EnCoach Odoo 19 custom modules
Full backend implementation with custom Odoo modules: - encoach_api: Core API, user management, JWT auth - encoach_exam: Exam generation (reading, writing, listening, speaking) - encoach_evaluate: AI-powered evaluation (writing, speaking) - encoach_training: Training tips and walkthrough - encoach_storage: File storage management - encoach_payment: Stripe, PayPal, Paymob integration - encoach_mail: Email notifications Made-with: Cursor
This commit is contained in:
Talal Sharabi
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encoach_ai_grading/services/__init__.py
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encoach_ai_grading/services/__init__.py
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from . import grading_service
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encoach_ai_grading/services/grading_service.py
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encoach_ai_grading/services/grading_service.py
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import json
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import logging
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import httpx
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from odoo.addons.encoach_ai.models.constants import GPT_MODELS, TEMPERATURE
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from odoo.addons.encoach_ai.services.openai_service import EncoachOpenAIService
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_logger = logging.getLogger(__name__)
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WRITING_RESPONSE_TEMPLATE = json.dumps({
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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": {"grade": 0.0, "comment": "..."},
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"Coherence and Cohesion": {"grade": 0.0, "comment": "..."},
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"Lexical Resource": {"grade": 0.0, "comment": "..."},
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"Grammatical Range and Accuracy": {"grade": 0.0, "comment": "..."},
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},
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})
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SPEAKING_RESPONSE_TEMPLATE = json.dumps({
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"comment": "extensive comment about answer quality",
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"overall": 0.0,
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"task_response": {
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"Fluency and Coherence": {"grade": 0.0, "comment": "extensive comment..."},
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"Lexical Resource": {"grade": 0.0, "comment": "..."},
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"Grammatical Range and Accuracy": {"grade": 0.0, "comment": "..."},
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"Pronunciation": {"grade": 0.0, "comment": "..."},
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},
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})
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SHORT_ANSWER_TEMPLATE = json.dumps({
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"exercises": [
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{"id": "exercise-id", "correct": True, "correct_answer": "the correct answer"},
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],
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})
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SUMMARY_TEMPLATE = json.dumps({
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"sections": [
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{
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"code": "section-code",
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"name": "Section Name",
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"grade": 0.0,
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"evaluation": "Detailed evaluation text...",
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"suggestions": "Improvement suggestions...",
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"bullet_points": ["point 1", "point 2"],
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},
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],
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})
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SPEAKING_TASK_INSTRUCTIONS = {
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1: (
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'Address the student as "you". '
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"If the answers are not 2 or 3 sentences long, "
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"warn the student that they should be."
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),
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2: 'Address the student as "you".',
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3: (
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'Address the student as "you" and pay special attention '
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"to coherence between the answers."
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),
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}
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class EncoachGradingService:
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def __init__(self, env):
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self.env = env
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self.ai = EncoachOpenAIService(env)
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# ------------------------------------------------------------------
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# Writing grading
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# ------------------------------------------------------------------
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def grade_writing(self, question, answer, task, attachment=None):
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"""Grade a writing answer using GPT-4o with IELTS writing rubric.
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Returns dict with comment, overall, task_response, fixed_text,
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perfect_answer, and ai_detection results.
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"""
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system_msg = (
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"You are a helpful assistant designed to output JSON "
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f"on this format: {WRITING_RESPONSE_TEMPLATE}"
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)
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if task == 1:
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user_prompt = (
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f"Evaluate the given Writing Task {task} response based on the "
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"IELTS grading system, ensuring a strict assessment that penalizes "
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"errors. Deduct points for deviations from the task, and assign a "
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"score of 0 if the response fails to address the question. "
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"Additionally, provide a detailed commentary highlighting both "
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"strengths and weaknesses in the response.\n"
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'Refer to the parts of the letter as: "Greeting Opener", '
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'"bullet 1", "bullet 2", "bullet 3", '
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'"closer (restate the purpose of the letter)", "closing greeting".\n'
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f'Question: "{question}"\nAnswer: "{answer}"'
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)
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else:
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user_prompt = (
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f"Evaluate the given Writing Task {task} response based on the "
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"IELTS grading system, ensuring a strict assessment that penalizes "
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"errors. Deduct points for deviations from the task, and assign a "
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"score of 0 if the response fails to address the question. "
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"Additionally, provide a detailed commentary highlighting both "
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"strengths and weaknesses in the response.\n"
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f'Question: "{question}"\nAnswer: "{answer}"'
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)
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messages = [
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{"role": "system", "content": system_msg},
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]
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if attachment and task == 1:
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messages.append({
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"role": "user",
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"content": [
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{"type": "text", "text": user_prompt},
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{
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"type": "image_url",
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"image_url": {"url": attachment},
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},
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],
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})
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else:
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messages.append({"role": "user", "content": user_prompt})
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result = self.ai.prediction(
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model=GPT_MODELS["grading"],
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messages=messages,
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temperature=TEMPERATURE["grading"],
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fields_to_check=["comment"],
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)
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if not result:
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return None
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result["fixed_text"] = self._get_fixed_text(answer)
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result["perfect_answer"] = self._get_perfect_answer(question)
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result["ai_detection"] = self._detect_ai(answer)
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return result
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# ------------------------------------------------------------------
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# Speaking grading
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# ------------------------------------------------------------------
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def grade_speaking(self, task, qa_pairs):
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"""Grade speaking using GPT-4o with IELTS speaking rubric.
