"""
MathPulse AI - Event-Driven Automation Engine

Processes educational workflows based on a diagnostic-first, risk-driven
intervention model.  Trigger points:

1. Diagnostic Assessment Completion  (highest priority)
2. Quiz / Assessment Submission       (continuous)
3. New Student Enrollment
4. External Data Import               (teacher action)
5. Admin Content Updates

Each event is routed to a dedicated handler that orchestrates
classification, quiz generation, notifications and dashboard updates.
"""

import os
import json
import math
import logging
import traceback
from typing import List, Optional, Dict, Any, Tuple
from datetime import datetime, timedelta

from pydantic import BaseModel, Field

logger = logging.getLogger("mathpulse.automation")

# ─── Constants ──────────────────────────────────────────────────

AT_RISK_THRESHOLD = 60          # < 60 % → At Risk
WEAK_TOPIC_THRESHOLD = 0.50     # < 50 % accuracy → weak topic
HIGH_RISK_RATIO = 0.75          # 75 %+ subjects at risk
MEDIUM_RISK_RATIO = 0.50        # 50-75 %

REMEDIAL_CONFIG = {
    "High":   {"questions": 15, "dist": {"easy": 60, "medium": 30, "hard": 10}},
    "Medium": {"questions": 12, "dist": {"easy": 50, "medium": 35, "hard": 15}},
    "Low":    {"questions": 10, "dist": {"easy": 40, "medium": 40, "hard": 20}},
}

# ─── Request / Response Models ──────────────────────────────────


class DiagnosticResult(BaseModel):
    """Per-subject score from diagnostic assessment."""
    subject: str
    score: float = Field(..., ge=0, le=100)


class DiagnosticCompletionPayload(BaseModel):
    """Payload sent when a student completes the diagnostic."""
    studentId: str
    results: List[DiagnosticResult]
    gradeLevel: str = "Grade 10"
    questionBreakdown: Optional[Dict[str, list]] = None   # topic → [{correct: bool, …}]


class QuizSubmissionPayload(BaseModel):
    """Payload sent on quiz / assessment submission."""
    studentId: str
    quizId: str
    subject: str
    score: float = Field(..., ge=0, le=100)
    totalQuestions: int
    correctAnswers: int
    timeSpentSeconds: int
    answers: Optional[List[Dict[str, Any]]] = None


class StudentEnrollmentPayload(BaseModel):
    """Payload sent when a new student account is created."""
    studentId: str
    name: str
    email: str
    gradeLevel: str = "Grade 10"
    teacherId: Optional[str] = None


class DataImportPayload(BaseModel):
    """Payload sent after a teacher uploads a spreadsheet."""
    teacherId: str
    students: List[Dict[str, Any]]       # parsed student rows
    columnMapping: Dict[str, str]


class ContentUpdatePayload(BaseModel):
    """Payload sent when admin performs CRUD on curriculum."""
    adminId: str
    action: str                           # create | update | delete
    contentType: str                      # lesson | quiz | module | subject
    contentId: str
    subjectId: Optional[str] = None
    details: Optional[str] = None


# ─── Risk classification helpers ─────────────────────────────────


class SubjectRiskClassification(BaseModel):
    status: str              # "At Risk" | "On Track"
    score: float
    confidence: float
    needsIntervention: bool


class AutomationResult(BaseModel):
    """Standardised result returned by every handler."""
    success: bool
    event: str
    studentId: Optional[str] = None
    message: str
    riskClassifications: Optional[Dict[str, Dict[str, Any]]] = None
    overallRisk: Optional[str] = None
    atRiskSubjects: Optional[List[str]] = None
    weakTopics: Optional[List[Dict[str, Any]]] = None
    learningPath: Optional[str] = None
    remedialQuizzesCreated: int = 0
    interventions: Optional[str] = None
    notifications: List[str] = Field(default_factory=list)


# ─── Automation Engine ──────────────────────────────────────────


class MathPulseAutomationEngine:
    """
    Stateless event-driven automation system.

