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import os
import streamlit as st
from PIL import Image
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
# IMG_PATH = 'imgs'
def main():
st.title("AI MNIST")
file = st.file_uploader('η»εγγ’γγγγΌγγγ¦γγ γγ.', type=['jpg', 'jpeg', 'png'])
if file:
st.markdown(f'{file.name} γγ’γγγγΌγγγΎγγ.')
img_path = os.path.join(file.name)
# η»εγδΏεγγ
with open(img_path, 'wb') as f:
f.write(file.read())
# δΏεγγη»εγ葨瀺
img = Image.open(img_path)
st.image(img)
# η»εγArrayε½’εΌγ«ε€ζ
img = load_img(img_path, target_size=(28, 28), color_mode = 'grayscale')
img_array = img_to_array(img)
img_array = img_array.reshape((1, 28, 28))
img_array = img_array/255
# δΏεγγγ’γγ«γεΌγ³εΊγ
model_path = os.path.join('model.h5')
model = load_model(model_path)
result = model.predict(img_array)
prediction = result.argmax()
st.text_area("γγγ―:", prediction, height=20)
if __name__ == '__main__':
main() |