feat(course-plan): GE1-style AI course planning with deliverables, resources, media, assignments
Based on UTAS GE1 Course Outline structure (Reading/Writing 10hrs + Listening/Speaking 8hrs) New Models: - encoach.course.plan.deliverable: Explicit learning outcome tracking by week/skill - encoach.course.plan.resource.dep: Resource dependencies (textbooks, videos, etc.) - encoach.course.plan.assignment: Assign plans to classes/students with progress tracking - encoach.course.plan.assignment.deliverable: Per-student deliverable completion status Extended Models: - course.plan.material: Added media fields (media_type, media_asset_url, media_asset_id, media_generation_prompt, media_metadata_json) for rich content - New material types: video_lesson, audio_recording, image_visual, interactive, assessment AI Agent Tools (agent_tools.py): - deliverables.detect: Parse course outlines (like GE1 PDF) and extract structured outcomes - deliverables.fetch: Get deliverables for AI to reference when generating - resources.fetch: Check available resources before generating content - resources.save: Persist resource dependencies - media.suggest_visuals: AI suggests images/diagrams for materials - media.generate_image: Generate educational images (DALL-E integration ready) - media.generate_audio: Generate TTS audio (ElevenLabs/Polly integration ready) - assignment.*: Create assignments and track progress Pipeline Enhancements (course_plan_pipeline.py): - generate_deliverables_from_outline(): Parse PDF/text outlines into structured deliverables - generate_week_materials_with_resources(): Resource-aware content generation - suggest_media_for_material(): AI visual aid suggestions - generate_media_for_material(): Actual image/audio generation New AI Agents (agents_defaults.xml): - deliverable_detector: Parses GE1-style outlines, extracts deliverables week-by-week - media_generator: Creates images/audio for teaching materials - Updated course_planner & course_week_materials with resource tools REST APIs (course_plan.py): POST /api/ai/course-plan/<id>/deliverables/detect - Parse outline GET /api/ai/course-plan/<id>/deliverables - List deliverables PUT /api/ai/course-plan/deliverables/<id> - Update status GET /api/ai/course-plan/<id>/resources - List resources POST /api/ai/course-plan/<id>/resources - Add resource POST /api/ai/course-plan/materials/<id>/media/suggest - Get visual suggestions POST /api/ai/course-plan/materials/<id>/media/generate - Generate image/audio POST /api/ai/course-plan/<id>/assignments - Assign to class/student GET /api/ai/course-plan/<id>/assignments - List assignments GET /api/ai/course-plan/assignments/<id> - Get with progress PUT /api/ai/course-plan/assignments/<id>/deliverables/<del_id> - Update status Security: Added ir.model.access.csv entries for all new models Made-with: Cursor
This commit is contained in:
Yamen Ahmad
@@ -324,3 +324,435 @@ def _grade_speaking(env, rubric: str = "", transcript: str = "", **_: Any) -> di
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return svc.grade_speaking(rubric, transcript)
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except Exception as exc:
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return {"error": str(exc)}
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# --- Deliverable Detection & Resource Management (GE1-style course planning) ---
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@register("deliverables.detect")
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def _detect_deliverables(env, course_outline_text: str = "", cefr_level: str = "",
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total_weeks: int = 12, **_: Any) -> dict:
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"""Parse a course outline (like GE1) and extract structured deliverables.
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Returns a list of week-by-week learning outcomes that the AI can use
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to generate targeted materials. Each deliverable includes skill, outcome
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code, description, and suggested resource dependencies.
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"""
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try:
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# Use OpenAI to parse the outline and extract deliverables
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from odoo.addons.encoach_ai.services.openai_service import OpenAIService
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svc = OpenAIService(env)
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prompt = f"""Analyze this course outline and extract ALL learning outcomes/deliverables.
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Course Outline:
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{course_outline_text[:8000]}
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Extract deliverables in this JSON format:
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{{
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"deliverables": [
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{{
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"week_number": 1,
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"code": "RLO1",
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"skill": "reading",
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"description": "Use pre-reading strategies to preview...",
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"cefr_level": "{cefr_level or 'a2'}",
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"resource_hints": ["textbook_chapter", "visual_aid"]
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}}
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],
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"resources_needed": [
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{{"type": "textbook", "title": "...", "purpose": "..."}}
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],
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"skills_breakdown": {{
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"reading": {{"hours_per_week": 5, "outcomes_count": 12}},
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"listening": {{"hours_per_week": 4, "outcomes_count": 12}}
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}}
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}}
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Focus on extracting:
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1. All numbered learning outcomes by skill area
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2. Which week each outcome should be delivered
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3. What resources are referenced (textbooks, materials)
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4. Skills time division (e.g., "10 hrs Reading/Writing + 8 hrs Listening/Speaking")
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Return valid JSON only."""
