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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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28
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,
@@ -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 {
@@ -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 {
@@ -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