Create app.py

app.py

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+import streamlit as st
+import cv2
+from streamlit_drawable_canvas import st_canvas
+from keras.models import load_model
+import numpy as np
+
+
+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)
+
+@st.cache_resource
+def load_mnist_model():
+    return load_model("mnist.keras")
+
+model = load_mnist_model()
+
+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",
+)
+
+if canvas_result.image_data is not None:
+    st.image(canvas_result.image_data, caption="Original Drawing")
+    img = cv2.cvtColor(canvas_result.image_data.astype("uint8"), cv2.COLOR_RGBA2GRAY)
+    img = 255 - img
+    img_resized = cv2.resize(img, (28, 28))
+    img_normalized = img_resized / 255.0
+    final_img = img_normalized.reshape(1, 28, 28, 1)
+    st.image(img_resized, caption="Preprocessed (28x28)")
+    prediction = model.predict(final_img)
+    st.write("Prediction:", np.argmax(prediction))