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Add perfect answers to speaking

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This commit is contained in:
Cristiano Ferreira
2023-12-05 21:43:19 +00:00
parent 57d6e7ffde
commit 50c39e5f9c
2 changed files with 71 additions and 18 deletions
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87
app.py
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@@ -317,11 +317,25 @@ def grade_speaking_task_1():
download_firebase_file(FIREBASE_BUCKET, answer_firebase_path, sound_file_name)
answer = speech_to_text(sound_file_name)
if has_10_words(answer):
messages = get_grading_messages(QuestionType.SPEAKING_1, question, answer)
token_count = reduce(lambda count, item: count + count_tokens(item)['n_tokens'],
map(lambda x: x["content"], filter(lambda x: "content" in x, messages)), 0)
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, GRADING_FIELDS, GRADING_TEMPERATURE)
os.remove(sound_file_name)
message = (
"Grade this Speaking Part 1 answer according to ielts grading system and provide an elaborated "
"comment where you deep dive into what is wrong and right about the answer."
"Please assign a grade of 0 if the answer provided does not address the question."
"Provide your answer on the following json format: {'comment': 'comment about answer quality', 'overall': 0.0, "
"'task_response': {'Fluency and Coherence': 0.0, 'Lexical Resource': 0.0, 'Grammatical Range and Accuracy': 0.0, "
"'Pronunciation': 0.0}}\n The question was '" + question + "' and the answer was '" + answer + "'")
token_count = count_tokens(message)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
["comment"],
GEN_QUESTION_TEMPERATURE)
perfect_answer_message = ("Provide a perfect answer according to ielts grading system to the following "
"Speaking Part 1 question: '" + question + "'")
token_count = count_tokens(perfect_answer_message)["n_tokens"]
response['perfect_answer'] = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT,
perfect_answer_message,
token_count,
None,
GEN_QUESTION_TEMPERATURE)
return response
else:
return {
@@ -369,11 +383,25 @@ def grade_speaking_task_2():
download_firebase_file(FIREBASE_BUCKET, answer_firebase_path, sound_file_name)
answer = speech_to_text(sound_file_name)
if has_10_words(answer):
messages = get_grading_messages(QuestionType.SPEAKING_2, question, answer)
token_count = reduce(lambda count, item: count + count_tokens(item)['n_tokens'],
map(lambda x: x["content"], filter(lambda x: "content" in x, messages)), 0)
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, GRADING_FIELDS, GRADING_TEMPERATURE)
os.remove(sound_file_name)
message = (
"Grade this Speaking Part 2 answer according to ielts grading system and provide an elaborated "
"comment where you deep dive into what is wrong and right about the answer."
"Please assign a grade of 0 if the answer provided does not address the question."
"Provide your answer on the following json format: {'comment': 'comment about answer quality', 'overall': 0.0, "
"'task_response': {'Fluency and Coherence': 0.0, 'Lexical Resource': 0.0, 'Grammatical Range and Accuracy': 0.0, "
"'Pronunciation': 0.0}}\n The question was '" + question + "' and the answer was '" + answer + "'")
token_count = count_tokens(message)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
["comment"],
GEN_QUESTION_TEMPERATURE)
perfect_answer_message = ("Provide a perfect answer according to ielts grading system to the following "
"Speaking Part 2 question: '" + question + "'")
token_count = count_tokens(perfect_answer_message)["n_tokens"]
response['perfect_answer'] = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT,
perfect_answer_message,
token_count,
None,
GEN_QUESTION_TEMPERATURE)
return response
else:
return {
@@ -436,12 +464,12 @@ def grade_speaking_task_3():
try:
data = request.get_json()
answers = data.get('answers')
perfect_answers = []
for item in answers:
sound_file_name = AUDIO_FILES_PATH + str(uuid.uuid4())
download_firebase_file(FIREBASE_BUCKET, item["answer"], sound_file_name)
answer_text = speech_to_text(sound_file_name)
item["answer_text"] = answer_text
item["answer"] = answer_text
os.remove(sound_file_name)
if not has_10_words(answer_text):
return {
@@ -454,11 +482,36 @@ def grade_speaking_task_3():
"Pronunciation": 0
}
}
perfect_answer_message = ("Provide a perfect answer according to ielts grading system to the following "
"Speaking Part 3 question: '" + item["question"] + "'")
token_count = count_tokens(perfect_answer_message)["n_tokens"]
perfect_answers.append(make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT,
perfect_answer_message,
token_count,
None,
GEN_QUESTION_TEMPERATURE))
message = (
"Grade this Speaking Part 3 answer according to ielts grading system and provide "
"an elaborated comment where you deep dive into what is wrong and right about the answers."
"Please assign a grade of 0 if the answer provided does not address the question."
"\n\n The questions and answers are: \n\n'")
messages = get_speaking_grading_messages(answers)
token_count = reduce(lambda count, item: count + count_tokens(item)['n_tokens'],
map(lambda x: x["content"], filter(lambda x: "content" in x, messages)), 0)
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, GRADING_FIELDS, GRADING_TEMPERATURE)
formatted_text = ""
for i, entry in enumerate(answers, start=1):
formatted_text += f"**Question {i}:**\n{entry['question']}\n\n"
formatted_text += f"**Answer {i}:**\n{entry['answer']}\n\n"
message += formatted_text
message += ("'\n\nProvide your answer on the following json format: {'comment': 'comment about answer quality', "
"'overall': 0.0, 'task_response': {'Fluency and Coherence': 0.0, 'Lexical Resource': 0.0, "
"'Grammatical Range and Accuracy': 0.0, 'Pronunciation': 0.0}}")
token_count = count_tokens(message)["n_tokens"]
response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
["comment"],
GEN_QUESTION_TEMPERATURE)
for i, answer in enumerate(perfect_answers, start=1):
response['perfect_answer_' + str(i)] = answer
return response
except Exception as e:
return str(e), 400
@@ -522,7 +575,7 @@ def save_speaking():
print("Failed to create video for part 3 question: " + question)
template["exercises"][2]["prompts"] = sp3_questions
template["exercises"][2]["title"] = exercises[2]["topic"]
(result, id) = save_to_db("speaking", template)
if result:
return {**template, "id": id}