app.py

import gradio as gr
import numpy as np
import cv2
from PIL import Image
import tensorflow as tf

# Load the trained model
model = tf.keras.models.load_model('mnist_model.h5')

def cnn_predict_digit(image):
    # Handle Gradio Sketchpad dictionary input
    if isinstance(image, dict) and 'composite' in image:
        image = image['composite']
    
    # Convert to grayscale if RGB
    if image.ndim == 3 and image.shape[2] == 3:
        image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    
    # Invert colors (white background → black background)
    image = 255 - image
    
    # Resize to 28x28
    image = cv2.resize(image, (28, 28))
    
    # Normalize and reshape
    image = image.astype('float32') / 255.0
    image = image.reshape(1, 28, 28, 1)
    
    # Predict
    prediction = model.predict(image)
    pred_label = np.argmax(prediction, axis=1)[0]
    
    return str(pred_label)

with gr.Blocks() as interface:
    gr.Markdown(
        """
        ## ✍️ Digit Classification using Convolutional Neural Network
        Draw a digit in the sketchpad below (0 to 9), then click **Submit** to see the prediction.
        """
    )
    with gr.Row():
        sketchpad = gr.Sketchpad(image_mode='L')
        output = gr.Label()
    gr.Button("Submit").click(cnn_predict_digit, inputs=sketchpad, outputs=output)
    gr.ClearButton([sketchpad, output])

interface.launch()