import gradio as gr
import tensorflow as tf
import numpy as np
import cv2

model = tf.keras.models.load_model("mymodel/mymodel")

def predict(img):
    z = tf.keras.preprocessing.image.img_to_array(img)
    kernel = np.ones((5,5),np.uint8)
    z = cv2.dilate(z ,kernel,iterations = 1)
    z = np.expand_dims(z, axis=0)
    y = model.predict(z)
    ysoft = tf.nn.softmax(y)
    ymax = np.argmax(ysoft)
    return int(ymax)

sp = gr.Sketchpad(tool="sketch", shape=(140,100), image_mode="L", label='arabic numeral', invert_colors=False).style(height=200, width=280)
gr.Label()
gr.Interface(fn=predict, 
             inputs=sp,
             outputs="label",
             live=True,
             examples=[
             ["writer001_pass01_digit2.png"],
             ["writer001_pass01_digit4.png"],
             ["writer001_pass07_digit9.png"],
             ["writer594_pass06_digit7.png"]]).launch()