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app.py

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+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")

digit_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:442fec1bc3a965146a4ce13413f210a8f433014f7c324fffee4b6557b2f63eda
+size 7150272