feat: Generation Page AI workflows + AI/Vector modules + exam session fixes
Generation Page (complete rebuild): - Full production-parity exam generation wizard with 4 IELTS modules - Reading: AI passage gen, 5 exercise types (MCQ, Fill, Write, T/F, Match) - Listening: 4 section types, AI context gen, TTS audio gen (ElevenLabs) - Writing: Task 1/2, AI instruction gen, word limits, marks - Speaking: 3 parts, AI script gen, avatar video gen (7 avatars) - Per-module config: timer, CEFR difficulty, access, approval, rubrics - Exam submission workflow (draft/published) Exam Structures: - New encoach.exam.structure model + CRUD controller - ExamStructuresPage wired to real API AI Module (encoach_ai): - OpenAI service, ElevenLabs TTS, AWS Polly, ELAI avatars - AI settings model with Odoo config parameters - 7 generation endpoints (passage, exercises, instructions, scripts, context) Vector Module (encoach_vector): - pgvector integration for RAG-based content search - Embedding service with sentence-transformers Exam Session Fixes: - Fixed ExamSession.tsx field mapping (question_type→type, exam_title→title) - Fixed submit payload to include attempt_id and answers - Fixed normalizeType to handle null/undefined Tested: 12/12 API tests passed, browser-verified with real OpenAI calls Made-with: Cursor
This commit is contained in:
Yamen Ahmad
@@ -5,7 +5,7 @@
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'summary': 'Exam scoring, grading queue, feedback, and score release management',
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'author': 'EnCoach',
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'license': 'LGPL-3',
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'depends': ['encoach_core', 'encoach_exam_template', 'encoach_course_gen', 'encoach_resources'],
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'depends': ['encoach_core', 'encoach_exam_template', 'encoach_course_gen', 'encoach_resources', 'encoach_ai'],
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'data': [
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'security/ir.model.access.csv',
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'views/student_attempt_views.xml',
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@@ -338,34 +338,52 @@ class EncoachGradingController(http.Controller):
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student_response = ans.answer if ans else ''
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suggested_score = question.marks * 0.5
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suggested_feedback = (
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f"AI suggestion for {question.skill} {question.question_type} question. "
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f"Student provided a response of {len(student_response)} characters. "
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f"Suggested mid-range score based on rubric criteria."
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)
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confidence = 0.6
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if not student_response:
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suggested_score = 0.0
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suggested_feedback = "No response provided by student."
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confidence = 0.95
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return _json_response({
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'suggested_score': 0.0,
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'suggested_feedback': 'No response provided by student.',
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'confidence': 0.95,
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})
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rubric = None
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rubric_text = "IELTS Band Descriptors"
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if attempt.exam_id and attempt.exam_id.template_id:
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Rubric = request.env['encoach.rubric'].sudo()
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rubric = Rubric.search([
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('skill', '=', question.skill),
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], limit=1)
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rubric_rec = Rubric.search([('skill', '=', question.skill)], limit=1)
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if rubric_rec:
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rubric_text = rubric_rec.name
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if rubric:
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suggested_feedback += f" Rubric '{rubric.name}' criteria should be applied."
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return _json_response({
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'suggested_score': round(suggested_score, 1),
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'suggested_feedback': suggested_feedback,
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'confidence': confidence,
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})
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try:
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from odoo.addons.encoach_ai.services.openai_service import OpenAIService
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ai = OpenAIService(request.env)
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skill = question.skill or 'writing'
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if skill in ('speaking',):
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result = ai.grade_speaking(rubric_text, student_response)
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else:
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result = ai.grade_writing(
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rubric_text,
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question.body or question.name or '',
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student_response,
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)
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overall = result.get('overall_band', 0)
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suggested_score = min(overall / 9.0 * question.marks, question.marks)
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return _json_response({
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'suggested_score': round(suggested_score, 1),
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'suggested_feedback': result.get('feedback', ''),
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'confidence': 0.85,
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'scores': result.get('scores', {}),
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'suggestions': result.get('suggestions', []),
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})
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except Exception as ai_err:
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_logger.warning('AI grading unavailable, using heuristic: %s', ai_err)
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suggested_score = question.marks * 0.5
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return _json_response({
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'suggested_score': round(suggested_score, 1),
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'suggested_feedback': (
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f"AI grading unavailable ({ai_err}). "
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f"Heuristic: mid-range score for {len(student_response)} char response."
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),
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'confidence': 0.4,
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})
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except Exception as e:
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_logger.exception('ai_suggest failed')
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@@ -1,67 +1,60 @@
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"""AI-powered speaking assessment using encoach_ai services."""
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import logging
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_logger = logging.getLogger(__name__)
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class SpeakingEvaluator:
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"""AI-powered speaking assessment using Whisper + GPT."""
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"""AI-powered speaking assessment using Whisper + GPT via encoach_ai."""
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def __init__(self, env=None):
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self.env = env
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def transcribe_audio(self, audio_path_or_bytes):
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"""Transcribe audio using the encoach_ai WhisperService."""
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try:
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from odoo.addons.encoach_ai.services.whisper_service import WhisperService
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whisper = WhisperService(self.env)
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if isinstance(audio_path_or_bytes, (bytes, bytearray)):
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return whisper.transcribe(audio_path_or_bytes, use_api=True)
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with open(audio_path_or_bytes, "rb") as f:
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return whisper.transcribe(f.read(), use_api=True)
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except ImportError:
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_logger.warning("encoach_ai not installed, falling back to direct whisper")
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return self._fallback_transcribe(audio_path_or_bytes)
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except Exception as e:
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_logger.error("Transcription error: %s", e)
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return {"text": "", "language": "en", "segments": [], "error": str(e)}
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def evaluate_speaking(self, transcription, rubric_criteria, target_band=6.0):
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"""Evaluate speaking using encoach_ai OpenAIService."""
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try:
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from odoo.addons.encoach_ai.services.openai_service import OpenAIService
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ai = OpenAIService(self.env)
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result = ai.grade_speaking(
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f"Target Band: {target_band}\n{rubric_criteria}",
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transcription,
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)
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return result
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except ImportError:
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_logger.warning("encoach_ai not installed")
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return {"overall_band": 0, "feedback": "AI evaluation not available"}
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except Exception as e:
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_logger.error("Speaking evaluation error: %s", e)
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return {"overall_band": 0, "feedback": f"Evaluation error: {e}"}
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@staticmethod
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def transcribe_audio(audio_path):
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"""Transcribe audio using Whisper."""
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def _fallback_transcribe(audio_path):
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"""Direct whisper fallback if encoach_ai is not available."""
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try:
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import whisper
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model = whisper.load_model("base")
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result = model.transcribe(audio_path)
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return {
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'text': result['text'],
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'language': result.get('language', 'en'),
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'segments': result.get('segments', []),
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"text": result["text"],
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"language": result.get("language", "en"),
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"segments": result.get("segments", []),
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}
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except ImportError:
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_logger.warning("whisper not installed")
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return {'text': '', 'language': 'en', 'segments': [], 'error': 'Whisper not available'}
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@staticmethod
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def evaluate_speaking(transcription, rubric_criteria, target_band=6.0):
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"""Evaluate speaking using OpenAI GPT."""
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try:
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import openai
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prompt = (
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"You are an IELTS speaking examiner. Evaluate the following speaking response.\n\n"
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f"Target Band: {target_band}\n\n"
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f"Rubric Criteria:\n{rubric_criteria}\n\n"
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f"Transcription:\n{transcription}\n\n"
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"Provide scores for each criterion (0-9 scale) and detailed feedback.\n"
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"Return JSON format:\n"
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"{\n"
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' "fluency_coherence": {"score": X, "feedback": "..."},\n'
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' "lexical_resource": {"score": X, "feedback": "..."},\n'
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' "grammatical_range": {"score": X, "feedback": "..."},\n'
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' "pronunciation": {"score": X, "feedback": "..."},\n'
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' "overall_band": X,\n'
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' "general_feedback": "..."\n'
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"}"
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)
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client = openai.OpenAI()
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response = client.chat.completions.create(
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model="gpt-4",
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messages=[
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{"role": "system", "content": "You are an expert IELTS speaking examiner."},
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{"role": "user", "content": prompt},
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],
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temperature=0.3,
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)
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import json
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result = json.loads(response.choices[0].message.content)
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return result
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except ImportError:
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_logger.warning("openai not installed")
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return {'overall_band': 0, 'general_feedback': 'AI evaluation not available', 'error': 'OpenAI not available'}
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except Exception as e:
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_logger.error("Speaking evaluation error: %s", e)
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return {'overall_band': 0, 'general_feedback': f'Evaluation error: {e}'}
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return {"text": "", "language": "en", "segments": [], "error": "Whisper not available"}
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