import numpy as np
#from neural_network import *
from numpy import loadtxt
import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout

products = ['glass', 'mixed', 'metal', 'paper']
n_products = len(products)
data = pd.read_csv("X_test.csv")
dff = data.loc[:, data.columns != 'Unnamed: 0']
def b():
    a = dff.sample()
    pred(a)

model = tf.keras.models.load_model('./saved_model')


def pred(a):
  print(a)
  prediction_proba = model.predict(np.array(a))
  prediction = np.argmax(prediction_proba)
  text_representation = products[prediction]
  print(text_representation)
  print(prediction)
  return prediction