app.py

import gradio as gr
import torch
import numpy as np

device = 'cuda' if torch.cuda.is_available() else 'cpu'

with open('cnn_model.bin', 'rb') as f:
    nn = torch.load(f, map_location=torch.device('cpu'))

nn.to(device)


def predict(input):
    if input is None:
        return 'None'

    x = np.array([[input]])
    x = torch.tensor(x).to(device)
    p = nn(x)
    p = p[0].cpu().detach().numpy()

    return dict(enumerate(p.tolist()))


desc = """\
This project uses a Convolutional Neural Network to classify handwritten digits.
Trained on the MNIST dataset.
Use most of the drawing area for better results.
"""

demo = gr.Interface(
    fn=predict,
    title='ConvNet for handwritten digits classification',
    description=desc,
    inputs=[
        gr.Sketchpad(
            shape=(28, 28),
            brush_radius=1.2,
        )
    ],
    outputs=[
        gr.Label(
            num_top_classes=3,
            scale=3,
        )
    ],
    live=True,
    allow_flagging='never',
).launch()