scripts/production/enterprise_analytics_dashboard.py

import asyncio
from collections import defaultdict
from datetime import datetime, timedelta
import logging
from typing import Any, Dict, List, Optional, Tuple
from fastapi import APIRouter, Depends, HTTPException, Request
import pandas as pd
import plotly.graph_objects as go
from pydantic import BaseModel, Field

logger = logging.getLogger(__name__)


# Analytics Models
class AnalyticsTimeRange(BaseModel):
    """Analytics Time Range"""

    start_date: str = Field(..., description="Start date (YYYY-MM-DD)")
    end_date: str = Field(..., description="End date (YYYY-MM-DD)")
    granularity: str = Field(
        "daily", description="Time granularity (hourly, daily, weekly, monthly)"
    )


class ChatMetrics(BaseModel):
    """Chat Conversation Metrics"""

    total_conversations: int = Field(0, description="Total conversations")
    active_conversations: int = Field(0, description="Active conversations")
    average_response_time: float = Field(0.0, description="Average response time in ms")
    user_satisfaction_score: float = Field(
        0.0, description="User satisfaction score (1-5)"
    )
    messages_per_conversation: float = Field(
        0.0, description="Average messages per conversation"
    )
    total_messages: int = Field(0, description="Total messages")
    active_users: int = Field(0, description="Active users")
    conversation_duration_avg: float = Field(
        0.0, description="Average conversation duration in seconds"
    )


class VoiceMetrics(BaseModel):
    """Voice Integration Metrics"""

    voice_commands_processed: int = Field(0, description="Voice commands processed")
    average_processing_time: float = Field(
        0.0, description="Average processing time in ms"
    )
    recognition_accuracy: float = Field(0.0, description="Speech recognition accuracy")
    tts_requests: int = Field(0, description="Text-to-speech requests")
    voice_messages_sent: int = Field(0, description="Voice messages sent")
    command_success_rate: float = Field(0.0, description="Command success rate")
    popular_commands: List[str] = Field(
        default_factory=list, description="Popular voice commands"
    )


class FileMetrics(BaseModel):
    """File Processing Metrics"""

    files_uploaded: int = Field(0, description="Files uploaded")
    images_processed: int = Field(0, description="Images processed")
    documents_analyzed: int = Field(0, description="Documents analyzed")
    audio_files_transcribed: int = Field(0, description="Audio files transcribed")
    total_storage_used_mb: float = Field(0.0, description="Total storage used in MB")
    average_file_size_kb: float = Field(0.0, description="Average file size in KB")
    file_processing_success_rate: float = Field(
        0.0, description="File processing success rate"
    )


class PerformanceMetrics(BaseModel):
    """System Performance Metrics"""

    uptime_percentage: float = Field(0.0, description="Uptime percentage")
    average_response_time_ms: float = Field(
        0.0, description="Average response time in ms"
    )
    concurrent_users: int = Field(0, description="Concurrent users")
    api_requests_per_minute: int = Field(0, description="API requests per minute")
    error_rate: float = Field(0.0, description="Error rate percentage")
    memory_usage_mb: int = Field(0, description="Memory usage in MB")
    cpu_usage_percent: float = Field(0.0, description="CPU usage percentage")


class UserBehaviorMetrics(BaseModel):
    """User Behavior Analytics"""

    user_retention_rate: float = Field(0.0, description="User retention rate")
    feature_adoption_rate: float = Field(0.0, description="Feature adoption rate")
    session_duration_avg: float = Field(0.0, description="Average session duration")
    daily_active_users: int = Field(0, description="Daily active users")
    monthly_active_users: int = Field(0, description="Monthly active users")
    user_engagement_score: float = Field(0.0, description="User engagement score")
    popular_features: List[str] = Field(
        default_factory=list, description="Popular features"
    )


class BusinessMetrics(BaseModel):
    """Business Performance Metrics"""

    roi_percentage: float = Field(0.0, description="Return on investment percentage")
    cost_savings: float = Field(0.0, description="Cost savings in USD")
    productivity_improvement: float = Field(
        0.0, description="Productivity improvement percentage"
    )
    support_ticket_reduction: float = Field(
        0.0, description="Support ticket reduction percentage"
    )
    user_satisfaction_trend: List[float] = Field(
        default_factory=list, description="User satisfaction trend"
    )
    feature_usage_growth: float = Field(
        0.0, description="Feature usage growth percentage"
    )


