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Runtime error
| import streamlit as st | |
| def app(): | |
| with open('style.css') as f: | |
| st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True) | |
| footer = """ | |
| <div class="footer-custom"> | |
| 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> | | |
| Guidance & Feedback - Maren Bernlöhr | Manuel Kuhn </a> | |
| </div> | |
| """ | |
| st.markdown(footer, unsafe_allow_html=True) | |
| st.subheader("Intro") | |
| intro = """ | |
| <div class="text"> | |
| The manual extraction of relevant information from text documents is a time-consuming task for any policy analyst. | |
| 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. | |
| 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. | |
| <ul> | |
| <li>Analizing the policy document</li> | |
| <li>finding SDG related content</li> | |
| <li>Make it searchable</li> | |
| <li>compare it to the national NDC</li> | |
| </ul> | |
| </div> | |
| <br> | |
| """ | |
| st.markdown(intro, unsafe_allow_html=True) | |
| st.image("lfqa.png", caption="LFQA Architecture") | |
| st.subheader("UI/UX") | |
| st.write("Each sentence in the generated answer ends with a coloured tooltip; the colour ranges from red to green. " | |
| "The tooltip contains a value representing answer sentence similarity to a specific sentence in the " | |
| "Wikipedia context passages retrieved. Mouseover on the tooltip will show the sentence from the " | |
| "Wikipedia context passage. If a sentence similarity is 1.0, the seq2seq model extracted and " | |
| "copied the sentence verbatim from Wikipedia context passages. Lower values of sentence " | |
| "similarity indicate the seq2seq model is struggling to generate a relevant sentence for the question " | |
| "asked.") | |
| st.image("wikipedia_answer.png", caption="Answer with similarity tooltips") | |