import streamlit as st
import sys,os


sys.path.append(f'{os.getcwd()}/utils')
from utils.model_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 Model Panel for OCT Image Analysis")
st.write(
        """
       The users can find following information regarding the AI model developed for the task: what the mode does, how it was developed and how it is used. Thus, we provide answers 
       to those questions via below described tabs.
        """)

list_test = """<ul>
                <li>Model generic information: This tab contains summary of the model’s details, including its main features, capabilities, and intended use. 
                    It also hihglights the model’s behaviour, such as the type of data inputs(image, feature etc) it can handle and the types of outputs it produces.</li>
                <li>Model development information: The tab includes information on the hyperparameters, model development framework such as Tensorflow or PyTorch, and other technical details. It also includes a complete analysis of the model’s inference performance, such as the model size, 
                        hardware-specific (GPU and CPU) inference time,and speed, as well as reproducibility check list.</li>
                <li>Model deployment information: This tab provides information on how the model is used in production, including details on the inference speed and latency,  
                        and how users can access and interact with the model in production.</li>
                </ul>"""
st.markdown(list_test, unsafe_allow_html=True)

if backend == "Developer":
    get_product_dev_page_layout()