feat(v3): restructure project + add complete frontend
- Restructure: move backend from new_project/ to backend/ - Add full React/TypeScript frontend (37 pages, 17 services, 16 type defs, 11 query hooks) - Add docs/ with SRS specs, user stories, and workflow documentation - Update .gitignore for new directory layout Workflows implemented: WF1 User Signup, WF2 Placement Test, WF3 Exam Configuration, WF4 General English Exam, WF5 Course Generation, WF6 Entity Student Onboarding, AI Course Generation, Adaptive Learning Engine UI, White-Label Branding, Score Release Made-with: Cursor
This commit is contained in:
Yamen Ahmad
2
custom_addons/encoach_ai_course/services/__init__.py
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custom_addons/encoach_ai_course/services/__init__.py
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from .english_pipeline import EnglishPipeline
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from .ielts_pipeline import IeltsPipeline
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124
custom_addons/encoach_ai_course/services/english_pipeline.py
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custom_addons/encoach_ai_course/services/english_pipeline.py
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import json
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import logging
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try:
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from odoo.addons.encoach_ai.services.openai_service import OpenAIService
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except ImportError:
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OpenAIService = None
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try:
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from odoo.addons.encoach_quality_gate.services.content_gate import ContentSourceGate
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except ImportError:
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ContentSourceGate = None
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_logger = logging.getLogger(__name__)
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class EnglishPipeline:
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"""AI content generation pipeline for General English courses."""
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def __init__(self, env):
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self.env = env
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self.ai = OpenAIService(env)
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def generate_content(self, env, gap_profile, cefr_level):
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"""Generate General English content based on gap analysis.
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Args:
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env: Odoo environment.
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gap_profile: dict with 'skill_gaps' list describing weak areas.
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cefr_level: target CEFR level string (e.g. 'B1').
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Returns:
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dict with generated content.
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"""
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skill_gaps = gap_profile.get('skill_gaps', [])
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gaps_description = ', '.join(skill_gaps) if skill_gaps else 'general skills'
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messages = [
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{'role': 'system', 'content': (
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'You are an expert English language curriculum designer. '
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'Return JSON with keys: "title", "objectives", "units" '
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'(each unit has "topic", "grammar_focus", "vocabulary", '
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'"reading_text", "exercises").'
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)},
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{'role': 'user', 'content': (
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f'Generate a General English course at CEFR {cefr_level} level. '
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f'Focus on these gap areas: {gaps_description}. '
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f'Include practical, real-world content appropriate for the level.'
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)},
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]
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try:
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content = self.ai.chat_json(messages, temperature=0.8)
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except Exception as e:
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_logger.error("English content generation failed: %s", e)
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env['encoach.ai.generation.log'].create({
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'course_type': 'general_english',
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'brief': json.dumps(gap_profile),
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'status': 'rejected',
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'error_log': str(e),
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})
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return {'error': str(e)}
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resource = env['encoach.ai.generation.log'].create({
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'course_type': 'general_english',
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'brief': json.dumps(gap_profile),
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'status': 'quality_check',
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'attempts': 1,
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})
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# Apply content source gate logic
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if ContentSourceGate is not None:
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try:
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ContentSourceGate.apply_gate(resource)
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except Exception as e:
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_logger.warning("ContentSourceGate.apply_gate failed: %s", e)
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return content
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def quality_check(self, env, content, cefr_level):
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"""Run quality gate checks on generated content.
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Args:
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env: Odoo environment.
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content: dict of generated content to validate.
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cefr_level: target CEFR level for alignment check.
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Returns:
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dict with 'passed' bool and 'issues' list.
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"""
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issues = []
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if not content.get('units'):
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issues.append('No units generated')
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else:
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for i, unit in enumerate(content['units'], 1):
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if not unit.get('exercises'):
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issues.append(f'Unit {i} has no exercises')
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if not unit.get('reading_text'):
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issues.append(f'Unit {i} has no reading text')
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if not content.get('objectives'):
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issues.append('No learning objectives defined')
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messages = [
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{'role': 'system', 'content': (
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'You are a CEFR alignment expert. Return JSON with keys: '
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'"aligned" (bool) and "issues" (list of strings).'
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)},
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{'role': 'user', 'content': (
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f'Check if this content is aligned with CEFR {cefr_level}: '
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f'{json.dumps(content)}'
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)},
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]
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try:
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alignment = self.ai.chat_json(messages, temperature=0.3)
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if not alignment.get('aligned'):
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issues.extend(alignment.get('issues', ['CEFR misalignment detected']))
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except Exception as e:
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_logger.warning("CEFR alignment check failed: %s", e)
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issues.append(f'CEFR alignment check error: {e}')
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return {'passed': len(issues) == 0, 'issues': issues}
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170
custom_addons/encoach_ai_course/services/ielts_pipeline.py
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custom_addons/encoach_ai_course/services/ielts_pipeline.py
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import json
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import logging
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try:
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from odoo.addons.encoach_ai.services.openai_service import OpenAIService
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except ImportError:
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OpenAIService = None
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try:
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from odoo.addons.encoach_quality_gate.services.content_gate import ContentSourceGate
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except ImportError:
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ContentSourceGate = None
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_logger = logging.getLogger(__name__)
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SKILL_PROMPTS = {
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'listening': (
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'Generate an IELTS Listening section. Return JSON with keys: '
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'"script", "speakers", "duration_estimate", "questions".'
