app.py

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

# Load the model from Hugging Face
model = tf.saved_model.load('https://huggingface.co/nivashuggingface/digit-recognition/resolve/main/saved_model')

def preprocess_image(img):
    """Preprocess the drawn image for prediction"""
    # Convert to grayscale and resize
    img = img.convert('L')
    img = img.resize((28, 28))
    # Convert to numpy array and normalize
    img_array = np.array(img)
    img_array = img_array.astype('float32') / 255.0
    # Add batch dimension
    img_array = np.expand_dims(img_array, axis=0)
    # Add channel dimension
    img_array = np.expand_dims(img_array, axis=-1)
    return img_array

def predict_digit(img):
    """Predict digit from drawn image"""
    # Preprocess the image
    processed_img = preprocess_image(img)
    # Make prediction
    predictions = model(processed_img)
    predicted_digit = tf.argmax(predictions, axis=1).numpy()[0]
    # Get confidence scores
    confidence_scores = tf.nn.softmax(predictions[0]).numpy()
    # Create result string
    result = f"Predicted Digit: {predicted_digit}\n\nConfidence Scores:\n"
    for i, score in enumerate(confidence_scores):
        result += f"Digit {i}: {score:.2%}\n"
    return result

# Create Gradio interface
iface = gr.Interface(
    fn=predict_digit,
    inputs=gr.Image(type="pil", label="Draw a digit (0-9)"),
    outputs=gr.Textbox(label="Prediction Results"),
    title="Digit Recognition with CNN",
    description="Draw a digit (0-9) in the box below. The model will predict which digit you drew.",
    examples=[
        ["examples/0.png"],
        ["examples/1.png"],
        ["examples/2.png"],
    ],
    theme=gr.themes.Soft()
)

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