249 lines
9.4 KiB
Python
249 lines
9.4 KiB
Python
from typing import List, Dict
|
|
|
|
from app.services.abc import IWritingService, ILLMService, IAIDetectorService
|
|
from app.configs.constants import GPTModels, TemperatureSettings, FieldsAndExercises
|
|
from app.helpers import TextHelper, ExercisesHelper
|
|
|
|
|
|
class WritingService(IWritingService):
|
|
|
|
def __init__(self, llm: ILLMService, ai_detector: IAIDetectorService):
|
|
self._llm = llm
|
|
self._ai_detector = ai_detector
|
|
|
|
async def get_writing_task_general_question(self, task: int, topic: str, difficulty: str):
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: {"prompt": "prompt content"}'
|
|
)
|
|
},
|
|
*self._get_writing_messages(task, topic, difficulty)
|
|
]
|
|
|
|
llm_model = GPTModels.GPT_3_5_TURBO if task == 1 else GPTModels.GPT_4_O
|
|
|
|
response = await self._llm.prediction(
|
|
llm_model,
|
|
messages,
|
|
["prompt"],
|
|
TemperatureSettings.GEN_QUESTION_TEMPERATURE
|
|
)
|
|
|
|
question = response["prompt"].strip()
|
|
|
|
return {
|
|
"question": self._add_newline_before_hyphen(question) if task == 1 else question,
|
|
"difficulty": difficulty,
|
|
"topic": topic
|
|
}
|
|
|
|
@staticmethod
|
|
def _get_writing_messages(task: int, topic: str, difficulty: str) -> List[Dict]:
|
|
# TODO: Should the muslim disclaimer be added to task 2?
|
|
task_prompt = (
|
|
'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, '
|
|
f'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 '
|
|
f'of {difficulty} difficulty and does not contain forbidden subjects in muslim countries.'
|
|
) if task == 1 else (
|
|
f'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 '
|
|
f'analysis of contrasting perspectives on the topic of "{topic}".'
|
|
)
|
|
|
|
task_instructions = (
|
|
'The prompt should end with "In the letter you should" followed by 3 bullet points of what '
|
|
'the answer should include.'
|
|
) if task == 1 else (
|
|
'The question should lead to an answer with either "theories", "complicated information" or '
|
|
'be "very descriptive" on the topic.'
|
|
)
|
|
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": task_prompt
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": task_instructions
|
|
}
|
|
]
|
|
|
|
return messages
|
|
|
|
async def grade_writing_task(self, task: int, question: str, answer: str):
|
|
bare_minimum = 100 if task == 1 else 180
|
|
|
|
if not TextHelper.has_words(answer):
|
|
return self._zero_rating("The answer does not contain enough english words.")
|
|
elif not TextHelper.has_x_words(answer, bare_minimum):
|
|
return self._zero_rating("The answer is insufficient and too small to be graded.")
