app.py

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

# Load the CNN model from the .h5 file
model = tf.keras.models.load_model('mnist_cnn_model.h5')

def predict_digit(image):
    # Preprocess the input image
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    image = image / 255.0  # Normalize pixel values

    # Make predictions
    predictions = model.predict(image)

    # Get the predicted digit
    predicted_digit = np.argmax(predictions)

    return predicted_digit

# Define Gradio interface
gr.Interface(
    title="Reconnaissance d'écriture manuscrite des données MNIST by Papa Sega",
    fn=predict_digit, 
    inputs=gr.Sketchpad(label="Desinner le chiffre ici",  height=300, width=800),
    outputs="number",
    live=True
).launch(debug=True)