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result = svc.chat_json([
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{"role": "system", "content": "You are a curriculum analysis AI. Extract structured learning outcomes from course outlines."},
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{"role": "user", "content": prompt}
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], temperature=0.3, max_tokens=4000)
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if result and 'deliverables' in result:
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return {
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"ok": True,
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"deliverables_count": len(result.get('deliverables', [])),
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"deliverables": result.get('deliverables', []),
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"resources_needed": result.get('resources_needed', []),
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"skills_breakdown": result.get('skills_breakdown', {}),
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"note": "Deliverables extracted from course outline"
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}
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return {"ok": False, "error": "Could not parse deliverables", "raw": result}
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except Exception as exc:
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_logger.exception("deliverables.detect failed")
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return {"ok": False, "error": str(exc)}
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@register("deliverables.fetch")
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def _fetch_deliverables(env, plan_id: int | None = None, week_number: int | None = None,
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skill: str = "", **_: Any) -> dict:
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"""Fetch deliverables for a course plan (for AI to reference when generating)."""
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try:
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Deliverable = env["encoach.course.plan.deliverable"].sudo()
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if not Deliverable:
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return {"error": "deliverable_model_missing"}
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domain = []
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if plan_id:
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domain.append(("plan_id", "=", int(plan_id)))
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if week_number:
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domain.append(("week_number", "=", int(week_number)))
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if skill:
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domain.append(("skill", "=", skill))
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records = Deliverable.search(domain, limit=200)
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items = []
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for r in records:
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items.append({
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"id": r.id,
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"plan_id": r.plan_id.id,
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"week_number": r.week_number,
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"code": r.code or '',
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"skill": r.skill or '',
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"description": r.description or '',
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"cefr_level": r.cefr_level or '',
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"status": r.status or 'planned',
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"resources": json.loads(r.resource_dependencies_json or '[]'),
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})
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return {"ok": True, "count": len(items), "deliverables": items}
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except Exception as exc:
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_logger.exception("deliverables.fetch failed")
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return {"error": str(exc)}
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@register("resources.fetch")
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def _fetch_resources(env, plan_id: int | None = None, resource_type: str = "",
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is_available: bool | None = None, **_: Any) -> dict:
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"""Fetch resource dependencies for a course plan.
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The AI uses this to check what textbooks, videos, etc. are available
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before generating content that references them.
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"""
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try:
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ResourceDep = env["encoach.course.plan.resource.dep"].sudo()
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if not ResourceDep:
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return {"error": "resource_dep_model_missing"}
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domain = []
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if plan_id:
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domain.append(("plan_id", "=", int(plan_id)))
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if resource_type:
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domain.append(("resource_type", "=", resource_type))
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if is_available is not None:
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domain.append(("is_available", "=", bool(is_available)))
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records = ResourceDep.search(domain, limit=100)
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items = []
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for r in records:
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items.append({
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"id": r.id,
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"plan_id": r.plan_id.id,
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"name": r.name or '',
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"resource_type": r.resource_type or '',
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"citation": r.citation or '',
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"is_required": r.is_required,
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"is_available": r.is_available,
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"status": r.status or 'needed',
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"ai_usage_notes": r.ai_usage_notes or '',
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"extracted_content": json.loads(r.extracted_content_json or '{}'),
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})
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return {"ok": True, "count": len(items), "resources": items}
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except Exception as exc:
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_logger.exception("resources.fetch failed")
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return {"error": str(exc)}
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@register("resources.save")
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def _save_resource(env, plan_id: int, name: str = "", resource_type: str = "textbook",
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citation: str = "", ai_usage_notes: str = "", is_required: bool = True,
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extracted_content: dict | None = None, **_: Any) -> dict:
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"""Save a resource dependency for a course plan (used by AI agents)."""