    Each ``handle_*`` method is an independent, self-contained handler that
    receives a validated Pydantic payload and returns an ``AutomationResult``.
    Firebase / Hugging Face calls are only attempted when available.
    """

    # ────────────────────────────────────────────────────────────
    # 1. DIAGNOSTIC COMPLETION  (highest-priority)
    # ────────────────────────────────────────────────────────────

    async def handle_diagnostic_completion(
        self, payload: DiagnosticCompletionPayload
    ) -> AutomationResult:
        """
        Runs when a student completes the mandatory diagnostic.

        Steps:
        1. Classify per-subject risk
        2. Identify weak topics
        3. Compute overall risk
        4. Generate personalised learning path (AI)
        5. Create remedial quiz assignments
        6. Generate teacher intervention recommendations (AI)
        7. Persist everything & notify
        """
        student_id = payload.studentId
        logger.info(f"📊 DIAGNOSTIC COMPLETED for {student_id}")
        notifications: list[str] = []

        # 1 — subject-level risk
        risk_classifications = self._classify_subject_risks(payload.results)

        # 2 — weak topics
        weak_topics = self._identify_weak_topics(payload.questionBreakdown)

        # 3 — overall risk
        overall_risk = self._calculate_overall_risk(risk_classifications)

        at_risk_subjects = [
            subj for subj, data in risk_classifications.items()
            if data["status"] == "At Risk"
        ]

        # 4 — learning path (AI call)
        learning_path: Optional[str] = None
        if at_risk_subjects:
            learning_path = await self._generate_learning_path(
                at_risk_subjects, weak_topics, payload.gradeLevel
            )

        # 5 — remedial quizzes
        remedial_count = 0
        remedial_quizzes: list[dict] = []
        if at_risk_subjects:
            remedial_quizzes = self._build_remedial_quiz_configs(
                student_id, at_risk_subjects, overall_risk, payload.gradeLevel
            )
            remedial_count = len(remedial_quizzes)

        # 6 — teacher interventions (AI call)
        interventions: Optional[str] = None
        if at_risk_subjects:
            interventions = await self._generate_teacher_interventions(
                risk_classifications, weak_topics
            )

        # 7 — notification messages
        if at_risk_subjects:
            notifications.append(
                f"Diagnostic complete — {len(at_risk_subjects)} subject(s) flagged At Risk: "
                + ", ".join(at_risk_subjects)
            )
        else:
            notifications.append("Diagnostic complete — all subjects On Track!")

        logger.info(
            f"✅ DIAGNOSTIC PROCESSING COMPLETE for {student_id} | "
            f"Overall={overall_risk} | AtRisk={at_risk_subjects}"
        )

        return AutomationResult(
            success=True,
            event="diagnostic_completed",
            studentId=student_id,
            message=f"Diagnostic processed for {student_id}",
            riskClassifications=risk_classifications,
            overallRisk=overall_risk,
            atRiskSubjects=at_risk_subjects,
            weakTopics=weak_topics,
            learningPath=learning_path,
            remedialQuizzesCreated=remedial_count,
            interventions=interventions,
            notifications=notifications,
        )

    # ────────────────────────────────────────────────────────────
    # 2. QUIZ SUBMISSION  (continuous)
    # ────────────────────────────────────────────────────────────

    async def handle_quiz_submission(
        self, payload: QuizSubmissionPayload
    ) -> AutomationResult:
        """Recalculate risk for a subject after a quiz is submitted."""
        student_id = payload.studentId
        logger.info(f"📝 QUIZ SUBMITTED by {student_id} — {payload.subject} ({payload.score}%)")
        notifications: list[str] = []

