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import gradio as gr
import pickle
with open('model_91_7248.bin', 'rb') as f:
nn = pickle.load(f)
def predict(input):
if input is None:
return 'None'
x = input.reshape((784, 1))
p = nn.feed_forward(x).reshape((10,))
return dict(enumerate(p))
demo = gr.Interface(
fn=predict,
title='Simple NeuralNet for handwritten digits classification',
description='Created using Python and Numpy only.\nFor a more robust model check this out: <a href="https://huggingface.co/spaces/ffcm/cnn-pytorch-mnist">ConvNet for handwritten digits classification</a>',
inputs=[
gr.Sketchpad(
shape=(28, 28),
brush_radius=1.2,
)
],
outputs=[
gr.Label(
num_top_classes=3,
scale=3,
)
],
live=True,
allow_flagging=False,
).launch()
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