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import streamlit as st
import tensorflow as tf
from tensorflow import keras
import numpy as np
from PIL import Image
import io
from streamlit_drawable_canvas import st_canvas
# Set page config
st.set_page_config(page_title="MNIST Digit Recognition", page_icon="✏️", layout="centered")
# Load the saved model
@st.cache_resource()
def load_model():
return tf.keras.models.load_model('mnist_model.h5',
custom_objects={'InputLayer': keras.layers.InputLayer},
compile=False)
model = load_model()
# Custom CSS
st.markdown("""
<style>
.big-font {
font-size:30px !important;
font-weight: bold;
color: #1E90FF;
}
.result {
font-size: 24px;
font-weight: bold;
color: #32CD32;
}
.footer {
font-size: 14px;
text-align: center;
padding: 20px;
}
</style>
""", unsafe_allow_html=True)
# Create a Streamlit app
st.markdown("<p class='big-font'>MNIST Digit Recognition</p>", unsafe_allow_html=True)
st.write("Draw a digit or upload an image to see the model's prediction!")
# Add option to choose between drawing and uploading
option = st.radio("Choose input method:", ('Draw', 'Upload'))
if option == 'Draw':
# Create a canvas component
canvas_result = st_canvas(
fill_color="rgba(255, 255, 255, 0.3)",
stroke_width=20,
stroke_color="#FFFFFF",
background_color="#000000",
height=280,
width=280,
drawing_mode="freedraw",
key="canvas",
)
if canvas_result.image_data is not None:
image = Image.fromarray(canvas_result.image_data.astype('uint8'))
else:
image = None
else: # Upload option
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(io.BytesIO(uploaded_file.read()))
st.image(image, caption='Uploaded Image.', use_column_width=True)
else:
st.write("Please upload an image.")
image = None
# Add a button to make a prediction
if st.button('Predict', key='predict_button'):
if image is not None:
# Preprocess the image
image = image.convert('L') # Convert to grayscale
image = image.resize((28, 28))
image_array = np.array(image) / 255.0 # Normalize
image_array = image_array.reshape(1, 28, 28, 1).astype('float32')
# Make a prediction
with st.spinner('Predicting...'):
prediction = model.predict(image_array)
predicted_digit = np.argmax(prediction)
# Display the results
st.markdown(f"<p class='result'>Predicted Digit: {predicted_digit}</p>", unsafe_allow_html=True)
#st.balloons()
else:
st.warning("Please draw or upload an image before predicting.")
st.markdown("---")
st.markdown("<div class='footer'>Created with ❤️ using Streamlit and TensorFlow<br>by <a href='https://github.com/joshsalako' target='_blank'>Joshua Salako</a></div>", unsafe_allow_html=True)
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