Aurora Mpox Sentinela OMS

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'] = 'alert@yourdomain.com'
        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.

La proposition mentionne data_collection.py, data_analysis.py, response_system.py et app.py ; commencez par vérifier si ces modules et leur configuration des dépendances existent dans le dépôt. Examinez le flux de données, le chemin du modèle, les entrées de l’API et la configuration SMTP avant l’implémentation. Les critères d’achèvement ne sont pas spécifiés ; le comportement attendu et les tests d’acceptation doivent donc être clarifiés.