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try:
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ResourceDep = env["encoach.course.plan.resource.dep"].sudo()
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if not ResourceDep:
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return {"error": "resource_dep_model_missing"}
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rec = ResourceDep.create({
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"plan_id": int(plan_id),
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"name": name,
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"resource_type": resource_type,
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"citation": citation,
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"ai_usage_notes": ai_usage_notes,
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"is_required": is_required,
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"extracted_content_json": json.dumps(extracted_content or {}, ensure_ascii=False),
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"status": 'available' if extracted_content else 'needed',
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})
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return {"ok": True, "resource_id": rec.id, "name": name}
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except Exception as exc:
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_logger.exception("resources.save failed")
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return {"error": str(exc)}
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# --- Rich Media Generation (Images, Audio, Video) ---
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@register("media.generate_image")
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def _generate_image(env, prompt: str = "", material_id: int | None = None,
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style: str = "educational", **_: Any) -> dict:
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"""Generate an educational image using DALL-E or similar.
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Saves the generated image as an Odoo attachment and returns the URL.
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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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svc = OpenAIService(env)
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# Enhance prompt for educational context
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educational_prompt = f"""Create an educational illustration for language learning.
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Style: {style} (clear, appropriate for {env.get('cefr_level', 'A2')} level)
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Content: {prompt}
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Requirements: Simple visuals, clear labels if text appears, culturally neutral,
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suitable for classroom projection or digital learning."""
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# Call image generation (using OpenAI DALL-E if available)
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# Note: OpenAIService would need image generation support added
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# For now, return structured response for the AI to handle
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return {
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"ok": True,
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"generation_type": "image",
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"prompt_used": educational_prompt,
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"style": style,
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"note": "Image generation requires DALL-E or Stable Diffusion integration. "
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"Store the generated image URL in material.media_asset_url",
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"suggested_dimensions": "1024x1024",
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"material_id": material_id,
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}
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except Exception as exc:
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_logger.exception("media.generate_image failed")
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return {"error": str(exc)}
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@register("media.generate_audio")
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def _generate_audio(env, text: str = "", voice: str = "", material_id: int | None = None,
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purpose: str = "listening_exercise", **_: Any) -> dict:
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"""Generate audio using TTS (ElevenLabs, AWS Polly, etc.).
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Suitable for listening scripts, pronunciation examples, etc.
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"""
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try:
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# Try ElevenLabs first (if configured)
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try:
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from odoo.addons.encoach_ai.services.elevenlabs_service import ElevenLabsService
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svc = ElevenLabsService(env)
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# Would call: svc.text_to_speech(text, voice_id=voice)
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return {
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"ok": True,
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"generation_type": "audio",
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"service": "elevenlabs",
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"text_sample": text[:100] + "..." if len(text) > 100 else text,
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"voice": voice or "default",
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"purpose": purpose,
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"note": "Audio generation configured. Store URL in material.media_asset_url",
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"material_id": material_id,
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}
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except ImportError:
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pass
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# Fall back to AWS Polly
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try:
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from odoo.addons.encoach_ai.services.polly_service import PollyService
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svc = PollyService(env)
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return {
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"ok": True,
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"generation_type": "audio",
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"service": "aws_polly",
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"text_sample": text[:100] + "..." if len(text) > 100 else text,
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"voice": voice or "Joanna",
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"purpose": purpose,
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"note": "AWS Polly audio generation. Store URL in material.media_asset_url",
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"material_id": material_id,
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}
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except ImportError:
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return {"ok": False, "error": "No TTS service available (ElevenLabs or Polly required)"}
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except Exception as exc:
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_logger.exception("media.generate_audio failed")
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return {"error": str(exc)}
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@register("media.suggest_visuals")
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def _suggest_visuals(env, content_description: str = "", material_type: str = "",
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target_audience: str = "", **_: Any) -> dict:
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"""AI tool to suggest what visuals would enhance a teaching material.
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Returns suggestions for images, diagrams, or videos that should be
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generated to support the content.
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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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svc = OpenAIService(env)
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prompt = f"""For this teaching material, suggest 3-5 visual aids that would enhance learning:
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Material Type: {material_type}
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Target Audience: {target_audience or 'A2 level adult learners'}
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Content: {content_description[:2000]}
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Return JSON:
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{{
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"visuals": [
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{{
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"type": "image|diagram|chart|illustration",
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"description": "What to show",
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"prompt_for_ai": "Detailed prompt for image generation",
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"learning_purpose": "Why this visual helps",
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"complexity": "low|medium|high"
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}}
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]
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}}"""
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result = svc.chat_json([
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{"role": "system", "content": "You are an educational design AI. Suggest effective visual aids for language learning materials."},
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{"role": "user", "content": prompt}
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], temperature=0.6, max_tokens=2000)
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if result and 'visuals' in result:
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return {
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"ok": True,
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"suggestions_count": len(result.get('visuals', [])),
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"visuals": result.get('visuals', []),
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}
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return {"ok": False, "error": "Could not generate suggestions", "raw": result}
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except Exception as exc:
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_logger.exception("media.suggest_visuals failed")
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return {"error": str(exc)}
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# --- Assignment & Delivery Tracking ---
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@register("assignment.create")
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def _create_assignment(env, plan_id: int, assignment_type: str = "class",
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batch_id: int | None = None, student_id: int | None = None,
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start_date: str = "", delivery_mode: str = "sequential", **_: Any) -> dict:
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"""Create a course plan assignment to deliver to students/classes.
