app.py

import gradio as gr
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout
from tensorflow import keras

import cv2

model2 = keras.models.load_model("final_model.h5")

def sketch_recognition(test_img):
  test_img = test_img/255
  test_img = cv2.resize(test_img,(28,28))
  test_input = test_img.reshape((1,28,28,1))
  label = [x for x in range(10)]
  prediction = model2.predict(test_input)[0]
  confidences = {label[i]:float(prediction[i]) for i in range(10)}
  return confidences

gr.Interface(fn=sketch_recognition, inputs="sketchpad", outputs=gr.outputs.Label(num_top_classes=3)).launch()