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qa_pairs: list of dicts with 'question' and 'answer' keys.
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"""
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system_msg = (
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"You are a helpful assistant designed to output JSON "
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f"on this format: {SPEAKING_RESPONSE_TEMPLATE}"
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)
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qa_text = "\n".join(
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f'Question: "{p["question"]}"\nAnswer: "{p["answer"]}"'
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for p in qa_pairs
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)
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task_instruction = SPEAKING_TASK_INSTRUCTIONS.get(task, "")
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user_prompt = (
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f"Evaluate the given Speaking Part {task} response based on the "
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"IELTS grading system, ensuring a strict assessment that penalizes "
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"errors. Deduct points for deviations from the task, and assign a "
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"score of 0 if the response fails to address the question. "
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"Additionally, provide detailed commentary highlighting both "
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f"strengths and weaknesses in the response. {task_instruction}\n"
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f"{qa_text}"
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)
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messages = [
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{"role": "system", "content": system_msg},
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{"role": "user", "content": user_prompt},
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]
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result = self.ai.prediction(
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model=GPT_MODELS["grading"],
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messages=messages,
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temperature=TEMPERATURE["grading"],
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fields_to_check=["comment"],
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)
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if not result:
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return None
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if task == 2 and qa_pairs:
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combined_answer = " ".join(p["answer"] for p in qa_pairs)
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result["fixed_text"] = self._get_fixed_text(combined_answer)
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result["perfect_answer"] = self._get_perfect_answer(
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qa_pairs[0]["question"]
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)
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return result
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# ------------------------------------------------------------------
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# Short-answer grading
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# ------------------------------------------------------------------
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def grade_short_answers(self, text, questions, answers):
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"""Evaluate short answers against a reading/listening passage."""
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system_msg = (
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"You are a helpful assistant designed to output JSON "
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f"on this format: {SHORT_ANSWER_TEMPLATE}"
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)
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qa_text = "\n".join(
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f'Q{i + 1}: "{q}" — Student answer: "{a}"'
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for i, (q, a) in enumerate(zip(questions, answers))
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)
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user_prompt = (
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"Evaluate each student answer against the passage. For each answer, "
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"determine if it is correct and provide the correct answer.\n\n"
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f'Passage: "{text}"\n\n{qa_text}'
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)
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messages = [
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{"role": "system", "content": system_msg},
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{"role": "user", "content": user_prompt},
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]
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return self.ai.prediction(
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model=GPT_MODELS["grading"],
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messages=messages,
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temperature=TEMPERATURE["grading"],
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check_blacklisted=False,
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)
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# ------------------------------------------------------------------
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# Grading summary
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# ------------------------------------------------------------------
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def generate_grading_summary(self, sections):
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"""Generate a summary for an entire exam session using GPT-3.5-turbo."""
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system_msg = (
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"You are a helpful assistant designed to output JSON "
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f"on this format: {SUMMARY_TEMPLATE}"
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)
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user_prompt = (
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"Generate a detailed evaluation summary for the following IELTS exam "
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"sections. For each section, provide an evaluation, suggestions for "
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"improvement, and key bullet points.\n\n"
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+ json.dumps(sections)
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)
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messages = [
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{"role": "system", "content": system_msg},
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{"role": "user", "content": user_prompt},
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]
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return self.ai.prediction(
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model=GPT_MODELS["secondary"],
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messages=messages,
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temperature=TEMPERATURE["grading"],
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check_blacklisted=False,
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)
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# ------------------------------------------------------------------
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# AI detection (GPTZero)
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# ------------------------------------------------------------------
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def _detect_ai(self, text):
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"""Call GPTZero API to detect AI-generated text."""
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api_key = (
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self.env["ir.config_parameter"]
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.sudo()
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.get_param("encoach.gptzero_api_key", "")
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)
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if not api_key:
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_logger.warning("GPTZero API key not configured")
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return None
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try:
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resp = httpx.post(
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"https://api.gptzero.me/v2/predict/text",
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headers={
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"x-api-key": api_key,
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"Content-Type": "application/json",
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},
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json={
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"document": text,
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"version": "",
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"multilingual": False,
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},
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timeout=30,
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)
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resp.raise_for_status()
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return resp.json()
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except Exception:
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_logger.exception("GPTZero API call failed")
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return None
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# ------------------------------------------------------------------
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# Helper prompts
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# ------------------------------------------------------------------
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def _get_perfect_answer(self, question):
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messages = [
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{"role": "system", "content": "You are an IELTS writing expert."},
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{
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"role": "user",
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"content": (
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"Write a perfect answer for this IELTS writing task: "
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f'"{question}"'
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),
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},
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]
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result = self.ai.prediction(
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model=GPT_MODELS["secondary"],
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messages=messages,
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temperature=TEMPERATURE["grading"],
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response_format={"type": "json_object"},
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check_blacklisted=False,
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)
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if result and "answer" in result:
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return result["answer"]
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return result
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def _get_fixed_text(self, text):
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messages = [
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{"role": "system", "content": "You are a grammar correction assistant. Output JSON."},
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{
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"role": "user",
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"content": (
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"Fix the grammatical and spelling errors in this text, "
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f'keeping the original meaning: "{text}"'
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),
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},
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]
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result = self.ai.prediction(
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model=GPT_MODELS["secondary"],
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messages=messages,
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temperature=TEMPERATURE["grading"],
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response_format={"type": "json_object"},
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check_blacklisted=False,
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)
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if result and "text" in result:
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return result["text"]
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return result
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