import appStore.target as target_extraction
import appStore.netzero as netzero
import appStore.sector as sector
import appStore.adapmit as adapmit
import appStore.ghg as ghg
import appStore.policyaction as policyaction
import appStore.conditional as conditional
import appStore.indicator as indicator
import appStore.doc_processing as processing
from utils.uploadAndExample import add_upload
import streamlit as st

st.set_page_config(page_title = 'Climate Policy Intelligence', 
                   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;'> Climate Policy Understanding App </h2>", unsafe_allow_html=True)
        st.write(' ')

with st.expander("ℹ️ - About this app", expanded=False):
    st.write(
        """
        Climate Policy Understanding 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.

        What Happens in 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.
        
        Classifers:
          - **Netzero**: Detects if any Netzero commitment is present in paragraph or not.
          - **GHG**: Detects if any GHG related information present in paragraph or not.
          - **Sector**: Detects which sectors are spoken/discussed about in paragraph.
          - **Adaptation-Mitigation**: Detects if the paragraph is related to Adaptation and/or Mitigation.
        """)
    st.caption("""**Target** (Definition): Targets are an intention to achieve a specific result, \
            for example, to reduce GHG emissions to a specific level \
            (a GHG target) or increase energy efficiency or renewable \
            energy to a specific level (a non-GHG target), typically by \ 
            a certain date""")
    st.caption("""**Economy-wide Target** (Definition): Certain Target are applicable \
                not at specific Sector level but are applicable at economic \
                wide scale. """)               
    st.write("")
apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
        policyaction.app, conditional.app, sector.app]#, indicator.app]
 #conditional.app, sector.app]
        #adapmit.app]
         
        #  policyaction.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)
    prg.empty()

    
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':
        # st.dataframe(st.session_state['key1'])
        target_extraction.target_display()
    elif topic == 'Action':
        policyaction.action_display()
    else: 
        policyaction.policy_display()
    # st.write(st.session_state.key1)