import streamlit as st


def app():
    # if 'file' in st.session_state:
    #   file = st.session_state['file']
    # else:
    #   st.sidebar.markdown(" :cloud: Upload document ")
    #   uploaded_file = st.sidebar.file_uploader('', type=['pdf', 'docx', 'txt']) #Upload PDF File
    #   st.session_state['file'] = uploaded_file
    
    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("Policy Action Tracker Manual")
    intro = """
    <div class="text">
    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.  

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.
        <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("appStore/img/pic1.png", caption="NDC Coherence")
    #st.subheader("Methodology")
    #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")