269 lines
10 KiB
Python
269 lines
10 KiB
Python
import asyncio
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from typing import List, Dict
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from app.services.abc import IWritingService, ILLMService, IAIDetectorService
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from app.configs.constants import GPTModels, TemperatureSettings, FieldsAndExercises
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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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*self._get_writing_args(task, topic, difficulty)
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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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question = response["prompt"].strip()
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return {
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"question": self._add_newline_before_hyphen(question) if task == 1 else question,
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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_args(task: int, topic: str, difficulty: str) -> List[Dict]:
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writing_args = {
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"1": {
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"prompt": (
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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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),
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"instructions": (
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'The prompt should end with "In the letter you should" followed by 3 bullet points of what '
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'the answer should include.'
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)
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},
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"2": {
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# TODO: Should the muslim disclaimer be here as well?
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"prompt": (
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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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"instructions": (
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'The question should lead to an answer with either "theories", "complicated information" or '
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'be "very descriptive" on the topic.'
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)
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}
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}
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messages = [
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{
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"role": "user",
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"content": writing_args[str(task)]["prompt"]
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},
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{
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"role": "user",
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"content": writing_args[str(task)]["instructions"]
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}
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]
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return messages
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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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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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template = self._get_writing_template()
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messages = [
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{
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"role": "system",
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"content": (
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f'You are a helpful assistant designed to output JSON on this format: {template}'
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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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'Additionally, provide a detailed commentary highlighting both strengths and '
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'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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if task == 1:
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messages.append({
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"role": "user",
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"content": (
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'Refer to the parts of the letter as: "Greeting Opener", "bullet 1", "bullet 2", '
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'"bullet 3", "closer (restate the purpose of the letter)", "closing greeting"'
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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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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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evaluation_promise = 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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perfect_answer_minimum = 150 if task == 1 else 250
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perfect_answer_promise = self._get_perfect_answer(question, perfect_answer_minimum)
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fixed_text_promise = self._get_fixed_text(answer)
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ai_detection_promise = self._ai_detector.run_detection(answer)
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prediction_result, perfect_answer_result, fixed_text_result, ai_detection_result = await asyncio.gather(
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evaluation_promise,
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perfect_answer_promise,
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fixed_text_promise,
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ai_detection_promise
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)
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response = prediction_result
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response["perfect_answer"] = perfect_answer_result["perfect_answer"]
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response["overall"] = ExercisesHelper.fix_writing_overall(
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response["overall"],
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response["task_response"]
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)
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response['fixed_text'] = fixed_text_result
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if ai_detection_result is not None:
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response['ai_detection'] = ai_detection_result
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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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{
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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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'{"fixed_text": "fixed test with no misspelling errors"}'
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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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'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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]
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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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async def _get_perfect_answer(self, question: str, size: int) -> Dict:
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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": "perfect answer for the question"}'
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)
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},
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{
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"role": "user",
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"content": f'Write a perfect answer for this writing exercise of a IELTS exam. Question: {question}'
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},
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{
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"role": "user",
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"content": f'The answer must have at least {size} words'
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}
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]
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return await self._llm.prediction(
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GPTModels.GPT_4_O,
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messages,
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["perfect_answer"],
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TemperatureSettings.GEN_QUESTION_TEMPERATURE
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)
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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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'Task Achievement': {
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"grade": 0.0,
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"comment": ""
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},
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'Coherence and Cohesion': {
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"grade": 0.0,
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"comment": ""
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},
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'Lexical Resource': {
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"grade": 0.0,
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"comment": ""
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},
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'Grammatical Range and Accuracy': {
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"grade": 0.0,
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"comment": ""
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}
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}
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}
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@staticmethod
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def _get_writing_template():
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return {
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"comment": "comment about student's response quality",
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"overall": 0.0,
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"task_response": {
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"Task Achievement": {
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"grade": 0.0,
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"comment": "comment about Task Achievement of the student's response"
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},
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"Coherence and Cohesion": {
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"grade": 0.0,
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"comment": "comment about Coherence and Cohesion of the student's response"
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},
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"Lexical Resource": {
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"grade": 0.0,
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"comment": "comment about Lexical Resource of the student's response"
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},
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"Grammatical Range and Accuracy": {
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"grade": 0.0,
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"comment": "comment about Grammatical Range and Accuracy of the student's response"
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}
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}
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}
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@staticmethod
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def _add_newline_before_hyphen(s):
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return s.replace(" -", "\n-")
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