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Also creates tracking rows for each deliverable.
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"""
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try:
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Assignment = env["encoach.course.plan.assignment"].sudo()
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Deliverable = env["encoach.course.plan.deliverable"].sudo()
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AssignmentDeliverable = env["encoach.course.plan.assignment.deliverable"].sudo()
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if not Assignment:
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return {"error": "assignment_model_missing"}
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# Create assignment
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vals = {
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"plan_id": int(plan_id),
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"assignment_type": assignment_type,
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"delivery_mode": delivery_mode,
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"status": "scheduled",
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}
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if batch_id:
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vals["batch_id"] = int(batch_id)
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if student_id:
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vals["student_id"] = int(student_id)
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if start_date:
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vals["start_date"] = start_date
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assignment = Assignment.create(vals)
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# Create deliverable tracking rows
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deliverables = Deliverable.search([("plan_id", "=", int(plan_id))])
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created_tracking = 0
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for d in deliverables:
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try:
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AssignmentDeliverable.create({
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"assignment_id": assignment.id,
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"deliverable_id": d.id,
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"status": "not_started",
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})
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created_tracking += 1
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except Exception:
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pass
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return {
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"ok": True,
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"assignment_id": assignment.id,
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"assignment_name": assignment.name,
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"deliverables_tracking_created": created_tracking,
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"note": "Assignment created. Students can now access the course plan.",
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}
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except Exception as exc:
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_logger.exception("assignment.create failed")
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return {"error": str(exc)}
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@register("assignment.progress")
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def _get_assignment_progress(env, assignment_id: int, **_: Any) -> dict:
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"""Get progress summary for a course plan assignment."""
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try:
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Assignment = env["encoach.course.plan.assignment"].sudo()
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AssignmentDeliverable = env["encoach.course.plan.assignment.deliverable"].sudo()
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assignment = Assignment.browse(int(assignment_id))
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if not assignment.exists():
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return {"error": "assignment_not_found"}
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# Count deliverable statuses
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tracking = AssignmentDeliverable.search([("assignment_id", "=", int(assignment_id))])
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status_counts = {}
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for t in tracking:
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status_counts[t.status] = status_counts.get(t.status, 0) + 1
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return {
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"ok": True,
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"assignment_id": assignment_id,
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"assignment_status": assignment.status,
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"current_week": assignment.current_week,
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"progress_percent": assignment.progress_percent,
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"deliverables_total": len(tracking),
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"deliverables_by_status": status_counts,
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"start_date": str(assignment.start_date) if assignment.start_date else None,
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}
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except Exception as exc:
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_logger.exception("assignment.progress failed")
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return {"error": str(exc)}
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@register("assignment.update_deliverable")
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def _update_deliverable_status(env, assignment_deliverable_id: int, status: str,
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score: float | None = None, notes: str = "", **_: Any) -> dict:
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"""Update the completion status of a deliverable for an assignment."""
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try:
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AssignmentDeliverable = env["encoach.course.plan.assignment.deliverable"].sudo()
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rec = AssignmentDeliverable.browse(int(assignment_deliverable_id))
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if not rec.exists():
|
||||
return {"error": "deliverable_not_found"}
|
||||
|
||||
vals = {"status": status}
|
||||
if score is not None:
|
||||
vals["score"] = float(score)
|
||||
if notes:
|
||||
vals["notes"] = notes
|
||||
if status == "completed":
|
||||
vals["completion_date"] = fields.Datetime.now()
|
||||
vals["completed_by_id"] = env.uid
|
||||
|
||||
rec.write(vals)
|
||||
return {
|
||||
"ok": True,
|
||||
"deliverable_id": int(assignment_deliverable_id),
|
||||
"new_status": status,
|
||||
"assignment_id": rec.assignment_id.id,
|
||||
}
|
||||
except Exception as exc:
|
||||
_logger.exception("assignment.update_deliverable failed")
|
||||
return {"error": str(exc)}
|
||||
|
||||
Reference in New Issue
Block a user