"""
Copyright 2024-2026 ReplyPier

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

from fastapi import APIRouter, Depends, Query, HTTPException
from sqlalchemy.orm import Session
from app.database import get_db
from app.core.auth import get_current_user, require_permissions
from app.services.feature_gate import check_feature_access
from app.models.user import User
from app.models.agent import Agent
from app.models.customer import Customer
from app.models.rating import Rating
from app.models.session_to_agent import SessionToAgent, SessionStatus
from app.models.chat_history import ChatHistory
from datetime import datetime, timedelta, timezone
from sqlalchemy import func, and_, or_, desc, distinct, case
from typing import Optional, List
from app.core.logger import get_logger
from uuid import UUID

logger = get_logger(__name__)
UPGRADE_MESSAGE = "Analytics are available on paid plans. Upgrade to see them."


def analytics_enabled(current_user: User = Depends(get_current_user), db: Session = Depends(get_db)) -> None:
    """Every analytics endpoint is a plan feature: the dashboard's upgrade
    overlay is not the gate, this is."""
    check_feature_access(db, current_user.organization_id, "analytics", UPGRADE_MESSAGE)


router = APIRouter(dependencies=[Depends(analytics_enabled)])

def get_time_range_dates(time_range: str) -> tuple[datetime, datetime]:
    """Get start and end dates based on time range"""
    # Use timezone-aware UTC so comparisons against timestamptz columns are correct
    # regardless of the database session timezone (naive bounds get interpreted in
    # the session tz, which silently drops recent rows when that tz isn't UTC).
    end_date = datetime.now(timezone.utc)
    
    if time_range == '24h':
        start_date = end_date - timedelta(days=1)
    elif time_range == '7d':
        start_date = end_date - timedelta(days=7)
    elif time_range == '30d':
        start_date = end_date - timedelta(days=30)
    else:  # 90d
        start_date = end_date - timedelta(days=90)
    
    return start_date, end_date

def get_interval(time_range: str) -> str:
    """Get SQL interval based on time range"""
    if time_range == '24h':
        return 'hour'
    elif time_range == '7d':
        return 'day'
    elif time_range == '30d':
        return 'day'
    else:  # 90d
        return 'week'

@router.get("/agent-performance")
async def get_agent_performance(
    time_range: str = Query('7d', regex='^(24h|7d|30d|90d)$'),
    db: Session = Depends(get_db),
    current_user: User = Depends(require_permissions("view_analytics"))
):
    """Get agent performance analytics data for the organization"""
    try:
        start_date, end_date = get_time_range_dates(time_range)
        org_id = current_user.organization_id

        # Get bot agent performance
        bot_agents_query = db.query(
            Agent.id,
            Agent.name,
            func.count(SessionToAgent.session_id).label('total_chats'),
            func.sum(
                case((SessionToAgent.status == SessionStatus.CLOSED, 1), else_=0)
            ).label('closed_chats'),
            func.avg(Rating.rating).label('avg_rating'),
            func.count(Rating.id).label('rating_count')
        ).outerjoin(
            SessionToAgent, 
            and_(
                SessionToAgent.agent_id == Agent.id,
                SessionToAgent.assigned_at.between(start_date, end_date)
            )
        ).outerjoin(
            Rating,
            and_(
                Rating.session_id == SessionToAgent.session_id,
                Rating.created_at.between(start_date, end_date)
            )
        ).filter(
            Agent.organization_id == org_id
        ).group_by(
            Agent.id
        ).order_by(
            desc('total_chats')
        )

        bot_agents = bot_agents_query.all()

        # Get human agent performance
        human_agents_query = db.query(
            User.id,
            User.full_name.label('name'),
            func.count(SessionToAgent.session_id).label('total_chats'),
            func.sum(
                case((SessionToAgent.status == SessionStatus.CLOSED, 1), else_=0)
            ).label('closed_chats'),
            func.avg(Rating.rating).label('avg_rating'),
            func.count(Rating.id).label('rating_count')
        ).outerjoin(
            SessionToAgent, 
            and_(
                SessionToAgent.user_id == User.id,
                SessionToAgent.assigned_at.between(start_date, end_date)
            )
        ).outerjoin(
            Rating,
            and_(
                Rating.session_id == SessionToAgent.session_id,
                Rating.created_at.between(start_date, end_date)
            )
        ).filter(
            User.organization_id == org_id
        ).group_by(
            User.id
        ).order_by(
            desc('total_chats')
        )

        human_agents = human_agents_query.all()

