import numpy as np
import gradio as gr
import tensorflow as tf

model = tf.keras.models.load_model('model1.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=10),
    live=True
)

iface.launch(share=True)