ium_z487175/DL-prediction.py

import pickle
import os
import numpy as np

workspace_path = os.getenv('WORKSPACE')
pickle_path = os.path.join(workspace_path, 'model_with_data.pickle')

if os.path.exists(pickle_path):
    with open(pickle_path, 'rb') as file:
        loaded_data = pickle.load(file)
else:
    ## aby było mozna uruchomić lokalnie
    with open('model_with_data.pickle', 'rb') as file:
        loaded_data = pickle.load(file)
        
# Wczytanie modelu
model = loaded_data[0]

#Wczytanie danych testowych
X_test_scaled = loaded_data[3]
y_test_encoded = loaded_data[4]

# Predykcja

y_pred = model.predict(X_test_scaled)
y_pred_classess = np.argmax(y_pred, axis=1)

# Zapisanie wyników predykcji do pliku

np.savetxt('results_prediction.csv', y_pred, delimiter=',')