        # Format the results
        bot_results = []
        for agent in bot_agents:
            bot_results.append({
                "id": str(agent.id),
                "name": agent.name,
                "total_chats": agent.total_chats,
                "closed_chats": agent.closed_chats,
                "avg_rating": float(agent.avg_rating) if agent.avg_rating else 0,
                "rating_count": agent.rating_count
            })

        human_results = []
        for agent in human_agents:
            human_results.append({
                "id": str(agent.id),
                "name": agent.name or "Unknown User",
                "total_chats": agent.total_chats,
                "closed_chats": agent.closed_chats,
                "avg_rating": float(agent.avg_rating) if agent.avg_rating else 0,
                "rating_count": agent.rating_count
            })

        return {
            "bot_agents": bot_results,
            "human_agents": human_results,
            "time_range": time_range
        }
    except Exception as e:
        logger.error(f"Error getting agent performance analytics: {str(e)}")
        raise

@router.get("")
async def get_analytics(
    time_range: str = Query('7d', regex='^(24h|7d|30d|90d)$'),
    db: Session = Depends(get_db),
    current_user: User = Depends(require_permissions("view_analytics"))
):
    """Get analytics data for the organization"""
    try:
        start_date, end_date = get_time_range_dates(time_range)
        interval = get_interval(time_range)
        org_id = current_user.organization_id

        # Get conversation metrics
        conversations_query = db.query(
            func.count(SessionToAgent.session_id).label('total'),
            func.date_trunc(interval, SessionToAgent.assigned_at).label('period')
        ).filter(
            SessionToAgent.organization_id == org_id,
            SessionToAgent.assigned_at.between(start_date, end_date)
        ).group_by('period').order_by('period')

        conversations = conversations_query.all()
        
        # Get previous period data for comparison
        prev_start = start_date - (end_date - start_date)
        prev_total = db.query(func.count(SessionToAgent.session_id)).filter(
            SessionToAgent.organization_id == org_id,
            SessionToAgent.assigned_at.between(prev_start, start_date)
        ).scalar() or 0

        current_total = sum(c.total for c in conversations)
        conv_change = ((current_total - prev_total) / prev_total * 100) if prev_total > 0 else 0

        # Get AI chat closures
        ai_closures_query = db.query(
            func.count(SessionToAgent.session_id).label('total'),
            func.date_trunc(interval, SessionToAgent.updated_at).label('period')
        ).filter(
            SessionToAgent.organization_id == org_id,
            SessionToAgent.updated_at.between(start_date, end_date),
            SessionToAgent.status == SessionStatus.CLOSED,
            SessionToAgent.user_id == None  # AI closures have no user_id
        ).group_by('period').order_by('period')

        ai_closures = ai_closures_query.all()
        
        current_ai_closures = sum(c.total for c in ai_closures)
        prev_ai_closures = db.query(func.count(SessionToAgent.session_id)).filter(
            SessionToAgent.organization_id == org_id,
            SessionToAgent.updated_at.between(prev_start, start_date),
            SessionToAgent.status == SessionStatus.CLOSED,
            SessionToAgent.user_id == None
        ).scalar() or 0

        ai_closures_change = ((current_ai_closures - prev_ai_closures) / prev_ai_closures * 100) if prev_ai_closures > 0 else 0

        # Get human transfers
        transfers_query = db.query(
            func.count(SessionToAgent.session_id).label('total'),
            func.date_trunc(interval, SessionToAgent.updated_at).label('period')
        ).filter(
            SessionToAgent.organization_id == org_id,
            SessionToAgent.updated_at.between(start_date, end_date),
            SessionToAgent.user_id != None  # Human transfers have a user_id
        ).group_by('period').order_by('period')

        transfers = transfers_query.all()
        
        current_transfers = sum(t.total for t in transfers)
        prev_transfers = db.query(func.count(SessionToAgent.session_id)).filter(
            SessionToAgent.organization_id == org_id,
            SessionToAgent.updated_at.between(prev_start, start_date),
            SessionToAgent.user_id != None  # Human transfers have a user_id
        ).scalar() or 0

        transfers_change = ((current_transfers - prev_transfers) / prev_transfers * 100) if prev_transfers > 0 else 0

        # Get bot ratings
        bot_ratings_query = db.query(
            func.avg(Rating.rating).label('avg_rating'),
            func.count(Rating.id).label('total'),
            func.date_trunc(interval, Rating.created_at).label('period')
        ).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).filter(
            Rating.organization_id == org_id,
            Rating.created_at.between(start_date, end_date),
            SessionToAgent.user_id == None  # Bot sessions have no user_id
        ).group_by('period').order_by('period')

        bot_ratings = bot_ratings_query.all()

