| DEFINE FUNCTION predict(filename): |
| #Step 1 |
| SET my_image TO plt.imread(os.path.join(‘uploads’, filename)) |
| #Step 2 |
| SET my_image_read TO resize(my_image (224,224,1)) |
| #Step 3 |
| CALL #F with graph.as_default(): |
| CALL #F set_session(sess) |
| SET #Fmodel TO tf.keras.models.load_model(“model_name.h5”) |
| CALL #F model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate =),#‘adam’, |
| loss = ‘categorical_cross-entropy’, |
| metrics = [‘accuracy’]) |
| SET #F probabilities TO model.predict(np.array([my_image_read])) [0,:] |
| CALL #F OUTPUT(probabilities) |
| #Step 4 |
| SET number_to_class TO [‘fifty’, ‘five’, ‘hundred’, ‘ten’, ‘twohundred’] |
| SET #F index TO np.argsort(probabilities) |
| SET predictions TO {“class1”: number_to_class[index [3]], |
| “class2”:number_to_class[index [2]], |
| “class3”:number_to_class[index [1]], |
| “prob1”:probabilities[index [3]], |
| “prob2”:probabilities[index [2]], |
| “prob3”:probabilities[index [1]]} |
| #Step 5 |
| CALL #F RETURN render template(‘predict.html’, predictions = predictions) |
