app.py

#Part 1
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import gradio as gr
import tensorflow as tf
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier

#step 1
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

input_module1 = gr.inputs.Image(label = "test_image", image_mode='L', shape = (28,28))

input_module2 = gr.inputs.Slider(1, 10, step=1, label = "k")

output_module1 = gr.outputs.Textbox(label = "Predicted Digit")

output_module2 = gr.outputs.Image(label = "Predicted Probability per class")

def image_classification(input1, input2):
    print(input1.shape)
    print(input2)

    image = input1.reshape(1, 28 *28)
    X_train = x_train.reshape(60000, 28*28)
    X_test = x_test.reshape(10000, 28*28)
   
    kNN_classifier = KNeighborsClassifier(n_neighbors=input2)
    kNN_classifier.fit(X_train, y_train)
    
    y_test_predicted_label = kNN_classifier.predict(image)

    output1 = y_test_predicted_label# text output example
    output2 = np.random.rand(2,2) # image-like array output example
    return output1,output2

# Step 6.4: Put all three component together into the gradio's interface function 
gr.Interface(fn=image_classification, 
             inputs=[input_module1, input_module2], 
             outputs=[output_module1, output_module2]
            ).launch(debug = True)