AI-Powered Logging and Testing Dashboard

A standalone application for monitoring and analyzing logs, tests, and code quality using AI-powered insights.

Features

• Real-time log monitoring and analysis
• Test coverage and results visualization
• Security vulnerability detection
• Code quality metrics
• AI-powered chat interface for data analysis
• Interactive dashboard with visualizations
• Integration with DeepSeek R1 via OLLAMA

System Requirements

Minimum Requirements

• CPU: 4 cores
• RAM: 8GB
• Storage: 20GB
• GPU: Optional (CPU-only mode supported)

Recommended Requirements

• CPU: 8+ cores
• RAM: 16GB+
• Storage: 50GB+
• GPU: NVIDIA GPU with 8GB+ VRAM

Setup

1. OLLAMA Setup

Standard Installation

# Install OLLAMA
curl https://ollama.ai/install.sh | sh

# Pull DeepSeek R1 model
ollama pull deepseek-coder:r1

# Verify installation
ollama list

Low-Resource Installation

For systems with limited resources, use the quantized model:

# Pull quantized model (requires less VRAM)
ollama pull deepseek-coder:r1-q4_K_M

Performance Optimization

Add these environment variables for better performance:

# For NVIDIA GPUs
export CUDA_VISIBLE_DEVICES=0
export OLLAMA_HOST=0.0.0.0
export OLLAMA_MODELS=/path/to/models

# For CPU-only systems
export OLLAMA_CPU_THREADS=4
export OLLAMA_GPU_LAYERS=0

2. Backend Setup

1. Create a virtual environment:

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment variables:

cp .env.example .env
# Edit .env with your OLLAMA configuration

4. Run the Flask application:

flask run

The backend will be available at http://localhost:5000

3. Frontend Setup

1. Install dependencies:

cd frontend
npm install

2. Start the development server:

npm start

The frontend will be available at http://localhost:3000

System Under Test (SUT) Integration

1. Log Collection

Add logging configuration to your SUT:

# Example logging configuration
import logging
from logging.handlers import RotatingFileHandler

def setup_logging():
    logger = logging.getLogger('sut')
    logger.setLevel(logging.INFO)
    
    # File handler
    handler = RotatingFileHandler(
        'logs/sut.log',
        maxBytes=10000000,
        backupCount=5
    )
    
    # Format
    formatter = logging.Formatter(
        '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
    )
    handler.setFormatter(formatter)
    
    logger.addHandler(handler)
    return logger

2. Test Integration

Configure your test framework to output results in a compatible format:

# Example test configuration
import pytest
import json

def pytest_terminal_summary(terminalreporter):
    results = {
        'total_tests': terminalreporter._numcollected,
        'passed_tests': len(terminalreporter.stats.get('passed', [])),
        'failed_tests': len(terminalreporter.stats.get('failed', [])),
        'coverage': terminalreporter.stats.get('coverage', {}).get('total', 0)
    }
    
    with open('test_results.json', 'w') as f:
        json.dump(results, f)

3. Security Scanning

Integrate security scanning tools:

# Example security scan integration
from bandit import run_bandit

def run_security_scan():
    results = run_bandit(['-r', '.'])
    return {
        'vulnerabilities': results.get('vulnerabilities', []),
        'score': results.get('score', 0)
    }

4. Performance Monitoring

Add performance monitoring:

# Example performance monitoring
import psutil
import time

def monitor_performance():
    metrics = {
        'cpu_percent': psutil.cpu_percent(),
        'memory_percent': psutil.virtual_memory().percent,
        'disk_usage': psutil.disk_usage('/').percent
    }
    return metrics

API Integration

1. Log Analysis

import requests

def analyze_logs(log_file):
    with open(log_file, 'r') as f:
        logs = f.read()
    
    response = requests.post(
        'http://localhost:5000/api/ai/analyze-logs',
        json={'logs': logs}
    )
    return response.json()

