pages/3_🎯_Evaluation_Panel.py

import streamlit as st
import sys,os

sys.path.append(f'{os.getcwd()}/utils')

from utils.eval_users import get_product_dev_page_layout

# 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

st.title("Explore Performance Panel for OCT Image Analysis")
st.write(
        """
       This panel provides information on the evaluation of the AI model’s performance, including details on the metrics used and the results of the evaluation. USerrs can also find 
       our notes regarding the issues. The performance metric visualizations and samples of failure and success cases are given in in this panel as well.""")

list_test = """<ul>
                    <li>Evaluation Metrics: This tab explains the details of the performance metrics and how each metric is calculated. 
                        Users can also find the characteristics of the evaluation data set. </li>
                    <li>Performance Summary: This tab includes visualizations of the performance metrics over test set.</li>
                    <li>Limitations: This tab provides examples of observed failure and success cases, along with visualizations and any possible observations behind the failure cases.</li>
                </ul>"""
st.markdown(list_test, unsafe_allow_html=True)

if backend == "Developer":
    get_product_dev_page_layout()