class AnalyticsSummary(BaseModel):
    """Comprehensive Analytics Summary"""

    timestamp: str = Field(..., description="Analytics generation timestamp")
    time_range: AnalyticsTimeRange
    chat_metrics: ChatMetrics
    voice_metrics: VoiceMetrics
    file_metrics: FileMetrics
    performance_metrics: PerformanceMetrics
    user_behavior_metrics: UserBehaviorMetrics
    business_metrics: BusinessMetrics
    overall_health_score: float = Field(
        0.0, description="Overall system health score (0-100)"
    )


class TrendAnalysis(BaseModel):
    """Trend Analysis Results"""

    metric_name: str = Field(..., description="Metric name")
    current_value: float = Field(0.0, description="Current value")
    previous_value: float = Field(0.0, description="Previous period value")
    change_percentage: float = Field(0.0, description="Change percentage")
    trend_direction: str = Field(
        "stable", description="Trend direction (up, down, stable)"
    )
    confidence_score: float = Field(0.0, description="Trend confidence score")


class AnomalyDetection(BaseModel):
    """Anomaly Detection Results"""

    metric_name: str = Field(..., description="Metric name")
    detected_at: str = Field(..., description="Detection timestamp")
    severity: str = Field(
        "low", description="Anomaly severity (low, medium, high, critical)"
    )
    description: str = Field(..., description="Anomaly description")
    suggested_action: str = Field(..., description="Suggested action")


class EnterpriseAnalyticsDashboard:
    """Enterprise Analytics Dashboard Service"""

    def __init__(self):
        self.router = APIRouter()
        self.analytics_data = defaultdict(list)
        self.setup_routes()

    def setup_routes(self):
        """Setup analytics dashboard routes"""
        self.router.add_api_route(
            "/analytics/dashboard/summary",
            self.get_dashboard_summary,
            methods=["POST"],
            summary="Get comprehensive analytics summary",
        )
        self.router.add_api_route(
            "/analytics/dashboard/chat-metrics",
            self.get_chat_metrics,
            methods=["POST"],
            summary="Get chat conversation metrics",
        )
        self.router.add_api_route(
            "/analytics/dashboard/voice-metrics",
            self.get_voice_metrics,
            methods=["POST"],
            summary="Get voice integration metrics",
        )
        self.router.add_api_route(
            "/analytics/dashboard/file-metrics",
            self.get_file_metrics,
            methods=["POST"],
            summary="Get file processing metrics",
        )
        self.router.add_api_route(
            "/analytics/dashboard/performance-metrics",
            self.get_performance_metrics,
            methods=["POST"],
            summary="Get system performance metrics",
        )
        self.router.add_api_route(
            "/analytics/dashboard/user-behavior",
            self.get_user_behavior_metrics,
            methods=["POST"],
            summary="Get user behavior analytics",
        )
        self.router.add_api_route(
            "/analytics/dashboard/business-metrics",
            self.get_business_metrics,
            methods=["POST"],
            summary="Get business performance metrics",
        )
        self.router.add_api_route(
            "/analytics/dashboard/trends",
            self.get_trend_analysis,
            methods=["POST"],
            summary="Get trend analysis",
        )
        self.router.add_api_route(
            "/analytics/dashboard/anomalies",
            self.get_anomaly_detection,
            methods=["POST"],
            summary="Get anomaly detection results",
        )
        self.router.add_api_route(
            "/analytics/dashboard/visualization/{chart_type}",
            self.get_visualization_data,
            methods=["POST"],
            summary="Get visualization data for charts",
        )
        self.router.add_api_route(
            "/analytics/dashboard/export",
            self.export_analytics_data,
            methods=["POST"],
            summary="Export analytics data",
        )

    async def get_dashboard_summary(
        self, time_range: AnalyticsTimeRange
    ) -> AnalyticsSummary:
        """Get comprehensive analytics dashboard summary"""
        try:
            # Generate mock analytics data
            chat_metrics = await self._generate_chat_metrics(time_range)
            voice_metrics = await self._generate_voice_metrics(time_range)
            file_metrics = await self._generate_file_metrics(time_range)
            performance_metrics = await self._generate_performance_metrics(time_range)
            user_behavior_metrics = await self._generate_user_behavior_metrics(
                time_range
            )
            business_metrics = await self._generate_business_metrics(time_range)

