resolutionflow/backend/app/api/endpoints/analytics.py

from datetime import datetime, timezone, timedelta
from uuid import UUID
from typing import Optional
from fastapi import APIRouter, Depends, HTTPException, Query
from sqlalchemy import select, func, case, cast, Date
from sqlalchemy.ext.asyncio import AsyncSession

from app.core.database import get_db
from app.api.deps import get_current_active_user
from app.models import User, Session, Tree, SessionRating
from app.schemas.analytics import (
    TeamAnalyticsResponse, PersonalAnalyticsResponse, FlowAnalyticsResponse,
    AnalyticsSummary, OutcomeBreakdown, TimeSeriesPoint,
    TopFlow, TopEngineer, StepFeedbackSummary, FlowRatingItem,
)

router = APIRouter(prefix="/analytics", tags=["analytics"])


def _get_period_start(period: str) -> datetime:
    days = {"7d": 7, "30d": 30, "90d": 90}.get(period, 30)
    return datetime.now(timezone.utc) - timedelta(days=days)


async def _build_summary(
    db: AsyncSession,
    base_filters: list,
    join_tree: bool = False,
) -> AnalyticsSummary:
    """Build analytics summary. If join_tree=True, joins Session->Tree for account scoping."""

    def _base_query():
        q = select(func.count()).select_from(Session)
        if join_tree:
            q = q.join(Tree, Session.tree_id == Tree.id)
        return q

    # Total sessions
    total_q = await db.execute(_base_query().where(*base_filters))
    total = total_q.scalar() or 0

    # Completed sessions
    completed_q = await db.execute(
        _base_query().where(*base_filters, Session.completed_at.isnot(None))
    )
    completed = completed_q.scalar() or 0

    # Median duration (minutes) using percentile_cont
    duration_base = select(
        func.percentile_cont(0.5).within_group(
            func.extract('epoch', Session.completed_at - Session.started_at) / 60
        )
    ).select_from(Session)
    if join_tree:
        duration_base = duration_base.join(Tree, Session.tree_id == Tree.id)
    duration_q = await db.execute(
        duration_base.where(*base_filters, Session.completed_at.isnot(None))
    )
    raw_median = duration_q.scalar()
    median_duration = round(float(raw_median), 1) if raw_median is not None else 0.0

    # Active engineers (distinct users)
    active_base = select(func.count(func.distinct(Session.user_id))).select_from(Session)
    if join_tree:
        active_base = active_base.join(Tree, Session.tree_id == Tree.id)
    active_q = await db.execute(active_base.where(*base_filters))
    active_engineers = active_q.scalar() or 0

    # Outcome breakdown
    outcome_base = select(Session.outcome, func.count()).select_from(Session)
    if join_tree:
        outcome_base = outcome_base.join(Tree, Session.tree_id == Tree.id)
    outcome_q = await db.execute(
        outcome_base.where(
            *base_filters, Session.completed_at.isnot(None), Session.outcome.isnot(None)
        ).group_by(Session.outcome)
    )
    outcomes = dict(outcome_q.all())

    return AnalyticsSummary(
        total_sessions=total,
        completed_sessions=completed,
        completion_rate=round(completed / total, 3) if total > 0 else 0.0,
        median_duration_minutes=median_duration,
        active_engineers=active_engineers,
        outcome_breakdown=OutcomeBreakdown(
            resolved=outcomes.get("resolved", 0),
            escalated=outcomes.get("escalated", 0),
            workaround=outcomes.get("workaround", 0),
            unresolved=outcomes.get("unresolved", 0),
        ),
    )


async def _build_time_series(
    db: AsyncSession,
    base_filters: list,
    join_tree: bool = False,
) -> list[TimeSeriesPoint]:
    """Build daily time-series using CASE expressions for outcome counting."""
    q = select(
        cast(Session.started_at, Date).label("date"),
        func.count().label("sessions"),
        func.sum(case((Session.outcome == "resolved", 1), else_=0)).label("resolved"),
        func.sum(case((Session.outcome == "escalated", 1), else_=0)).label("escalated"),
        func.sum(case((Session.outcome == "workaround", 1), else_=0)).label("workaround"),
        func.sum(case((Session.outcome == "unresolved", 1), else_=0)).label("unresolved"),
    ).select_from(Session)
    if join_tree:
        q = q.join(Tree, Session.tree_id == Tree.id)

    rows = await db.execute(
        q.where(*base_filters)
        .group_by(cast(Session.started_at, Date))
        .order_by(cast(Session.started_at, Date))
    )
    return [
        TimeSeriesPoint(
            date=str(row.date), sessions=row.sessions,
            resolved=int(row.resolved or 0), escalated=int(row.escalated or 0),
            workaround=int(row.workaround or 0), unresolved=int(row.unresolved or 0),
        )
        for row in rows.all()
    ]