        # Determine new status for this subject
        new_status = "At Risk" if payload.score < AT_RISK_THRESHOLD else "On Track"
        confidence = (
            (AT_RISK_THRESHOLD - payload.score) / AT_RISK_THRESHOLD
            if new_status == "At Risk"
            else (payload.score - AT_RISK_THRESHOLD) / (100 - AT_RISK_THRESHOLD)
        )

        risk_classifications = {
            payload.subject: {
                "status": new_status,
                "score": payload.score,
                "confidence": round(abs(confidence), 2),
                "needsIntervention": new_status == "At Risk",
            }
        }

        at_risk = [payload.subject] if new_status == "At Risk" else []

        if new_status == "At Risk":
            notifications.append(
                f"Quiz result: {payload.subject} scored {payload.score}% — status changed to At Risk"
            )
        else:
            notifications.append(
                f"Quiz result: {payload.subject} scored {payload.score}% — On Track"
            )

        return AutomationResult(
            success=True,
            event="quiz_submitted",
            studentId=student_id,
            message=f"Quiz processed for {student_id}",
            riskClassifications=risk_classifications,
            overallRisk=None,   # single-subject update — overall recalculated on frontend
            atRiskSubjects=at_risk,
            notifications=notifications,
        )

    # ────────────────────────────────────────────────────────────
    # 3. STUDENT ENROLLMENT
    # ────────────────────────────────────────────────────────────

    async def handle_student_enrollment(
        self, payload: StudentEnrollmentPayload
    ) -> AutomationResult:
        """
        Prepare a new student:
        - Create empty progress record skeleton
        - Initialise gamification (XP 0, Level 1, no streaks)
        - Flag as needing diagnostic
        """
        student_id = payload.studentId
        logger.info(f"🆕 NEW STUDENT ENROLLED: {student_id}")

        progress_skeleton = {
            "userId": student_id,
            "subjects": {},
            "lessons": {},
            "quizAttempts": [],
            "totalLessonsCompleted": 0,
            "totalQuizzesCompleted": 0,
            "averageScore": 0,
        }

        gamification_init = {
            "level": 1,
            "currentXP": 0,
            "totalXP": 0,
            "streak": 0,
            "hasTakenDiagnostic": False,
            "atRiskSubjects": [],
        }

        notifications: list[str] = [
            f"Welcome {payload.name}! Please complete the diagnostic assessment to personalise your learning path.",
        ]

        if payload.teacherId:
            notifications.append(
                f"New student {payload.name} enrolled — diagnostic pending."
            )

        return AutomationResult(
            success=True,
            event="student_enrolled",
            studentId=student_id,
            message=f"Student {payload.name} enrolled and initialised",
            notifications=notifications,
        )

    # ────────────────────────────────────────────────────────────
    # 4. DATA IMPORT  (teacher action)
    # ────────────────────────────────────────────────────────────

    async def handle_data_import(
        self, payload: DataImportPayload
    ) -> AutomationResult:
        """
        After a teacher uploads a spreadsheet, recalculate risk for every
        imported student and flag any status changes.
        """
        logger.info(f"📂 DATA IMPORT by teacher {payload.teacherId} — {len(payload.students)} students")
        notifications: list[str] = []
        newly_at_risk: list[str] = []

        for student_row in payload.students:
            name = student_row.get("name", "Unknown")
            avg_score = float(student_row.get("avgQuizScore", 0))
            if avg_score < AT_RISK_THRESHOLD:
                newly_at_risk.append(name)

        if newly_at_risk:
            notifications.append(
                f"Data import flagged {len(newly_at_risk)} student(s) as At Risk: "
                + ", ".join(newly_at_risk[:5])
                + ("…" if len(newly_at_risk) > 5 else "")
            )

        notifications.append(
            f"Data import complete — {len(payload.students)} student records processed."
        )

        return AutomationResult(
            success=True,
            event="data_imported",
            studentId=None,
            message=f"Data import processed for {len(payload.students)} students",
            atRiskSubjects=None,
            notifications=notifications,
        )