|
|
else:
|
|
template = self._get_writing_template()
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
f'You are a helpful assistant designed to output JSON on this format: {template}'
|
|
)
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
f'Evaluate the given Writing Task {task} response based on the IELTS grading system, '
|
|
'ensuring a strict assessment that penalizes errors. Deduct points for deviations '
|
|
'from the task, and assign a score of 0 if the response fails to address the question. '
|
|
'Additionally, provide a detailed commentary highlighting both strengths and '
|
|
'weaknesses in the response. '
|
|
f'\n Question: "{question}" \n Answer: "{answer}"')
|
|
}
|
|
]
|
|
|
|
if task == 1:
|
|
messages.append({
|
|
"role": "user",
|
|
"content": (
|
|
'Refer to the parts of the letter as: "Greeting Opener", "bullet 1", "bullet 2", '
|
|
'"bullet 3", "closer (restate the purpose of the letter)", "closing greeting"'
|
|
)
|
|
})
|
|
|
|
llm_model = GPTModels.GPT_3_5_TURBO if task == 1 else GPTModels.GPT_4_O
|
|
temperature = (
|
|
TemperatureSettings.GRADING_TEMPERATURE
|
|
if task == 1 else
|
|
TemperatureSettings.GEN_QUESTION_TEMPERATURE
|
|
)
|
|
|
|
response = await self._llm.prediction(
|
|
llm_model,
|
|
messages,
|
|
["comment"],
|
|
temperature
|
|
)
|
|
|
|
perfect_answer_minimum = 150 if task == 1 else 250
|
|
perfect_answer = await self._get_perfect_answer(question, perfect_answer_minimum)
|
|
|
|
response["perfect_answer"] = perfect_answer["perfect_answer"]
|
|
response["overall"] = ExercisesHelper.fix_writing_overall(response["overall"], response["task_response"])
|
|
response['fixed_text'] = await self._get_fixed_text(answer)
|
|
|
|
ai_detection = await self._ai_detector.run_detection(answer)
|
|
if ai_detection is not None:
|
|
response['ai_detection'] = ai_detection
|
|
|
|
return response
|
|
|
|
async def _get_fixed_text(self, text):
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: '
|
|
'{"fixed_text": "fixed test with no misspelling errors"}'
|
|
)
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": (
|
|
'Fix the errors in the given text and put it in a JSON. '
|
|
f'Do not complete the answer, only replace what is wrong. \n The text: "{text}"'
|
|
)
|
|
}
|
|
]
|
|
|
|
response = await self._llm.prediction(
|
|
GPTModels.GPT_3_5_TURBO,
|
|
messages,
|
|
["fixed_text"],
|
|
0.2,
|
|
False
|
|
)
|
|
return response["fixed_text"]
|
|
|
|
async def _get_perfect_answer(self, question: str, size: int) -> Dict:
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": (
|
|
'You are a helpful assistant designed to output JSON on this format: '
|
|
'{"perfect_answer": "perfect answer for the question"}'
|
|
)
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": f'Write a perfect answer for this writing exercise of a IELTS exam. Question: {question}'
|
|
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": f'The answer must have at least {size} words'
|
|
}
|
|
]
|
|
return await self._llm.prediction(
|
|
GPTModels.GPT_4_O,
|
|
messages,
|
|
["perfect_answer"],
|
|
TemperatureSettings.GEN_QUESTION_TEMPERATURE
|
|
)
|
|
|
|
@staticmethod
|
|
def _zero_rating(comment: str):
|
|
return {
|
|
'comment': comment,
|
|
'overall': 0,
|
|
'task_response': {
|
|
'Task Achievement': {
|
|
"grade": 0.0,
|
|
"comment": ""
|
|
},
|
|
'Coherence and Cohesion': {
|
|
"grade": 0.0,
|
|
"comment": ""
|
|
},
|
|
'Lexical Resource': {
|
|
"grade": 0.0,
|
|
"comment": ""
|
|
},
|
|
'Grammatical Range and Accuracy': {
|
|
"grade": 0.0,
|
|
"comment": ""
|
|
}
|
|
}
|
|
}
|
|
|
|
@staticmethod
|
|
def _get_writing_template():
|
|
return {
|
|
"comment": "comment about student's response quality",
|
|
"overall": 0.0,
|
|
"task_response": {
|
|
"Task Achievement": {
|
|
"grade": 0.0,
|
|
"comment": "comment about Task Achievement of the student's response"
|
|
},
|
|
"Coherence and Cohesion": {
|
|
"grade": 0.0,
|
|
"comment": "comment about Coherence and Cohesion of the student's response"
|
|
},
|
|
"Lexical Resource": {
|
|
"grade": 0.0,
|
|
"comment": "comment about Lexical Resource of the student's response"
|
|
},
|
|
"Grammatical Range and Accuracy": {
|
|
"grade": 0.0,
|
|
"comment": "comment about Grammatical Range and Accuracy of the student's response"
|
|
}
|
|
}
|
|
}
|
|
|
|
@staticmethod
|
|
def _add_newline_before_hyphen(s):
|
|
return s.replace(" -", "\n-")
|
|
|