            # Calculate overall health score
            health_score = self._calculate_health_score(
                chat_metrics, performance_metrics, user_behavior_metrics
            )

            return AnalyticsSummary(
                timestamp=datetime.utcnow().isoformat(),
                time_range=time_range,
                chat_metrics=chat_metrics,
                voice_metrics=voice_metrics,
                file_metrics=file_metrics,
                performance_metrics=performance_metrics,
                user_behavior_metrics=user_behavior_metrics,
                business_metrics=business_metrics,
                overall_health_score=health_score,
            )

        except Exception as e:
            logger.error(f"Failed to generate dashboard summary: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate analytics summary"
            )

    async def get_chat_metrics(self, time_range: AnalyticsTimeRange) -> ChatMetrics:
        """Get chat conversation metrics"""
        try:
            return await self._generate_chat_metrics(time_range)
        except Exception as e:
            logger.error(f"Failed to generate chat metrics: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate chat metrics"
            )

    async def get_voice_metrics(self, time_range: AnalyticsTimeRange) -> VoiceMetrics:
        """Get voice integration metrics"""
        try:
            return await self._generate_voice_metrics(time_range)
        except Exception as e:
            logger.error(f"Failed to generate voice metrics: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate voice metrics"
            )

    async def get_file_metrics(self, time_range: AnalyticsTimeRange) -> FileMetrics:
        """Get file processing metrics"""
        try:
            return await self._generate_file_metrics(time_range)
        except Exception as e:
            logger.error(f"Failed to generate file metrics: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate file metrics"
            )

    async def get_performance_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> PerformanceMetrics:
        """Get system performance metrics"""
        try:
            return await self._generate_performance_metrics(time_range)
        except Exception as e:
            logger.error(f"Failed to generate performance metrics: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate performance metrics"
            )

    async def get_user_behavior_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> UserBehaviorMetrics:
        """Get user behavior analytics"""
        try:
            return await self._generate_user_behavior_metrics(time_range)
        except Exception as e:
            logger.error(f"Failed to generate user behavior metrics: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate user behavior metrics"
            )

    async def get_business_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> BusinessMetrics:
        """Get business performance metrics"""
        try:
            return await self._generate_business_metrics(time_range)
        except Exception as e:
            logger.error(f"Failed to generate business metrics: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate business metrics"
            )

    async def get_trend_analysis(
        self, time_range: AnalyticsTimeRange
    ) -> List[TrendAnalysis]:
        """Get trend analysis for key metrics"""
        try:
            return await self._generate_trend_analysis(time_range)
        except Exception as e:
            logger.error(f"Failed to generate trend analysis: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate trend analysis"
            )

    async def get_anomaly_detection(
        self, time_range: AnalyticsTimeRange
    ) -> List[AnomalyDetection]:
        """Get anomaly detection results"""
        try:
            return await self._generate_anomaly_detection(time_range)
        except Exception as e:
            logger.error(f"Failed to generate anomaly detection: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate anomaly detection"
            )

    async def get_visualization_data(
        self, chart_type: str, time_range: AnalyticsTimeRange
    ) -> Dict[str, Any]:
        """Get visualization data for charts"""
        try:
            return await self._generate_visualization_data(chart_type, time_range)
        except Exception as e:
            logger.error(f"Failed to generate visualization data: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to generate visualization data"
            )

    async def export_analytics_data(
        self, time_range: AnalyticsTimeRange, format: str = "json"
    ) -> Dict[str, Any]:
        """Export analytics data in specified format"""
        try:
            return await self._export_analytics_data(time_range, format)
        except Exception as e:
            logger.error(f"Failed to export analytics data: {e}")
            raise HTTPException(
                status_code=500, detail="Failed to export analytics data"
            )

    async def _generate_chat_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> ChatMetrics:
        """Generate chat conversation metrics"""
        # Mock data - in production, query from database
        return ChatMetrics(
            total_conversations=1500,
            active_conversations=45,
            average_response_time=180.5,
            user_satisfaction_score=4.7,
            messages_per_conversation=8.3,
            total_messages=12450,
            active_users=89,
            conversation_duration_avg=420.2,
        )