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),
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'reading': (
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'Generate an IELTS Reading passage with questions. Return JSON with keys: '
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'"passage", "word_count", "questions", "question_types".'
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),
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'writing': (
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'Generate an IELTS Writing task. Return JSON with keys: '
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'"task_type", "prompt", "requirements", "sample_band_descriptors".'
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),
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'speaking': (
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'Generate an IELTS Speaking part. Return JSON with keys: '
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'"part", "questions", "cue_card" (if part 2), "follow_up_questions".'
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),
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}
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IELTS_FORMAT_RULES = {
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'listening': {'required_keys': ['script', 'questions'], 'min_questions': 10},
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'reading': {'required_keys': ['passage', 'questions'], 'min_word_count': 500},
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'writing': {'required_keys': ['prompt', 'requirements']},
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'speaking': {'required_keys': ['questions']},
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}
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class IeltsPipeline:
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"""AI content generation pipeline for IELTS skill-specific content."""
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def __init__(self, env):
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self.env = env
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self.ai = OpenAIService(env)
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def generate_content(self, env, skill, brief):
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"""Generate IELTS skill-specific content.
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Args:
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env: Odoo environment.
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skill: one of 'listening', 'reading', 'writing', 'speaking'.
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brief: dict with generation parameters.
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Returns:
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dict with generated content.
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"""
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system_prompt = SKILL_PROMPTS.get(skill, SKILL_PROMPTS['reading'])
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brief_text = json.dumps(brief) if isinstance(brief, dict) else str(brief)
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messages = [
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{'role': 'system', 'content': (
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f'You are an expert IELTS content creator. {system_prompt}'
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)},
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{'role': 'user', 'content': (
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f'Generate IELTS {skill} content based on this brief: {brief_text}'
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)},
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]
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try:
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content = self.ai.chat_json(messages, temperature=0.8)
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except Exception as e:
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_logger.error("IELTS %s content generation failed: %s", skill, e)
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return {'error': str(e)}
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resource = env['encoach.ai.ielts.generation.log'].create({
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'skill': skill,
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'brief': brief_text,
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'status': 'format_check',
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'attempts': 1,
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})
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# Apply content source gate logic
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if ContentSourceGate is not None:
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try:
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ContentSourceGate.apply_gate(resource)
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except Exception as e:
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_logger.warning("ContentSourceGate.apply_gate failed: %s", e)
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return content
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def format_check(self, env, content, skill):
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"""Validate IELTS format compliance for a given skill.
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Args:
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env: Odoo environment.
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content: dict of generated content.
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skill: IELTS skill type.
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Returns:
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dict with 'passed' bool and 'errors' list.
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"""
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errors = []
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rules = IELTS_FORMAT_RULES.get(skill, {})
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for key in rules.get('required_keys', []):
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if key not in content or not content[key]:
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errors.append(f'Missing required key: {key}')
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if skill == 'listening':
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questions = content.get('questions', [])
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min_q = rules.get('min_questions', 10)
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if len(questions) < min_q:
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errors.append(
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f'Listening requires at least {min_q} questions, '
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f'got {len(questions)}'
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)
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if skill == 'reading':
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passage = content.get('passage', '')
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min_wc = rules.get('min_word_count', 500)
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word_count = len(passage.split()) if passage else 0
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if word_count < min_wc:
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errors.append(
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f'Reading passage must be at least {min_wc} words, '
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f'got {word_count}'
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)
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if skill == 'speaking' and not content.get('questions'):
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errors.append('Speaking section requires at least one question')
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return {'passed': len(errors) == 0, 'errors': errors}
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def band_calibration(self, env, content, target_band):
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"""Check content aligns with target IELTS band level.
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Args:
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env: Odoo environment.
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content: dict of generated content.
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target_band: float target band score (e.g. 6.5).
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Returns:
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dict with 'passed' bool and 'issues' list.
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"""
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messages = [
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{'role': 'system', 'content': (
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'You are an IELTS band calibration expert. Evaluate whether '
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'the provided content matches the target band level. '
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'Return JSON with keys: "calibrated" (bool), "estimated_band" (float), '
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'"issues" (list of strings).'
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)},
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{'role': 'user', 'content': (
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f'Target band: {target_band}. '
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f'Content: {json.dumps(content)}'
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)},
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]
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try:
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result = self.ai.chat_json(messages, temperature=0.3)
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except Exception as e:
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_logger.error("Band calibration failed: %s", e)
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return {'passed': False, 'issues': [f'Band calibration error: {e}']}
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return {
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'passed': result.get('calibrated', False),
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'issues': result.get('issues', []),
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}
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