        # Get human ratings
        human_ratings_query = db.query(
            func.avg(Rating.rating).label('avg_rating'),
            func.count(Rating.id).label('total'),
            func.date_trunc(interval, Rating.created_at).label('period')
        ).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).filter(
            Rating.organization_id == org_id,
            Rating.created_at.between(start_date, end_date),
            SessionToAgent.user_id != None  # Human sessions have a user_id
        ).group_by('period').order_by('period')

        human_ratings = human_ratings_query.all()

        # Calculate rating averages and changes
        current_bot_avg = db.query(func.avg(Rating.rating)).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).filter(
            Rating.organization_id == org_id,
            Rating.created_at.between(start_date, end_date),
            SessionToAgent.user_id == None
        ).scalar() or 0

        prev_bot_avg = db.query(func.avg(Rating.rating)).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).filter(
            Rating.organization_id == org_id,
            Rating.created_at.between(prev_start, start_date),
            SessionToAgent.user_id == None
        ).scalar() or 0

        current_human_avg = db.query(func.avg(Rating.rating)).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).filter(
            Rating.organization_id == org_id,
            Rating.created_at.between(start_date, end_date),
            SessionToAgent.user_id != None
        ).scalar() or 0

        prev_human_avg = db.query(func.avg(Rating.rating)).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).filter(
            Rating.organization_id == org_id,
            Rating.created_at.between(prev_start, start_date),
            SessionToAgent.user_id != None
        ).scalar() or 0

        # Calculate rating counts
        bot_count = db.query(func.count(Rating.id)).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).filter(
            Rating.organization_id == org_id,
            SessionToAgent.user_id == None
        ).scalar() or 0

        human_count = db.query(func.count(Rating.id)).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).filter(
            Rating.organization_id == org_id,
            SessionToAgent.user_id != None
        ).scalar() or 0

        # Calculate rating changes
        bot_change = ((current_bot_avg - prev_bot_avg) / prev_bot_avg * 100) if prev_bot_avg > 0 else 0
        human_change = ((current_human_avg - prev_human_avg) / prev_human_avg * 100) if prev_human_avg > 0 else 0

        return {
            "conversations": {
                "total": current_total,
                "change": conv_change,
                "trend": "up" if conv_change >= 0 else "down",
                "data": [r.total for r in conversations],
                "labels": [r.period.strftime("%Y-%m-%d") for r in conversations]
            },
            "aiClosures": {
                "total": current_ai_closures,
                "change": ai_closures_change,
                "trend": "up" if ai_closures_change >= 0 else "down",
                "data": [r.total for r in ai_closures],
                "labels": [r.period.strftime("%Y-%m-%d") for r in ai_closures]
            },
            "transfers": {
                "total": current_transfers,
                "change": transfers_change,
                "trend": "up" if transfers_change >= 0 else "down",
                "data": [r.total for r in transfers],
                "labels": [r.period.strftime("%Y-%m-%d") for r in transfers]
            },
            "ratings": {
                "bot": {
                    "data": [float(r.avg_rating or 0) for r in bot_ratings],
                    "labels": [r.period.strftime("%Y-%m-%d") for r in bot_ratings],
                    "change": bot_change,
                    "trend": "up" if bot_change >= 0 else "down"
                },
                "human": {
                    "data": [float(r.avg_rating or 0) for r in human_ratings],
                    "labels": [r.period.strftime("%Y-%m-%d") for r in human_ratings],
                    "change": human_change,
                    "trend": "up" if human_change >= 0 else "down"
                },
                "bot_avg": float(current_bot_avg),
                "human_avg": float(current_human_avg),
                "bot_count": bot_count,
                "human_count": human_count,
                "bot_change": bot_change,
                "human_change": human_change,
                "bot_trend": "up" if bot_change >= 0 else "down",
                "human_trend": "up" if human_change >= 0 else "down"
            }
        }
    except Exception as e:
        logger.error(f"Error getting analytics: {str(e)}")
        raise 

@router.get("/customer-analytics")
async def get_customer_analytics(
    time_range: str = Query('7d', pattern='^(24h|7d|30d|90d)$'),
    page: int = Query(1, ge=1, description="Page number"),
    page_size: int = Query(10, ge=1, le=100, description="Items per page"),
    db: Session = Depends(get_db),
    current_user: User = Depends(require_permissions("view_analytics"))
):
    """Get customer analytics data for the organization"""
    try:
        start_date, end_date = get_time_range_dates(time_range)
        org_id = current_user.organization_id

