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f1e84d5 7148c74 8179f09 0799f1a f1e84d5 ca01695 e5a478f f1e84d5 e5a478f 7148c74 e5a478f f1e84d5 e5a478f f1e84d5 e5a478f f1e84d5 e5a478f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | 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))
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