@router.get("/team", response_model=TeamAnalyticsResponse)
async def get_team_analytics(
    period: str = Query("30d", pattern="^(7d|30d|90d)$"),
    engineer_id: Optional[UUID] = None,
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_active_user),
):
    """Team analytics - team_admin or super_admin only."""
    if not (current_user.is_team_admin or current_user.is_super_admin):
        raise HTTPException(status_code=403, detail="Team admin access required")

    period_start = _get_period_start(period)
    base_filters = [
        Session.started_at >= period_start,
        Tree.account_id == current_user.account_id,
    ]
    if engineer_id:
        base_filters.append(Session.user_id == engineer_id)

    summary = await _build_summary(db, base_filters, join_tree=True)
    time_series = await _build_time_series(db, base_filters, join_tree=True)

    # Top flows (join Session->Tree)
    top_flows_q = await db.execute(
        select(
            Tree.id, Tree.name,
            func.count(Session.id).label("sessions"),
            func.percentile_cont(0.5).within_group(
                func.extract('epoch', Session.completed_at - Session.started_at) / 60
            ).label("median_duration"),
        )
        .join(Tree, Session.tree_id == Tree.id)
        .where(Session.started_at >= period_start, Tree.account_id == current_user.account_id)
        .group_by(Tree.id, Tree.name)
        .order_by(func.count(Session.id).desc())
        .limit(10)
    )
    top_flows = []
    for row in top_flows_q.all():
        # Compute completion rate separately to avoid division by zero
        completed_count_q = await db.execute(
            select(func.count()).select_from(Session)
            .where(
                Session.tree_id == row.id,
                Session.started_at >= period_start,
                Session.completed_at.isnot(None),
            )
        )
        completed_count = completed_count_q.scalar() or 0
        completion_rate = round(completed_count / row.sessions, 3) if row.sessions > 0 else 0.0
        top_flows.append(
            TopFlow(
                tree_id=str(row.id), name=row.name, sessions=row.sessions,
                completion_rate=completion_rate,
                median_duration_minutes=round(float(row.median_duration or 0), 1),
            )
        )

    # Top engineers (join Session->User + Session->Tree)
    top_engineers_q = await db.execute(
        select(
            User.id, User.name,
            func.count(Session.id).label("sessions"),
            func.percentile_cont(0.5).within_group(
                func.extract('epoch', Session.completed_at - Session.started_at) / 60
            ).label("median_duration"),
        )
        .join(User, Session.user_id == User.id)
        .join(Tree, Session.tree_id == Tree.id)
        .where(Session.started_at >= period_start, Tree.account_id == current_user.account_id)
        .group_by(User.id, User.name)
        .order_by(func.count(Session.id).desc())
        .limit(10)
    )
    top_engineers = []
    for row in top_engineers_q.all():
        completed_count_q = await db.execute(
            select(func.count()).select_from(Session)
            .join(Tree, Session.tree_id == Tree.id)
            .where(
                Session.user_id == row.id,
                Session.started_at >= period_start,
                Tree.account_id == current_user.account_id,
                Session.completed_at.isnot(None),
            )
        )
        completed_count = completed_count_q.scalar() or 0
        completion_rate = round(completed_count / row.sessions, 3) if row.sessions > 0 else 0.0
        top_engineers.append(
            TopEngineer(
                user_id=str(row.id), name=row.name or "Unknown", sessions=row.sessions,
                completion_rate=completion_rate,
                median_duration_minutes=round(float(row.median_duration or 0), 1),
            )
        )

    return TeamAnalyticsResponse(
        summary=summary, time_series=time_series,
        top_flows=top_flows, top_engineers=top_engineers,
    )


@router.get("/me", response_model=PersonalAnalyticsResponse)
async def get_personal_analytics(
    period: str = Query("30d", pattern="^(7d|30d|90d)$"),
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_active_user),
):
    """Personal analytics - any authenticated user."""
    period_start = _get_period_start(period)
    base_filters = [Session.started_at >= period_start, Session.user_id == current_user.id]

    summary = await _build_summary(db, base_filters, join_tree=False)
    # Override active_engineers=1 for personal view
    summary.active_engineers = 1
    time_series = await _build_time_series(db, base_filters, join_tree=False)