    async def _generate_voice_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> VoiceMetrics:
        """Generate voice integration metrics"""
        # Mock data - in production, query from database
        return VoiceMetrics(
            voice_commands_processed=450,
            average_processing_time=1200.5,
            recognition_accuracy=0.92,
            tts_requests=280,
            voice_messages_sent=670,
            command_success_rate=0.88,
            popular_commands=[
                "create_task",
                "schedule_meeting",
                "search_information",
                "send_message",
                "set_reminder",
            ],
        )

    async def _generate_file_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> FileMetrics:
        """Generate file processing metrics"""
        # Mock data - in production, query from database
        return FileMetrics(
            files_uploaded=670,
            images_processed=230,
            documents_analyzed=310,
            audio_files_transcribed=130,
            total_storage_used_mb=245.7,
            average_file_size_kb=1560.3,
            file_processing_success_rate=0.96,
        )

    async def _generate_performance_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> PerformanceMetrics:
        """Generate system performance metrics"""
        # Mock data - in production, collect from monitoring system
        return PerformanceMetrics(
            uptime_percentage=99.9,
            average_response_time_ms=180.2,
            concurrent_users=25,
            api_requests_per_minute=45,
            error_rate=0.02,
            memory_usage_mb=245,
            cpu_usage_percent=12.5,
        )

    async def _generate_user_behavior_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> UserBehaviorMetrics:
        """Generate user behavior analytics"""
        # Mock data - in production, analyze user behavior patterns
        return UserBehaviorMetrics(
            user_retention_rate=0.85,
            feature_adoption_rate=0.72,
            session_duration_avg=1200.5,
            daily_active_users=150,
            monthly_active_users=450,
            user_engagement_score=4.3,
            popular_features=[
                "chat",
                "voice_commands",
                "file_upload",
                "workflow_automation",
                "search",
            ],
        )

    async def _generate_business_metrics(
        self, time_range: AnalyticsTimeRange
    ) -> BusinessMetrics:
        """Generate business performance metrics"""
        # Mock data - in production, calculate from business data
        return BusinessMetrics(
            roi_percentage=45.7,
            cost_savings=125000.0,
            productivity_improvement=32.5,
            support_ticket_reduction=58.3,
            user_satisfaction_trend=[4.2, 4.3, 4.5, 4.6, 4.7],
            feature_usage_growth=28.9,
        )

    async def _generate_trend_analysis(
        self, time_range: AnalyticsTimeRange
    ) -> List[TrendAnalysis]:
        """Generate trend analysis for key metrics"""
        trends = [
            TrendAnalysis(
                metric_name="user_satisfaction_score",
                current_value=4.7,
                previous_value=4.5,
                change_percentage=4.4,
                trend_direction="up",
                confidence_score=0.92,
            ),
            TrendAnalysis(
                metric_name="average_response_time",
                current_value=180.5,
                previous_value=195.2,
                change_percentage=-7.5,
                trend_direction="down",
                confidence_score=0.88,
            ),
            TrendAnalysis(
                metric_name="active_users",
                current_value=89,
                previous_value=85,
                change_percentage=4.7,
                trend_direction="up",
                confidence_score=0.85,
            ),
            TrendAnalysis(
                metric_name="error_rate",
                current_value=0.02,
                previous_value=0.03,
                change_percentage=-33.3,
                trend_direction="down",
                confidence_score=0.90,
            ),
        ]
        return trends

    async def _generate_anomaly_detection(
        self, time_range: AnalyticsTimeRange
    ) -> List[AnomalyDetection]:
        """Generate anomaly detection results"""
        anomalies = [
            AnomalyDetection(
                metric_name="api_response_time",
                detected_at=datetime.utcnow().isoformat(),
                severity="medium",
                description="API response time increased by 45% in the last hour",
                suggested_action="Check server load and database performance",
            ),
            AnomalyDetection(
                metric_name="memory_usage",
                detected_at=datetime.utcnow().isoformat(),
                severity="low",
                description="Memory usage spike detected during peak hours",
                suggested_action="Monitor memory usage and consider scaling",
            ),
        ]
        return anomalies

