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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)
iface = gr.Interface(predict_image, inputs="sketchpad", outputs="label")
iface.launch(debug='True') |