app.py

import gradio as gr
import numpy as np 
from PIL import Image, ImageOps
import tensorflow as tf

# Load the model 
model = tf.keras.models.load_model("cnn_model.h5")


def predict(image_array):
    try:
        
        if isinstance(image_array, dict) and 'composite' in image_array:
            image_array = image_array['composite']
        
        if image_array is None or np.sum(image_array) == 0:
            return "Please draw a digit."
            
        
        image = Image.fromarray(image_array.astype("uint8"), mode="L")
        
    
        image = image.resize((28, 28), Image.LANCZOS)
        
        image = ImageOps.invert(image)  
        
        image_array = np.array(image).astype("float32") / 255.0
        image_array = image_array.reshape(1, 28, 28, 1)
        
        # Make prediction
        logits = model.predict(image_array, verbose=0)
        prediction = int(np.argmax(logits))
        
        confidence = float(tf.nn.softmax(logits)[0][prediction])
        
        return f"Digit: {prediction} (confidence: {confidence:.2%})"
    except Exception as err:
        return f"Runtime error: {str(err)}"

gr.Interface(
    fn=predict,
    inputs=gr.Sketchpad(
        image_mode="L", 
        canvas_size=(280, 280),  # Larger canvas
        type="numpy",
        brush_radius=10,         # Thicker brush
    ),
    outputs="text",
    title="EMNIST Digit Classifier",
    description="Draw a digit (0-9) in the center of the canvas. For best results with EMNIST, make your digit large and clear."
).launch()