| import gradio as gr | |
| import tensorflow as tf | |
| import keras | |
| from matplotlib import pyplot as plt | |
| import numpy as np | |
| objects = tf.keras.datasets.mnist | |
| (training_images, training_labels), (test_images, test_labels) = objects.load_data() | |
| training_images = training_images / 255.0 | |
| test_images = test_images / 255.0 | |
| from keras.layers import Flatten, Dense | |
| model = tf.keras.models.Sequential([Flatten(input_shape=(28,28)), | |
| Dense(256, activation='relu'), | |
| Dense(256, activation='relu'), | |
| Dense(128, activation='relu'), | |
| Dense(10, activation=tf.nn.softmax)]) | |
| model.compile(optimizer = 'adam', | |
| loss = 'sparse_categorical_crossentropy', | |
| metrics=['accuracy']) | |
| model.fit(training_images, training_labels, epochs=10) | |
| test=test_images[0].reshape(-1,28,28) | |
| pred=model.predict(test) | |
| print(pred) | |
| def predict_image(img): | |
| img_3d=img.reshape(-1,28,28) | |
| im_resize=img_3d/255.0 | |
| prediction=model.predict(im_resize) | |
| pred=np.argmax(prediction) | |
| return pred | |
| iface = gr.Interface( | |
| fn= predict_image, | |
| inputs= gr.Image(height=28, width=28, image_mode='L', sources='clipboard'), | |
| outputs='label' | |
| ) | |
| iface.launch(debug='True') |