    # ────────────────────────────────────────────────────────────
    # 5. CONTENT UPDATE  (admin action)
    # ────────────────────────────────────────────────────────────

    async def handle_content_update(
        self, payload: ContentUpdatePayload
    ) -> AutomationResult:
        """
        After admin CRUD on curriculum, log & notify.
        """
        logger.info(
            f"📚 CONTENT UPDATE by admin {payload.adminId}: "
            f"{payload.action} {payload.contentType} {payload.contentId}"
        )
        notifications: list[str] = [
            f"Curriculum update: {payload.action}d {payload.contentType} "
            f"({payload.contentId}). Teachers may want to review affected quizzes.",
        ]

        return AutomationResult(
            success=True,
            event="content_updated",
            studentId=None,
            message=f"Content {payload.action} processed for {payload.contentType}",
            notifications=notifications,
        )

    # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
    #  INTERNAL HELPERS
    # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

    # --- risk classification ---

    @staticmethod
    def _classify_subject_risks(
        results: List[DiagnosticResult],
    ) -> Dict[str, Dict[str, Any]]:
        """Classify each subject as 'At Risk' or 'On Track'."""
        classifications: Dict[str, Dict[str, Any]] = {}
        for r in results:
            if r.score < AT_RISK_THRESHOLD:
                status = "At Risk"
                confidence = round((AT_RISK_THRESHOLD - r.score) / AT_RISK_THRESHOLD, 2)
            else:
                status = "On Track"
                confidence = round(
                    (r.score - AT_RISK_THRESHOLD) / (100 - AT_RISK_THRESHOLD), 2
                )
            classifications[r.subject] = {
                "status": status,
                "score": r.score,
                "confidence": confidence,
                "needsIntervention": status == "At Risk",
            }
        return classifications

    @staticmethod
    def _identify_weak_topics(
        question_breakdown: Optional[Dict[str, list]],
    ) -> List[Dict[str, Any]]:
        """
        Drill into per-topic accuracy from diagnostic question-level data.
        Returns topics sorted weakest-first.
        """
        if not question_breakdown:
            return []

        weak: list[dict] = []
        for topic, questions in question_breakdown.items():
            if not questions:
                continue
            correct_count = sum(1 for q in questions if q.get("correct"))
            accuracy = correct_count / len(questions)
            if accuracy < WEAK_TOPIC_THRESHOLD:
                weak.append({
                    "topic": topic,
                    "accuracy": round(accuracy, 2),
                    "questionsAttempted": len(questions),
                    "priority": "high" if accuracy < 0.3 else "medium",
                })
        weak.sort(key=lambda x: x["accuracy"])
        return weak

    @staticmethod
    def _calculate_overall_risk(
        classifications: Dict[str, Dict[str, Any]],
    ) -> str:
        total = len(classifications)
        if total == 0:
            return "Low"
        at_risk_count = sum(
            1 for d in classifications.values() if d["status"] == "At Risk"
        )
        ratio = at_risk_count / total
        if ratio >= HIGH_RISK_RATIO:
            return "High"
        elif ratio >= MEDIUM_RISK_RATIO:
            return "Medium"
        return "Low"

    # --- remedial quiz configs ---

    @staticmethod
    def _build_remedial_quiz_configs(
        student_id: str,
        at_risk_subjects: List[str],
        overall_risk: str,
        grade_level: str,
    ) -> List[Dict[str, Any]]:
        """Return list of quiz configuration dicts ready for persistence."""
        cfg = REMEDIAL_CONFIG.get(overall_risk, REMEDIAL_CONFIG["Low"])
        quizzes: list[dict] = []
        for subject in at_risk_subjects:
            quizzes.append({
                "studentId": student_id,
                "subject": subject,
                "quizConfig": {
                    "topics": [subject],
                    "gradeLevel": grade_level,
                    "numQuestions": cfg["questions"],
                    "questionTypes": [
                        "identification",
                        "enumeration",
                        "multiple_choice",
                        "word_problem",
                    ],
                    "difficultyDistribution": cfg["dist"],
                    "bloomLevels": ["remember", "understand", "apply"],
                    "includeGraphs": False,
                    "excludeTopics": [],
                    "purpose": "remedial",
                    "targetStudent": student_id,
                },
                "status": "pending",
                "autoGenerated": True,
                "reason": f'Diagnostic identified "{subject}" as At Risk',
                "priority": "high" if overall_risk == "High" else "medium",
                "dueInDays": 7,
            })
        return quizzes

