Create new file

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)
+    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")
+    
+