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import streamlit as st |
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def app(): |
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with open('style.css') as f: |
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st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True) |
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footer = """ |
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<div class="footer-custom"> |
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Developer - <a href="https://www.linkedin.com/in/erik-lehmann-giz/" target="_blank">Erik Lehmann</a> | |
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<a href="https://www.linkedin.com/in/jonas-nothnagel-bb42b114b/" target="_blank">Jonas Nothnagel</a> | |
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<a href="https://www.linkedin.com/in/prashantpsingh/" target="_blank">Prashant Singh</a> | |
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Guidance & Feedback - Maren Bernlöhr | Manuel Kuhn </a> |
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</div> |
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""" |
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st.markdown(footer, unsafe_allow_html=True) |
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st.subheader("Intro") |
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intro = """ |
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<div class="text"> |
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The manual extraction of relevant information from text documents is a time-consuming task for any policy analyst. |
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As the amount and length of public policy documents in relation to sustainable development (such as National Development Plans and |
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Nationally Determined Contributions) continuously increases, a major challenge for policy action tracking – the evaluation of stated |
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goals and targets and their actual implementation on the ground – arises. Luckily, Artificial Intelligence (AI) and Natural Language Processing (NLP) |
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methods can help in shortening and easing this task for policy analysts. |
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For this purpose, the United Nations Sustainable Development Solutions Network (SDSN) and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH |
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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 |
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documents faster to facilitate evidence-based decision-making processes in sustainable development and beyond. |
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<ul> |
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<li>Analizing the policy document</li> |
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<li>finding SDG related content</li> |
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<li>Make it searchable</li> |
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<li>compare it to the national NDC</li> |
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</ul> |
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</div> |
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<br> |
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""" |
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st.markdown(intro, unsafe_allow_html=True) |
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st.image("lfqa.png", caption="LFQA Architecture") |
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st.subheader("UI/UX") |
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st.write("Each sentence in the generated answer ends with a coloured tooltip; the colour ranges from red to green. " |
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"The tooltip contains a value representing answer sentence similarity to a specific sentence in the " |
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"Wikipedia context passages retrieved. Mouseover on the tooltip will show the sentence from the " |
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"Wikipedia context passage. If a sentence similarity is 1.0, the seq2seq model extracted and " |
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"copied the sentence verbatim from Wikipedia context passages. Lower values of sentence " |
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"similarity indicate the seq2seq model is struggling to generate a relevant sentence for the question " |
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"asked.") |
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st.image("wikipedia_answer.png", caption="Answer with similarity tooltips") |
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