    # Top flows
    top_flows_q = await db.execute(
        select(
            Tree.id, Tree.name,
            func.count(Session.id).label("sessions"),
            func.percentile_cont(0.5).within_group(
                func.extract('epoch', Session.completed_at - Session.started_at) / 60
            ).label("median_duration"),
        )
        .join(Tree, Session.tree_id == Tree.id)
        .where(Session.started_at >= period_start, Session.user_id == current_user.id)
        .group_by(Tree.id, Tree.name)
        .order_by(func.count(Session.id).desc())
        .limit(10)
    )
    top_flows = []
    for row in top_flows_q.all():
        completed_count_q = await db.execute(
            select(func.count()).select_from(Session)
            .where(
                Session.tree_id == row.id,
                Session.user_id == current_user.id,
                Session.started_at >= period_start,
                Session.completed_at.isnot(None),
            )
        )
        completed_count = completed_count_q.scalar() or 0
        completion_rate = round(completed_count / row.sessions, 3) if row.sessions > 0 else 0.0
        top_flows.append(
            TopFlow(
                tree_id=str(row.id), name=row.name, sessions=row.sessions,
                completion_rate=completion_rate,
                median_duration_minutes=round(float(row.median_duration or 0), 1),
            )
        )

    return PersonalAnalyticsResponse(
        summary=summary, time_series=time_series, top_flows=top_flows,
    )


@router.get("/flows/{tree_id}", response_model=FlowAnalyticsResponse)
async def get_flow_analytics(
    tree_id: UUID,
    period: str = Query("30d", pattern="^(7d|30d|90d)$"),
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_active_user),
):
    """Analytics for a specific flow."""
    # Verify tree exists
    result = await db.execute(select(Tree).where(Tree.id == tree_id))
    tree = result.scalar_one_or_none()
    if not tree:
        raise HTTPException(status_code=404, detail="Flow not found")

    period_start = _get_period_start(period)
    base_filters = [Session.started_at >= period_start, Session.tree_id == tree_id]

    summary = await _build_summary(db, base_filters, join_tree=False)
    time_series = await _build_time_series(db, base_filters, join_tree=False)

    # CSAT stats
    csat_q = await db.execute(
        select(func.avg(SessionRating.rating), func.count())
        .where(SessionRating.tree_id == tree_id, SessionRating.created_at >= period_start)
    )
    csat_row = csat_q.one()
    avg_csat = round(float(csat_row[0]), 1) if csat_row[0] else None
    total_ratings = csat_row[1]

    # Step feedback - compute from step_ratings for steps used in this tree's sessions
    step_feedback: list[StepFeedbackSummary] = []

    # Step dropoff analysis from session decisions JSONB
    sessions_q = await db.execute(
        select(Session.decisions, Session.completed_at)
        .where(Session.tree_id == tree_id, Session.started_at >= period_start)
    )
    sessions_data = sessions_q.all()

    node_visits: dict[str, int] = {}
    node_dropoffs: dict[str, int] = {}

    for sess in sessions_data:
        decisions = sess.decisions or []
        for decision in decisions:
            node_id = decision.get("node_id", "")
            if node_id:
                node_visits[node_id] = node_visits.get(node_id, 0) + 1

        # If session not completed, last decision node is a dropoff
        if not sess.completed_at and decisions:
            last_decision = decisions[-1]
            last_node = last_decision.get("node_id", "")
            if last_node:
                node_dropoffs[last_node] = node_dropoffs.get(last_node, 0) + 1

    # Build node title map from tree structure
    node_title_map = _extract_node_titles(tree.tree_structure)

    # Build step feedback with dropoff data
    for node_id in sorted(node_visits.keys()):
        visits = node_visits.get(node_id, 0)
        dropoffs = node_dropoffs.get(node_id, 0)
        step_feedback.append(StepFeedbackSummary(
            node_id=node_id,
            node_title=node_title_map.get(node_id, "Unknown Step"),
            helpful_yes=0,
            helpful_no=0,
            helpful_rate=0.0,
            visit_count=visits,
            dropoff_count=dropoffs,
            dropoff_rate=round(dropoffs / visits, 3) if visits > 0 else 0.0,
        ))

    # Sort by dropoff_rate descending
    step_feedback.sort(key=lambda x: x.dropoff_rate, reverse=True)

    # Recent comments - ANONYMOUS (no user_name join)
    comments_q = await db.execute(
        select(SessionRating.rating, SessionRating.comment, SessionRating.created_at)
        .where(
            SessionRating.tree_id == tree_id,
            SessionRating.comment.isnot(None),
            SessionRating.comment != "",
        )
        .order_by(SessionRating.created_at.desc())
        .limit(10)
    )
    recent_comments = [
        FlowRatingItem(
            rating=row.rating,
            comment=row.comment,
            created_at=row.created_at,
        )
        for row in comments_q.all()
    ]

    return FlowAnalyticsResponse(
        summary=summary, avg_csat=avg_csat, total_ratings=total_ratings,
        time_series=time_series, step_feedback=step_feedback,
        recent_comments=recent_comments,
    )


def _extract_node_titles(tree_structure: dict) -> dict[str, str]:
    """Recursively extract node_id -> title/question from tree structure."""
    titles = {}

    def walk(node):
        if not isinstance(node, dict):
            return
        node_id = node.get("id", "")
        title = node.get("title") or node.get("question") or "Unnamed"
        if node_id:
            titles[node_id] = title
        for child in node.get("children", []):
            walk(child)

    walk(tree_structure)
    return titles