    async def _generate_visualization_data(
        self, chart_type: str, time_range: AnalyticsTimeRange
    ) -> Dict[str, Any]:
        """Generate visualization data for charts"""
        if chart_type == "user_engagement":
            return {
                "chart_type": "line",
                "title": "User Engagement Over Time",
                "data": {
                    "labels": ["Week 1", "Week 2", "Week 3", "Week 4", "Current"],
                    "datasets": [
                        {
                            "label": "Daily Active Users",
                            "data": [120, 135, 142, 148, 150],
                            "borderColor": "rgb(75, 192, 192)",
                            "backgroundColor": "rgba(75, 192, 192, 0.2)",
                        }
                    ],
                },
            }
        elif chart_type == "response_time":
            return {
                "chart_type": "bar",
                "title": "Average Response Time by Feature",
                "data": {
                    "labels": ["Chat", "Voice", "File Upload", "Search", "Workflow"],
                    "datasets": [
                        {
                            "label": "Response Time (ms)",
                            "data": [180, 1200, 450, 320, 890],
                            "backgroundColor": [
                                "rgba(255, 99, 132, 0.8)",
                                "rgba(54, 162, 235, 0.8)",
                                "rgba(255, 205, 86, 0.8)",
                                "rgba(75, 192, 192, 0.8)",
                                "rgba(153, 102, 255, 0.8)",
                            ],
                        }
                    ],
                },
            }
        elif chart_type == "feature_usage":
            return {
                "chart_type": "doughnut",
                "title": "Feature Usage Distribution",
                "data": {
                    "labels": [
                        "Chat",
                        "Voice Commands",
                        "File Processing",
                        "Workflows",
                        "Search",
                    ],
                    "datasets": [
                        {
                            "data": [45, 25, 15, 10, 5],
                            "backgroundColor": [
                                "#FF6384",
                                "#36A2EB",
                                "#FFCE56",
                                "#4BC0C0",
                                "#9966FF",
                            ],
                        }
                    ],
                },
            }
        else:
            return {
                "chart_type": "line",
                "title": "Default Chart",
                "data": {"labels": [], "datasets": []},
            }

    async def _export_analytics_data(
        self, time_range: AnalyticsTimeRange, format: str = "json"
    ) -> Dict[str, Any]:
        """Export analytics data in specified format"""
        summary = await self.get_dashboard_summary(time_range)

        if format == "csv":
            # Generate CSV data
            import csv
            import io

            output = io.StringIO()
            writer = csv.writer(output)

            # Write header
            writer.writerow(["Metric Category", "Metric Name", "Value", "Timestamp"])

            # Write data
            metrics_data = [
                (
                    "Chat",
                    "Total Conversations",
                    summary.chat_metrics.total_conversations,
                    summary.timestamp,
                ),
                (
                    "Chat",
                    "Active Conversations",
                    summary.chat_metrics.active_conversations,
                    summary.timestamp,
                ),
                (
                    "Chat",
                    "Average Response Time",
                    summary.chat_metrics.average_response_time,
                    summary.timestamp,
                ),
                (
                    "Voice",
                    "Commands Processed",
                    summary.voice_metrics.voice_commands_processed,
                    summary.timestamp,
                ),
                (
                    "Voice",
                    "Recognition Accuracy",
                    summary.voice_metrics.recognition_accuracy,
                    summary.timestamp,
                ),
                (
                    "File",
                    "Files Uploaded",
                    summary.file_metrics.files_uploaded,
                    summary.timestamp,
                ),
                (
                    "File",
                    "Storage Used (MB)",
                    summary.file_metrics.total_storage_used_mb,
                    summary.timestamp,
                ),
                (
                    "Performance",
                    "Uptime Percentage",
                    summary.performance_metrics.uptime_percentage,
                    summary.timestamp,
                ),
                (
                    "Performance",
                    "Error Rate",
                    summary.performance_metrics.error_rate,
                    summary.timestamp,
                ),
                (
                    "Business",
                    "ROI Percentage",
                    summary.business_metrics.roi_percentage,
                    summary.timestamp,
                ),
                (
                    "Business",
                    "Cost Savings",
                    summary.business_metrics.cost_savings,
                    summary.timestamp,
                ),
            ]

            for category, name, value, timestamp in metrics_data:
                writer.writerow([category, name, value, timestamp])

            return {
                "format": "csv",
                "filename": f"analytics_export_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.csv",
                "data": output.getvalue(),
                "record_count": len(metrics_data),
            }
        else:
            # Default JSON format
            return {
                "format": "json",
                "filename": f"analytics_export_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.json",
                "data": summary.dict(),
                "record_count": 1,
            }

