:
return { return {
@@ -488,11 +501,11 @@ def get_speaking_task_2_question():
topic = request.args.get("topic", default=random.choice(mti_topics)) topic = request.args.get("topic", default=random.choice(mti_topics))
try: try:
gen_sp2_question = "Create a question of " + difficulty + " difficulty for IELTS Speaking Part 2 that encourages candidates to narrate a personal experience " \ gen_sp2_question = "Create a question of " + difficulty + " 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 " \ "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 " \ "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." \ "reasons it left a lasting impression. Make sure that the generated question does not contain forbidden subjects in muslim countries." \
"Provide your response in this json format: {'topic': 'topic','question': 'question', " \ "Provide your response in this json format: {'topic': 'topic','question': 'question', " \
"'prompts': ['prompt_1', 'prompt_2', 'prompt_3']}" "'prompts': ['prompt_1', 'prompt_2', 'prompt_3']}"
token_count = count_tokens(gen_sp2_question)["n_tokens"] token_count = count_tokens(gen_sp2_question)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_sp2_question, token_count, GEN_FIELDS, response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_sp2_question, token_count, GEN_FIELDS,
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
@@ -511,11 +524,11 @@ def get_speaking_task_3_question():
topic = request.args.get("topic", default=random.choice(mti_topics)) topic = request.args.get("topic", default=random.choice(mti_topics))
try: try:
gen_sp3_question = "Formulate a set of 3 questions of " + difficulty + " difficulty for IELTS Speaking Part 3 that encourage candidates to engage in a " \ gen_sp3_question = "Formulate a set of 3 questions of " + difficulty + " difficulty for IELTS Speaking Part 3 that encourage candidates to engage in a " \
"meaningful discussion on the topic of '" + topic + "'. Provide inquiries, ensuring " \ "meaningful discussion on the topic of '" + topic + "'. Provide inquiries, ensuring " \
"they explore various aspects, perspectives, and implications related to the topic. " \ "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." \ "Make sure that the generated question does not contain forbidden subjects in muslim countries." \
"Provide your response in this json format: {'topic': 'topic','questions': ['question', " \ "Provide your response in this json format: {'topic': 'topic','questions': ['question', " \
"'question', 'question']}" "'question', 'question']}"
token_count = count_tokens(gen_sp3_question)["n_tokens"] token_count = count_tokens(gen_sp3_question)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_sp3_question, token_count, GEN_FIELDS, response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_sp3_question, token_count, GEN_FIELDS,
GEN_QUESTION_TEMPERATURE) GEN_QUESTION_TEMPERATURE)
@@ -586,13 +599,18 @@ def grade_speaking_task_3():
token_count = count_tokens(message)["n_tokens"] token_count = count_tokens(message)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count, response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
["comment"], ["comment"],
GEN_QUESTION_TEMPERATURE) GRADING_TEMPERATURE)
for i, answer in enumerate(perfect_answers, start=1): for i, answer in enumerate(perfect_answers, start=1):
response['perfect_answer_' + str(i)] = answer response['perfect_answer_' + str(i)] = answer
for i, answer in enumerate(text_answers, start=1): for i, answer in enumerate(text_answers, start=1):
response['transcript_' + str(i)] = answer response['transcript_' + str(i)] = answer
response['fixed_text_' + str(i)] = get_speaking_corrections(answer) response['fixed_text_' + str(i)] = get_speaking_corrections(answer)
if response["overall"] == "0.0" or response["overall"] == 0.0:
response["overall"] = round((response["task_response"]["Fluency and Coherence"] + response["task_response"][
"Lexical Resource"] + response["task_response"]["Grammatical Range and Accuracy"] +
response["task_response"]["Pronunciation"]) / 4, 1)
return response return response
except Exception as e: except Exception as e:
return str(e), 400 return str(e), 400