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import base64
import streamlit as st
from PIL import Image
import numpy as np
from keras.models import model_from_json
st.markdown('<h1 style="color:black;">CNN Image classification model</h1>', unsafe_allow_html=True)
st.markdown('<h2 style="color:black;">The image classification model classifies images into zebra and horse', unsafe_allow_html=True)
st.cache(allow_output_mutation=True)
def get_base64_of_bin_file(bin_file):
with open(bin_file, 'rb') as f:
data = f.read()
return base64.b64encode(data).decode()
def set_png_as_page_bg(png_file):
bin_str = get_base64_of_bin_file(png_file)
page_bg_img = '''
<style>
.stApp {
background-image: url("data:image/png;base64,%s");
background-size: cover;
background-repeat: no-repeat;
background-attachment: scroll; # doesn't work
}
</style>
''' % bin_str
st.markdown(page_bg_img, unsafe_allow_html=True)
return
set_png_as_page_bg('background.webp')
# def load_model():
# # load json and create model
# json_file = open('model.json', 'r')
# loaded_model_json = json_file.read()
# json_file.close()
# CNN_class_index = model_from_json(loaded_model_json)
# # load weights into new model
# model = CNN_class_index.load_weights("model.h5")
# #model= tf.keras.load_model('model.h5')
# #CNN_class_index = json.load(open(f"{os.getcwd()}F:\Machine Learning Resources\ZebraHorse\model.json"))
# return model, CNN_class_index
def load_model():
# Load the model architecture
with open('model.json', 'r') as f:
model = model_from_json(f.read())
# Load the model weights
model.load_weights('model.h5')
#CNN_class_index = json.load(open(f"{os.getcwd()}F:\Machine Learning Resources\ZebraHorse\model.json"))
return model
def image_transformation(image):
#image = Image._resize_dispatcher(image, (256, 256))
# image= np.resize((256,256))
image = np.array(image)
# np.save('images.npy', image)
# image = np.load('images.npy', allow_pickle=True)
return image
def image_prediction(image, model):
image = image_transformation(image=image)
outputs = model.predict(image)
_, y_hat = outputs.max(1)
predicted_idx = str(y_hat.item())
return predicted_idx
def main():
image_file = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'],label_visibility="visible")
if image_file:
left_column, right_column = st.columns(2)
left_column.image(image_file, caption="Uploaded image", use_column_width=True)
image = Image.open(image_file)
image = image_transformation(image=image)
pred_button = st.button("Predict")
model = load_model()
# label = ['Zebra', 'Horse']
# label = np.array(label).reshape(1, -1)
# ohe= OneHotEncoder()
# labels = ohe.fit_transform(label).toarray()
if pred_button:
image_prediction(image, model)
outputs = model.predict(image)
_, y_hat = outputs.max(1)
predicted_idx = str(y_hat.item())
right_column.title("Prediction")
right_column.write(predicted_idx)
if __name__ == '__main__':
main() |