    def _calculate_health_score(
        self,
        chat_metrics: ChatMetrics,
        performance_metrics: PerformanceMetrics,
        user_behavior_metrics: UserBehaviorMetrics,
    ) -> float:
        """Calculate overall system health score"""
        # Weighted average of key metrics
        uptime_score = performance_metrics.uptime_percentage
        response_time_score = max(
            0, 100 - (performance_metrics.average_response_time_ms / 10)
        )
        user_satisfaction_score = (
            chat_metrics.user_satisfaction_score * 20
        )  # Convert 1-5 to 0-100
        error_rate_score = max(0, 100 - (performance_metrics.error_rate * 1000))
        engagement_score = (
            user_behavior_metrics.user_engagement_score * 20
        )  # Convert 1-5 to 0-100

        weights = {
            "uptime": 0.25,
            "response_time": 0.20,
            "user_satisfaction": 0.25,
            "error_rate": 0.15,
            "engagement": 0.15,
        }

        health_score = (
            uptime_score * weights["uptime"]
            + response_time_score * weights["response_time"]
            + user_satisfaction_score * weights["user_satisfaction"]
            + error_rate_score * weights["error_rate"]
            + engagement_score * weights["engagement"]
        )

        return round(health_score, 2)


# Initialize enterprise analytics dashboard
enterprise_analytics_dashboard = EnterpriseAnalyticsDashboard()

# Analytics API Router for inclusion in main application
router = enterprise_analytics_dashboard.router


# Additional analytics endpoints
@router.get("/analytics/dashboard/health")
async def analytics_dashboard_health():
    """Health check for analytics dashboard"""
    return {
        "status": "healthy",
        "service": "enterprise_analytics_dashboard",
        "available_metrics": [
            "chat_metrics",
            "voice_metrics",
            "file_metrics",
            "performance_metrics",
            "user_behavior_metrics",
            "business_metrics",
        ],
        "supported_charts": [
            "user_engagement",
            "response_time",
            "feature_usage",
        ],
        "export_formats": ["json", "csv"],
    }


@router.get("/analytics/dashboard/realtime")
async def get_realtime_metrics():
    """Get real-time analytics metrics"""
    # Mock real-time data
    return {
        "timestamp": datetime.utcnow().isoformat(),
        "active_conversations": 25,
        "concurrent_users": 89,
        "api_requests_per_minute": 45,
        "memory_usage_mb": 245,
        "cpu_usage_percent": 12.5,
        "response_time_ms": 180.2,
        "error_rate": 0.02,
    }


@router.post("/analytics/dashboard/predictive")
async def get_predictive_analytics(time_range: AnalyticsTimeRange):
    """Get predictive analytics and forecasts"""
    # Mock predictive data
    return {
        "timestamp": datetime.utcnow().isoformat(),
        "time_range": time_range,
        "predictions": {
            "user_growth": {
                "next_week": 165,
                "next_month": 195,
                "confidence": 0.85,
            },
            "storage_usage": {
                "next_week": 280.5,
                "next_month": 345.2,
                "confidence": 0.92,
            },
            "api_requests": {
                "next_week": 52,
                "next_month": 68,
                "confidence": 0.78,
            },
        },
        "recommendations": [
            "Consider scaling storage capacity in 2 weeks",
            "Monitor API rate limits for increased usage",
            "Optimize database queries for better performance",
        ],
    }


@router.get("/analytics/dashboard/comparison")
async def get_comparison_analytics(current_period: str, previous_period: str):
    """Get comparison analytics between periods"""
    # Mock comparison data
    return {
        "current_period": current_period,
        "previous_period": previous_period,
        "comparisons": {
            "active_users": {"current": 150, "previous": 135, "change": 11.1},
            "user_satisfaction": {"current": 4.7, "previous": 4.5, "change": 4.4},
            "response_time": {"current": 180.5, "previous": 195.2, "change": -7.5},
            "error_rate": {"current": 0.02, "previous": 0.03, "change": -33.3},
        },
        "insights": [
            "User satisfaction improved by 4.4% compared to previous period",
            "Response time decreased by 7.5%, indicating performance improvements",
            "Error rate reduced by 33.3%, showing increased system stability",
        ],
    }


logger.info("Enterprise Analytics Dashboard initialized")