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# import gradio as gr
# import numpy as np
# from modelutil import create_model
# def predict_digit(image):
# try:
# if image == None: pass
# except:
# model = create_model()
# predictions = model.predict(image.reshape(1, 28, 28))
# return np.argmax(predictions)
# gr.Interface(
# title="MNIST Digit Classifier by Papa Sega",
# fn=predict_digit,
# inputs=gr.Sketchpad( label="Draw a digit"),
# outputs="number",
# live=True
# ).launch()
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="MNIST Digit Classifier by Papa Sega",
fn=predict_digit,
inputs=gr.Sketchpad(label="Draw a digit", height=500, width=500),
outputs="number",
live=True
).launch()
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