        # Get base customer query
        customer_query = db.query(
            Customer.id,
            Customer.email,
            Customer.full_name,
            func.count(func.distinct(SessionToAgent.session_id)).label('total_chats'),
            func.max(SessionToAgent.assigned_at).label('last_interaction'),
            func.avg(Rating.rating).label('avg_rating'),
            func.count(Rating.id).label('rating_count')
        ).outerjoin(
            SessionToAgent, 
            and_(
                Customer.id == SessionToAgent.customer_id,
                SessionToAgent.assigned_at.between(start_date, end_date)
            )
        ).outerjoin(
            Rating,
            and_(
                Rating.customer_id == Customer.id,
                Rating.organization_id == Customer.organization_id
            )
        ).filter(
            Customer.organization_id == org_id
        ).group_by(
            Customer.id
        ).order_by(
            Customer.created_at.desc()
        )

        # Get total count for pagination
        total_count = customer_query.count()
        
        # Apply pagination
        customers = customer_query.offset((page - 1) * page_size).limit(page_size).all()

        result = []
        for customer in customers:
            result.append({
                "id": str(customer.id),
                "email": customer.email,
                "full_name": customer.full_name,
                "total_chats": customer.total_chats,
                "last_interaction": customer.last_interaction.isoformat() if customer.last_interaction else None,
                "avg_rating": float(customer.avg_rating) if customer.avg_rating else 0,
                "rating_count": customer.rating_count
            })

        return {
            "customers": result,
            "time_range": time_range,
            "pagination": {
                "page": page,
                "page_size": page_size,
                "total_count": total_count,
                "total_pages": (total_count + page_size - 1) // page_size
            }
        }
    except Exception as e:
        logger.error(f"Error getting customer analytics: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error getting customer analytics: {str(e)}")

@router.get("/customer-details/{customer_id}")
async def get_customer_details(
    customer_id: UUID,
    time_range: str = Query('7d', regex='^(24h|7d|30d|90d)$'),
    db: Session = Depends(get_db),
    current_user: User = Depends(require_permissions("view_analytics"))
):
    """Get detailed information about a specific customer"""
    try:
        start_date, end_date = get_time_range_dates(time_range)
        org_id = current_user.organization_id

        # Verify customer belongs to organization
        customer = db.query(Customer).filter(
            Customer.id == customer_id,
            Customer.organization_id == org_id
        ).first()

        if not customer:
            raise HTTPException(status_code=404, detail="Customer not found")

        # Get customer feedback
        feedback_query = db.query(
            Rating.rating,
            Rating.feedback,
            Rating.created_at,
            Agent.name.label('agent_name'),
            User.full_name.label('user_name')
        ).join(
            SessionToAgent, Rating.session_id == SessionToAgent.session_id
        ).outerjoin(
            Agent, SessionToAgent.agent_id == Agent.id
        ).outerjoin(
            User, SessionToAgent.user_id == User.id
        ).filter(
            Rating.customer_id == customer_id,
            Rating.organization_id == org_id
        ).order_by(
            desc(Rating.created_at)
        )

        feedback = feedback_query.all()

        # Format the results
        feedback_results = []
        for item in feedback:
            agent_name = item.agent_name
            if item.user_name:  # If handled by a human agent
                agent_name = item.user_name
                
            feedback_results.append({
                "rating": item.rating,
                "feedback": item.feedback,
                "created_at": item.created_at.isoformat(),
                "agent_name": agent_name
            })

        return {
            "feedback": feedback_results
        }
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error getting customer details: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))


@router.get("/sentiment")
async def get_sentiment_analytics(
    time_range: str = Query('7d', regex='^(24h|7d|30d|90d)$'),
    db: Session = Depends(get_db),
    current_user: User = Depends(require_permissions("view_analytics"))
):
    """Get sentiment distribution and trends for the organization"""
    try:
        start_date, end_date = get_time_range_dates(time_range)
        interval = get_interval(time_range)
        org_id = current_user.organization_id

        # Sentiment distribution (counts of positive/neutral/negative)
        distribution = db.query(
            ChatHistory.sentiment_label,
            func.count(ChatHistory.id).label('count')
        ).filter(
            ChatHistory.organization_id == org_id,
            ChatHistory.created_at.between(start_date, end_date),
            ChatHistory.message_type == 'user',
            ChatHistory.sentiment_label.isnot(None)
        ).group_by(
            ChatHistory.sentiment_label
        ).all()

        dist_dict = {"positive": 0, "neutral": 0, "negative": 0}
        for row in distribution:
            if row.sentiment_label in dist_dict:
                dist_dict[row.sentiment_label] = row.count

        total_analyzed = sum(dist_dict.values())

