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Browse files- app.py +61 -0
- digit_recognizer_model.h5 +3 -0
- requirements.txt +5 -0
app.py
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import streamlit as st
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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import cv2
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from PIL import Image, ImageOps
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# Load the trained model
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model = load_model('digit_recognizer_model.h5')
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# Streamlit app title
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st.title("Handwritten Digit Recognizer")
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# Instructions
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st.write("Draw a digit below and click 'Predict' to see the model's prediction.")
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# Create a canvas component
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from streamlit_drawable_canvas import st_canvas
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# Set up the canvas
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canvas_result = st_canvas(
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fill_color="black", # Drawing background color
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stroke_width=10,
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stroke_color="white",
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background_color="black",
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height=280,
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width=280,
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drawing_mode="freedraw",
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key="canvas",
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)
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# Predict button
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if st.button('Predict'):
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if canvas_result.image_data is None:
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st.write("Please draw a digit first!")
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else:
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# Convert the canvas image to grayscale
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img = cv2.cvtColor(canvas_result.image_data.astype('uint8'), cv2.COLOR_BGR2GRAY)
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# Resize to 28x28 pixels, the input size for the model
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img_resized = cv2.resize(img, (28, 28))
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# Invert the image (white background, black digit)
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img_resized = cv2.bitwise_not(img_resized)
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# Normalize the image
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img_resized = img_resized / 255.0
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# Reshape for the model: (1, 28, 28, 1)
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img_resized = img_resized.reshape(1, 28, 28, 1)
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# Predict the digit
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prediction = model.predict(img_resized)
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predicted_digit = np.argmax(prediction)
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# Display the prediction
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st.write(f"Predicted Digit: {predicted_digit}")
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# Clear button
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if st.button('Clear'):
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st.experimental_rerun()
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digit_recognizer_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:ec8b515e8b1648a977bd6f4cb60139263d18ac09c8db773d21ada331afac2024
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size 2742848
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requirements.txt
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streamlit
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tensorflow
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opencv-python-headless
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Pillow
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streamlit-drawable-canvas
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