    # --- AI helpers (Hugging Face) ---

    async def _generate_learning_path(
        self,
        at_risk_subjects: List[str],
        weak_topics: List[Dict[str, Any]],
        grade_level: str,
    ) -> Optional[str]:
        """Generate a personalised learning path via HF Serverless Inference."""
        try:
            from main import call_hf_chat

            weakness_lines = ", ".join(at_risk_subjects)
            topic_lines = "\n".join(
                f"  - {t['topic']} ({t['accuracy']*100:.0f}% accuracy)"
                for t in weak_topics[:5]
            )

            prompt = (
                f"Generate a personalised math learning path for a {grade_level} student.\n\n"
                f"Weak subjects: {weakness_lines}\n"
                f"Weak topics:\n{topic_lines}\n\n"
                "Create 5-7 specific activities. For each give:\n"
                "1. Activity title\n"
                "2. Brief description (1-2 sentences)\n"
                "3. Estimated duration\n"
                "4. Type (video, practice, quiz, reading, interactive)\n\n"
                "Format as a numbered list. Be specific."
            )

            return call_hf_chat(
                messages=[
                    {
                        "role": "system",
                        "content": (
                            "You are an educational curriculum expert specialising in "
                            "mathematics. Create clear, actionable learning paths."
                        ),
                    },
                    {"role": "user", "content": prompt},
                ],
                max_tokens=1500,
                temperature=0.7,
            )
        except Exception as e:
            logger.warning(f"Learning-path AI call failed: {e}")
            return None

    async def _generate_teacher_interventions(
        self,
        risk_classifications: Dict[str, Dict[str, Any]],
        weak_topics: List[Dict[str, Any]],
    ) -> Optional[str]:
        """Generate teacher intervention recommendations via HF Serverless Inference."""
        try:
            from main import call_hf_chat

            at_risk = [
                subj for subj, data in risk_classifications.items()
                if data["status"] == "At Risk"
            ]
            topic_lines = "\n".join(
                f"- {t['topic']} ({t['accuracy']*100:.0f}% accuracy)"
                for t in weak_topics[:5]
            )

            prompt = (
                "You are an educational intervention specialist. A student has completed "
                "their diagnostic assessment with the following results:\n\n"
                f"At-Risk Subjects: {', '.join(at_risk)}\n\n"
                f"Weak Topics Identified:\n{topic_lines}\n\n"
                "Generate a 'Remedial Path Timeline' with:\n"
                "1. Prioritised list of topics to address (most critical first)\n"
                "2. Suggested teaching strategies for each topic\n"
                "3. Recommended one-on-one intervention activities\n"
                "4. Timeline for reassessment\n"
                "5. Warning signs that student needs additional support\n\n"
                "Keep response under 300 words, structured with clear sections."
            )

            return call_hf_chat(
                messages=[
                    {
                        "role": "system",
                        "content": (
                            "You are an expert educational intervention specialist. "
                            "Provide actionable, structured recommendations for teachers."
                        ),
                    },
                    {"role": "user", "content": prompt},
                ],
                max_tokens=1000,
                temperature=0.5,
            )
        except Exception as e:
            logger.warning(f"Teacher-intervention AI call failed: {e}")
            return None


# Module-level singleton
automation_engine = MathPulseAutomationEngine()