Created
June 17, 2022 19:44
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Function to test predictions
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| from PIL import Image | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from keras import layers | |
| import os | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def download_and_predict(url, filename): | |
| test_model = keras.models.load_model(f"{hotDogDir}hotdog_classifier_v4.keras") | |
| # download the image from the url and save | |
| os.system("curl -s {} -o {}".format(url, filename)) | |
| # open the image | |
| img = Image.open(filename) | |
| # save the image | |
| img.save(filename) | |
| # convert it to RGB | |
| img = img.convert('RGB') | |
| # show image | |
| plt.imshow(img) | |
| plt.axis('off') | |
| # resize the image for VGG16 model | |
| img = img.resize((256, 256)) | |
| # calculate probabilities of images if they are hotdogs or not | |
| img_test = tf.expand_dims(np.array(img), axis=0) | |
| predicted = test_model.predict(img_test)[0][0] | |
| if predicted > 0.8: | |
| plt.title(f"predicted : Not Hot Dog") | |
| else: | |
| plt.title(f"predicted : Hot Dog") | |
| plt.axis('off') |
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