Mohitha/Handwritten-Digit-Recognition-Calculator

1import streamlit as st2from streamlit_drawable_canvas import st_canvas3from tensorflow.keras.models import load_model4import numpy as np5from PIL import Image, ImageOps6 7# Load pre-trained model (should be trained on MNIST)8model = load_model("mnist_calc.keras")9 10st.title("🧮 Handwritten Digit Calculator with Deep Learning")11 12# Canvas settings13stroke_width = st.slider("Stroke width: ", 1, 25, 9)14canvas_result_1 = st_canvas(15    fill_color="#000000",16    stroke_width=stroke_width,17    stroke_color="#FFFFFF",18    background_color="#000000",19    width=200,20    height=200,21    drawing_mode="freedraw",22    key="canvas1"23)24 25operator = st.selectbox("Select Operation", ["+", "-", "*", "/"])26 27canvas_result_2 = st_canvas(28    fill_color="#000000",29    stroke_width=stroke_width,30    stroke_color="#FFFFFF",31    background_color="#000000",32    width=200,33    height=200,34    drawing_mode="freedraw",35    key="canvas2"36)37 38def preprocess(image_data):39    img = Image.fromarray(image_data)40    img = img.convert("L")  # Convert to grayscale41    img = ImageOps.invert(img)42    img = img.resize((28, 28))43    img = np.array(img).astype("float32") / 255.044    img = img.reshape(1, 28, 28, 1)45    return img46 47def predict_digit(canvas_data):48    if canvas_data is not None:49        img = canvas_data.image_data50        img = preprocess(img)51        prediction = model.predict(img)52        digit = np.argmax(prediction)53        return digit54    return None55 56if st.button("Calculate"):57    digit1 = predict_digit(canvas_result_1)58    digit2 = predict_digit(canvas_result_2)59 60    if digit1 is not None and digit2 is not None:61        if operator == "+":62            result = digit1 + digit263        elif operator == "-":64            result = digit1 - digit265        elif operator == "*":66            result = digit1 * digit267        elif operator == "/":68            result = digit1 / digit2 if digit2 != 0 else "∞"69 70        st.markdown(f"**Predicted:** {digit1} {operator} {digit2} = **{result}**")71    else:72        st.warning("Please draw digits on both canvases.")73