import pandas as pd
import numpy as np

# Make numpy values easier to read.
np.set_printoptions(precision=3, suppress=True)

import tensorflow as tf
from tensorflow.keras import layers

ecvalve = pd.read_csv(
    "/home/fferreira/Documents/teste_ecvalve.csv",
    names=[ "frequency", "state", "pressure", "capacity"])
print("Data shape:", ecvalve.shape)
print("Columns:", ecvalve.columns)

ecvalve.head()
ecvalve_features = ecvalve.copy()
ecvalve_labels = ecvalve_features.pop('state')

ecvalve_features = np.array(ecvalve_features)

ecvalve_model = tf.keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(ecvalve_features.shape[1],)),
    layers.Dense(32, activation='relu'),  # Add another layer
    layers.Dense(1, activation='sigmoid')  # Sigmoid for binary classification
])
ecvalve_model.compile(loss=tf.keras.losses.BinaryCrossentropy(), optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])

ecvalve_model.fit(ecvalve_features, ecvalve_labels, epochs=20)

ecvalve_model.save('EcValve_1.h5')