2. Test Analysis

def analyze_tests(test_results):
    response = requests.post(
        'http://localhost:5000/api/ai/analyze-tests',
        json={'test_results': test_results}
    )
    return response.json()

3. Security Analysis

def analyze_security(scan_results):
    response = requests.post(
        'http://localhost:5000/api/ai/analyze-security',
        json={'scan_results': scan_results}
    )
    return response.json()

Usage

1. Open the dashboard in your browser

2. View real-time analysis of:

   • Security metrics
   • Code quality
   • Test coverage
   • Performance metrics

3. Use the chat interface to:

   • Ask questions about logs
   • Get test results
   • Request security analysis
   • Generate reports

Demo Data

The application uses generated demo data for demonstration purposes. The data includes:

• Random test results
• Sample log entries
• Security analysis
• Code quality metrics
• Performance indicators

API Endpoints

• GET /api/ai/analysis: Get comprehensive analysis data
• POST /api/ai/chat: Interact with the AI assistant
• POST /api/ai/analyze-logs: Analyze log files
• POST /api/ai/analyze-tests: Analyze test results
• POST /api/ai/analyze-security: Analyze security scan results

Development

The application is structured as follows:

ai-logger/
├── backend/
│   ├── app/
│   │   ├── routes/
│   │   ├── services/
│   │   └── utils/
│   └── requirements.txt
└── frontend/
    └── src/
        └── components/
            └── AIDashboard/

🔧 Troubleshooting Guide

Backend Issues

Flask Server Won't Start

1. Virtual Environment Issues

   # Recreate virtual environment
   rm -rf venv
   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
   pip install -r requirements.txt

2. Port Already in Use

   # Check what's using port 5000
   lsof -i :5000    # On Unix/Mac
   netstat -ano | findstr :5000  # On Windows

3. Module Not Found Errors

   # Verify all dependencies are installed
   pip install -r requirements.txt
   
   # If python-ollama fails, try:
   pip install python-ollama==0.2.4

Frontend Issues

npm start Fails

1. Node Modules Issues

   # Clean install
   rm -rf node_modules
   rm package-lock.json
   npm install

2. Port 3000 in Use

   # Kill process using port 3000
   lsof -i :3000    # On Unix/Mac
   netstat -ano | findstr :3000  # On Windows

OLLAMA Connection Issues

1. Verify OLLAMA is Running

   # Check OLLAMA status
   curl http://localhost:11434/api/version

2. Common Error Messages and Solutions:

   • "Connection refused": OLLAMA service not running
   • "Model not found": Need to pull the model first
   • "An error occurred": Check backend logs for details

CORS Issues

1. 403 Forbidden Errors

   • Check browser console for specific CORS errors
   • Verify frontend URL matches CORS configuration
   • Ensure all required headers are allowed

2. API Connection Failed

   • Confirm backend URL is correct in frontend config
   • Check if backend server is running
   • Verify network connectivity

Environment Setup

1. Environment Variables

   # Backend (.env file)
   FLASK_APP=app
   FLASK_ENV=development
   
   # Frontend (.env file)
   REACT_APP_API_URL=http://localhost:5000

2. File Permissions

   # Fix permissions if needed
   chmod +x venv/bin/activate
   chmod -R 755 .

Quick Verification Steps

1. Backend Health Check:

   curl http://localhost:5000/api/health

2. Frontend Build Check:

   npm run build

3. OLLAMA Model Check:

   curl http://localhost:11434/api/tags

Still Having Issues?

1. Check the logs:

   • Backend: Flask server output
   • Frontend: Browser console (F12)
   • OLLAMA: System logs

2. Common Root Causes:

   • Missing dependencies
   • Incorrect file permissions
   • Port conflicts
   • Network/firewall restrictions
   • Incompatible package versions

3. If problems persist:

   • Open an issue on GitHub
   • Include error messages and logs
   • Describe your environment setup
   • List steps to reproduce the issue