"""
Enhanced visualization engine for ARF Demo
"""
import plotly.graph_objects as go
import plotly.express as px
import numpy as np
import pandas as pd
from typing import Dict, List, Any, Optional
import logging

logger = logging.getLogger(__name__)


class EnhancedVisualizationEngine:
    """Enhanced visualization engine with multiple chart types"""
    
    def __init__(self):
        self.color_palette = {
            "primary": "#3b82f6",
            "success": "#10b981",
            "warning": "#f59e0b",
            "danger": "#ef4444",
            "info": "#8b5cf6",
            "dark": "#1e293b",
            "light": "#f8fafc"
        }
    
    def create_executive_dashboard(self, data: Optional[Dict] = None) -> go.Figure:
        """Create executive dashboard with ROI visualization"""
        if data is None:
            data = {"roi_multiplier": 5.2}
        
        roi_multiplier = data.get("roi_multiplier", 5.2)
        
        # Create a multi-panel executive dashboard
        fig = go.Figure()
        
        # Main ROI gauge
        fig.add_trace(go.Indicator(
            mode="number+gauge",
            value=roi_multiplier,
            title={"text": "<b>ROI Multiplier</b><br>Investment Return"},
            domain={'x': [0.25, 0.75], 'y': [0.6, 1]},
            gauge={
                'axis': {'range': [0, 10], 'tickwidth': 1},
                'bar': {'color': self.color_palette["success"]},
                'steps': [
                    {'range': [0, 2], 'color': '#e5e7eb'},
                    {'range': [2, 4], 'color': '#d1d5db'},
                    {'range': [4, 6], 'color': '#10b981'},
                    {'range': [6, 10], 'color': '#059669'}
                ],
                'threshold': {
                    'line': {'color': "black", 'width': 4},
                    'thickness': 0.75,
                    'value': roi_multiplier
                }
            }
        ))
        
        # Add secondary metrics as subplots
        fig.add_trace(go.Indicator(
            mode="number",
            value=85,
            title={"text": "MTTR Reduction"},
            number={'suffix': "%", 'font': {'size': 24}},
            domain={'x': [0.1, 0.4], 'y': [0.2, 0.5]}
        ))
        
        fig.add_trace(go.Indicator(
            mode="number",
            value=94,
            title={"text": "Detection Accuracy"},
            number={'suffix': "%", 'font': {'size': 24}},
            domain={'x': [0.6, 0.9], 'y': [0.2, 0.5]}
        ))
        
        fig.update_layout(
            height=700,
            paper_bgcolor="rgba(0,0,0,0)",
            plot_bgcolor="rgba(0,0,0,0)",
            font={'family': "Arial, sans-serif"},
            margin=dict(t=50, b=50, l=50, r=50)
        )
        
        return fig
    
    def create_telemetry_plot(self, scenario_name: str, anomaly_detected: bool = True) -> go.Figure:
        """Create telemetry plot for a scenario"""
        # Generate realistic telemetry data
        time_points = np.arange(0, 100, 1)
        
        # Different patterns for different scenarios
        if "Cache" in scenario_name:
            base_data = 100 + 50 * np.sin(time_points * 0.2)
            noise = np.random.normal(0, 8, 100)
            metric_name = "Cache Hit Rate (%)"
            normal_range = (70, 95)
        elif "Database" in scenario_name:
            base_data = 70 + 30 * np.sin(time_points * 0.15)
            noise = np.random.normal(0, 6, 100)
            metric_name = "Connection Pool Usage"
            normal_range = (20, 60)
        elif "Memory" in scenario_name:
            base_data = 50 + 40 * np.sin(time_points * 0.1)
            noise = np.random.normal(0, 10, 100)
            metric_name = "Memory Usage (%)"
            normal_range = (40, 80)
        else:
            base_data = 80 + 20 * np.sin(time_points * 0.25)
            noise = np.random.normal(0, 5, 100)
            metric_name = "System Load"
            normal_range = (50, 90)
        
        data = base_data + noise
        
        fig = go.Figure()
        
        if anomaly_detected:
            # Normal operation
            fig.add_trace(go.Scatter(
                x=time_points[:70],
                y=data[:70],
                mode='lines',
                name='Normal Operation',
                line=dict(color=self.color_palette["primary"], width=3),
                fill='tozeroy',
                fillcolor='rgba(59, 130, 246, 0.1)'
            ))
            
            # Anomaly period
            fig.add_trace(go.Scatter(
                x=time_points[70:],
                y=data[70:],
                mode='lines',
                name='Anomaly Detected',
                line=dict(color=self.color_palette["danger"], width=3, dash='dash'),
                fill='tozeroy',
                fillcolor='rgba(239, 68, 68, 0.1)'
            ))
            
