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import gradio as gr
import tensorflow as tf
import keras
from keras.datasets import mnist
import matplotlib.pyplot as plt
import random
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
def sample_digit(digit):
rn = 0
# pick a random digit from 60,000 in the training set until a desired match is found
while(train_labels[rn] != digit):
rn = int(random.random() * 60000)
digit_img = train_images[rn]
fig = plt.figure()
plt.imshow(digit_img, cmap=plt.cm.binary)
out_txt = "train_images[%d]" % rn
return fig, out_txt
iface = gr.Interface(
fn = sample_digit,
inputs = [
#gr.inputs.Dropdown([0, 1, 2, 3])
#gr.inputs.Number()
gr.inputs.Slider(minimum=0, maximum=9, step=1)
],
outputs=[gr.outputs.Image(type='plot'), 'text'],
title='MNIST Digit Sampler',
description='Pick a random digit from the MNIST dataset'
)
iface.launch()
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