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import streamlit as st
from torchvision.transforms import functional as F
import gc
import numpy as np
from modules.streamlit_utils import *
def main():
is_mobile, screen_width = configure_page()
display_banner(is_mobile)
display_title(is_mobile)
display_sidebar()
initialize_session_state()
cropped_image = None
img_selected = load_example_image()
uploaded_file = load_user_image(img_selected, is_mobile)
if uploaded_file is not None:
cropped_image = display_image(uploaded_file, screen_width, is_mobile)
if uploaded_file is not None:
get_score_threshold(is_mobile)
if st.button("🚀 Launch Prediction"):
st.session_state.image = launch_prediction(cropped_image, st.session_state.score_threshold, is_mobile, screen_width)
st.session_state.original_prediction = st.session_state.prediction.copy()
st.rerun()
# Create placeholders for all sections
prediction_result_placeholder = st.empty()
additional_options_placeholder = st.empty()
modeler_placeholder = st.empty()
if 'prediction' in st.session_state and uploaded_file:
if st.session_state.image != cropped_image:
print('Image has changed')
# Delete the prediction
del st.session_state.prediction
return
with prediction_result_placeholder.container():
if is_mobile:
display_options(st.session_state.crop_image, st.session_state.score_threshold, is_mobile, int(5/6*screen_width))
else:
with st.expander("Show result of prediction"):
display_options(st.session_state.crop_image, st.session_state.score_threshold, is_mobile, int(5/6*screen_width))
if not is_mobile:
with additional_options_placeholder.container():
modify_results()
with modeler_placeholder.container():
modeler_options(is_mobile)
display_bpmn_modeler(is_mobile, screen_width)
else:
prediction_result_placeholder.empty()
additional_options_placeholder.empty()
modeler_placeholder.empty()
# Create a lot of space for scrolling
for _ in range(50):
st.text("")
gc.collect()
if __name__ == "__main__":
print('Starting the app...')
main()
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