app.py

import gradio as gr
import tensorflow as tf

model = tf.keras.models.load_model('model.h5')

def recognize_digit(image):
    if image is not None:
        image = image.reshape((1, 28, 28, 1)).astype('float32')/255

        prediction = model.predict(image)

        return {str(i):float(prediction[0][i]) for i in range(10)}
    else:
        return ''
    
iface = gr.Interface(
    fn = recognize_digit,
    inputs=gr.Image(shape=(28, 28),image_mode='L',invert_colors=True,source='canvas'),
    outputs=gr.Label(num_top_classes=3),
    live=True
)
iface.launch()
    

# ref : https://www.youtube.com/watch?v=3DGLznJorT8