app.py

import scipy
import gradio as gr
import numpy as np
import tensorflow as tf
def digit_KNN_prediction(test_image, K=5):
  test_image_flatten = test_image.reshape((-1, 28*28))
  from sklearn.neighbors import KNeighborsClassifier
  (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
  img_shape = x_train.shape
  n_samples = img_shape[0]
  width = img_shape[1]
  height = img_shape[2]
  x_train_flatten = x_train.reshape(n_samples, width*height)
  KNN_classifier = KNeighborsClassifier(n_neighbors=K)
  KNN_classifier.fit(x_train_flatten, y_train)
  ans = KNN_classifier.predict(test_image_flatten)
  return ans[0]

input_image = gr.inputs.Image(shape=(28, 28), image_mode='L')
input_K = gr.inputs.Slider(1, 13, step = 2, default = 5)

output_label = gr.outputs.Textbox(label="Predicted Digit")
gr.Interface(fn=digit_KNN_prediction, 
                         inputs = [input_image, input_K], 
                         outputs = [output_label],
                         title = "Digit classification using KNN algorithm", 
                         ).launch(debug=True)