import streamlit as st

import functions as f

import components.components as comp

# TITLE TAB

st.set_page_config(page_title="Segmentation 🫁")

# HEADER

def header_page():

    st.markdown("<h1 style='text-align: center;'>Modern Lung Segmentation <br> 🫁</h1>", unsafe_allow_html=True)

# FUNCTIONS

f.modelsCheck()

def inputFileUploaderSelected():

    st.session_state.input_selected = 'file_uploader'

def inputCameraSelected():

    st.session_state.input_selected = 'camera'

# BODY

def body_page():

    st.image('BG.png')

    # Select Model

    l_col_select_model, r_col_select_model = st.columns([2, 1])

    with l_col_select_model:

        st.markdown("<h4 style='text-align: center; margin:15px'>Select model to create lung segmentation:</h4>", unsafe_allow_html=True)

    with r_col_select_model:

        eval_df = f.getEvaluationDF()

        UNet_IoU = eval_df.loc[eval_df['Model'] == 'UNet', 'IoU'].values[0]

        UNetPP_IoU = eval_df.loc[eval_df['Model'] == 'UNet++', 'IoU'].values[0]

        AttUNet_IoU = eval_df.loc[eval_df['Model'] == 'AttUNet', 'IoU'].values[0]

        R2UNet_IoU = eval_df.loc[eval_df['Model'] == 'R2UNet', 'IoU'].values[0]

        options = [

            "UNet ~ IoU: {}%".format(int(UNet_IoU*100)),

            "UNet++ ~ IoU: {}%".format(int(UNetPP_IoU*100)),

            "AttUNet ~ IoU: {}%".format(int(AttUNet_IoU*100)),

            "R2UNet ~ IoU: {}%".format(int(R2UNet_IoU*100)),

            "All Models At Once"

        ]

        used_model = st.selectbox(label='',

                                    options=options)

        st.session_state.used_model = used_model

    # Select Input

    buff1, mid_col_input_1 , buff2, mid_col_input_2, buff3 = st.columns([2, 2, 1, 2, 2])

    btn_mid_col_input_1 = mid_col_input_1.button('Camera (Just 1 Image)', type='primary', on_click=inputCameraSelected)

    btn_mid_col_input_2 = mid_col_input_2.button('Upload File (More Images)', type='primary', on_click=inputFileUploaderSelected)

    if ('input_selected' in st.session_state):

        if ('camera' in st.session_state.input_selected):

            # Camera Input

            images = st.camera_input(label='Masukan Gambar', key='images', label_visibility='hidden')

            if images:

                f.saveSegmentation()

                f.showSegmentationFromCamera()

        elif ('file_uploader' in st.session_state.input_selected):

            # File Uploader Input

            images = st.file_uploader(label='Masukan Gambar', accept_multiple_files=True, key='images', type=['jpg', 'jpeg', 'png', 'bmp'], label_visibility='hidden')

            if images:

                f.saveSegmentation()

                f.showSegmentationFromFileUploader()

# SIDEBAR

with st.sidebar:

    description = "Modern lung segmentation applications aim to identify and separate the lungs from radiological images, such as Chest X-Rays (CXRs). Lung segmentation improves the accuracy of image classification model training and assists medical teams in their observations. The accuracy level is measured using the Intersection over Union (IoU) method in several models that utilize a dataset containing 3000 images and their corresponding masks."

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

    st.markdown("<span style='font-size:17px; text-align:justify font-weight:bold;'>Model Specification : </span>", unsafe_allow_html=True)

    st.write(f.getEvaluationDF())

# 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()