#import appStore.identifier as target_identifier
import appStore.doc_processing as processing
from utils.uploadAndExample import add_upload
from PIL import Image
import streamlit as st

####################################### Dashboard ######################################################

# App 

st.set_page_config(page_title = 'Vulnerable Groups Identification', 
                   initial_sidebar_state='expanded', layout="wide") 

with st.sidebar:
    # upload and example doc
    choice = st.sidebar.radio(label = 'Select the Document',
                            help = 'You can upload the document \
                            or else you can try a example document', 
                            options = ('Upload Document', 'Try Example'), 
                            horizontal = True)
    add_upload(choice)

with st.container():
        st.markdown("<h2 style='text-align: center; color: black;'> Vulnerable Groups Identification </h2>", unsafe_allow_html=True)
        st.write(' ')

with st.expander("ℹ️ - About this app", expanded=False):
    st.write(
        """
        The Vulnerable Groups Identification App is an open-source\
        digital tool which aims to assist policy analysts and \
        other users in extracting and filtering relevant \
        information from public documents.
        """)
    st.write('**Definitions**')

    st.caption("""
            - **Place holder**: Place holder \
            Place holder \
            Place holder \
            Place holder \ 
            Place holder
              """)
   
    st.write("""
           What happens in the background?
            
           - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
           In this step the document is broken into smaller paragraphs \
           (based on word/sentence count).
           - Step 2: The paragraphs are fed to **Target Classifier** which detects if
           the paragraph contains any *Target* related information or not.
           - Step 3: The paragraphs which are detected containing some target \
           related information are then fed to multiple classifier to enrich the 
           Information Extraction.
    
           The Step 2 and 3 are repated then similarly for Action and  Policies & Plans.
           """)
                  
    st.write("")


# apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
#         policyaction.app, conditional.app, sector.app, adapmit.app,indicator.app]

# multiplier_val =1/len(apps)
# if st.button("Analyze Document"):
#     prg = st.progress(0.0)
#     for i,func in enumerate(apps):
#         func()
#         prg.progress((i+1)*multiplier_val)

    
# if 'key1' in st.session_state:
#     with st.sidebar:
#         topic = st.radio(
#                         "Which category you want to explore?",
#                         ('Target', 'Action', 'Policies/Plans'))
    
#     if topic == 'Target':
#         target_extraction.target_display()
#     elif topic == 'Action':
#         policyaction.action_display()
#     else: 
#         policyaction.policy_display()
#     # st.write(st.session_state.key1)