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SDSN-demo / appStore /info.py
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import streamlit as st
def app():
with open('style.css') as f:
st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
st.markdown("<h2 style='text-align: center; \
color: black;'> Climate Policy Action Tracker Manual</h2>",
unsafe_allow_html=True)
st.markdown("<div style='text-align: center; \
color: grey;'>The Policy Action Tracker is an open-source\
digital tool which aims to assist policy analysts and \
other users in extracting and filtering relevant \
information from public documents.</div>",
unsafe_allow_html=True)
footer = """
<div class="footer-custom">
Guidance & Feedback - <a href="https://www.linkedin.com/in/maren-bernlöhr-149891222" target="_blank">Maren Bernlöhr</a> |
<a href="https://www.linkedin.com/in/manuelkuhm" target="_blank">Manuel Kuhm</a> |
Developer - <a href="https://www.linkedin.com/in/erik-lehmann-giz/" target="_blank">Erik Lehmann</a> |
<a href="https://www.linkedin.com/in/jonas-nothnagel-bb42b114b/" target="_blank">Jonas Nothnagel</a> |
<a href="https://www.linkedin.com/in/prashantpsingh/" target="_blank">Prashant Singh</a> |
</div>
"""
st.markdown(footer, unsafe_allow_html=True)
c1, c2, c3 = st.columns([8,1,12])
with c1:
st.image("docStore/img/ndc.png")
with c3:
st.markdown('<div style="text-align: justify;">The manual extraction \
of relevant information from text documents is a \
time-consuming task for any policy analysts. As the amount and length of \
public policy documents in relation to sustainable development (such as \
National Development Plans and Nationally Determined Contributions) \
continuously increases, a major challenge for policy action tracking – the \
evaluation of stated goals and targets and their actual implementation on \
the ground – arises. Luckily, Artificial Intelligence (AI) and Natural \
Language Processing (NLP) methods can help in shortening and easing this \
task for policy analysts.</div><br>',
unsafe_allow_html=True)
intro = """
<div style="text-align: justify;">
For this purpose, the United Nations Sustainable Development Solutions \
Network (SDSN) and the Deutsche Gesellschaft für Internationale \
Zusammenarbeit (GIZ) GmbH are collaborating since 2021 in the development \
of an AI-powered open-source web application that helps find and extract \
relevant information from public policy documents faster to facilitate \
evidence-based decision-making processes in sustainable development and beyond.
The collaboration aims to determine the potential of NLP methods for \
tracking policy implementation and coherence in the context of the \
Sustainable Development Goals (SDGs) and the Paris Climate Agreement. \
Nationally determined contributions (NDCs) will serve as a starting \
point for the analysis and evaluation in a specific national context. \
Under the Paris Climate Agreement, NDCs embody the efforts of each \
country to reduce national emissions and thus contribute to the \
achievement of the long-term goals of the Agreement – to increase the \
ability to adapt to adverse impacts of climate change and foster \
climate resilience and low greenhouse gas emissions development, in a \
manner that does not threaten food production. The Paris Climate \
Agreement (Article 4, Paragraph 2)1 requires each Party to prepare, \
communicate and maintain successive NDCs. Thus, they serve as a \
comparable, accessible, and widely acknowledged starting point for \
analysis. However, the agreed and communicated goals and measures must \
also be reflected in national strategies, statements, and other \
government publications to be implemented timely, as well as effectively.\
At best, the activities and measures should have an allocated budget. \
Given the complexity, the manual evaluation of policy documents and \
other publications has been very time-consuming and has presented a \
significant challenge for policy analysts and makers alike. In \
consequence, the open-source web application aims to support the process\
through suitable AI-powered and NLP methods. In the following, the \
application’s functionalities are explained in more detail.
</div>
<br>
"""
st.markdown(intro, unsafe_allow_html=True)
st.image("docStore/img/pic1.png")