dashboard.py

"""
Web Dashboard for System Monitoring
Displays real-time metrics and AI predictions in browser
"""

from flask import Flask, render_template, jsonify
import psutil
from datetime import datetime
from collections import deque
from predictor import SystemHealthPredictor
import threading
import time

app = Flask(__name__)

metrics_history = deque(maxlen=50)
predictor = SystemHealthPredictor()
current_prediction = {'status': 'Initializing...', 'at_risk': False}

predictor.load_model()


def get_current_metrics():

    net_io = psutil.net_io_counters()

    return {
        'timestamp': datetime.now().strftime("%H:%M:%S"),
        'cpu_percent': round(psutil.cpu_percent(interval=1), 1),
        'memory_percent': round(psutil.virtual_memory().percent, 1),
        'disk_percent': round(psutil.disk_usage('/').percent, 1),
        'network_sent_mb': round(net_io.bytes_sent / (1024 * 1024), 2),
        'network_recv_mb': round(net_io.bytes_recv / (1024 * 1024), 2)
    }


def background_monitor():

    global current_prediction

    while True:
        try:

            metrics = get_current_metrics()
            metrics_history.append(metrics)


            if len(metrics_history) >= 5:
                recent = list(metrics_history)[-5:]
                prediction = predictor.predict(metrics, recent)


                if prediction and not prediction.get('error'):
                    current_prediction = prediction
                else:

                    current_prediction = {
                        'status': 'COLLECTING DATA',
                        'at_risk': False,
                        'risk_probability': 0,
                        'confidence': 0
                    }
            else:

                current_prediction = {
                    'status': 'COLLECTING DATA',
                    'at_risk': False,
                    'risk_probability': 0,
                    'confidence': 0
                }

            time.sleep(3)
        except Exception as e:
            print(f"Error in background monitor: {e}")
            current_prediction = {
                'status': 'ERROR',
                'at_risk': False,
                'risk_probability': 0,
                'confidence': 0
            }
            time.sleep(5)


@app.route('/')
def index():

    return render_template('dashboard.html')


@app.route('/api/metrics')
def api_metrics():

    if len(metrics_history) == 0:
        return jsonify({'error': 'No data yet'})

    current = metrics_history[-1]

    return jsonify({
        'current': current,
        'prediction': current_prediction,
        'history': list(metrics_history)
    })


@app.route('/api/stats')
def api_stats():

    cpu_freq = psutil.cpu_freq()
    mem = psutil.virtual_memory()
    disk = psutil.disk_usage('/')

    return jsonify({
        'cpu_count': psutil.cpu_count(),
        'cpu_freq': round(cpu_freq.current, 1) if cpu_freq else 'N/A',
        'memory_total_gb': round(mem.total / (1024 ** 3), 1),
        'disk_total_gb': round(disk.total / (1024 ** 3), 1),
        'uptime_hours': round((time.time() - psutil.boot_time()) / 3600, 1)
    })


if __name__ == '__main__':

    monitor_thread = threading.Thread(target=background_monitor, daemon=True)
    monitor_thread.start()

    print("\n" + "=" * 60)
    print("🚀 Starting Web Dashboard...")
    print("=" * 60)
    print("\n📊 Dashboard will be available at:")
    print("   http://localhost:5000")
    print("\n⚠️  Make sure you've trained the model first:")
    print("   python predictor.py")
    print("\n   Press Ctrl+C to stop\n")

    app.run(debug=True, host='0.0.0.0', port=5000, use_reloader=False)