Zabbix MCP Server

from typing import List, Dict, Any import plotly.graph_objects as go import plotly.express as px from datetime import datetime, timedelta import pandas as pd import json class VisualizationService: def __init__(self): self.colors = { 'critical': '#FF0000', 'high': '#FFA500', 'medium': '#FFFF00', 'low': '#00FF00' } def create_event_timeline(self, events: List[Dict[str, Any]]) -> str: """Tạo biểu đồ timeline cho các sự kiện""" df = pd.DataFrame(events) df['timestamp'] = pd.to_datetime(df['timestamp']) fig = go.Figure() for severity in ['critical', 'high', 'medium', 'low']: mask = df['severity'] == severity if mask.any(): fig.add_trace(go.Scatter( x=df[mask]['timestamp'], y=[severity] * len(df[mask]), mode='markers', name=severity.capitalize(), marker=dict( color=self.colors[severity], size=10 ) )) fig.update_layout( title='Event Timeline', xaxis_title='Time', yaxis_title='Severity', showlegend=True ) return fig.to_json() def create_severity_distribution(self, events: List[Dict[str, Any]]) -> str: """Tạo biểu đồ phân phối mức độ nghiêm trọng""" df = pd.DataFrame(events) severity_counts = df['severity'].value_counts() fig = go.Figure(data=[ go.Pie( labels=severity_counts.index, values=severity_counts.values, hole=.3 ) ]) fig.update_layout( title='Severity Distribution', showlegend=True ) return fig.to_json() def create_trend_analysis(self, events: List[Dict[str, Any]], window: str = '1d') -> str: """Tạo biểu đồ phân tích xu hướng""" df = pd.DataFrame(events) df['timestamp'] = pd.to_datetime(df['timestamp']) df.set_index('timestamp', inplace=True) # Resample theo window trend = df.resample(window).size() fig = go.Figure() fig.add_trace(go.Scatter( x=trend.index, y=trend.values, mode='lines+markers', name='Event Count' )) fig.update_layout( title=f'Event Trend Analysis ({window})', xaxis_title='Time', yaxis_title='Number of Events', showlegend=True ) return fig.to_json() def create_host_analysis(self, events: List[Dict[str, Any]]) -> str: """Tạo biểu đồ phân tích theo host""" df = pd.DataFrame(events) host_counts = df['host'].value_counts() fig = go.Figure(data=[ go.Bar( x=host_counts.index, y=host_counts.values ) ]) fig.update_layout( title='Events by Host', xaxis_title='Host', yaxis_title='Number of Events', showlegend=False ) return fig.to_json() def create_analysis_report(self, events: List[Dict[str, Any]], analyses: List[Dict[str, Any]]) -> Dict[str, Any]: """Tạo báo cáo phân tích tổng hợp""" return { 'timeline': self.create_event_timeline(events), 'severity_distribution': self.create_severity_distribution(events), 'trend_analysis': self.create_trend_analysis(events), 'host_analysis': self.create_host_analysis(events), 'summary': { 'total_events': len(events), 'total_analyses': len(analyses), 'average_confidence': sum(a.get('confidence', 0) for a in analyses) / len(analyses) if analyses else 0, 'most_common_host': pd.DataFrame(events)['host'].mode().iloc[0] if events else None, 'most_common_severity': pd.DataFrame(events)['severity'].mode().iloc[0] if events else None } }