import gradio as gr
import numpy as np
import cv2



def classify(input):
    image = input['layers'][0]
    print("Type of image variable:", type(image))
    dimensions = image.shape
    print("Dimensions of the image:", dimensions)
    # Resize image to 28x28 using interpolation
    resized_image = cv2.resize(image, (28, 28))
     # Convert image to grayscale
    gray_image = cv2.cvtColor(resized_image, cv2.COLOR_RGBA2GRAY)
    # Reshape the image to add a single channel dimension
    final_image = np.expand_dims(gray_image, axis=-1)
    
    print("Final image shape:", final_image.shape)
    print("Final image:", final_image)
    
    #input = np.reshape(input, (1, 28, 28))
    #print(input)
    return ''

label = gr.Label(num_top_classes=10)

interface = gr.Interface(fn=classify, inputs="sketchpad", outputs=label,
live=True)
interface.launch()