app.py

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

# Title and description for the interface
title = "Welcome to your first sketch recognition app!"
head = "<center>The robot was trained to classify numbers (0 to 9). To test it, write your number in the space provided.</center>"


# Load the trained model
model = tf.keras.models.load_model("number_recognition_model_colab.keras")


def recognize_digit(image):
    if image is not None:
        image = image.reshape((1, 28, 28, 1)).astype('float32')/255

        prediction = model.predict(image)

        return {str(i): float(prediction[0][i]) for i in range(10)}
    else:
        return ''


# Build and launch the Gradio interface
demo = gr.Interface(
    fn = recognize_digit,
    inputs = gr.Image(
        shape=(28, 28), 
        image_mode='L', 
        invert_colors=True, 
        source='canvas',
        brush_radius=1, 
        tool="color-sketch",
    ),
    outputs = gr.Label(num_top_classes=3),
    live = True
).launch(share=True)