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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
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()
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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()
<title>TechGuardian Dashboard</title>Status dos Sistemas
Dados Coletados
Anomalias Detectadas
The text was updated successfully, but these errors were encountered: