Aurora Mpox Sentinela OMS

[felipeliliti]

Aurora Mpox Sentinel.

1. data_collection.py

Este módulo coleta e armazena dados em tempo real.

import pandas as pd
import requests

def fetch_health_data(api_endpoint):
    try:
        response = requests.get(api_endpoint)
        response.raise_for_status()
        data = response.json()
        df = pd.DataFrame(data)
        df.to_csv('health_data.csv', index=False)
        return df
    except requests.exceptions.RequestException as e:
        print(f"Error fetching data: {e}")
        return pd.DataFrame()

2. data_analysis.py

Este módulo realiza a análise e previsão usando modelos avançados.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
import tensorflow as tf

def load_model(model_path):
    return tf.keras.models.load_model(model_path)

def preprocess_data(df):
    # Example preprocessing
    df.fillna(0, inplace=True)
    X = df[['feature1', 'feature2']]  # Replace with actual features
    return X

def train_model(X_train, y_train):
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(32, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(X_train, y_train, epochs=10)
    return model

def predict(model, X):
    return model.predict(X)

# Example usage
if __name__ == "__main__":
    df = pd.read_csv('health_data.csv')
    X = preprocess_data(df)
    y = df['target']  # Example target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = train_model(X_train, y_train)
    y_pred = model.predict(X_test)
    print(classification_report(y_test, y_pred))

3. response_system.py

Este módulo lida com a resposta e envio de alertas.

import smtplib
from email.mime.text import MIMEText

def send_alert(email_recipient, subject, message):
    try:
        msg = MIMEText(message)
        msg['Subject'] = subject
        msg['From'] = '[email protected]'
        msg['To'] = email_recipient

        with smtplib.SMTP('smtp.yourdomain.com', 587) as server:
            server.starttls()
            server.login('your_username', 'your_password')
            server.sendmail(msg['From'], [msg['To']], msg.as_string())
    except Exception as e:
        print(f"Error sending alert: {e}")

4. app.py

Este módulo cria uma interface web usando Flask.

from flask import Flask, request, jsonify
import pandas as pd
from data_analysis import load_model, preprocess_data, predict
from response_system import send_alert

app = Flask(__name__)

# Load pre-trained model
model = load_model('model_path')

@app.route('/predict', methods=['POST'])
def predict_endpoint():
    data = request.json
    df = pd.DataFrame(data)
    preprocessed_data = preprocess_data(df)
    predictions = predict(model, preprocessed_data)
    return jsonify(predictions.tolist())

@app.route('/alert', methods=['POST'])
def alert():
    data = request.json
    email = data['email']
    subject = data['subject']
    message = data['message']
    send_alert(email, subject, message)
    return 'Alert sent!', 200

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0')

Notas Finais

1. Instale as Dependências:

   Certifique-se de que você tem todas as bibliotecas necessárias instaladas:

   pip install pandas scikit-learn tensorflow flask requests

2. Modelo de Machine Learning:

   Certifique-se de que o modelo treinado esteja salvo e acessível no caminho especificado (model_path). Se você não tiver um modelo treinado, pode usar o código de treinamento fornecido em data_analysis.py para criar um.

3. Segurança e Configuração:

   • Email: Configure o servidor SMTP e as credenciais no módulo response_system.py.
   • Proteção de Dados: Certifique-se de que todas as medidas de segurança e privacidade estão implementadas conforme necessário.