            # Add detection point
            fig.add_vline(
                x=70,
                line_dash="dash",
                line_color=self.color_palette["success"],
                annotation_text="ARF Detection",
                annotation_position="top"
            )
        else:
            # All normal
            fig.add_trace(go.Scatter(
                x=time_points,
                y=data,
                mode='lines',
                name=metric_name,
                line=dict(color=self.color_palette["primary"], width=3),
                fill='tozeroy',
                fillcolor='rgba(59, 130, 246, 0.1)'
            ))
        
        # Add normal range
        fig.add_hrect(
            y0=normal_range[0],
            y1=normal_range[1],
            fillcolor="rgba(16, 185, 129, 0.1)",
            opacity=0.2,
            line_width=0,
            annotation_text="Normal Range",
            annotation_position="top left"
        )
        
        fig.update_layout(
            title=f"📈 {metric_name} - Live Telemetry",
            xaxis_title="Time (minutes)",
            yaxis_title=metric_name,
            height=300,
            margin=dict(l=20, r=20, t=50, b=20),
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)',
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            )
        )
        
        return fig
    
    def create_impact_gauge(self, scenario_name: str) -> go.Figure:
        """Create business impact gauge"""
        impact_map = {
            "Cache Miss Storm": {"revenue": 8500, "severity": "critical"},
            "Database Connection Pool Exhaustion": {"revenue": 4200, "severity": "high"},
            "Kubernetes Memory Leak": {"revenue": 5500, "severity": "high"},
            "API Rate Limit Storm": {"revenue": 3800, "severity": "medium"},
            "Network Partition": {"revenue": 12000, "severity": "critical"},
            "Storage I/O Saturation": {"revenue": 6800, "severity": "high"}
        }
        
        impact = impact_map.get(scenario_name, {"revenue": 5000, "severity": "medium"})
        
        fig = go.Figure(go.Indicator(
            mode="gauge+number",
            value=impact["revenue"],
            title={'text': "💰 Hourly Revenue Risk", 'font': {'size': 16}},
            number={'prefix': "$", 'font': {'size': 28}},
            gauge={
                'axis': {'range': [0, 15000], 'tickwidth': 1},
                'bar': {'color': self._get_severity_color(impact["severity"])},
                'steps': [
                    {'range': [0, 3000], 'color': '#10b981'},
                    {'range': [3000, 7000], 'color': '#f59e0b'},
                    {'range': [7000, 15000], 'color': '#ef4444'}
                ],
                'threshold': {
                    'line': {'color': "black", 'width': 4},
                    'thickness': 0.75,
                    'value': impact["revenue"]
                }
            }
        ))
        
        fig.update_layout(
            height=300,
            margin=dict(l=20, r=20, t=50, b=20),
            paper_bgcolor='rgba(0,0,0,0)'
        )
        
        return fig
    
    def create_agent_performance_chart(self) -> go.Figure:
        """Create agent performance comparison chart"""
        agents = ["Detection", "Recall", "Decision"]
        accuracy = [98.7, 92.0, 94.0]
        speed = [45, 30, 60]  # seconds
        confidence = [99.8, 92.0, 94.0]
        
        fig = go.Figure(data=[
            go.Bar(name='Accuracy (%)', x=agents, y=accuracy, 
                   marker_color=self.color_palette["primary"]),
            go.Bar(name='Speed (seconds)', x=agents, y=speed,
                   marker_color=self.color_palette["success"]),
            go.Bar(name='Confidence (%)', x=agents, y=confidence,
                   marker_color=self.color_palette["info"])
        ])
        
        fig.update_layout(
            title="🤖 Agent Performance Metrics",
            barmode='group',
            height=400,
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)',
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            )
        )
        
        return fig
    
    def create_timeline_comparison(self) -> go.Figure:
        """Create timeline comparison chart"""
        phases = ["Detection", "Analysis", "Decision", "Execution", "Recovery"]
        manual_times = [300, 1800, 1200, 1800, 3600]  # seconds
        arf_times = [45, 30, 60, 720, 0]
        
        # Convert to minutes for readability
        manual_times_min = [t/60 for t in manual_times]
        arf_times_min = [t/60 for t in arf_times]
        
        fig = go.Figure()
        
        fig.add_trace(go.Bar(
            name='Manual Process',
            x=phases,
            y=manual_times_min,
            marker_color=self.color_palette["danger"],
            text=[f"{t:.0f}m" for t in manual_times_min],
            textposition='auto'
        ))
        
        fig.add_trace(go.Bar(
            name='ARF Autonomous',
            x=phases,
            y=arf_times_min,
            marker_color=self.color_palette["success"],
            text=[f"{t:.0f}m" for t in arf_times_min],
            textposition='auto'
        ))
        
        total_manual = sum(manual_times_min)
        total_arf = sum(arf_times_min)
        
        fig.update_layout(
            title=f"⏰ Incident Timeline Comparison<br>"
                  f"<span style='font-size: 14px; color: #6b7280'>"
                  f"Total: {total_manual:.0f}m manual vs {total_arf:.0f}m ARF "
                  f"({((total_manual - total_arf) / total_manual * 100):.0f}% faster)</span>",
            barmode='group',
            height=400,
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)',
            legend=dict(
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            ),
            yaxis_title="Time (minutes)"
        )
        
        return fig
    
    def create_roi_simulation_chart(self, roi_data: Dict) -> go.Figure:
        """Create ROI simulation chart"""
        scenarios = ["Worst Case", "Base Case", "Best Case"]
        roi_values = [
            roi_data.get("worst_case", 4.0),
            roi_data.get("base_case", 5.2),
            roi_data.get("best_case", 6.5)
        ]
        
        fig = go.Figure(go.Bar(
            x=scenarios,
            y=roi_values,
            marker_color=[
                self.color_palette["warning"],
                self.color_palette["success"],
                self.color_palette["primary"]
            ],
            text=[f"{v:.1f}×" for v in roi_values],
            textposition='auto'
        ))
        
        fig.update_layout(
            title="📊 ROI Simulation Scenarios",
            yaxis_title="ROI Multiplier",
            height=400,
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)',
            yaxis=dict(range=[0, max(roi_values) * 1.2])
        )
        
        # Add industry average line
        fig.add_hline(
            y=5.2,
            line_dash="dash",
            line_color="gray",
            annotation_text="Industry Average",
            annotation_position="top right"
        )
        
        return fig
    
    def create_learning_graph(self, graph_type: str = "patterns") -> go.Figure:
        """Create learning engine visualization"""
        if graph_type == "patterns":
            return self._create_pattern_graph()
        elif graph_type == "dependencies":
            return self._create_dependency_graph()
        else:
            return self._create_action_graph()
    
    def _create_pattern_graph(self) -> go.Figure:
        """Create pattern recognition graph"""
        nodes = ["Cache Miss", "DB Pool", "Memory Leak", "API Limit", "Network"]
        connections = [
            ("Cache Miss", "DB Pool", 0.85),
            ("DB Pool", "Memory Leak", 0.72),
            ("Memory Leak", "API Limit", 0.65),
            ("API Limit", "Network", 0.58),
            ("Cache Miss", "Network", 0.45)
        ]
        
        fig = go.Figure()
        
        # Add nodes
        for node in nodes:
            fig.add_trace(go.Scatter(
                x=[np.random.random()],
                y=[np.random.random()],
                mode='markers+text',
                name=node,
                marker=dict(size=30, color=self.color_palette["primary"]),
                text=[node],
                textposition="top center"
            ))
        
        # Add edges
        for src, dst, weight in connections:
            fig.add_trace(go.Scatter(
                x=[np.random.random(), np.random.random()],
                y=[np.random.random(), np.random.random()],
                mode='lines',
                line=dict(width=weight * 5, color='gray'),
                showlegend=False
            ))
        
        fig.update_layout(
            title="🧠 RAG Memory - Incident Pattern Graph",
            height=500,
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)',
            showlegend=False,
            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
        )
        
        return fig
    
    def _create_dependency_graph(self) -> go.Figure:
        """Create system dependency graph"""
        fig = go.Figure(go.Sunburst(
            labels=["System", "Cache", "Database", "API", "User Service", "Payment"],
            parents=["", "System", "System", "System", "API", "API"],
            values=[100, 30, 40, 30, 15, 15],
            marker=dict(colors=px.colors.sequential.Blues)
        ))
        
        fig.update_layout(
            title="🔗 System Dependency Map",
            height=500,
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)'
        )
        
        return fig
    
    def _create_action_graph(self) -> go.Figure:
        """Create action-outcome graph"""
        actions = ["Scale Cache", "Restart DB", "Limit API", "Monitor Memory"]
        success_rates = [87, 92, 78, 85]
        
        fig = go.Figure(go.Bar(
            x=actions,
            y=success_rates,
            marker_color=self.color_palette["success"],
            text=[f"{rate}%" for rate in success_rates],
            textposition='auto'
        ))
        
        fig.update_layout(
            title="🎯 Action Success Rates",
            yaxis_title="Success Rate (%)",
            height=400,
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)',
            yaxis=dict(range=[0, 100])
        )
        
        return fig
    
    def _get_severity_color(self, severity: str) -> str:
        """Get color for severity level"""
        color_map = {
            "critical": self.color_palette["danger"],
            "high": self.color_palette["warning"],
            "medium": self.color_palette["info"],
            "low": self.color_palette["success"]
        }
        return color_map.get(severity.lower(), self.color_palette["info"])