148 lines
6.1 KiB
Python
148 lines
6.1 KiB
Python
from app.services.abc import IWritingService, ILLMService, IAIDetectorService
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from app.configs.constants import GPTModels, TemperatureSettings
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from app.helpers import TextHelper, ExercisesHelper
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class WritingService(IWritingService):
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def __init__(self, llm: ILLMService, ai_detector: IAIDetectorService):
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self._llm = llm
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self._ai_detector = ai_detector
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async def get_writing_task_general_question(self, task: int, topic: str, difficulty: str):
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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: {"prompt": "prompt content"}'
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)
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},
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{
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"role": "user",
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"content": self._get_writing_prompt(task, topic, difficulty)
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}
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]
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llm_model = GPTModels.GPT_3_5_TURBO if task == 1 else GPTModels.GPT_4_O
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response = await self._llm.prediction(
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llm_model,
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messages,
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["prompt"],
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TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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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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@staticmethod
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def _get_writing_prompt(task: int, topic: str, difficulty: str):
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return (
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'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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f'based on the topic of "{topic}", requiring the student to provide information, '
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'advice, or instructions within the letter. Make sure that the generated prompt is '
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f'of {difficulty} difficulty and does not contain forbidden subjects in muslim countries.'
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) if task == 1 else (
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f'Craft a comprehensive question of {difficulty} difficulty like the ones for IELTS '
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'Writing Task 2 General Training that directs the candidate to delve into an in-depth '
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f'analysis of contrasting perspectives on the topic of "{topic}".'
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)
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async def grade_writing_task(self, task: int, question: str, answer: str):
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bare_minimum = 100 if task == 1 else 180
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minimum = 150 if task == 1 else 250
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# TODO: left as is, don't know if this is intended or not
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llm_model = GPTModels.GPT_3_5_TURBO if task == 1 else GPTModels.GPT_4_O
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temperature = (
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TemperatureSettings.GRADING_TEMPERATURE
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if task == 1 else
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TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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if not TextHelper.has_words(answer):
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return self._zero_rating("The answer does not contain enough english words.")
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elif not TextHelper.has_x_words(answer, bare_minimum):
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return self._zero_rating("The answer is insufficient and too small to be graded.")
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else:
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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: '
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'{"perfect_answer": "example perfect answer", "comment": '
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'"comment about answer quality", "overall": 0.0, "task_response": '
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'{"Task Achievement": 0.0, "Coherence and Cohesion": 0.0, '
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'"Lexical Resource": 0.0, "Grammatical Range and Accuracy": 0.0 }'
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)
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},
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{
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"role": "user",
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"content": (
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f'Evaluate the given Writing Task {task} response based on the IELTS grading system, '
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'ensuring a strict assessment that penalizes errors. Deduct points for deviations '
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'from the task, and assign a score of 0 if the response fails to address the question. '
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f'Additionally, provide an exemplary answer with a minimum of {minimum} words, along with a '
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'detailed commentary highlighting both strengths and weaknesses in the response. '
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f'\n Question: "{question}" \n Answer: "{answer}"')
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},
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{
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"role": "user",
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"content": f'The perfect answer must have at least {minimum} words.'
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}
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]
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response = await self._llm.prediction(
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llm_model,
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messages,
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["comment"],
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temperature
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)
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response["overall"] = ExercisesHelper.fix_writing_overall(response["overall"], response["task_response"])
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response['fixed_text'] = await self._get_fixed_text(answer)
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ai_detection = await self._ai_detector.run_detection(answer)
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if ai_detection is not None:
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response['ai_detection'] = ai_detection
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return response
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async def _get_fixed_text(self, text):
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messages = [
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{"role": "system", "content": ('You are a helpful assistant designed to output JSON on this format: '
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'{"fixed_text": "fixed test with no misspelling errors"}')
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},
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{"role": "user", "content": (
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'Fix the errors in the given text and put it in a JSON. '
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f'Do not complete the answer, only replace what is wrong. \n The text: "{text}"')
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}
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]
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response = await self._llm.prediction(
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GPTModels.GPT_3_5_TURBO,
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messages,
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["fixed_text"],
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0.2,
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False
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)
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return response["fixed_text"]
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@staticmethod
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def _zero_rating(comment: str):
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return {
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'comment': comment,
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'overall': 0,
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'task_response': {
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'Coherence and Cohesion': 0,
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'Grammatical Range and Accuracy': 0,
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'Lexical Resource': 0,
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'Task Achievement': 0
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}
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}
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