Spaces:
Sleeping
Sleeping
#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 | |
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 | |
""") | |
#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 | |