import streamlit as st from streamlit_drawable_canvas import st_canvas from keras.models import load_model import numpy as np import cv2 # Page setup st.set_page_config(page_title="Digit Recognizer", layout="centered") st.markdown("

🧠 Handwritten Digit Recognizer

", unsafe_allow_html=True) st.markdown("---") # Sidebar settings st.sidebar.header("🛠 Drawing Settings") drawing_mode = st.sidebar.selectbox("Drawing tool:", ("freedraw", "line", "rect", "circle", "transform")) stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 10) stroke_color = st.sidebar.color_picker("Stroke color hex: ", "#000000") # black bg_color = st.sidebar.color_picker("Background color hex: ", "#FFFFFF") # white bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"]) realtime_update = st.sidebar.checkbox("Update in realtime", True) # Load model @st.cache_resource def load_mnist_model(): return load_model("mnist_model.keras") model = load_mnist_model() # Layout columns col1, col2 = st.columns(2) with col1: st.subheader("🖌️ Draw a Digit") canvas_result = st_canvas( fill_color="rgba(255, 165, 0, 0.3)", stroke_width=stroke_width, stroke_color=stroke_color, background_color=bg_color, update_streamlit=realtime_update, height=280, width=280, drawing_mode=drawing_mode, key="canvas" ) with col2: if canvas_result.image_data is not None: st.image(canvas_result.image_data, caption="Your Drawing") if st.button("Predict"): # Preprocess image img = cv2.cvtColor(canvas_result.image_data.astype("uint8"), cv2.COLOR_RGBA2GRAY) img = 255 - img # Invert img_resized = cv2.resize(img, (28, 28)) img_normalized = img_resized / 255.0 img_reshaped = img_normalized.reshape((1, 28, 28)) # Prediction prediction = model.predict(img_reshaped) predicted_digit = int(np.argmax(prediction)) # st.success(f"✅ Predicted Digit: **{predicted_digit}**") st.markdown("
", unsafe_allow_html=True) st.markdown( f"

✅ Predicted Digit: {predicted_digit}

", unsafe_allow_html=True ) st.markdown("
", unsafe_allow_html=True)