app.py

""" Resources:
        1- https://streamlit-emoji-shortcodes-streamlit-app-gwckff.streamlit.app/ 
        2- https://github.com/giswqs/streamlit-geospatial

"""

import streamlit as st
import streamlit.components.v1 as components

USER_GROUPS = ["Developer", "Manager", "Practitioner"]

st.set_page_config(layout="wide")


if 'user_group' not in st.session_state:
    index_tmp = 0
else: 
    index_tmp = USER_GROUPS.index(st.session_state['user_group'])


#Sidebar for USER GROUPS
st.sidebar.title("USER GROUPS")
backend = st.sidebar.selectbox(
            "Select User-Group ", USER_GROUPS, index=index_tmp
        )

st.session_state['user_group'] = backend


st.sidebar.title("About")
st.sidebar.info(
    """
    Web App URL: <http://testxaiprod-xaiframeworkdemo.rahtiapp.fi/Decision_Exploration_Panel>

    GitHub repository: <https://github.com/>
    """
)

st.sidebar.title("Contact")
st.sidebar.info(
    """
    Sinan Kaplan:[LinkedIn](https://www.linkedin.com//in/kaplansinan)
    """
)

# Customize page title
st.title("Explainable Artificial Intelligence Framework Case Study - Optical Coherence Tomography Image Analysis by Deep Learning")


markdown1 = """
The proposed framework, as presented in our paper, aims to assist in the detection of selected anomalies from Retinal Optical Coherence Tomography (OCT) images. To illustrate the usage of the framework, we have developed a case study that focuses on identifying Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and normal/healthy retinal conditions.

1. [Choroidal Neovascularization (CNV)](https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5) is a type of retinal disease that involves the growth of abnormal blood vessels in the choroid layer of the eye. These new blood vessels can lead to fluid accumulation beneath and within the layers of tissue, causing vision loss.

2. [Diabetic Macular Edema (DME)](https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5) is a common complication of diabetes that affects the retina responsible for central vision. This condition occurs when fluid accumulates in and around the macula, leading to retinal thickening and swelling, which can result in impaired vision or blindness if left untreated.

3. [Drusen](https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5) is a condition associated with early Age-related Macular Degeneration (AMD) in the retina. It involves the accumulation of small deposits of liquid material in the retina, which can lead to vision problems.

4. Finally, the framework includes a classification model for NORMAL or healthy retina, providing a baseline for comparison with the aforementioned anomalies.

"""
st.markdown(markdown1)
# st.markdown(
#     """
#     This system is developed to classifiy certain anamolies from OCT images. Those anamolies are  and [leafmap](https://leafmap.org). It is an open-source project and you are very welcome to contribute to the [GitHub repository](https://github.com/giswqs/streamlit-multipage-template).
#     """
# )

st.header("Framework Sections")

markdown = """
The framework consists of four distinct sections, each focusing on a different aspect of the Artificial Intelligence(AI) application.

1. The Data Panel provides comprehensive information about the data used in the framework, including the sources, pre-processing techniques, and any relevant characteristics.

2. The Model Panel offers insights into the AI model used in the framework, including its architecture, parameters, and training procedures.

3. The Performance Evaluation Panel can be used to analyze and report on the performance of the AI model over a test set, providing detailed metrics and visualization of results.

4. Finally, the Decision Exploration section is designed to explore global and instance-level explanations of the AI model's decision-making process.
"""

st.markdown(markdown)



st.header("How to Use the Framework")

markdown = """
To utilize the framework efficiently, following the instructions below should be helpful:

1. Navigate to the left sidebar and select a user group from the available options.

2. Begin exploring the framework by clicking on the different panels, which are located in the upper left corner of the screen.

It is recommend to follow the order of Data Panel, Model Panel, Performance Evaluation Panel, and Decision Exploration Panel to ensure a systematic understanding of the framework's functionality.
"""

st.markdown(markdown)