pages/1_💾_Data_Panel.py

# Read
import streamlit as st
import sys,os


sys.path.append(f'{os.getcwd()}/utils')
from utils.data_users import get_product_dev_page_layout,get_product_manager_page_layout,get_product_practitioner_page_layout


print(os.getcwd())
# st.write(st.session_state.user_group)
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

# #     with st.sidebar:
# st.sidebar.title("🎈Explore Data Panel")

st.title("Data Panel for OCT Image Analysis")

st.write(
        """
        ##
        To gain a comprehensive understanding of the AI system, examining the data has a crucial role. The Data Panel adopts a data-centric approach, providing detailed information about the following aspects of the data:
        """)


list_test = """<ul>
                <li>Data Source Information contains information related to the modality, format, domain, ethical considerations, including licensing and data version. </li>
                <li>Exploratory Data Stats presents exploratory data analysis information covering train/validation/test data division, summary statistics, and sample visualization from each category. </li>
                <li>Data Onboarding provides information about the data pre-processing and post-processing steps applied to the dataset before training, as well as any data augmentations that were used.</li>
 </ul>"""
st.markdown(list_test, unsafe_allow_html=True)


if backend == "Developer":
    get_product_dev_page_layout()


if backend == "Manager":
    get_product_manager_page_layout()

if backend == "Practitioner":
    get_product_practitioner_page_layout()