import streamlit as st

import functions as f

import components.components as comp

from pathlib import Path

import pandas as pd

# TITLE TAB

st.set_page_config(page_title="UNet++")

# FUNCTIONS

f.modelsCheck()

description_UNetPP = Path('models/UNet++/description_UNet++.md').read_text()

evaluation_unetpp_df = pd.read_csv('./models/UNet++/csv_logger_UNet++.csv')

test_iou = evaluation_unetpp_df.iloc[-1]['test_iou']

testing_time = evaluation_unetpp_df.iloc[-1]['testing_time']

# HEADER

def header_page():

    st.markdown("<h1 style='text-align: center;'>UNet++ Architecture</h1>", unsafe_allow_html=True)

    st.image('models/UNet++/UNet++.png')

# BODY

def body_page():

    st.markdown(description_UNetPP, unsafe_allow_html=True)

# SIDEBAR

with st.sidebar:

    description = "UNet++ is a convolutional neural network architecture designed for biomedical image segmentation tasks. It consists of an encoding path that extracts features through convolutions and downsampling, and a decoding path that restores the image resolution through upsampling and information merging. UNet++'s unique architecture effectively combines local image details with global contextual information, resulting in improved segmentation accuracy."

    model_result = 'The UNet++ model in the Modern Lung Segmentation application achieved an Intersection over Union (IoU) of {:.3f} during testing, with a training time of {:.3f}s.'.format(test_iou, testing_time)

    st.markdown(f'<p style="font-size:15px; text-align:justify">{description}', unsafe_allow_html=True)

    st.markdown(f'<p style="font-size:15px; text-align:justify">{model_result}', unsafe_allow_html=True)

# FOOTER

def footer_page():

    # st.markdown("<p><br></p>", unsafe_allow_html=True)

    # st.markdown("<div style='margin-top:200px;'></div>", unsafe_allow_html=True)

    comp.margin_top(200)

    st.markdown("<p style='text-align: center; font-style:italic;'>Copyright ⓒ 2023 - By Achmad Bauravindah</p>", unsafe_allow_html=True)

if __name__ == '__main__':

    header_page()

    body_page()

    footer_page()