        # Sentiment trend over time
        trend = db.query(
            func.date_trunc(interval, ChatHistory.created_at).label('period'),
            func.avg(ChatHistory.sentiment_score).label('avg_score'),
            func.count(ChatHistory.id).label('message_count')
        ).filter(
            ChatHistory.organization_id == org_id,
            ChatHistory.created_at.between(start_date, end_date),
            ChatHistory.message_type == 'user',
            ChatHistory.sentiment_score.isnot(None)
        ).group_by('period').order_by('period').all()

        # Average sentiment score
        avg_score = db.query(
            func.avg(ChatHistory.sentiment_score)
        ).filter(
            ChatHistory.organization_id == org_id,
            ChatHistory.created_at.between(start_date, end_date),
            ChatHistory.message_type == 'user',
            ChatHistory.sentiment_score.isnot(None)
        ).scalar() or 0.0

        # Previous period comparison
        prev_start = start_date - (end_date - start_date)
        prev_avg = db.query(
            func.avg(ChatHistory.sentiment_score)
        ).filter(
            ChatHistory.organization_id == org_id,
            ChatHistory.created_at.between(prev_start, start_date),
            ChatHistory.message_type == 'user',
            ChatHistory.sentiment_score.isnot(None)
        ).scalar() or 0.0

        score_change = ((float(avg_score) - float(prev_avg)) / abs(float(prev_avg)) * 100) if prev_avg else 0.0

        # Sessions with negative sentiment (for agent attention)
        negative_sessions = db.query(
            SessionToAgent.session_id,
            SessionToAgent.sentiment_label,
            SessionToAgent.sentiment_score,
            SessionToAgent.status
        ).filter(
            SessionToAgent.organization_id == org_id,
            SessionToAgent.assigned_at.between(start_date, end_date),
            SessionToAgent.sentiment_label == 'negative'
        ).order_by(
            SessionToAgent.sentiment_score.asc()
        ).limit(10).all()

        return {
            "distribution": dist_dict,
            "total_analyzed": total_analyzed,
            "avg_score": round(float(avg_score), 4),
            "score_change": round(score_change, 2),
            "score_trend": "up" if score_change >= 0 else "down",
            "trend": {
                "data": [round(float(r.avg_score or 0), 4) for r in trend],
                "labels": [r.period.strftime("%Y-%m-%d") for r in trend],
                "message_counts": [r.message_count for r in trend]
            },
            "negative_sessions": [
                {
                    "session_id": str(s.session_id),
                    "sentiment_label": s.sentiment_label,
                    "sentiment_score": round(float(s.sentiment_score), 4) if s.sentiment_score else None,
                    "status": s.status
                }
                for s in negative_sessions
            ],
            "time_range": time_range
        }
    except Exception as e:
        logger.error(f"Error getting sentiment analytics: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error getting sentiment analytics: {str(e)}")


@router.get("/session-sentiment/{session_id}")
async def get_session_sentiment(
    session_id: UUID,
    db: Session = Depends(get_db),
    current_user: User = Depends(require_permissions("view_analytics"))
):
    """Get sentiment breakdown for a specific session"""
    try:
        org_id = current_user.organization_id

        # Verify session belongs to organization
        session = db.query(SessionToAgent).filter(
            SessionToAgent.session_id == session_id,
            SessionToAgent.organization_id == org_id
        ).first()

        if not session:
            raise HTTPException(status_code=404, detail="Session not found")

        # Get per-message sentiment for customer messages
        messages = db.query(
            ChatHistory.id,
            ChatHistory.message,
            ChatHistory.sentiment_label,
            ChatHistory.sentiment_score,
            ChatHistory.created_at
        ).filter(
            ChatHistory.session_id == session_id,
            ChatHistory.message_type == 'user',
            ChatHistory.sentiment_label.isnot(None)
        ).order_by(
            ChatHistory.created_at.asc()
        ).all()

        return {
            "session_id": str(session_id),
            "overall_sentiment": {
                "label": session.sentiment_label,
                "score": round(float(session.sentiment_score), 4) if session.sentiment_score else None
            },
            "messages": [
                {
                    "id": m.id,
                    "message": m.message[:200] if m.message else None,  # Truncate for privacy
                    "sentiment_label": m.sentiment_label,
                    "sentiment_score": round(float(m.sentiment_score), 4) if m.sentiment_score else None,
                    "created_at": m.created_at.isoformat()
                }
                for m in messages
            ]
        }
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error getting session sentiment: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))