app.py

import streamlit as st
from utils.uploadAndExample import add_upload

####################################### 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
              """)
    #c1, c2, c3 =  st.columns([12,1,10])
    #with c1:
        #image = Image.open('docStore/img/flow.jpg') 
        #st.image(image)
    #with c3:
        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)

#st.title("Identify references to vulnerable groups.")

#st.write("""Vulnerable groups encompass various communities and individuals who are disproportionately affected by the impacts of climate change
#due to their socioeconomic status, geographical location, or inherent characteristics. By incorporating the needs and perspectives of these groups 
#into national climate policies, governments can ensure equitable outcomes, promote social justice, and strive to build resilience within the most marginalized populations, 
#fostering a more sustainable and inclusive society as we navigate the challenges posed by climate change.This app allows you to identify whether a text contains any 
#references to vulnerable groups, for example when talking about policy documents.""")

# Document upload
#uploaded_file = st.file_uploader("Upload your file here")

# Create text input box
#input_text = st.text_area(label='Please enter your text here', value="This policy has been implemented to support women.")

#st.write('Prediction:', model(input_text))

######################################### Model #########################################################

# Load the model
#model = SetFitModel.from_pretrained("leavoigt/vulnerable_groups")

# Define the classes
#id2label = {
 #   0: 'Agricultural communities',
 #   1: 'Children and Youth',
 #   2: 'Coastal communities',
 #   3: 'Drought-prone regions',
 #   4: 'Economically disadvantaged communities',
 #   5: 'Elderly population',
 #   6: 'Ethnic minorities and indigenous people',
 #   7: 'Informal sector workers',
 #   8: 'Migrants and Refugees',
 #   9: 'Other',
 #   10: 'People with Disabilities',
 #   11: 'Rural populations',
 #   12: 'Sexual minorities (LGBTQI+)',
 #   13: 'Urban populations',
 #   14: 'Women'}


### Process document to paragraphs 
# Source: https://blog.jcharistech.com/2021/01/21/how-to-save-uploaded-files-to-directory-in-streamlit-apps/

# Store uploaded file temporarily in directory to get file path (necessary for processing)
# def save_uploadedfile(upl_file):
#      with open(os.path.join("tempDir",upl_file.name),"wb") as f:
#          f.write(upl_file.getbuffer())
#      return st.success("Saved File:{} to tempDir".format(upl_file.name))

# if uploaded_file is not None: 
#     # Save the file 
#     file_details = {"FileName": uploaded_file.name, "FileType": uploaded_file.type}
#     save_uploadedfile(uploaded_file)

#     #Get the file path