app.py

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

# 1. Load the trained model
model = tf.keras.models.load_model('digit_recognizer.keras')

# 2. Define the classification function
def classify_digit(image):
    if image is None:
        return None
    
    # Preprocessing to match MNIST data format
    # Convert to grayscale if it isn't already
    if len(image.shape) == 3:
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
    # Resize the image to 28x28 pixels
    image = cv2.resize(image, (28, 28))
    
    # Reshape to (1, 28, 28, 1) to match model input shape
    # The '1' indicates a batch size of 1
    image = image.reshape(1, 28, 28, 1)
    
    # Normalize pixel values (0 to 1) just like in the training notebook
    image = image / 255.0
    
    # Predict
    prediction = model.predict(image).flatten()
    
    # Return dictionary for Gradio Label output
    return {str(i): float(prediction[i]) for i in range(10)}

# 3. Build the Gradio Interface
# We use Sketchpad so users can draw the digit
interface = gr.Interface(
    fn=classify_digit,
    inputs=gr.Sketchpad(label="Draw a Digit"),
    outputs=gr.Label(num_top_classes=3),
    title="Handwritten Digit Recognizer",
    description="Draw a digit (0-9) on the canvas to see if the Neural Network recognizes it."
)

# 4. Launch
if __name__ == "__main__":
    interface.launch()