encoach_backend_v4/custom_addons/encoach_ai/services/coach_service.py

"""AI Coaching service — conversational assistant, tips, study suggestions."""

import json
import logging

_logger = logging.getLogger(__name__)


class CoachService:
    """High-level AI coaching: chat, tips, explanations, writing help, study plans."""

    def __init__(self, env):
        from .openai_service import OpenAIService
        self.env = env
        self.ai = OpenAIService(env)

    def _log(self, action, latency_ms=0, status="success", error=None, inp=None, out=None):
        try:
            self.env["encoach.ai.log"].sudo().create({
                "service": "coach",
                "action": action,
                "latency_ms": latency_ms,
                "status": status,
                "error_message": error,
                "input_preview": (inp or "")[:500],
                "output_preview": (out or "")[:500],
            })
        except Exception:
            _logger.warning("Failed to log coach call", exc_info=True)

    def chat(self, message, *, history=None, student_context=None):
        """Multi-turn coaching conversation with RAG context."""
        import time
        t0 = time.time()
        messages = [
            {"role": "system", "content": (
                "You are EnCoach AI — a friendly, expert IELTS and English learning coach. "
                "You help students with study strategies, explain concepts, motivate them, "
                "and answer questions about their learning journey. "
                "Be encouraging but honest. Keep responses concise (under 150 words). "
                "If asked about scores or progress, reference the student context provided."
            )},
        ]
        if student_context:
            messages.append({"role": "system", "content": f"Student context: {json.dumps(student_context)}"})
        for h in (history or []):
            messages.append({"role": h.get("role", "user"), "content": h["content"]})
        messages.append({"role": "user", "content": message})
        reply = self.ai.chat_with_context(
            messages, message,
            content_types=["course", "resource", "module", "feedback"],
            model=self.ai.fast_model, action="coach_chat", max_tokens=512,
        )
        self._log("coach_chat", int((time.time() - t0) * 1000), inp=message[:500], out=reply[:500])
        return {"reply": reply}

    def get_tip(self, context="general"):
        """Get a contextual learning tip, enriched with knowledge base content."""
        import time
        t0 = time.time()
        vector_context = self.ai._get_vector_context(context, content_types=["resource", "feedback"], limit=3)
        kb_text = self.ai._format_context(vector_context) if vector_context else ""

        system_prompt = (
            "Generate a single, practical English learning or IELTS preparation tip. "
            "Make it specific and actionable. Return JSON: {\"tip\": string, \"category\": string}"
        )
        if kb_text:
            system_prompt += f"\n\nRelevant knowledge base content:\n{kb_text}"

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": f"Context: {context}"},
        ]
        result = self.ai.chat_json(messages, model=self.ai.fast_model, action="coach_tip", max_tokens=256)
        self._log("coach_tip", int((time.time() - t0) * 1000), inp=context, out=json.dumps(result)[:500])
        return result

    def explain(self, score_data, student_context=""):
        """Explain a grade or assessment result."""
        import time
        t0 = time.time()
        explanation = self.ai.explain_grade(score_data, student_context)
        self._log("coach_explain", int((time.time() - t0) * 1000), out=explanation[:500])
        return {"explanation": explanation}

    def suggest(self, student_profile):
        """Suggest next study actions."""
        import time
        t0 = time.time()
        result = self.ai.suggest_study_plan(student_profile)
        self._log("coach_suggest", int((time.time() - t0) * 1000), out=json.dumps(result)[:500])
        return result

    def writing_help(self, task, draft, help_type="improve"):
        """Help with writing tasks."""
        import time
        t0 = time.time()
        result = self.ai.writing_help(task, draft, help_type)
        self._log("coach_writing", int((time.time() - t0) * 1000), inp=draft[:200], out=json.dumps(result)[:500])
        return result

    def get_hint(self, question_context):
        """Give a hint for a question without revealing the answer."""
        import time
        t0 = time.time()
        messages = [
            {"role": "system", "content": (
                "Give a helpful hint for this question WITHOUT revealing the answer. "
                "Guide the student's thinking. Return JSON: {\"hint\": string, \"strategy\": string}"
            )},
            {"role": "user", "content": json.dumps(question_context)},
        ]
        result = self.ai.chat_json(messages, model=self.ai.fast_model, action="coach_hint", max_tokens=256)
        self._log("coach_hint", int((time.time() - t0) * 1000), out=json.dumps(result)[:500])
        return result