import numpy as np
import tensorflow as tf
import os


#Import Data
PATH = os.path.join(os.getcwd(), "crop-data")

test_data = os.path.join(PATH, "test")
test_data_32 = tf.keras.utils.image_dataset_from_directory(test_data,
                                                                 shuffle=True,
                                                                 batch_size=32,
                                                                 image_size=(160,160))

test_data_64 = tf.keras.utils.image_dataset_from_directory(test_data,
                                                                 shuffle=True,
                                                                 batch_size=64,
                                                                 image_size=(160,160))

AUTOTUNE = tf.data.AUTOTUNE
test_data_32 = test_data_32.prefetch(buffer_size=AUTOTUNE)
test_data_64 = test_data_64.prefetch(buffer_size=AUTOTUNE)

test_data_32 = test_data_32.map(lambda x,y: (x, tf.one_hot(y,38)))
test_data_64 = test_data_64.map(lambda x,y: (x, tf.one_hot(y,38)))


model1 = tf.keras.models.load_model("crop-classifier.keras")
model2 = tf.keras.models.load_model("crop-classifier-64-batch.keras")
model3 = tf.keras.models.load_model("crop-classifier-callbacks.keras")

result = model1.evaluate(test_data_32)
print(f"Test Loss: {result[0]}")
print(f"Test Accuracy: {result[1]}")

result = model2.evaluate(test_data_64)
print(f"Test Loss: {result[0]}")
print(f"Test Accuracy: {result[1]}")

result = model3.evaluate(test_data_64)
print(f"Test Loss: {result[0]}")
print(f"Test Accuracy: {result[1]}")