import pandas as pd
import numpy as np
import requests

def collect_data(api_url):
response = requests.get(api_url)
data = response.json()
df = pd.DataFrame(data)
return df

Exemplo de URL de API para coleta de dados

api_url = "https://example.com/api/system_status"
data = collect_data(api_url)from sklearn.ensemble import IsolationForest

def detect_anomalies(data):
model = IsolationForest(contamination=0.01)
model.fit(data)
data['anomaly'] = model.predict(data)
anomalies = data[data['anomaly'] == -1]
return anomalies

anomalies = detect_anomalies(data)from twilio.rest import Client

def send_alert(anomalies):
if not anomalies.empty:
account_sid = 'your_account_sid'
auth_token = 'your_auth_token'
client = Client(account_sid, auth_token)
message = client.messages.create(
body=f"Anomalias detectadas: {anomalies}",
from_='+1234567890',
to='+0987654321'
)
print(message.sid)

send_alert(anomalies)
from flask import Flask, render_template
import pandas as pd

app = Flask(name)

@app.route('/')
def index():
data = collect_data(api_url)
anomalies = detect_anomalies(data)
return render_template('index.html', tables=[data.to_html(classes='data', header="true"), anomalies.to_html(classes='data', header="true")])

if name == 'main':
app.run(debug=True)from apscheduler.schedulers.blocking import BlockingScheduler

scheduler = BlockingScheduler()

@scheduler.scheduled_job('interval', minutes=5)
def scheduled_job():
data = collect_data(api_url)
anomalies = detect_anomalies(data)
send_alert(anomalies)

scheduler.start()

Status dos Sistemas

Dados Coletados

Anomalias Detectadas