example_usage.py

#!/usr/bin/env python3
"""
Example usage of the Log Analyzer modules
Demonstrates how to use the log collector and LLM analyzer programmatically
"""

import yaml
from log_collector import LogCollector
from llm_analyzer import LLMAnalyzer


def main():
    print("=" * 60)
    print("Linux Log Analyzer - Example Usage")
    print("=" * 60)
    
    # Load configuration
    print("\n1. Loading configuration...")
    with open('config.yaml', 'r') as f:
        config = yaml.safe_load(f)
    print("✓ Configuration loaded")
    
    # Initialize log collector
    print("\n2. Initializing log collector...")
    collector = LogCollector(config)
    print("✓ Log collector initialized")
    
    # Collect logs
    print("\n3. Collecting logs from system...")
    logs = collector.collect_all_logs()
    print(f"✓ Collected {len(logs)} log entries")
    
    if not logs:
        print("\n⚠ No logs collected. Check your configuration and permissions.")
        return
    
    # Display sample logs
    print("\n4. Sample log entries:")
    print("-" * 60)
    for i, log in enumerate(logs[:5]):
        print(f"\nLog {i+1}:")
        print(f"  Timestamp: {log.timestamp}")
        print(f"  Source: {log.source}")
        print(f"  Priority: {log.priority}")
        print(f"  Message: {log.message[:100]}...")
    
    # Initialize LLM analyzer
    print("\n5. Initializing LLM analyzer...")
    analyzer = LLMAnalyzer(config)
    print("✓ LLM analyzer initialized")
    
    # Extract insights
    print("\n6. Extracting statistical insights...")
    insights = analyzer.extract_key_insights(logs)
    print(f"✓ Insights extracted:")
    print(f"  - Total logs: {insights['total_logs']}")
    print(f"  - Errors: {insights['error_count']}")
    print(f"  - Warnings: {insights['warning_count']}")
    print(f"  - Unique sources: {len(insights['sources'])}")
    
    # Perform LLM analysis
    print("\n7. Performing LLM analysis...")
    print("   (This requires a running LLM API endpoint)")
    
    try:
        result = analyzer.analyze_logs(logs[:50], analysis_type='summary')
        
        if result['success']:
            print("✓ Analysis completed successfully")
            print("\n" + "=" * 60)
            print("ANALYSIS RESULTS")
            print("=" * 60)
            print(result['analysis'])
            print("=" * 60)
            print(f"\nModel used: {result['model']}")
            print(f"Logs analyzed: {result['logs_analyzed']}")
        else:
            print(f"✗ Analysis failed: {result['error']}")
            print("\nNote: Make sure your LLM API endpoint is running and accessible.")
            print(f"Current endpoint: {config['llm']['api_base']}")
    
    except Exception as e:
        print(f"✗ Error during analysis: {e}")
        print("\nTroubleshooting:")
        print("1. Check if your LLM API is running")
        print("2. Verify the API endpoint in config.yaml")
        print("3. Ensure the API key is correct")
    
    print("\n" + "=" * 60)
    print("Example completed!")
    print("=" * 60)
    print("\nTo use the web interface, run:")
    print("  streamlit run app.py")


if __name__ == "__main__":
    main()

# Made with Bob