app.py

import streamlit as st
from streamlit_drawable_canvas import st_canvas
import numpy as np
from PIL import Image
import tensorflow as tf
from tensorflow.keras.models import load_model

# pip install streamlit-drawable-canvas

model = load_model("digit_model.h5")

st.title("Digit Recognition :writing_hand:")
st.write("Write a number between 0-9 on the board and let's see if the model can identify it!")
st.write('(it lives some hard time predicting 1. try to fill the whole space equaly.)')

numbers=[":zero:", ":one:", ":two:", ":three:", ":four:", ":five:", ":six:", ":seven:", ":eight:", ":nine:"]


canvas_result = st_canvas(
    stroke_width=20,
    stroke_color="rgb(255, 255, 255)",
    background_color="rgb(33, 62, 40)",
    update_streamlit=True, 
    width=200,
    height=200,
    drawing_mode="freedraw",
    key="canvas",
)
if st.button("Predict"):
    image_data = np.array(canvas_result.image_data)
    image_data = image_data.astype(np.uint8)
    image = Image.fromarray(image_data)
    image = image.resize((28, 28)).convert("L")
    image = np.array(image).reshape((1, 28, 28, 1)) / 255.0

    prediction = model.predict(image)
    predicted_class = np.argmax(prediction)
    st.write(f"Your number is  {numbers[predicted_class]}  If this was wrong, your handwriting sucks!")

st.image("https://i.ytimg.com/vi/NlUVkNJ3Rcw/maxresdefault.jpg")