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import os
import streamlit as st
import tensorflow as tf
from tensorflow_serving.apis import prediction_service_pb2_grpc
from client import style_transfer_serving
import grpc
def main():
options = [
('grpc.max_send_message_length', 200 * 1024 * 1024),
('grpc.max_receive_message_length', 200 * 1024 * 1024)
]
channel = grpc.insecure_channel('localhost:8500', options=options)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
st.title("Neural Style-Transfer App")
col1, col2 = st.beta_columns(2)
content_file = st.sidebar.file_uploader('Upload Image', type=['jpg', 'jpeg', 'png'])
style_file = st.sidebar.file_uploader('Upload Style', type=['jpg', 'jpeg', 'png'])
style_options = st.sidebar.selectbox(label='Example Styles', options=os.listdir('assets/template_styles'))
col1.subheader('Content Image')
col2.subheader('Style Image')
show_image = col1.empty()
show_style = col2.empty()
st.subheader('Style Transfer')
show_transfer = st.empty()
style = None
content = None
if content_file:
content = content_file.getvalue()
show_image.image(content, use_column_width=True)
if style_file:
style = style_file.getvalue()
show_style.image(style, use_column_width=True)
elif style_options is not None:
with open(os.path.join('assets/template_styles', style_options), 'rb') as f:
style = f.read()
show_style.image(style, use_column_width=True)
if content is not None and style is not None:
content_image = tf.io.decode_image(content)
style_image = tf.image.resize(tf.io.decode_image(style), (256, 256))
with st.spinner('Generating style transfer...'):
style_transfer = style_transfer_serving(stub, content_image, style_image)
show_transfer.image(style_transfer, use_column_width=True)
if __name__ == "__main__":
main()
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